Smart power consumption management method and device, electronic equipment and storage medium
The smart electricity management system classifies and predicts energy consumption to proactively manage critical equipment, ensuring stability and efficiency in complex environments, addressing the inefficiencies of existing systems.
Patent Information
- Application Number
- CN202411265811.4
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- Filing Date
- 2024-09-10
- Publication Date
- 2024-12-27
- Estimated Expiration
- Not applicable · inactive patent
AI Technical Summary
Existing smart electricity management systems are inadequate in places with complex electrical equipment, such as hospitals and large shopping malls, leading to potential safety hazards and inefficiencies due to reliance on alarms for danger response.
A method and device for smart electricity management that classifies power-consuming equipment into key and conventional types based on functional information, historical data analysis, and real-time data, predicting energy consumption and detecting potential hazards to enable proactive management.
Ensures stable operation of critical equipment, optimizes power consumption efficiency, and enhances flexibility and adaptability in managing power usage, reducing risks and improving overall management effectiveness.
Smart Images
Figure CN119204530A_ABST
Abstract
Description
Technical Field
[0001] The present application relates to the field of data management technology, for example, to a smart electricity management method, device, electronic device and storage medium. Background Art
[0002] With the rapid development of science and technology and social economy, energy demand continues to increase, and various types of power equipment have also increased significantly. This has led to the effective management and monitoring of power use becoming particularly important. The rapid development of technologies such as the Internet of Things, big data, cloud computing, and artificial intelligence has provided strong technical support for smart power management. These technologies can monitor the operating status of power equipment in real time, collect and analyze data, and provide power users with smarter and more efficient power management solutions.
[0003] In the related art, the management method of smart electricity consumption is mainly based on alarm. However, in some places with special functions such as hospitals, large residential areas, large shopping malls, etc., the large number of electrical equipment and the complex use methods make the places have various safety hazards. Relying on alarms when danger occurs may cause huge losses to the places. Therefore, the current management effect of smart electricity consumption is poor. Summary of the invention
[0004] The present application aims to provide a smart electricity management method, device, electronic device and storage medium.
[0005] According to one aspect of the present application, a smart electricity management method is proposed, including: obtaining the functional information of the target electricity consumption site, and determining the key electricity equipment and conventional electricity equipment in the target electricity consumption site based on the functional information; determining the predicted energy consumption data of the key electricity equipment based on the historical management data and measured operation data of the key electricity equipment in a preset historical selection period; determining the operation energy consumption data based on the real-time electricity demand, environmental data and predicted energy consumption data of the target electricity consumption site; determining the electricity consumption hazard information at the current moment based on the equipment operation information and operation energy consumption data of the conventional electricity equipment.
[0006] According to some embodiments, key power equipment and conventional power equipment in a target power consumption location are determined based on functional information, including: determining multiple power equipment in the target power consumption location based on the functional information; obtaining historical power consumption data corresponding to each of the multiple power equipment; and dividing the multiple power equipment into key power equipment and conventional power equipment based on the historical power consumption data.
[0007] According to some embodiments, multiple power-consuming devices are divided into critical power-consuming devices and conventional power-consuming devices based on historical power consumption data, including: analyzing historical power consumption data to determine the power demand and power consumption period characteristics of each of the multiple power-consuming devices; determining critical power-consuming devices and conventional power-consuming devices among the multiple power-consuming devices based on preset site power consumption priorities, power demand and power consumption period characteristics.
[0008] According to some embodiments, predicted energy consumption data of key power-consuming equipment is determined based on historical management data and measured operation data of the key power-consuming equipment within a preset historical selection period, including: obtaining historical management data; extracting historical maintenance information, historical operation parameters and historical management information from the historical management data; and determining predicted energy consumption data based on the historical maintenance information, historical operation parameters, historical management information and measured operation data.
[0009] According to some embodiments, predicted energy consumption data is determined based on historical maintenance information, historical operating parameters, historical management information and measured operating data, including: determining the maintenance cycle of the target power consumption site, the maintenance time and fault information corresponding to the key power equipment and conventional power equipment respectively based on the historical maintenance information; determining the equipment loss parameters corresponding to the key power equipment and conventional power equipment respectively at the current moment based on the maintenance cycle, maintenance time, fault information and preset equipment life information; training an energy consumption prediction model based on the equipment loss parameters, historical operating parameters and historical management information; inputting the measured operating data into the energy consumption prediction model, and outputting the determined predicted energy consumption data.
[0010] According to some embodiments, the above method further includes: obtaining site application arrangement information of the target power consumption site at the current moment; and determining the real-time power demand according to the site application arrangement information.
[0011] According to some embodiments, operating energy consumption data is determined based on the real-time power demand, environmental data and predicted energy consumption data of the target power consumption site, including: updating the real-time power demand based on the environmental data to determine the target power demand; determining the impact information corresponding to the environmental data based on pre-set environmental impact information; adjusting the predicted energy consumption data based on the target power demand and the impact information corresponding to the environmental data to determine the operating energy consumption data.
[0012] According to one aspect of the present application, a smart electricity management device is provided, comprising: The equipment determination module is used to obtain the functional information of the target power consumption site and determine the key power consumption equipment and conventional power consumption equipment of the target power consumption site according to the functional information; The predicted energy consumption data determination module is used to determine the predicted energy consumption data of key power-consuming equipment based on the historical management data and measured operation data of the key power-consuming equipment within a preset historical selection period; An operation energy consumption data determination module is used to determine the operation energy consumption data according to the real-time power demand, environmental data and predicted energy consumption data of the target power consumption site; The power consumption hidden danger information determination module is used to determine the power consumption hidden danger information at the current moment based on the equipment operation information and operation energy consumption data of conventional power-consuming equipment.
[0013] Optionally, the device determination module is specifically used for: According to the functional information, multiple electrical equipment in the target electrical consumption location are determined; Obtain historical power consumption data corresponding to multiple power-consuming devices; Based on historical electricity consumption data, multiple power-consuming equipment are divided into key power-consuming equipment and conventional power-consuming equipment.
[0014] Optionally, when the device determination module divides a plurality of power-consuming devices into key power-consuming devices and conventional power-consuming devices according to historical power-consuming data, the device determination module is specifically configured to: Analyze historical electricity consumption data to determine the electricity demand and electricity consumption period characteristics of each of multiple electricity-consuming devices; According to the preset site power priority, power demand and power period characteristics, key power equipment and conventional power equipment are determined among multiple power equipment.
[0015] Optionally, the predicted energy consumption data determination module is specifically used for: Obtain historical management data; Extract historical maintenance information, historical operating parameters and historical management information from historical management data; Determine the predicted energy consumption data based on historical maintenance information, historical operating parameters, historical management information and measured operating data.
[0016] Optionally, when the predicted energy consumption data determination module determines the predicted energy consumption data according to the historical maintenance information, the historical operating parameters, the historical management information and the measured operating data, it is specifically used to: Based on historical maintenance information, determine the maintenance cycle of the target power consumption site, the maintenance time and fault information of key power consumption equipment and conventional power consumption equipment; According to the maintenance cycle, maintenance time, fault information and preset equipment life information, determine the equipment loss parameters corresponding to the key power-consuming equipment and conventional power-consuming equipment at the current moment; Train energy consumption prediction models based on equipment loss parameters, historical operating parameters, and historical management information; The measured operating data is input into the energy consumption prediction model, and the output determines the predicted energy consumption data.
[0017] Optionally, the smart electricity management device further includes a demand determination module, which is used to: Obtaining the site application arrangement information of the target power consumption site at the current moment; Determine real-time power demand based on site application scheduling information.
[0018] Optionally, the operation energy consumption data determination module is specifically used for: Update the real-time power demand based on environmental data to determine the target power demand; Determine the impact information corresponding to the environmental data according to the preset environmental impact information; According to the impact information corresponding to the target electricity demand and environmental data, the predicted energy consumption data is adjusted to determine the operating energy consumption data.
[0019] According to one aspect of the present application, an electronic device is proposed, which includes: a processor; and a memory storing a computer program, wherein when the computer program is executed by the processor, the processor executes the smart electricity management method as described above.
[0020] According to one aspect of the present application, a non-transitory computer-readable medium is provided, on which readable instructions are stored. When the instructions are executed by a processor, the processor executes the smart electricity management method as described above.
[0021] It should be understood that the foregoing general description and the following detailed description are exemplary only and are not restrictive of the present application. Beneficial Effects
[0022] Through the above-mentioned embodiments provided by the present application, by obtaining the functional information of the target power consumption site, the accurate classification of power consumption equipment is achieved, and key power consumption equipment and conventional power consumption equipment are identified. This classification method helps to differentiate the management and maintenance of equipment of different importance, ensure the stable operation of key equipment, and optimize the power consumption efficiency of conventional equipment. Combined with the real-time power demand, environmental data and predicted energy consumption data of the target power consumption site, the operating energy consumption data is dynamically determined. This real-time response mechanism ensures the flexibility and adaptability of power management, can quickly respond to various emergencies and changes, and ensure the continuity and stability of power consumption. Combined with the real-time power demand, environmental data and predicted energy consumption data of the target power consumption site, the operating energy consumption data is dynamically determined. This real-time response mechanism ensures the flexibility and adaptability of power management, can quickly respond to various emergencies and changes, and improves the management effect of smart power management as a whole. BRIEF DESCRIPTION OF THE DRAWINGS
[0023] In order to more clearly illustrate the technical solutions in the embodiments of the present application, the drawings required for use in the description of the embodiments 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 exceeding the scope of protection required by the present application.
[0024] Figure 1 A schematic diagram of an application scenario provided for an embodiment of the present application; Figure 2 A flowchart of a smart electricity management method provided in an embodiment of the present application; Figure 3 A block diagram of a smart electricity management device provided in an embodiment of the present application; Figure 4 A schematic diagram of the structure of an electronic device provided in an embodiment of the present application. DETAILED DESCRIPTION
[0025] Example embodiments will now be described more fully with reference to the accompanying drawings. However, example embodiments can be implemented in many forms and should not be construed as limited to the embodiments set forth herein; rather, these embodiments are provided so that this application will be comprehensive and complete and will fully convey the concepts of the example embodiments to those skilled in the art. The same reference numerals in the figures represent the same or similar parts, and thus their repeated description will be omitted.
[0026] In addition, described feature, structure or characteristic can be combined in one or more embodiments in any suitable manner. In the following description, many specific details are provided to provide a full understanding of the embodiments of the present application. However, those skilled in the art will appreciate that the technical scheme of the present application can be put into practice without one or more of the specific details, or other methods, components, devices, steps, etc. can be adopted. In other cases, known methods, devices, realizations or operations are not shown or described in detail to avoid blurring the various aspects of the application.
[0027] The block diagrams shown in the accompanying drawings are merely functional entities and do not necessarily correspond to physically independent entities. That is, these functional entities may be implemented in software form, or in one or more hardware modules or integrated circuits, or in different networks and / or processor devices and / or microcontroller devices.
[0028] The flowcharts shown in the accompanying drawings are only exemplary and do not necessarily include all the contents and operations / steps, nor must they be executed in the order described. For example, some operations / steps can be decomposed, and some operations / steps can be combined or partially combined, so the actual execution order may change according to actual conditions.
[0029] It should be understood that although the terms first, second, third, etc. may be used herein to describe various components, these components should not be limited by these terms. These terms are used to distinguish one component from another component. Therefore, the first component discussed below can be referred to as the second component without departing from the teachings of the concepts of the present application. As used herein, the term "and / or" includes any one of the associated listed items and all combinations of one or more.
[0030] Figure 1 A schematic diagram of an application scenario according to an exemplary embodiment is shown. In a place with special functions, a large number of electrical equipment may be set up to support the place to realize its functions. These electrical equipment may include equipment that is indispensable in the place, such as medical equipment in a hospital, and may also include some equipment that can be restored to use after a short disconnection, such as a self-service registration machine, monitoring equipment in a certain area, etc. Figure 1 In the application scenario shown, the smart electricity management method of the present application can be applied to an electricity management server. The electricity management server can detect environmental data and analyze the current electricity hidden danger information based on environmental data, functional information, etc., so as to facilitate timely power regulation and advance maintenance of equipment.
[0031] For specific implementation methods, please refer to the following embodiments.
[0032] Figure 2 This is a flow chart of a smart electricity management method provided in an embodiment of the present application. The method of this embodiment can be applied to an electricity management server. Figure 2 As shown, the method includes: step S200, step S201, step S202 and step S203.
[0033] In step S200, the function information of the target power consumption site is obtained, and the key power consumption equipment and conventional power consumption equipment of the target power consumption site are determined according to the function information.
[0034] In this application, the target power consumption place is used to indicate the place where the power consumption management server performs power consumption management. Functional information can be used to indicate the role of the target power consumption place. For example, the main function of a hospital is to treat patients, the main function of a large residential area is to allow a large number of residents to have a good living experience, and the main function of a large shopping mall is to provide convenient and spacious entertainment venues, etc. The functional information of different power consumption places can be pre-set according to the specific usage conditions currently transmitted to you. The functional information can be a pre-set default setting, or it can be obtained by periodic input by relevant staff.
[0035] Key power-consuming equipment can be used to represent indispensable equipment in the target power-consuming place, such as medical equipment in a hospital, and conventional power-consuming equipment can be equipment that can be restored to use after a short disconnection, such as a self-service registration machine, monitoring equipment in a certain area, etc. In some implementations, multiple key power-consuming equipment and multiple conventional power-consuming equipment corresponding to target power-consuming places with different functional information can be pre-set.
[0036] According to an example embodiment, the function information of the target power consumption site may be obtained, and then the key power consumption equipment and conventional power consumption equipment corresponding to the function information may be found.
[0037] In step S201, predicted energy consumption data of key power-consuming equipment is determined based on historical management data and measured operation data of the key power-consuming equipment within a preset historical selection period.
[0038] In this application, the preset historical selection period may be one or more representative historical time periods, which may cover the operating status of the equipment in a period corresponding to a certain season or a certain stage. Generally, the period of the most recent month can be selected. Historical management data can be used to represent information such as equipment operation-related data, fault-related data, corresponding operating energy consumption data, and adjustment of power consumption strategies within the preset historical selection period. The measured operating data may include the current, voltage, operating time, etc. of each electrical equipment at the current moment.
[0039] According to the exemplary embodiment, an operation energy consumption data output model may be pre-established, and the predicted energy consumption data may be directly output by inputting historical management data and measured operation data within a preset historical selection period into the model.
[0040] In step S202, the operating energy consumption data is determined according to the real-time power demand, environmental data and predicted energy consumption data of the target power consumption site.
[0041] In this application, the real-time power demand can be used to represent the demand of the target power consumption place at the current moment. The power demand may be the same as that of the previous moments. The place may also have a sudden use. For example, a large stadium may be involved in some events, and the power demand at different times of the day of the event may also be different. In some implementations, the real-time power demand can be input externally.
[0042] The environmental data may include the installation locations of different electrical equipment, the setting location of the target electrical site, temperature, humidity, etc. The environmental data may be acquired in real time.
[0043] According to an example embodiment, an operation energy consumption data output model may be preset, and real-time power demand, environmental data, and predicted energy consumption data may be input into the model to directly output the operation energy consumption data.
[0044] In step S203, the current power consumption potential hazard information is determined based on the equipment operation information and operation energy consumption data of the conventional power-consuming equipment.
[0045] In this application, the equipment operation information of conventional electrical equipment may include the relevant electrical parameters of the equipment, the operating time, etc. The hidden danger information of electricity consumption can be used to characterize the hidden dangers existing under the operating energy consumption data of key electrical equipment and the equipment operation information of conventional electrical equipment at the current moment, such as electrical fire hazards and data security risks caused by faults such as component damage, network errors, and circuit board corrosion.
[0046] According to the example embodiment, a fault cause prediction model can be pre-set, and the fault cause can be directly output by inputting the equipment operation information and operation energy consumption data of conventional electrical equipment into the model. Possible risks corresponding to different fault causes can be pre-set, and after outputting the possible fault causes, the possible hidden dangers can be found accordingly, and the possible hidden dangers and the fault causes can be used as the power consumption hidden danger information.
[0047] In some implementations, after obtaining information about hidden dangers in electricity use, automatic power outages, switching to backup power, and other operations can be performed in a timely manner to provide protection.
[0048] This application achieves accurate classification of power-consuming equipment and identifies key power-consuming equipment and conventional power-consuming equipment by obtaining functional information of the target power-consuming sites. This classification method helps to differentiate the management and maintenance of equipment of different importance, ensure the stable operation of key equipment, and optimize the power efficiency of conventional equipment. The operating energy consumption data is dynamically determined in combination with the real-time power demand, environmental data, and predicted energy consumption data of the target power-consuming sites. This real-time response mechanism ensures the flexibility and adaptability of power management, can respond quickly to various emergencies and changes, and ensure the continuity and stability of power consumption. The operating energy consumption data is dynamically determined in combination with the real-time power demand, environmental data, and predicted energy consumption data of the target power-consuming sites. This real-time response mechanism ensures the flexibility and adaptability of power management, can respond quickly to various emergencies and changes, and improves the management effect of smart power management as a whole.
[0049] According to some embodiments, multiple power-consuming devices in a target power-consuming location may be determined based on functional information; historical power consumption data corresponding to each of the multiple power-consuming devices may be obtained; and the multiple power-consuming devices may be divided into key power-consuming devices and conventional power-consuming devices based on the historical power consumption data.
[0050] In this application, the functional information may include the equipment installed in the target power consumption site, that is, multiple power consumption equipment. The power consumption management server may pre-store the historical power consumption data of the power consumption equipment, and may pre-set the division method of conventional and key power consumption equipment. For example, when the power consumption data accounts for a certain threshold percentage of the whole, it can be regarded as a key power consumption equipment.
[0051] According to the example embodiment, relevant information of multiple power-consuming devices in the target power-consuming place, such as device ID, can be extracted from the functional information. Then, the pre-stored historical power consumption data corresponding to these power-consuming devices are obtained, and the historical power consumption data is analyzed by a pre-set division method to divide the key power-consuming devices and the conventional power-consuming devices.
[0052] This application divides multiple power-consuming equipment into key power-consuming equipment and conventional power-consuming equipment, which can achieve differentiated management of power-consuming equipment. Key power-consuming equipment is usually crucial to business operations, and its stable operation is directly related to the production efficiency and customer experience of the enterprise. Therefore, in terms of resource allocation, more attention and investment can be given to key power-consuming equipment, such as giving priority to its power supply, regular maintenance and overhaul, etc. For conventional power-consuming equipment, reasonable arrangements can be made according to actual conditions to avoid waste of resources. This differentiated resource allocation method helps to optimize the overall power consumption structure and improve resource utilization efficiency. By analyzing historical power consumption data, the energy consumption characteristics and laws of each device can be identified, and then effective energy-saving measures can be taken. For key power-consuming equipment, although its energy consumption is large, it can be reduced by optimizing operating parameters and adopting efficient energy-saving technologies. For conventional power-consuming equipment, unnecessary energy consumption can be reduced by reasonably controlling the use time and the number of uses. This energy-saving measure based on data analysis helps to reduce the overall energy consumption cost and improve the economic benefits of the target power consumption site.
[0053] According to some embodiments, historical electricity consumption data can be analyzed to determine the electricity demand and electricity consumption period characteristics of each of multiple electricity-consuming devices; based on preset site electricity consumption priorities, electricity demand and electricity consumption period characteristics, key electricity-consuming devices and conventional electricity-consuming devices can be determined among multiple electricity-consuming devices.
[0054] In this application, electricity demand can be used to indicate the overall trend of electricity consumption under different circumstances, and the characteristics of electricity consumption time periods can be used to indicate the situation of periods with more electricity consumption and periods with relatively normal electricity consumption. The priority of electricity consumption at a site can be set based on factors such as business needs, equipment importance, and safety requirements of the target electricity consumption site. The priority can indicate which equipment is critical electricity-consuming equipment (such as core machines on a production line, safety monitoring systems, etc.) and which equipment is conventional electricity-consuming equipment (such as lighting equipment, office computers, etc.).
[0055] According to the example embodiment, the historical power consumption data can be analyzed by means of statistical analysis, time series analysis, etc., and the power demand and power consumption period characteristics of each device can be determined by calculating the average power consumption, peak power consumption period, and low power consumption period of various power-consuming devices. Then, the power demand and power consumption period characteristics of each power-consuming device can be compared and analyzed with the preset power consumption priority standard. For equipment with large power consumption, power consumption period that is highly related to business operations, and is crucial to site operations, it is determined to be a critical power-consuming equipment; for equipment with relatively small power consumption, flexible power consumption period, and less impact on business operations, it is determined to be a conventional power-consuming equipment.
[0056] The classification results of this application are helpful to optimize the allocation of power resources. Critical power-consuming equipment is given priority to ensure its stable operation; conventional power-consuming equipment is flexibly scheduled according to actual needs to avoid waste of resources. This differentiated resource allocation method improves the overall power efficiency. The classification results are helpful to optimize the allocation of power resources. Critical power-consuming equipment is given priority to ensure its stable operation; conventional power-consuming equipment is flexibly scheduled according to actual needs to avoid waste of resources. This differentiated resource allocation method improves the overall power efficiency.
[0057] According to some embodiments, historical management data can be obtained; historical maintenance information, historical operating parameters and historical management information can be extracted from the historical management data; and predicted energy consumption data can be determined based on the historical maintenance information, historical operating parameters, historical management information and measured operating data.
[0058] In the present application, historical management information may include management information such as the device's usage time, load conditions, operating mode, and corresponding historical energy consumption data, which helps determine the energy consumption characteristics of the device under different conditions.
[0059] According to the example embodiment, the pre-stored historical management data is first obtained, and information related to equipment maintenance, such as maintenance time, maintenance content, replacement parts, etc., is extracted from the historical management data, and these data are used as historical maintenance information. Various parameters of the equipment during operation, such as current, voltage, power, temperature, humidity, etc., are extracted, that is, historical operation parameters, which directly reflect the operating status and energy consumption of the equipment.
[0060] Based on historical maintenance information, historical operating parameters and measured operating data, similar maintenance information, operating parameters and operating data can be compared in historical management information. If the overlap is greater than 90%, it can be regarded as similar. Similar historical energy consumption data can be used as the predicted energy consumption data at the current moment. This application can more comprehensively reflect the operating status and energy consumption characteristics of the equipment by comprehensively considering historical maintenance information, historical operating parameters, historical management information and measured operating data, thereby improving the accuracy of the prediction. Accurate energy consumption prediction helps companies formulate reasonable energy use plans, optimize energy allocation, reduce energy waste, and reduce operating costs. By analyzing historical maintenance information, potential problems and failure trends of equipment can be discovered in a timely manner, maintenance and maintenance can be carried out in advance, and increased energy consumption and downtime caused by equipment failure can be reduced.
[0061] According to some embodiments, the maintenance cycle of the target power consumption site, the maintenance time and fault information corresponding to the key power equipment and conventional power equipment can be determined based on historical maintenance information; the equipment loss parameters corresponding to the key power equipment and conventional power equipment at the current moment can be determined based on the maintenance cycle, maintenance time, fault information and preset equipment life information; the energy consumption prediction model is trained based on the equipment loss parameters, historical operating parameters and historical management information; the measured operating data is input into the energy consumption prediction model, and the predicted energy consumption data is determined by output.
[0062] According to an example embodiment, first, the maintenance cycle, last maintenance time, and previous maintenance records of each key power-consuming equipment and conventional power-consuming equipment are extracted from the historical maintenance information. Based on this information, the current maintenance status of the equipment, that is, the time interval from the last maintenance, is calculated. The fault information in the historical maintenance records is analyzed, including the fault type, fault frequency, fault repair time, etc. Combined with the maintenance cycle of the equipment, the failure risk of the equipment under different maintenance states is evaluated. Based on the maintenance cycle, maintenance time, fault information, and preset equipment life information (such as equipment design life, key component life, etc.), the loss parameters of the equipment at the current moment are calculated using a preset algorithm (such as an equipment loss rate model, a life assessment model, etc.). These parameters may include the overall loss rate of the equipment, the remaining life of key components, etc.
[0063] Take historical operating parameters (such as current, voltage, power, etc.), historical management information (such as load conditions, working mode, usage time, etc.) and determined equipment loss parameters as input features, and use historical energy consumption data as output labels. Select a suitable energy consumption prediction model based on the characteristics of the data and prediction requirements. Since energy consumption data usually has time series characteristics and is affected by many factors, you can choose a time series analysis model (such as ARIMA, LSTM, etc.) or a hybrid model combined with machine learning. The prepared data is divided into a training set and a validation set (or test set). The model is trained using the training set data, and the prediction performance of the model is evaluated using the validation set (or test set) data. Improve the prediction accuracy and generalization ability of the model by adjusting model parameters and optimizing algorithms.
[0064] Input the current measured operating data into the trained energy consumption prediction model. These data should include the real-time operating parameters of the equipment, current load conditions, working mode, etc., and directly output the predicted energy consumption data.
[0065] This application makes the energy consumption prediction model more accurately reflect the actual operating status and energy consumption characteristics of the equipment by comprehensively considering historical maintenance information, historical operating parameters, historical management information and measured operating data, as well as the introduction of equipment loss parameters.
[0066] According to some embodiments, the site application arrangement information of the target power consumption site at the current moment may be obtained; and the real-time power demand may be determined according to the site application arrangement information.
[0067] In this application, the site application arrangement information can be used to indicate the specific usage of the target power consumption site. For example, in a large shopping mall, an event may be held, and the time of the event will be arranged in a certain way, including what to do at different times and what kind of power consumption equipment needs to be used at different times as the site application arrangement information.
[0068] The corresponding real-time power demand under the application arrangement information of different places can be pre-set.
[0069] According to an example embodiment, the site application arrangement information of the target power consumption site at the current moment may be directly acquired, and then the corresponding real-time power consumption demand may be searched based on the site application arrangement information.
[0070] By acquiring and analyzing the real-time information of the application schedule of the venue, this application can more accurately predict and determine the real-time power demand, thereby improving the accuracy of power management. This helps to avoid the risk of energy waste and power shortage.
[0071] According to some embodiments, the real-time power demand can be updated based on environmental data to determine the target power demand; the impact information corresponding to the environmental data can be determined based on pre-set environmental impact information; and the predicted energy consumption data can be adjusted based on the target power demand and the impact information corresponding to the environmental data to determine the operating energy consumption data.
[0072] First, obtain the environmental data of the target power consumption site in real time, including but not limited to indoor temperature, humidity, light intensity, personnel density, etc. These data can be collected in real time by IoT sensors and transmitted to the data processing system.
[0073] According to an example embodiment, the initial real-time power demand is dynamically adjusted based on the collected environmental data. For example, if the indoor temperature is higher than a preset threshold, the power demand of the air conditioning system may be increased; if the light intensity is sufficient, the power consumption of the lighting system may be reduced. A mapping relationship or algorithm with the power demand may be preset to determine the target power demand.
[0074] An environmental impact information database can be established in advance, which contains the specific impact information of different environmental data (such as temperature, humidity, light, etc.) on the energy consumption of electrical equipment. This information may exist in the form of tables, functions or models, describing the quantitative relationship between environmental variables and equipment energy consumption. According to the real-time collected environmental data, the corresponding impact information is queried in the environmental impact information database. This information will be used for subsequent adjustments to the predicted energy consumption data.
[0075] The target electricity demand, the impact information corresponding to the environmental data and the predicted energy consumption data are combined, and a preset calculation method is used, which includes weighting, multiplication or other suitable mathematical operations. The calculation method can be set based on the actual application process, and the predicted energy consumption data is adjusted to obtain the operating energy consumption data.
[0076] This application collects environmental data in real time and dynamically adjusts electricity demand, combined with comprehensive adjustments of environmental impact information, to make the prediction of operating energy consumption data more accurate. Dynamic adjustments based on environmental data can ensure that unnecessary power consumption is minimized while meeting the needs of venue application arrangements. For example, reducing the use of lighting systems when there is sufficient light, or reducing the load of air conditioning systems when the temperature is appropriate, thereby optimizing energy efficiency.
[0077] The following describes an apparatus embodiment of the present application, which can be used to execute the method embodiment of the present application. For details not disclosed in the apparatus embodiment of the present application, reference can be made to the method embodiment of the present application.
[0078] Figure 3 This is a block diagram of a smart electricity management device provided in an embodiment of the present application. Figure 3 As shown, the smart electricity management device 300 includes a device determination module 301, a predicted energy consumption data determination module 302, an operation energy consumption data determination module 303 and a power consumption hidden danger information determination module 304.
[0079] The equipment determination module 301 is used to obtain the function information of the target power consumption site, and determine the key power consumption equipment and conventional power consumption equipment of the target power consumption site according to the function information; The predicted energy consumption data determination module 302 is used to determine the predicted energy consumption data of the key power-consuming equipment according to the historical management data and the measured operation data of the key power-consuming equipment in the preset historical selection period; The operation energy consumption data determination module 303 is used to determine the operation energy consumption data according to the real-time power demand, environmental data and predicted energy consumption data of the target power consumption site; The power usage hidden danger information determination module 304 is used to determine the power usage hidden danger information at the current moment according to the equipment operation information and operation energy consumption data of the conventional power-consuming equipment.
[0080] Optionally, the device determination module 301 is specifically configured to: According to the functional information, multiple electrical equipment in the target electrical consumption location are determined; Obtain historical power consumption data corresponding to multiple power-consuming devices; Based on historical electricity consumption data, multiple power-consuming equipment are divided into key power-consuming equipment and conventional power-consuming equipment.
[0081] Optionally, when the device determination module 301 divides a plurality of power-consuming devices into key power-consuming devices and conventional power-consuming devices according to historical power-consuming data, it is specifically configured to: Analyze historical electricity consumption data to determine the electricity demand and electricity consumption period characteristics of each of multiple electricity-consuming devices; According to the preset site power priority, power demand and power period characteristics, key power equipment and conventional power equipment are determined among multiple power equipment.
[0082] Optionally, the predicted energy consumption data determination module 302 is specifically used for: Obtain historical management data; Extract historical maintenance information, historical operating parameters and historical management information from historical management data; Determine the predicted energy consumption data based on historical maintenance information, historical operating parameters, historical management information and measured operating data.
[0083] Optionally, when the predicted energy consumption data determining module 302 determines the predicted energy consumption data according to the historical maintenance information, the historical operating parameters, the historical management information and the measured operating data, it is specifically used to: Based on historical maintenance information, determine the maintenance cycle of the target power consumption site, the maintenance time and fault information of key power consumption equipment and conventional power consumption equipment; According to the maintenance cycle, maintenance time, fault information and preset equipment life information, determine the equipment loss parameters corresponding to the key power-consuming equipment and conventional power-consuming equipment at the current moment; Train energy consumption prediction models based on equipment loss parameters, historical operating parameters, and historical management information; The measured operating data is input into the energy consumption prediction model, and the output determines the predicted energy consumption data.
[0084] Optionally, the smart electricity management device 300 further includes a demand determination module 305, which is used to: Obtaining the site application arrangement information of the target power consumption site at the current moment; Determine real-time power demand based on site application scheduling information.
[0085] Optionally, the operation energy consumption data determination module 303 is specifically used for: Update the real-time power demand based on environmental data to determine the target power demand; Determine the impact information corresponding to the environmental data according to the preset environmental impact information; According to the impact information corresponding to the target electricity demand and environmental data, the predicted energy consumption data is adjusted to determine the operating energy consumption data.
[0086] The device performs functions similar to the method provided above. For other functions, please refer to the previous description and will not be repeated here.
[0087] Figure 4 A schematic diagram of the structure of an electronic device provided in an embodiment of the present application, such as Figure 4 As shown, the electronic device 400 of this embodiment may include: a memory 401 and a processor 402 .
[0088] The memory 401 stores a computer program. When the computer program is executed by the processor 402, the processor 402 executes the method in the above embodiment.
[0089] The processor 402 and the memory 401 are connected, for example, via a bus.
[0090] Optionally, the electronic device 400 may further include a transceiver. It should be noted that in actual applications, the number of transceivers is not limited to one, and the structure of the electronic device 400 does not constitute a limitation on the embodiments of the present application.
[0091] Processor 402 may be a CPU (Central Processing Unit), a general-purpose processor, a DSP (Digital Signal Processor), an ASIC (Application Specific Integrated Circuit), an FPGA (Field Programmable Gate Array), or other programmable logic devices, transistor logic devices, hardware components, or any combination thereof. It may implement or execute various exemplary logic blocks, modules, and circuits described in conjunction with the disclosure of this application. Processor 402 may also be a combination that implements computing functions, such as a combination of one or more microprocessors, a combination of a DSP and a microprocessor, and the like.
[0092] The bus may include a path to transmit information between the above components. The bus may be a PCI (Peripheral Component Interconnect) bus or an EISA (Extended Industry Standard Architecture) bus. The bus may be divided into an address bus, a data bus, a control bus, etc. For ease of representation, only one thick line is used in the figure, but it does not mean that there is only one bus or one type of bus.
[0093] The memory 401 may be a ROM (Read Only Memory) or other types of static storage devices that can store static information and instructions, a RAM (Random Access Memory) or other types of dynamic storage devices that can store information and instructions, or an EEPROM (Electrically Erasable Programmable Read Only Memory), a CD-ROM (Compact Disc Read Only Memory) or other optical disk storage, optical disk storage (including compressed optical disk, laser disk, optical disk, digital versatile disk, Blu-ray disk, etc.), a magnetic disk storage medium or other magnetic storage device, or any other medium that can be used to carry or store the desired program code in the form of instructions or data structures and can be accessed by a computer, but is not limited thereto.
[0094] The memory 401 is used to store the application code for executing the solution of the present application, and the execution is controlled by the processor 402. The processor 402 is used to execute the application code stored in the memory 401 to implement the contents shown in the above method embodiment.
[0095] The electronic devices include, but are not limited to, mobile terminals such as mobile phones, laptop computers, digital broadcast receivers, PDAs (personal digital assistants), PADs (tablet computers), PMPs (portable multimedia players), vehicle-mounted terminals (such as vehicle-mounted navigation terminals), and fixed terminals such as digital TVs, desktop computers, etc. It can also be a server, etc. Figure 4 The electronic device shown is merely an example and should not bring any limitation to the functions and scope of use of the embodiments of the present application.
[0096] The electronic device of this embodiment can be used to execute the method of any of the above embodiments, and its implementation principle and technical effect are similar, which will not be described in detail here.
[0097] The present application also provides a non-transitory computer-readable storage medium having computer-readable instructions stored thereon. When the aforementioned instructions are executed by a processor, the processor executes the method in the above embodiment.
[0098] Those skilled in the art can understand that all or part of the steps of implementing the above-mentioned method embodiments can be completed by hardware related to program instructions. The aforementioned program can be stored in a non-transient computer-readable storage medium. When the program is executed, the steps of the above-mentioned method embodiments are executed; and the aforementioned storage medium includes: ROM, RAM, disk or optical disk and other media that can store program codes.
[0099] The embodiments of the present application are described in detail above. Specific examples are used herein to illustrate the principles and implementation methods of the present application. The description of the above embodiments is only used to help understand the method and its core idea of the present application. At the same time, changes or deformations made by those skilled in the art based on the ideas of the present application, the specific implementation methods and the scope of application of the present application, all belong to the scope of protection of the present application. In summary, the content of this specification should not be construed as a limitation on the present application.
Claims
1. A smart electricity management method, characterized in that: include: Acquire the functional information of the target power consumption site, and determine the key power consumption equipment and conventional power consumption equipment of the target power consumption site according to the functional information; Determine the predicted energy consumption data of the key power-consuming equipment according to the historical management data and the measured operation data of the key power-consuming equipment within a preset historical selection period; Determine the operating energy consumption data according to the real-time power demand of the target power consumption site, environmental data and the predicted energy consumption data; The hidden danger information of electricity consumption at the current moment is determined according to the equipment operation information of the conventional electrical equipment and the operation energy consumption data.
2. The method according to claim 1, characterized in that: The step of determining the key power-consuming equipment and conventional power-consuming equipment of the target power-consuming place according to the functional information includes: Determine a plurality of electrical equipment at the target electrical location according to the functional information; Acquire historical power consumption data corresponding to each of the plurality of power-consuming devices; According to the historical power consumption data, the plurality of power-consuming devices are divided into the key power-consuming devices and the conventional power-consuming devices.
3. The method according to claim 2, characterized in that The dividing the plurality of power-consuming devices into the key power-consuming devices and the conventional power-consuming devices according to the historical power-consuming data comprises: Analyze the historical power consumption data to determine the power consumption demand and power consumption period characteristics of each of the multiple power consumption devices; The key power-consuming equipment and the conventional power-consuming equipment are determined from among the plurality of power-consuming equipment according to the preset site power-consuming priority, the power-consuming demand and the characteristics of the power-consuming period.
4. The method according to claim 1, characterized in that: The step of determining the predicted energy consumption data of the key power-consuming equipment according to the historical management data and the measured operation data of the key power-consuming equipment in a preset historical selection period includes: Acquiring the historical management data; Extracting historical maintenance information, historical operating parameters and historical management information from the historical management data; The predicted energy consumption data is determined based on the historical maintenance information, the historical operating parameters, the historical management information and the measured operating data.
5. The method according to claim 4, characterized in that The step of determining the predicted energy consumption data according to the historical maintenance information, the historical operating parameters, the historical management information and the measured operating data includes: Determine the maintenance period of the target power consumption site, the maintenance time and fault information corresponding to the key power consumption equipment and the conventional power consumption equipment respectively according to the historical maintenance information; Determine the equipment loss parameters corresponding to the key electrical equipment and the conventional electrical equipment at the current moment according to the maintenance cycle, the maintenance time, the fault information and the preset equipment life information; Training an energy consumption prediction model according to the equipment loss parameters, the historical operation parameters, and the historical management information; The measured operating data is input into the energy consumption prediction model, and the predicted energy consumption data is output to determine the predicted energy consumption data.
6. The method according to any one of claims 1 to 5, characterized in that: Also includes: Obtaining the site application arrangement information of the target power consumption site at the current moment; The real-time power demand is determined according to the site application arrangement information.
7. The method according to claim 1, characterized in that The step of determining the operating energy consumption data according to the real-time power demand of the target power consumption site, the environmental data and the predicted energy consumption data includes: updating the real-time power demand according to the environmental data to determine the target power demand; Determining the impact information corresponding to the environmental data according to the preset environmental impact information; The predicted energy consumption data is adjusted according to the target power demand and the impact information corresponding to the environmental data to determine the operating energy consumption data.
8. A smart electricity management device, characterized in that: include: An equipment determination module is used to obtain the functional information of the target power consumption site, and determine the key power consumption equipment and conventional power consumption equipment of the target power consumption site according to the functional information; A predicted energy consumption data determination module, used to determine the predicted energy consumption data of the key power-consuming equipment according to the historical management data and the measured operation data of the key power-consuming equipment within a preset historical selection period; An operation energy consumption data determination module, used to determine the operation energy consumption data according to the real-time power demand of the target power consumption site, environmental data and the predicted energy consumption data; The power usage hidden danger information determination module is used to determine the power usage hidden danger information at the current moment according to the equipment operation information of the conventional power-consuming equipment and the operation energy consumption data.
9. An electronic device, characterized in that: include: processor; A memory storing a computer program, which, when executed by the processor, enables the processor to execute the smart electricity management method as described in any one of claims 1 to 7.
10. A non-transitory computer-readable storage medium, characterized in that: Computer-readable instructions are stored thereon, and when the instructions are executed by a processor, the processor executes the smart electricity management method as described in any one of claims 1-7.