Energy optimization method and device for temporary power consumption scenario, equipment and medium

Abstract

The application discloses an energy optimization method and device for a temporary power consumption scene, equipment and a medium. The method comprises the following steps: for each power distribution node, collecting electrical equipment characteristics of at least one electrical equipment in a power distribution area corresponding to the power distribution node; performing feature extraction on the electrical equipment characteristics to determine an electrical characteristic vector of the electrical equipment; performing first category screening and second category screening on a device type cascade of the electrical equipment based on the electrical equipment characteristics and the electrical characteristic vector to determine a target device type to which the electrical equipment belongs; and performing energy optimization on a target construction power distribution system based on the target device type of the electrical equipment, an operation condition of the electrical equipment and cumulative energy consumption of the electrical equipment under the operation condition. Through the technical scheme, the energy utilization rate of the power distribution system is improved.

Description

Technical Field

[0001] This application relates to the field of energy management technology, and more particularly to the cross-disciplinary fields of smart construction sites, power load identification and energy dispatch optimization. Specifically, it relates to an energy optimization method, device, equipment and medium for temporary power consumption scenarios. Background Technology

[0002] Temporary power distribution systems at construction sites differ fundamentally from those in conventional buildings. They are characterized by a diverse range of equipment (from tower cranes and construction elevators with tens of kilowatts to hand tools with hundreds of kilowatts connected simultaneously), non-standard temporary wiring, frequent changes in electrical wiring topology, and a large number of inductive and nonlinear loads (such as welding machines and frequency converters), resulting in high harmonic content and complex transient waveforms in the power supply lines. These unique characteristics render conventional non-intrusive load monitoring methods based on steady-state active power changes severely ineffective in construction site scenarios. They struggle to accurately identify equipment types and operating states, making it difficult to optimize the power distribution system's energy performance based on the equipment type and operating status.

[0003] Therefore, there is an urgent need for a device-level energy consumption perception and multi-energy collaborative optimization method that can cope with the special scenarios of temporary power use on construction sites. Summary of the Invention

[0004] This application provides an energy optimization method, apparatus, equipment, and medium for temporary power consumption scenarios, in order to reduce the energy consumption of the power distribution system deployed in the temporary power consumption scenario and improve the energy utilization rate of the power distribution system.

[0005] According to one aspect of this application, an energy optimization method for temporary power consumption scenarios is provided, the method comprising: For each power distribution node, electrical equipment characteristics of at least one electrical device within the power distribution area corresponding to the power distribution node are collected; wherein, the electrical equipment characteristics include fundamental and harmonic characteristics, transient event waveform characteristics, power envelope characteristics, and environmental characteristics; Feature extraction is performed on the electrical equipment to determine the electrical feature vector of the electrical equipment; Based on the electrical equipment features and the electrical feature vector, the equipment types of the electrical equipment are cascaded and subjected to first category filtering and second category filtering to determine the target equipment type to which the electrical equipment belongs; Based on the target equipment type, operating conditions, and cumulative energy consumption of the electrical equipment under its operating conditions, energy optimization is performed on the target construction power distribution system; wherein, the target construction power distribution system refers to a power distribution system oriented towards temporary power consumption scenarios.

[0006] According to another aspect of this application, an energy optimization device for temporary power consumption scenarios is provided, the device comprising: The feature acquisition module is used to acquire electrical equipment features of at least one electrical device in the power distribution area corresponding to each power distribution node; wherein, the electrical equipment features include fundamental and harmonic features, transient event waveform features, power envelope features, and environmental features; The feature extraction module is used to extract features from the electrical equipment and determine the electrical feature vector of the electrical equipment. The category filtering module is used to perform first category filtering and second category filtering on the equipment type of the electrical equipment based on the electrical equipment features and the electrical feature vector, so as to determine the target equipment type to which the electrical equipment belongs; The energy optimization module is used to optimize the energy of the target construction power distribution system based on the target equipment type of the electrical equipment, the operating conditions of the electrical equipment, and the cumulative energy consumption of the operating conditions of the electrical equipment; wherein, the target construction power distribution system refers to the power distribution system for temporary power consumption scenarios.

[0007] According to another aspect of this application, an electronic device is provided, the electronic device comprising: One or more processors; Memory, used to store one or more programs; When the one or more programs are executed by the one or more processors, the one or more processors implement any of the energy optimization methods for temporary power consumption scenarios provided in the embodiments of this application.

[0008] According to another aspect of this application, a computer-readable storage medium is provided, on which a computer program is stored, which, when executed by a processor, implements any of the energy optimization methods for temporary power consumption scenarios provided in the embodiments of this application.

[0009] According to another aspect of this application, a computer program product is provided, including a computer program that, when executed by a processor, implements any of the energy optimization methods for temporary power consumption scenarios provided in the embodiments of this application.

[0010] This application utilizes non-intrusive monitoring of electrical equipment in the target construction power distribution system to uncover the unique characteristics of electrical equipment in temporary power supply scenarios. It extracts multi-dimensional electrical feature vectors to achieve robust identification of equipment types in high-noise environments. Using the equipment type identification results as prior knowledge, and combining them with the operating conditions and cumulative energy consumption of the equipment, the application optimizes the energy consumption of the target construction power distribution system. This reduces energy consumption in temporary power supply scenarios and improves the energy utilization rate of the power distribution system. Attached Figure Description

[0011] Figure 1 This is a flowchart of an energy optimization method for temporary power consumption scenarios provided in Embodiment 1 of this application; Figure 2 This is a flowchart of an energy optimization method for temporary power consumption scenarios provided in Embodiment 2 of this application; Figure 3 This is a schematic diagram of the structure of an energy optimization device for temporary power consumption scenarios provided in Embodiment 3 of this application; Figure 4 This is a schematic diagram of the structure of an electronic device that implements the energy optimization method for temporary power consumption scenarios in Embodiment 4 of this application. Detailed Implementation

[0012] To enable those skilled in the art to better understand the present application, the technical solutions in the embodiments of the present application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present application, and not all embodiments. Based on the embodiments in the present application, all other embodiments obtained by those of ordinary skill in the art without creative effort should fall within the scope of protection of the present application.

[0013] It should be noted that the terms "first," "second," etc., in the specification, claims, and accompanying drawings of this application are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence. It should be understood that such data can be interchanged where appropriate so that the embodiments of this application described herein can be implemented in orders other than those illustrated or described herein. Furthermore, the terms "comprising" and "having," and any variations thereof, are intended to cover non-exclusive inclusion; for example, a process, method, system, product, or apparatus that comprises a series of steps or units is not necessarily limited to those steps or units explicitly listed, but may include other steps or units not explicitly listed or inherent to such processes, methods, products, or apparatus.

[0014] Example 1 Figure 1 This is a flowchart illustrating an energy optimization method for temporary power consumption scenarios according to Embodiment 1 of this application. This embodiment is applicable to energy optimization of power distribution systems deployed in temporary power consumption scenarios. The optimization can be performed by an energy optimization device for temporary power consumption scenarios, which can be implemented in hardware and / or software. This energy optimization device can be configured in a computer device, such as a server used for power distribution management. Figure 1 As shown, the method includes: S110. For each power distribution node, collect the electrical equipment characteristics of at least one electrical device in the power distribution area corresponding to the power distribution node.

[0015] The electrical equipment characteristics can include fundamental and harmonic characteristics, transient event waveform characteristics, power envelope characteristics, and environmental characteristics. It should be noted that fundamental and harmonic characteristics refer to the amplitude and phase of the fundamental and harmonic components of voltage and current extracted through Fast Fourier Transform; transient event waveform characteristics can refer to the transient feature vector generated by the transient process of the current waveform during the start-up and shutdown of electrical equipment (e.g., starting current impact envelope, waveform distortion rate time-series change, reactive power transient response); power envelope characteristics can refer to the low-frequency envelope signals of active power, reactive power, and apparent power, which can be used to identify load rate change patterns during the operation of electrical equipment (e.g., the periodic lifting / slewing operation characteristics of tower cranes); environmental characteristics can refer to the environmental parameters of the area where the electrical equipment is located (e.g., temperature, humidity, and noise spectrum). Optionally, environmental characteristics can be used as auxiliary characteristics to distinguish equipment states with similar electrical characteristics but different physical operating conditions.

[0016] In one optional implementation, the construction power distribution system includes multiple distribution nodes of different levels, such as main incoming line cabinets, regional distribution boxes, and dedicated circuits for large equipment. Multimodal feature acquisition units can be deployed at different distribution nodes to collect the electrical equipment characteristics of the electrical equipment within their respective distribution areas. It should be noted that the multimodal feature acquisition units deployed in this embodiment of the invention differ from conventional smart meters in that they possess high-frequency sampling capabilities (sampling rate not less than 6.4kHz), enabling simultaneous acquisition of fundamental and harmonic characteristics, transient event waveform characteristics, and power envelope characteristics.

[0017] S120. Extract features from electrical equipment to determine the electrical feature vector of the electrical equipment.

[0018] Optionally, feature extraction is performed on the electrical equipment features to determine the electrical feature vector of the electrical equipment, including: monitoring feature mutations in the electrical equipment features of the electrical equipment; if an electrical equipment feature with a change exceeding a preset change threshold is detected within an adjacent monitoring period, it indicates that a load event has occurred in the electrical equipment; for the electrical equipment that has experienced a load event, feature extraction is performed on the electrical equipment features of the electrical equipment to determine the electrical feature vector of the electrical equipment.

[0019] Load events can include the start-up and shutdown of electrical equipment and the switching of electrical equipment operating conditions. It should be noted that, in this embodiment of the invention, the switching of electrical equipment can be used to characterize the situation where the electrical load changes due to changes in construction procedures. This can include equipment frequency conversion, load rate step switching, intermittent working mode switching, and mechanical gear / process switching, etc. For example, the power of the welding machine suddenly increases at the moment of arc ignition, the lifting and lowering changes of the pipe hoisting machine / tower crane, the valve adjustment of the water pump (e.g., switching from "low head and high flow" to "high head and low flow"), and the functional change of the air compressor ("loading to produce gas" and "unloading and idling", etc.).

[0020] It should be noted that, in this embodiment of the invention, the amplitude changes of the five harmonic components before and after the occurrence of a load event are extracted based on the fundamental and harmonic characteristics. For example, the amplitude changes of harmonic components between one monitoring cycle before and after the occurrence of a load event, and the amplitude changes of harmonic components between two monitoring cycles before and after the occurrence of a load event. Based on the transient event waveform characteristics, the ratio of the instantaneous current peak value at the time of the load event to the steady-state current after the occurrence of the load event, as well as the event duration, are extracted. Based on the power envelope characteristics, the active power step, reactive power step, and power factor changes before and after the occurrence of a load event can be extracted. Based on the environmental characteristics, the noise spectrum characteristics changes of the area where the electrical equipment is located at the time of the load event can be extracted. Optionally, since typical nonlinear loads on construction sites (such as welding machines and frequency conversion drive equipment) exhibit significant and distinguishable working fingerprints in the fifth harmonic frequency band, the amplitude changes of the five harmonic components before and after the occurrence of a load event can be extracted based on the fundamental and harmonic characteristics and used as the core identification features of the electrical feature vector.

[0021] Optionally, in one specific implementation, the characteristic mutation monitoring results may include mutations in fundamental current / power, mutations in higher harmonic distortion rate, and transient oscillation waveform changes. Mutations in fundamental current / power can characterize a change in the active power of the electrical equipment exceeding a first preset active power change threshold within adjacent monitoring periods. Mutations in higher harmonic distortion rate can characterize a change in the total active power of the electrical equipment less than a second preset active power change threshold within adjacent monitoring periods, but a change in the total harmonic distortion rate or the amplitude of a specific harmonic within adjacent monitoring periods exceeding a preset change rate threshold. Transient oscillation waveform changes can characterize the detection of voltage / current waveform segments with high-frequency oscillations (typically at the kHz level), rather than stable sine waves. For example, in a pipeline welding operation, when 2-3 welding machines start welding almost simultaneously, the total incoming current will suddenly increase from 100A to 400A within 1 second, at which point a mutation in fundamental current / power will be detected. Optionally, the preset change threshold, the first preset active power change threshold, the second preset active power change threshold, and the preset change rate threshold can be adaptively set according to those skilled in the art, and the first preset active power change threshold is greater than the second preset active power change threshold.

[0022] Optionally, in this embodiment of the invention, feature mutation monitoring can be performed by an edge computing gateway deployed at the power distribution node or by a server. By performing feature mutation monitoring by an edge computing gateway deployed at the power distribution node, the operating pressure on the server can be effectively reduced.

[0023] By monitoring the abrupt changes in the electrical characteristics of electrical equipment, it is possible to effectively detect electrical equipment that is in operation or in use.

[0024] S130. Based on the characteristics of electrical equipment and electrical feature vectors, perform first-category screening and second-category screening on the cascaded equipment types of electrical equipment to determine the target equipment type to which the electrical equipment belongs.

[0025] It should be noted that the second category screening is conducted based on the first category screening.

[0026] Optionally, after determining the target equipment type of the electrical equipment, the method further includes: continuously monitoring the electrical equipment characteristics of the electrical equipment of the target equipment type; issuing an abnormal alarm for the electrical equipment when the difference between the electrical equipment characteristics of the electrical equipment and the standard electrical equipment characteristic threshold exceeds a preset difference; identifying the equipment operating status of the electrical equipment of the target equipment type based on the electrical equipment characteristics; and determining the energy consumption information corresponding to the electrical equipment under different equipment operating statuses based on the equipment operating status of the electrical equipment of the target equipment type, which is used as the cumulative energy consumption of the electrical equipment under different operating conditions.

[0027] By continuously monitoring specific types of electrical equipment, the operating status and cumulative energy consumption of each item under different operating conditions of the equipment are determined, laying a data foundation for subsequent energy optimization of the target construction power distribution system.

[0028] S140. Based on the target equipment type, operating conditions, and cumulative energy consumption of the equipment under different operating conditions, optimize the energy distribution system for the target construction project.

[0029] Among them, the target construction power distribution system can refer to the power distribution system for temporary power use scenarios that is to be optimized for energy.

[0030] This application embodiment performs non-intrusive monitoring of electrical equipment in the target construction power distribution system, mines the special characteristics of electrical equipment in temporary power supply scenarios, extracts multi-dimensional electrical feature vectors, achieves robustness in identifying the type of electrical equipment in a high-noise environment, and uses the equipment type identification results as prior knowledge. Combined with the operating conditions of the electrical equipment and the cumulative energy consumption of the operating conditions of the electrical equipment, the energy of the target construction power distribution system is optimized, reducing the energy consumption of the power distribution system deployed in the temporary power supply scenario and improving the energy utilization rate of the power distribution system.

[0031] Example 2 Figure 2 This is a flowchart of an energy optimization method for temporary power consumption scenarios provided in Embodiment 2 of this application. Based on the technical solutions of the above embodiments, this embodiment further refines the process of "optimizing the energy of the target construction power distribution system based on the target equipment type, operating conditions, and cumulative energy consumption of the equipment under different operating conditions." It should be noted that for parts not detailed in this embodiment, please refer to the relevant descriptions in other embodiments. Figure 2 As shown, the method includes: S210. For each power distribution node, collect the electrical equipment characteristics of at least one electrical device in the power distribution area corresponding to the power distribution node.

[0032] S220. Extract features from electrical equipment to determine the electrical feature vector of the electrical equipment.

[0033] S230. Based on the characteristics of electrical equipment and electrical feature vectors, perform first-category screening and second-category screening on the cascaded equipment types of electrical equipment to determine the target equipment type to which the electrical equipment belongs.

[0034] Optionally, based on the characteristics of electrical equipment and electrical feature vectors, the equipment types of electrical equipment are cascaded to perform first-category screening and second-category screening to determine the target equipment type to which the electrical equipment belongs. This includes: performing first-category screening based on the harmonic fingerprint characteristics of the electrical equipment and a preset harmonic fingerprint database of electrical equipment to determine at least one candidate equipment type of the electrical equipment; and performing second-category screening based on the electrical feature vector of the electrical equipment and the equipment type identification model corresponding to the candidate equipment type to determine the target equipment type corresponding to the electrical equipment.

[0035] It should be noted that the harmonic fingerprint database of electrical equipment can be established by offline feature calibration of electrical equipment. Each type of electrical equipment in the harmonic fingerprint database has a corresponding unique harmonic fingerprint template.

[0036] Specifically, after determining at least one candidate device type for the electrical equipment, the electrical feature vector of the electrical equipment can be input into the device type recognition model corresponding to each candidate device type. This yields the device type recognition result and confidence score output by each model. The device type recognition result with the highest confidence score (positive value) is taken as the target device type for the electrical equipment. It should be noted that a positive output from the device type recognition model indicates that the currently input electrical feature vector matches the candidate device type to which the model belongs; a negative output indicates that the currently input electrical feature vector does not match the candidate device type to which the model belongs. Optionally, the device type recognition model can be a pre-trained neural network model for device type recognition.

[0037] By classifying and identifying target device types, the efficiency of target device type identification is improved, while the performance pressure during the target device type identification process is reduced.

[0038] S240. Based on the target equipment type and real-time operating conditions of the electrical equipment in at least one power distribution area, determine the real-time equipment power distribution ratio corresponding to the target construction power distribution system.

[0039] Among them, the real-time equipment power distribution can be used to characterize the power proportion of each target equipment type in the current total power load of the target construction power distribution system.

[0040] Specifically, the types of electrical equipment in each distribution area of ​​the target construction power distribution system can be summarized. The electrical equipment can be grouped according to the type of target equipment. Each target equipment type corresponds to an electrical equipment group, and each electrical equipment group contains at least one electrical equipment. Based on the real-time operating conditions of each electrical equipment in the electrical equipment group, the total power of the target equipment type corresponding to that electrical equipment group can be determined. Then, the real-time power distribution of the equipment in the target construction power distribution system can be determined based on the total power corresponding to each target equipment type.

[0041] S250. Based on the preset load mapping relationship between generator fuel consumption power and load composition, determine the optimal power of the generator under the real-time equipment power distribution conditions.

[0042] The load mapping relationship can be used to record the optimal fuel consumption power of the engine under different load composition ratios. Optionally, the load mapping relationship can be set by those skilled in the art based on prior knowledge in the field.

[0043] Optionally, the load type of the electrical equipment can be determined based on its corresponding target equipment type. The load type can include resistive loads, inductive loads, and nonlinear loads. When the target equipment type in the real-time equipment power distribution is different, the load composition corresponding to the real-time equipment power distribution will also be different. It should be noted that a resistive load can be used to characterize a load in which almost all electrical energy is converted into heat or light energy when current passes through the electrical equipment, and the current and voltage are always in phase. An inductive load can refer to a load in which electrical energy is mainly converted into magnetic field energy when current passes through the electrical equipment, and the current phase lags the voltage phase by 90 degrees. A nonlinear load can refer to a load in which the current and voltage are not linearly proportional when current passes through the electrical equipment, generating harmonic currents, and the current waveform of a nonlinear load is not a sine wave.

[0044] Optionally, in this embodiment of the invention, the load power ratio corresponding to each load type under the real-time equipment power ratio distribution can be determined according to the load type corresponding to the target equipment type. Then, the load composition score under the real-time equipment power ratio distribution can be determined according to the load weight corresponding to each load type. Finally, the optimal fuel consumption power of the generator can be determined in the load mapping relationship based on the load composition score. Optionally, the load weight can be set by those skilled in the art; the load weights corresponding to different load types can be the same or different.

[0045] Optionally, in another embodiment of the present invention, the construction procedure corresponding to the real-time operating status of each electrical device can be determined based on a preset mapping database of device operating status and construction procedures, and the construction time of the user device within the construction procedure can be monitored. This construction time is compared with the standard construction time corresponding to the construction procedure to determine the type of construction progress deviation and issue an alarm. For example, if the construction time is less than the standard construction time, the current construction procedure is in a delayed stage; if the construction time is greater than the standard construction time, the current construction procedure is in an advanced stage. Optionally, the mapping database of device operating status and construction procedures can be set by those skilled in the art based on prior knowledge, and the standard construction time corresponding to the construction procedure can be adapted to settings by those skilled in the art.

[0046] By indirectly sensing construction progress through non-invasive electrical monitoring methods, without relying on manual reporting or invasive construction monitoring systems, the efficiency of determining construction progress is improved, while reducing labor costs.

[0047] This application embodiment optimizes the generator's energy based on the equipment-level load decomposition results, achieving differentiated control of the generator's optimal operating point under different combinations of electrical equipment. This avoids coarse-grained planning of generator energy based on the total load curve of the target power distribution system, thereby improving the energy utilization rate of the power distribution system.

[0048] Example 3 Figure 3 This is a structural schematic diagram of an energy optimization device for temporary power consumption scenarios provided in Embodiment 3 of this application. It is applicable to situations where energy optimization is performed on power distribution systems deployed in temporary power consumption scenarios. This energy optimization device for temporary power consumption scenarios can be implemented in hardware and / or software, and can be configured in computer equipment, such as a server. Figure 3 As shown, the device includes: The feature acquisition module 310 is used to acquire electrical equipment features of at least one electrical device in the power distribution area corresponding to each power distribution node; wherein, the electrical equipment features include fundamental and harmonic features, transient event waveform features, power envelope features, and environmental features; Feature extraction module 320 is used to extract features from the electrical equipment and determine the electrical feature vector of the electrical equipment; The category filtering module 330 is used to perform first category filtering and second category filtering on the equipment type of the electrical equipment based on the electrical equipment features and the electrical feature vector, so as to determine the target equipment type to which the electrical equipment belongs; The energy optimization module 340 is used to optimize the energy of the target construction power distribution system based on the target equipment type of the electrical equipment, the operating conditions of the electrical equipment, and the cumulative energy consumption of the operating conditions of the electrical equipment; wherein, the target construction power distribution system refers to the power distribution system for temporary power consumption scenarios.

[0049] This application embodiment performs non-intrusive monitoring of electrical equipment in the target construction power distribution system, mines the special characteristics of electrical equipment in temporary power supply scenarios, extracts multi-dimensional electrical feature vectors, achieves robustness in identifying the type of electrical equipment in a high-noise environment, and uses the equipment type identification results as prior knowledge. Combined with the operating conditions of the electrical equipment and the cumulative energy consumption of the operating conditions of the electrical equipment, the energy of the target construction power distribution system is optimized, reducing the energy consumption of the power distribution system deployed in the temporary power supply scenario and improving the energy utilization rate of the power distribution system.

[0050] Optionally, the energy optimization module 340 includes: The power proportion distribution unit is used to determine the real-time equipment power proportion distribution corresponding to the target construction power distribution system based on the target equipment type and the real-time operating conditions of the electrical equipment in the at least one power distribution area; wherein, the real-time equipment power proportion distribution is used to characterize the power proportion of each target equipment type in the current total power load of the target construction power distribution system; The optimal power determination unit is used to determine the optimal power of the generator under the real-time equipment power ratio distribution conditions based on the preset load mapping relationship between the generator fuel consumption power and the load composition; wherein, the load mapping relationship is used to record the optimal fuel consumption power of the engine under different load composition ratio conditions.

[0051] Optionally, the load type of the electrical equipment is determined according to its corresponding target equipment type. The load type includes resistive load, inductive load and nonlinear load. When the target equipment type in the real-time equipment power ratio distribution is different, the load composition corresponding to the real-time equipment power ratio distribution is also different.

[0052] Optionally, the category filtering module 330 includes: The first screening unit is used to perform a first category screening based on the harmonic fingerprint characteristics of the electrical equipment and a preset harmonic fingerprint database of electrical equipment, and to determine at least one candidate equipment type of the electrical equipment. The second screening unit is used to perform a second category screening based on the electrical feature vector of the electrical equipment and the equipment type identification model corresponding to the candidate equipment type, so as to determine the target equipment type corresponding to the electrical equipment.

[0053] Optionally, the device may also include: The continuous monitoring module is used to continuously monitor the electrical equipment characteristics of the target equipment type. When the difference between the electrical equipment characteristics of the equipment and the standard electrical equipment characteristic threshold exceeds a preset difference, an abnormal alarm is issued for the equipment. Based on the electrical equipment characteristics, the operating status of the equipment of the target equipment type is identified. The working condition energy consumption determination module is used to determine the energy consumption information corresponding to the electrical equipment under different operating conditions based on the operating status of the electrical equipment of the target equipment type, and use it as the cumulative energy consumption of the electrical equipment under different operating conditions.

[0054] Optionally, the feature extraction module 320 includes: The mutation monitoring unit is used to monitor the electrical characteristics of electrical equipment. If an electrical equipment characteristic with a change exceeding a preset change threshold is detected within an adjacent monitoring period, it indicates that a load event has occurred in the electrical equipment. The load event includes the start-up and shutdown of the electrical equipment and the switching of the operating conditions of the electrical equipment. The feature extraction unit is used to extract the electrical features of the electrical equipment that has experienced a load event, and to determine the electrical feature vector of the electrical equipment.

[0055] The energy optimization device for temporary power consumption scenarios provided in this application can execute the energy optimization method for temporary power consumption scenarios provided in any embodiment of this application, and has the corresponding functional modules and beneficial effects for executing the energy optimization method for each temporary power consumption scenario.

[0056] According to embodiments of this application, this application also provides an electronic device, a readable storage medium, and a computer program product.

[0057] Example 4 Figure 4 This is a schematic diagram of the structure of an electronic device 410 implementing the energy optimization method for temporary power consumption scenarios according to embodiments of this application. The electronic device is intended to represent various forms of digital computers, such as laptop computers, desktop computers, workstations, personal digital assistants, servers, blade servers, mainframe computers, and other suitable computers. The electronic device may also represent various forms of mobile devices, such as personal digital processors, cellular phones, smartphones, wearable devices (such as helmets, glasses, watches, etc.), and other similar computing devices. The components shown herein, their connections and relationships, and their functions are merely examples and are not intended to limit the implementation of the present application described and / or claimed herein.

[0058] like Figure 4As shown, the electronic device 410 includes at least one processor 411 and a memory, such as a read-only memory 412 or a random access memory 413, communicatively connected to the at least one processor 411. The memory stores computer programs executable by the at least one processor. The processor 411 can perform various appropriate actions and processes based on the computer program stored in the read-only memory 412 or loaded from storage unit 418 into the random access memory 413. The random access memory 413 can also store various programs and data required for the operation of the electronic device 410. The processor 411, read-only memory 412, and random access memory 413 are interconnected via a bus 414. An input / output interface 415 is also connected to the bus 414.

[0059] Multiple components in electronic device 410 are connected to input / output interface 415, including: input unit 416, such as keyboard, mouse, etc.; output unit 417, such as various types of monitors, speakers, etc.; storage unit 418, such as disk, optical disk, etc.; and communication unit 419, such as network card, modem, wireless transceiver, etc. Communication unit 419 allows electronic device 410 to exchange information / data with other devices through computer networks such as the Internet and / or various telecommunications networks.

[0060] Processor 411 can be a variety of general-purpose and / or special-purpose processing components with processing and computing capabilities. Some examples of processor 411 include, but are not limited to, central processing units, graphics processing units, various special-purpose artificial intelligence computing chips, various processors running machine learning model algorithms, digital signal processors, and any suitable processor, controller, microcontroller, etc. Processor 411 performs the various methods and processes described above, such as energy optimization methods for temporary power consumption scenarios.

[0061] In some embodiments, the energy optimization method for temporary power consumption scenarios can be implemented as a computer program tangibly contained in a computer-readable storage medium, such as storage unit 418. In some embodiments, part or all of the computer program can be loaded and / or installed on electronic device 410 via read-only memory 412 and / or communication unit 419. When the computer program is loaded into random access memory 413 and executed by processor 411, one or more steps of the energy optimization method for temporary power consumption scenarios described above can be performed. Alternatively, in other embodiments, processor 411 can be configured for the energy optimization method for temporary power consumption scenarios by any other suitable means (e.g., by means of firmware).

[0062] Various embodiments of the systems and techniques described above herein can be implemented in digital electronic circuit systems, integrated circuit systems, field-programmable gate arrays, application-specific integrated circuits (ASICs), application-specific standard products (ASICs), systems-on-a-chip (SoCs), payload programmable logic devices, computer hardware, firmware, software, and / or combinations thereof. These various embodiments may include implementations in one or more computer programs that can be executed and / or interpreted on a programmable system including at least one programmable processor, which may be a dedicated or general-purpose programmable processor, capable of receiving data and instructions from a storage system, at least one input device, and at least one output device, and transmitting data and instructions to the storage system, the at least one input device, and the at least one output device.

[0063] Computer programs used to implement the methods of this application may be written in any combination of one or more programming languages. These computer programs may be provided to the processor of a general-purpose computer, a special-purpose computer, or other programmable energy optimization device for temporary power consumption scenarios, such that when executed by the processor, the computer programs cause the functions / operations specified in the flowcharts and / or block diagrams to be implemented. The computer programs may be executed entirely on a machine, partially on a machine, or as a standalone software package, partially on a machine and partially on a remote machine, or entirely on a remote machine or server.

[0064] In the context of this application, a computer-readable storage medium can be a tangible medium that may contain or store a computer program for use by or in conjunction with an instruction execution system, apparatus, or device. A computer-readable storage medium can be, but is not limited to, electronic, magnetic, optical, electromagnetic, infrared, or semiconductor systems, apparatus, or devices, or any suitable combination of the foregoing. Alternatively, a computer-readable storage medium can be a machine-readable signal medium. More specific examples of machine-readable storage media include electrical connections based on one or more wires, portable computer disks, hard disks, random access memory, read-only memory, erasable programmable read-only memory, optical fibers, portable compact disk read-only memory, optical storage devices, magnetic storage devices, or any suitable combination of the foregoing.

[0065] To provide interaction with a user, the systems and techniques described herein can be implemented on an electronic device having: a display device (e.g., a cathode ray tube or liquid crystal display monitor) for displaying information to the user; and a keyboard and pointing device (e.g., a mouse or trackball) through which the user provides input to the electronic device. Other types of devices can also be used to provide interaction with the user; for example, feedback provided to the user can be any form of sensory feedback (e.g., visual feedback, auditory feedback, or tactile feedback); and input from the user can be received in any form (including sound input, voice input, or tactile input).

[0066] The systems and technologies described herein can be implemented in computing systems that include backend components (e.g., as data servers), or computing systems that include middleware components (e.g., application servers), or computing systems that include frontend components (e.g., user computers with graphical user interfaces or web browsers through which users can interact with implementations of the systems and technologies described herein), or any combination of such backend, middleware, or frontend components. The components of the system can be interconnected via digital data communication of any form or medium (e.g., communication networks). Examples of communication networks include local area networks (LANs), wide area networks (WANs), blockchain networks, and the Internet.

[0067] A computing system can include clients and servers. Clients and servers are generally geographically separated and typically interact via communication networks. The client-server relationship is created by computer programs running on the respective computers and having a client-server relationship with each other. The server can be a cloud server, also known as a cloud computing server or cloud host, which is a host product within the cloud computing service system to address the shortcomings of traditional physical hosts and virtual private servers, such as high management difficulty and weak business scalability.

[0068] It should be understood that the various forms of processes shown above can be used to rearrange, add, or delete steps. For example, the steps described in this application can be executed in parallel, sequentially, or in different orders, as long as the desired result of the technical solution of this application can be achieved, and this is not limited herein.

[0069] The specific embodiments described above do not constitute a limitation on the scope of protection of this application. Those skilled in the art should understand that various modifications, combinations, sub-combinations, and substitutions can be made according to design requirements and other factors. Any modifications, equivalent substitutions, and improvements made within the spirit and principles of this application should be included within the scope of protection of this application.

Claims

1. An energy optimization method for temporary power consumption scenarios, characterized in that, include: For each power distribution node, electrical equipment characteristics of at least one electrical device within the power distribution area corresponding to the power distribution node are collected; wherein, the electrical equipment characteristics include fundamental and harmonic characteristics, transient event waveform characteristics, power envelope characteristics, and environmental characteristics; Feature extraction is performed on the electrical equipment to determine the electrical feature vector of the electrical equipment; Based on the electrical equipment features and the electrical feature vector, the equipment types of the electrical equipment are cascaded and subjected to first category filtering and second category filtering to determine the target equipment type to which the electrical equipment belongs; Based on the target equipment type, operating conditions, and cumulative energy consumption of the electrical equipment under its operating conditions, energy optimization is performed on the target construction power distribution system; wherein, the target construction power distribution system refers to the power distribution system for temporary power consumption scenarios that is to be optimized for energy.

2. The method according to claim 1, characterized in that, The energy optimization of the target construction power distribution system based on the target equipment type, operating conditions, and cumulative energy consumption of the electrical equipment under different operating conditions includes: Based on the target equipment type and real-time operating conditions of the electrical equipment in the at least one power distribution area, the real-time equipment power ratio distribution corresponding to the target construction power distribution system is determined; wherein, the real-time equipment power ratio distribution is used to characterize the power ratio of each target equipment type in the current total power load of the target construction power distribution system; Based on the preset load mapping relationship between generator fuel consumption power and load composition, the optimal power of the generator is determined under the real-time equipment power ratio distribution conditions; wherein, the load mapping relationship is used to record the optimal fuel consumption power of the engine under different load composition ratio conditions.

3. The method according to claim 2, characterized in that, The load type of the electrical equipment is determined according to its corresponding target equipment type. The load types include resistive loads, inductive loads, and nonlinear loads. When the target equipment types in the real-time equipment power distribution are different, the load composition corresponding to the real-time equipment power distribution will also be different.

4. The method according to claim 1, characterized in that, The step of performing a first category screening and a second category screening on the cascaded equipment types of the electrical equipment based on the electrical equipment features and the electrical feature vector to determine the target equipment type to which the electrical equipment belongs includes: Based on the harmonic fingerprint characteristics of the electrical equipment and the preset harmonic fingerprint database of electrical equipment, a first category screening is performed to determine at least one candidate equipment type of the electrical equipment; Based on the electrical feature vector of the electrical equipment and the equipment type identification model corresponding to the candidate equipment type, a second category screening is performed to determine the target equipment type corresponding to the electrical equipment.

5. The method according to claim 1, characterized in that, After determining the target equipment type of the electrical equipment, the process also includes: The electrical equipment characteristics of the target equipment type are continuously monitored. When the difference between the electrical equipment characteristics of the equipment and the standard electrical equipment characteristic threshold exceeds a preset difference, an abnormal alarm is issued for the equipment. Based on the electrical equipment characteristics, the operating status of the equipment of the target equipment type is identified. Based on the operating status of the electrical equipment of the target equipment type, determine the energy consumption information corresponding to the electrical equipment under different operating states, and use it as the cumulative energy consumption of the electrical equipment under different operating conditions.

6. The method according to claim 1, characterized in that, The step of extracting features from the electrical equipment to determine the electrical feature vector of the electrical equipment includes: The electrical equipment characteristics of the electrical equipment are monitored for sudden changes. If an electrical equipment characteristic with a change exceeding a preset change threshold is detected within an adjacent monitoring period, it indicates that a load event has occurred in the electrical equipment. The load event includes the start-up and shutdown of the electrical equipment and the switching of the operating conditions of the electrical equipment. For electrical equipment that experiences a load event, feature extraction is performed on the electrical equipment characteristics of the equipment to determine the electrical feature vector of the equipment.

7. An energy optimization device for temporary power consumption scenarios, characterized in that, include: The feature acquisition module is used to acquire electrical equipment features of at least one electrical device in the power distribution area corresponding to each power distribution node; wherein, the electrical equipment features include fundamental and harmonic features, transient event waveform features, power envelope features, and environmental features; The feature extraction module is used to extract features from the electrical equipment and determine the electrical feature vector of the electrical equipment. The category filtering module is used to perform first category filtering and second category filtering on the equipment type of the electrical equipment based on the electrical equipment features and the electrical feature vector, so as to determine the target equipment type to which the electrical equipment belongs; The energy optimization module is used to optimize the energy of the target construction power distribution system based on the target equipment type of the electrical equipment, the operating conditions of the electrical equipment, and the cumulative energy consumption of the operating conditions of the electrical equipment; wherein, the target construction power distribution system refers to the power distribution system for temporary power consumption scenarios.

8. An electronic device, characterized in that, include: One or more processors; Memory, used to store one or more programs; When the one or more programs are executed by the one or more processors, the one or more processors implement the energy optimization method for temporary power consumption scenarios as described in any one of claims 1-6.

9. A computer-readable storage medium having a computer program stored thereon, characterized in that, When executed by the processor, the program implements the energy optimization method for temporary power consumption scenarios as described in any one of claims 1-6.

10. A computer program product comprising a computer program that, when executed by a processor, implements the energy optimization method for temporary power consumption scenarios according to any one of claims 1-6.

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