Energy management method and system based on big data

Abstract

The embodiment of the specification provides an energy management method and system based on big data, and belongs to the field of control systems, wherein the system comprises a data acquisition module configured to acquire parameter information and historical operation information of multiple loads of a target building, and further configured to acquire historical operation information of elevators of the target building, and further configured to acquire personnel historical movement information of the target building; a data analysis module configured to determine the type of each load based on the parameter information and the historical operation information of the multiple loads, and further configured to determine the power supply mode of the load based on the type of each load, and further configured to determine an elevator optimized operation scheme based on the historical operation information of the elevators of the target building, and further configured to determine an illumination optimized operation scheme based on the personnel historical movement information of the target building, and has the advantages that the energy consumption of the building is analyzed and optimized, and the energy loss of the building is reduced.

Description

TECHNICAL FIELD

[0001] The present application relates to the field of control systems, in particular to an energy management method and system based on big data. BACKGROUND

[0002] With the rapid development of economy and the continuous improvement of residents' income level, people's demand for living environment comfort has also increased. Intelligent building, as a building that integrates communication, automatic control and other new technologies, can make various devices such as power and lighting in the building work in coordination, so it is widely used in residential areas. The Chinese patent with application number CN202210695570.1 discloses an intelligent monitoring system based on big data, which includes a signal acquisition module, a signal analysis module, a signal storage module, a database comparison module, a control terminal module, a warning module, a fire monitoring module, a signal transmission module, a signal management module, a cloud signal storage module, a cloud server module, a security monitoring module and a data processing module. The output end of the signal acquisition module is connected to the input end of the signal analysis module. The output end of the signal analysis module is connected to the signal storage module. The output end of the signal storage module transmits signals to the input end of the control terminal module through the database comparison module. At the same time, the control terminal module also inputs signals to the cloud server module through the signal transmission module. The signal transmission module also transmits signals to the input end of the signal management module. The input end of the signal management module is connected to the cloud signal storage module. The output end of the data processing module is connected to the input end of the security monitoring module. The output end of the security monitoring module is connected to the input end of the cloud server module. The above-mentioned intelligent monitoring system based on big data automatically monitors the air quality in the intelligent building during use, compares the collected data with the data in the database, and automatically controls the operation of the ventilation equipment, air conditioning equipment and humidification equipment according to the comparison result, so as to automatically adjust the air in the intelligent building and automatically monitor the condition in the intelligent building. However, the energy consumption in the residence is large because there is no self-management, which often causes a lot of energy waste. The above-mentioned system cannot monitor the energy supply and consumption, that is, it cannot control the energy consumption. In such a large energy consumption environment, because there are many energy consumption paths and many energy consumption devices, the overall energy consumption needs to be monitored by a large number of monitoring devices, and the monitoring data cannot form a whole energy consumption analysis, so the whole energy consumption cannot be optimized.

[0003] Therefore, it is necessary to provide an energy management method and system based on big data for analyzing and optimizing the energy consumption of a building. SUMMARY

[0004] One of the embodiments of the present specification provides a big data-based energy management system, comprising: a data acquisition module, configured to acquire parameter information and historical operation information of a plurality of loads of a target building, and further configured to acquire historical operation information of elevators of the target building, and further configured to acquire historical walking information of personnel of the target building; a data analysis module, configured to determine the type of each of the loads based on the parameter information and the historical operation information of the plurality of loads, and further configured to determine the power supply mode of each of the loads based on the type of each of the loads, and further configured to determine an optimized operation scheme of elevators based on the historical operation information of the elevators of the target building, and further configured to determine an optimized operation scheme of lighting based on the historical walking information of personnel of the target building.

[0005] In some embodiments, the data analysis module determines the type of each of the loads based on the parameter information and the historical operation information of the plurality of loads, comprising: clustering the plurality of loads based on the parameter information and the historical operation information of the plurality of loads to determine a clustering result; and determining the type of each of the loads based on the clustering result.

[0006] In some embodiments, the clustering of the plurality of loads based on the parameter information and the historical operation information of the plurality of loads to determine a clustering result comprises: determining the load similarity of any two loads based on the parameter information and the historical operation information of the two loads; and clustering the plurality of loads based on the load similarity to determine a plurality of load clusters, wherein the clustering result comprises the plurality of load clusters.

[0007] In some embodiments, the power supply system of the target building comprises a grid power supply unit and a new energy power supply unit; the data analysis module determines the power supply mode of each of the loads based on the type of each of the loads, comprising: for each of the clustering clusters, determining the power supply mode of the loads included in the clustering cluster based on the parameter information and the historical operation information of the clustering center of the clustering cluster, wherein the power supply mode is grid power supply unit power supply or new energy power supply unit power supply.

[0008] In some embodiments, the data acquisition module further comprises a first power supply monitoring unit and a second power supply monitoring unit, wherein the first power supply monitoring unit is configured to acquire a grid power supply state data sequence of the grid power supply unit, the grid power supply state data sequence comprising state data of the grid power supply unit at a plurality of time points in a current time period, and the second power supply monitoring unit is configured to acquire a new energy power supply state data sequence of the new energy power supply unit, the new energy power supply state data sequence comprising state data of the new energy power supply unit at a plurality of time points in a current time period; the system further comprises a central management module, configured to adjust the power supply mode of the loads based on the grid power supply state data sequence and the new energy power supply state data sequence.

[0009] In some embodiments, the data analysis module determines an elevator optimized operation scheme based on the elevator historical operation information of the target building, including: fitting the elevator historical operation information of the target building to determine the number of operating elevators in multiple time periods.

[0010] In some embodiments, the data analysis module determines a lighting optimized operation scheme based on the personnel historical walking information of the target building, including: fitting the personnel historical walking information of the target building to determine the inductive lighting public area in multiple time periods.

[0011] In some embodiments, the data acquisition module is further configured to acquire at least one conference room reservation request from a user terminal, wherein the conference room reservation request includes a target conference room and a reservation time period; and the data acquisition module is further configured to acquire access control data and manage the equipment in the target conference room based on the access control data of the target conference room and the reservation time period.

[0012] In some embodiments, the data acquisition module manages the equipment in the target conference room based on the access control data of the target conference room and the reservation time period, including: determining whether a meeting is opened based on the access control data of the target conference room, a relationship graph, and the reservation time period; and managing the equipment in the target conference room if it is determined that the meeting is opened.

[0013] One of the embodiments of the present specification provides an energy management method based on big data, including: acquiring historical operation information of multiple loads of a target building; acquiring elevator historical operation information of the target building;

[0014] acquiring personnel historical walking information of the target building; determining the type of each load based on parameter information and historical operation information of the multiple loads; determining the power supply mode of each load based on the type of each load; determining an elevator optimized operation scheme based on the elevator historical operation information of the target building; and determining a lighting optimized operation scheme based on the personnel historical walking information of the target building. BRIEF DESCRIPTION OF DRAWINGS

[0015] The present specification will be further illustrated in the form of exemplary embodiments, which will be described in detail with reference to the accompanying drawings. These embodiments are not limiting, and in these embodiments, the same reference numbers represent the same structures, wherein:

[0016] Figure 1 is an application scenario schematic diagram of an energy management system based on big data according to some embodiments of the present specification;

[0017] Figure 2is an exemplary block diagram of a big data based energy management system according to some embodiments of the present specification;

[0018] Figure 3 is an exemplary flow chart of a big data based energy management method according to some embodiments of the present specification.

[0019] In the drawings: 110, processing device; 120, network; 130, user terminal; 140, storage device. DETAILED DESCRIPTION

[0020] In order to more clearly illustrate the technical solutions of the embodiments of the present specification, the following will briefly introduce the drawings needed to be used in the embodiment description. Obviously, the drawings in the following description are only some examples or embodiments of the present specification, and for those skilled in the art, without paying creative labor, the present specification can also be applied to other similar scenarios according to these drawings. Unless it is clear from the language environment or otherwise stated, the same reference numbers in the drawings represent the same structure or operation.

[0021] It should be understood that the "system", "device", "unit" and / or "module" used herein is a method for distinguishing different components, elements, parts, sections or assemblies at different levels. However, if other words can achieve the same purpose, the words can be replaced by other expressions.

[0022] As shown in the specification and claims, unless the context clearly indicates otherwise, "one", "a", "an", and / or "the" do not refer to the singular, but also include the plural. Generally speaking, the terms "comprise" and "include" only indicate the inclusion of the steps and elements explicitly identified, and these steps and elements do not constitute an exclusive list, and the method or device can also include other steps or elements.

[0023] The flow chart is used in the present specification to illustrate the operations performed by the system according to the embodiments of the present specification. It should be understood that the preceding or subsequent operations are not necessarily executed in sequence. On the contrary, each step can be processed in reverse order or simultaneously. At the same time, other operations can also be added to these processes, or one or more steps of operation can be removed from these processes.

[0024] Figure 1 is an exemplary block diagram of a big data based energy management system according to some embodiments of the present specification;

[0025] As Figure 1As shown, the application scenario can include a processing device 110, a network 120, a user terminal 130, and a storage device 140. The application scenario can operate by controlling target devices according to the methods and / or processes disclosed in this specification.

[0026] The processing device 110 can be configured to process data and / or information from at least one component of the application scenario or an external data source (e.g., a cloud data center). The processing device 110 can access data and / or information from the user terminal 130 and the storage device 140 through the network 120. The processing device 110 can directly connect to the user terminal 130 and the storage device 140 to access information and / or data. For example, the processing device 110 can obtain parameter information and historical operation information of a plurality of loads of a target building, elevator historical operation information, and personnel historical walking information from the power distribution cabinet 150. For another example, the processing device 110 can determine the type of each load based on the parameter information and the historical operation information of the plurality of loads, determine the power supply mode of the load based on the type of each load, determine an elevator optimized operation scheme based on the elevator historical operation information of the target building, and determine a lighting optimized operation scheme based on the personnel historical walking information of the target building. The processing device 110 can be local or remote. In some embodiments, the processing device 110 can include a processor, which can include one or more sub-processors (e.g., a single-core processing device or a multi-core multi-core processing device). For example only, the processor can include a central processing unit (CPU), an application-specific integrated circuit (ASIC), an application-specific instruction-set processor (ASIP), a graphics processing unit (GPU), a physics processing unit (PPU), a digital signal processor (DSP), a field-programmable gate array (FPGA), a programmable logic device (PLD), a controller, a microcontroller unit, a reduced instruction set computer (RISC), a microprocessor, or the like, or any combination thereof.

[0027] The network 120 can include any suitable network that can facilitate the exchange of information and / or data of the application scenario. In some embodiments, one or more components of the application scenario (e.g., the processing device 110, the user terminal 130, and the storage device 140) can exchange information and / or data through the network 120. In some embodiments, the network 120 can be any one or more of a wired network or a wireless network. In some embodiments, the network 120 can include one or more network access points. For example, the network 120 can include wired or wireless network access points, such as base stations and / or network switching points, through which one or more components of the application scenario can connect to the network 120 to exchange data and / or information.

[0028] User terminal 130 refers to one or more terminals or software used by a user (e.g., a user of a target building, etc.). In some embodiments, user terminal 130 can include, but is not limited to, a smartphone, a tablet, a laptop, a desktop computer, etc. In some embodiments, user terminal 130 can interact with other components in the application scenario through network 120. For example, user terminal 130 can send one or more control instructions to processing device 110 to control processing device 110 to determine a type of each load based on parameter information and historical operation information of a plurality of loads, determine a power supply mode of the load based on the type of each load, determine an elevator optimization operation scheme based on historical operation information of an elevator of the target building, and determine a lighting optimization operation scheme based on historical walking information of personnel of the target building.

[0029] Storage device 140 can be used to store data, instructions, and / or any other information. In some embodiments, storage device 140 can store data and / or information obtained from processing device 110 and user terminal 130. For example, storage device 140 can store parameter information and historical operation information of a plurality of loads. For another example, storage device 140 can store a trained machine learning model. In some embodiments, storage device 140 can include a mass storage, a removable storage, a volatile read / write memory, a read-only memory (ROM), etc., or any combination thereof. An exemplary mass storage can include a disk, an optical disk, a solid-state disk, etc. An exemplary removable storage can include a flash drive, a floppy disk, an optical disk, a memory card, a compact disk, a magnetic tape, etc. An exemplary volatile read / write memory can include a random access memory (RAM). An exemplary RAM can include a dynamic random access memory (DRAM), a double data rate synchronous dynamic random access memory (DDR SDRAM), a static random access memory (SRAM), a thyristor random access memory (T-RAM), and a zero-capacitor random access memory (Z-RAM), etc. An exemplary ROM can include a mask read-only memory (MROM), a programmable read-only memory (PROM), an erasable programmable read-only memory (EPROM), an electrically erasable programmable read-only memory (EEPROM), a compact disk read-only memory (CD-ROM), and a digital versatile disk read-only memory, etc. In some embodiments, storage device 140 can be executed on a cloud platform. By way of example only, the cloud platform can include a private cloud, a public cloud, a hybrid cloud, a community cloud, a distributed cloud, an on-premises cloud, a multi-layer cloud, etc., or any combination thereof.

[0030] It should be noted that the application scenario is provided only for illustrative purposes and is not intended to limit the scope of the present specification. Numerous modifications or changes can be made by one of ordinary skill in the art based on the description of the present specification. For example, the application scenario can also include a database. However, these changes and modifications will not depart from the scope of the present specification.

[0031] Figure 2 is an exemplary block diagram of a big data based energy management system according to some embodiments of the present specification. As shown in Figure 2 , the big data based energy management system can include a data acquisition module, a data analysis module and a central management module.

[0032] The data acquisition module can be configured to acquire parameter information and historical operation information of a plurality of loads of a target building.

[0033] The parameter information of the load can include the name of the load, the rated input voltage, the rated input power, the component information, etc. The historical operation information of the load can include the working voltage, the working current, the operation time of each time period of the day, etc. Wherein, a day can be divided into a plurality of time periods, for example, divided into 24 time periods, wherein the time length of a time period is 60 minutes.

[0034] The data acquisition module can also be configured to acquire elevator historical operation information of the target building.

[0035] The elevator historical operation information of the target building can include the number of elevator operations in each time period of the day, the number of passengers carried by each elevator, the operation distance of each elevator, etc.

[0036] The data acquisition module can also be configured to acquire personnel historical movement information of the target building.

[0037] The personnel historical movement information of the target building can include the number of personnel passing through at each time period of the day at a plurality of locations of the target building and the length of stay of each personnel, etc.

[0038] In some embodiments, the data acquisition module further includes a first power supply monitoring unit and a second power supply monitoring unit, wherein the first power supply monitoring unit is configured to acquire a power grid power supply state data sequence of a power grid power supply unit, the power grid power supply state data sequence including state data (such as three-phase average line current, phase active power A, phase active power B, phase active power C, phase current A, phase current B, phase current C, three-phase active power, ambient temperature, ambient humidity, arc light intensity, vibration frequency, ambient noise, leakage situation, etc.) of the power grid power supply unit at a plurality of time points of a current time period, and the second power supply monitoring unit is configured to acquire a new energy power supply state data sequence of a new energy power supply unit, the new energy power supply state data sequence including state data (such as three-phase average line current, phase active power A, phase active power B, phase active power C, phase current A, phase current B, phase current C, three-phase active power, ambient temperature, ambient humidity, arc light intensity, vibration frequency, ambient noise, leakage situation, etc.) of the new energy power supply unit at a plurality of time points of the current time period.

[0039] In some embodiments, the data acquisition module can further predict a future power grid power supply state data sequence according to a plurality of historical power grid power supply state data sequences, wherein the historical power grid power supply state data sequence can include state data of the power grid power supply unit at a plurality of historical time points in a historical time period, and the future power grid power supply state data sequence can include state data of the power grid power supply unit at a plurality of future time points in a future time period.

[0040] In some embodiments, the data acquisition module can predict a future power grid power supply state data sequence according to a plurality of historical power grid power supply state data sequences through a first prediction model, wherein the first prediction model is a machine learning model for predicting the future power grid power supply state data sequence, and the first prediction model can include but is not limited to a neural network (NN), a convolutional neural network (CNN), a deep neural network (DNN), a recurrent neural network (RNN), or any combination thereof, for example, the first prediction model can be a model formed by combining a convolutional neural network and a deep neural network.

[0041] In some embodiments, the data acquisition module can further predict a future new energy power supply state data sequence according to a plurality of historical new energy power supply state data sequences, wherein the historical new energy power supply state data sequence can include state data of the new energy power supply unit at a plurality of historical time points in a historical time period, and the future new energy power supply state data sequence can include state data of the new energy power supply unit at a plurality of future time points in a future time period.

[0042] In some embodiments, the data acquisition module can predict a future new energy power supply state data sequence according to a plurality of historical new energy power supply state data sequences through a second prediction model, wherein the second prediction model is a machine learning model for predicting the future new energy power supply state data sequence, and the second prediction model can include but is not limited to a neural network (NN), a convolutional neural network (CNN), a deep neural network (DNN), a recurrent neural network (RNN), or any combination thereof, for example, the second prediction model can be a model formed by combining a convolutional neural network and a deep neural network.

[0043] The data analysis module can be used to determine the type of each load based on the parameter information and historical operation information of the plurality of loads.

[0044] In some embodiments, the load type can be a first type of load, a second type of load, or a third type of load, the first type of load is a continuous use load, the second type of load is a short-time or repeated short-time use load, and the third type of load is an occasional short-time use load.

[0045] In some embodiments, the data analysis module determines the type of each load based on the parameter information and the historical operation information of the plurality of loads, including: clustering the plurality of loads based on the parameter information and the historical operation information of the plurality of loads to determine a clustering result; and determining the type of each load based on the clustering result. In some embodiments, for any two loads, the data analysis module determines a load similarity between the two loads based on the parameter information and the historical operation information of the two loads; and clusters the plurality of loads based on the load similarity to determine a plurality of load clusters, where the clustering result includes the plurality of load clusters. In some embodiments, the data analysis module can determine the load similarity between the two loads based on the parameter information and the historical operation information of the two loads by a similarity algorithm (e.g., cosine similarity, Euclidean distance, etc.). In some embodiments, the data analysis module can cluster the plurality of loads based on the load similarity to determine the plurality of load clusters by a K-Means algorithm. It can be understood that the more similar the parameter information and the historical operation information of the two loads are, the higher the load similarity between the two loads is.

[0046] The data analysis module can also be used to determine the power supply mode of each load based on the type of each load.

[0047] The power supply system of the target building includes a grid power supply unit and a new energy power supply unit (e.g., solar power supply, wind power supply, etc.).

[0048] In order to reduce the grid function while ensuring the normal operation of the target building, the data analysis module can determine the power supply mode of each load based on the type of each load. For example, the first type of load and the second type of load can be powered by the grid power supply unit, and the third type of load can be powered by the new energy power supply unit.

[0049] In some embodiments, the data analysis module determines the power supply mode of each load based on the type of each load, including: for each clustering cluster, determining the power supply mode of the load included in the clustering cluster based on the parameter information and the historical operation information of the clustering center of the clustering cluster, where the power supply mode is grid power supply unit power supply or new energy power supply unit power supply. It can be understood that when the clustering center of the clustering cluster is determined to be a first type of load or a second type of load according to the parameter information and the historical operation information of the clustering center, the power supply mode of the load included in the clustering cluster is grid power supply unit power supply; and when the clustering center of the clustering cluster is determined to be a third type of load according to the parameter information and the historical operation information of the clustering center, the power supply mode of the load included in the clustering cluster is new energy power supply unit power supply.

[0050] The central management module can be used to adjust the power supply mode of the load based on the grid power supply state data sequence and the new energy power supply state data sequence.

[0051] It can be understood that the central management module can determine whether the power grid power supply unit has a partial failure according to the power grid power supply state data sequence, and if a partial failure occurs, the power supply mode of the load powered by the power grid power supply unit can be adjusted to the new energy power supply unit to maintain the normal operation of the equipment in the target building as much as possible.

[0052] In some embodiments, the central management module can determine whether the power grid power supply unit has a failure according to the power grid power supply state data sequence and the future power grid power supply state data sequence through a first failure determination model, wherein the failure prediction model is a machine learning model for determining whether the power grid power supply unit has a failure according to the power grid power supply state data sequence and the future power grid power supply state data sequence, and the first failure determination model can include but is not limited to a neural network (NN), a convolutional neural network (CNN), a deep neural network (DNN), a recurrent neural network (RNN), or any combination thereof, for example, the first failure determination model can be a model formed by combining a convolutional neural network and a deep neural network.

[0053] It can be understood that the central management module can determine whether the new energy power supply unit has a partial failure according to the new energy power supply state data sequence, and if a partial failure occurs, the power supply mode of the load powered by the new energy power supply unit can be adjusted to the power grid power supply unit to maintain the normal operation of the equipment in the target building as much as possible.

[0054] In some embodiments, the central management module can determine whether the new energy power supply unit has a failure according to the new energy power supply state data sequence and the future new energy power supply state data sequence through a second failure determination model, wherein the failure prediction model is a machine learning model for determining whether the new energy power supply unit has a failure according to the new energy power supply state data sequence and the future new energy power supply state data sequence, and the second failure determination model can include but is not limited to a neural network (NN), a convolutional neural network (CNN), a deep neural network (DNN), a recurrent neural network (RNN), or any combination thereof, for example, the second failure determination model can be a model formed by combining a convolutional neural network and a deep neural network.

[0055] The data analysis module can also be used to determine an elevator optimization operation scheme based on elevator historical operation information of the target building.

[0056] In some embodiments, the data analysis module determines an elevator optimization operation scheme based on elevator historical operation information of the target building, including fitting the elevator historical operation information of the target building to determine the number of operating elevators in multiple time periods. It can be understood that not all elevators are in operation at some time periods, which can meet user demand while saving energy consumption.

[0057] The data analysis module can also be used to determine the lighting optimization operation scheme based on the historical walking information of the personnel of the target building.

[0058] In some embodiments, the data analysis module determines the lighting optimization operation scheme based on the historical walking information of the personnel of the target building, including fitting the historical walking information of the personnel of the target building to determine the sensing lighting public area in multiple time periods. It can be understood that in a certain time period, the lighting device of the non-sensing lighting public area can be in an off state, which can meet the user demand while saving energy consumption.

[0059] In some embodiments, the data acquisition module is also used to acquire at least one conference room reservation request from the user terminal, wherein the conference room reservation request includes a target conference room and a reservation time period.

[0060] In some embodiments, the data acquisition module can also be used to acquire access control data of the target conference room.

[0061] In some embodiments, the central management module can manage the devices in the target conference room based on the access control data of the target conference room and the reservation time period. In some embodiments, the central management module can determine whether the user who reserves the target conference room enters the target conference room within the reservation time period according to the access control data of the target conference room, and if it is determined that the user who reserves the target conference room enters the target conference room within the reservation time period, the devices (such as lighting, display screen, air conditioning, fresh air system, etc.) in the target conference room are turned on.

[0062] In some embodiments, the central management module manages the devices in the target conference room based on the access control data of the target conference room and the reservation time period, including determining whether the meeting is opened based on the access control data of the target conference room, the relationship graph, and the reservation time period; and if it is determined that the meeting is opened, the devices in the target conference room are managed.

[0063] In some embodiments, the relationship graph can represent the relationship between multiple users, and the relationship graph can be composed of multiple user nodes. One user node can represent one user. When there is a connection between any two users, the user nodes corresponding to the two users are connected by an edge. The closer the connection between the two users, the shorter the edge between the two nodes. It can be understood that the edge between the user nodes corresponding to two users belonging to the same department is shorter than the edge between the user nodes corresponding to two users belonging to two departments. The edge between the user nodes corresponding to two users belonging to the same department and the same project is shorter than the edge between the user nodes corresponding to two users belonging to the same department and two projects.

[0064] It can be understood that the central management module determines the user entering the conference room according to the access control data of the target conference room, and if there is at least one user corresponding to the node and the user corresponding to the node of the target conference room The edge between the nodes on the relationship graph is less than the preset edge length, the equipment (such as lighting, display screen, air conditioner, fresh air system, etc.) in the target conference room is started.

[0065] It should be noted that the above description of the energy management system based on big data and its modules is for the convenience of description, and cannot limit the scope of the embodiments. It can be understood that for those skilled in the art, after understanding the principle of the system, the modules can be combined or connected to form a subsystem without departing from the principle. In some embodiments, Figure 2 The data acquisition module, data analysis module and central management module disclosed in the present application can be different modules in a system, or one module can realize the functions of two or more modules. For example, each module can share a storage module, and each module can have its own storage module. Variations such as this are within the scope of the present application.

[0066] Figure 3 is an exemplary flowchart of the energy management method based on big data according to some embodiments of the present application, as Figure 3 As shown, the energy management method based on big data can include the following steps. In some embodiments, the energy management method based on big data can be executed by the energy management system based on big data.

[0067] Step 310, obtaining historical running information of a plurality of loads of a target building. In some embodiments, step 310 can be executed by the data acquisition module.

[0068] Step 320, obtaining elevator historical running information of the target building. In some embodiments, step 320 can be executed by the data acquisition module.

[0069] Step 330, obtaining personnel historical walking information of the target building. In some embodiments, step 330 can be executed by the data acquisition module.

[0070] Step 340, determining the type of each load based on the parameter information and historical running information of the plurality of loads. In some embodiments, step 340 can be executed by the data analysis module.

[0071] Step 350, determining the power supply mode of the load based on the type of each load. In some embodiments, step 350 can be executed by the data analysis module.

[0072] At step 360, an elevator optimization operation scheme is determined based on the elevator historical operation information of the target building. In some embodiments, step 360 can be performed by the data analysis module.

[0073] At step 370, a lighting optimization operation scheme is determined based on the personnel historical walking information of the target building. In some embodiments, step 370 can be performed by the data analysis module.

[0074] For more description of the big data based energy management method, please refer to Figure 2 and the related description, which will not be repeated here.

[0075] The above has described the basic concept, and it is obvious that the above detailed disclosure is only as an example for the person skilled in the art, and does not constitute a limitation on the present specification. Although it is not explicitly stated here, the person skilled in the art can make various modifications, improvements and corrections to the present specification. Such modifications, improvements and corrections are suggested in the present specification, so such modifications, improvements and corrections still belong to the spirit and scope of the exemplary embodiments of the present specification.

[0076] At the same time, the present specification uses specific words to describe the embodiments of the present specification. As "one embodiment", "an embodiment", and / or "some embodiments" means a certain feature, structure or characteristic related to at least one embodiment of the present specification. Therefore, it should be emphasized and noted that the "an embodiment" or "one embodiment" or "one alternative embodiment" mentioned in different places in the present specification does not necessarily refer to the same embodiment. In addition, some features, structures or characteristics in one or more embodiments of the present specification can be properly combined.

[0077] In addition, unless the claim explicitly states, the order of the processing elements and sequences described in the present specification, the use of numerals and letters, or the use of other names, is not intended to limit the order of the processes and methods of the present specification. Although some currently considered useful embodiments of the invention are discussed in the above disclosure through various examples, it should be understood that such details are only for the purpose of illustration, and the additional claims are not limited to the disclosed embodiments, on the contrary, the claims are intended to cover all modifications and equivalent combinations that meet the spirit and scope of the embodiments of the present specification. For example, although the system components described above can be realized by hardware devices, they can also be realized only by software solutions, such as installing the described system on existing servers or mobile devices.

[0078] For simplicity of disclosure and to help understand one or more embodiments of the application, various features can sometimes be described in the context of a single embodiment, a figure, or a description of an embodiment. However, the disclosure should not be construed as requiring that such features be present in only one embodiment, figure, or description of an embodiment. In fact, one or more embodiments of the application can include all of the features described above in any combination.

[0079] Each patent, patent application, patent publication, and other material cited in this specification is hereby incorporated by reference in its entirety. In the event of inconsistencies between the disclosure of this specification and the documents, descriptions, and / or terminology of the incorporated materials, the disclosure of this specification is intended to prevail. It is expressly not intended that any portion of this specification be used to construe an exception to, or limitation on, any claim.

[0080] Finally, it should be understood that the embodiments described herein are merely exemplary of the principles of the application. Other alternatives can be constructed without departing from the scope of the application. Accordingly, the embodiments described herein are not intended to limit the scope of the application.

Claims

1. A big data based energy management system, characterized in that, The method comprises the following steps: The data acquisition module is configured to acquire parameter information and historical operation information of a plurality of loads of a target building, wherein a power supply system of the target building comprises a grid power supply unit and a new energy power supply unit, and the data acquisition module is further configured to acquire historical operation information of an elevator of the target building and historical walking information of personnel in the target building; The data analysis module is configured to determine a type of each of the loads based on the parameter information and the historical operation information of the plurality of loads, determine a power supply mode of each of the loads based on the type of each of the loads, determine an optimized operation scheme of the elevator based on the historical operation information of the elevator of the target building, and determine an optimized operation scheme of lighting based on the historical walking information of personnel in the target building; The central management module is configured to adjust the power supply mode of the loads; The data analysis module determines the type of each of the loads based on the parameter information and the historical operation information of the plurality of loads, and the method comprises the following steps: The plurality of loads are clustered based on the parameter information and the historical operation information of the plurality of loads to determine a clustering result, wherein the clustering result comprises a plurality of load clusters; The type of each of the loads is determined based on the clustering result; The data analysis module determines the power supply mode of each of the loads based on the type of each of the loads, and the method comprises the following steps: For each of the load clusters, the power supply mode of the loads included in the load cluster is determined based on parameter information and historical operation information of a cluster center of the load cluster, wherein when the cluster center is determined to be a first type of load or a second type of load according to the parameter information and the historical operation information of the cluster center, the power supply mode of the loads included in the load cluster is grid power supply unit power supply; and when the cluster center is determined to be a third type of load according to the parameter information and the historical operation information of the cluster center, the power supply mode of the loads included in the load cluster is new energy power supply unit power supply; The data acquisition module is further configured to acquire access control data of a target conference room, and the central management module is further configured to manage equipment in the target conference room based on the access control data of the target conference room and a reservation time period, and the method comprises the following steps: Determine whether the conference is started based on the access control data of the target conference room, the relationship graph and the reservation time period, wherein the relationship graph represents the relationship between multiple users, the relationship graph is composed of multiple user nodes, one user node represents one user, when there is a connection between any two users, the user nodes corresponding to the two users are connected by an edge, the closer the connection between the two users, the shorter the edge between the two nodes, the edge between the user nodes corresponding to two users belonging to the same department is shorter than the edge between the user nodes corresponding to two users belonging to two departments, the edge between the user nodes corresponding to two users belonging to the same department and the same project is shorter than the edge between the user nodes corresponding to two users belonging to the same department and two projects, determine the user entering the conference room according to the access control data of the target conference room, if there is at least one node corresponding to a user and the node corresponding to the user reserving the target conference room in the relationship graph is less than the preset edge length, start the equipment in the target conference room.

2. The big data based energy management system of claim 1, wherein, The parameters information and historical operation information of the plurality of loads are used to cluster the plurality of loads to determine a clustering result, including: For any two loads, the parameters information and historical operation information of the two loads are used to determine a load similarity of the two loads; The plurality of loads are clustered based on the load similarity to determine a plurality of load clusters.

3. The big data based energy management system as claimed in claim 1 wherein, The data acquisition module further includes a first power supply monitoring unit and a second power supply monitoring unit, wherein the first power supply monitoring unit is configured to acquire power grid power supply state data sequence of the power grid power supply unit, the power grid power supply state data sequence includes state data of the power grid power supply unit at a plurality of time points in a current time period, and the second power supply monitoring unit is configured to acquire new energy power supply state data sequence of the new energy power supply unit, the new energy power supply state data sequence includes state data of the new energy power supply unit at a plurality of time points in a current time period. The central management module adjusts the power supply mode of the load based on the power grid power supply state data sequence and the new energy power supply state data sequence.

4. The big data based energy management system of any of claims 1-3, wherein, The data analysis module determines an elevator optimization operation scheme based on the elevator historical operation information of the target building, including: The elevator historical operation information of the target building is fitted to determine the number of operating elevators in a plurality of time periods.

5. The big data based energy management system as claimed in any one of claims 1 to 3, wherein, The data analysis module determines a lighting optimization operation scheme based on the personnel historical movement information of the target building, including: The personnel historical movement information of the target building is fitted to determine the inductive lighting public area in a plurality of time periods.

6. A big data based energy management method, characterized by, The energy management system based on big data of claim 1, comprising: acquiring historical operation information of a plurality of loads of a target building; acquiring elevator historical operation information of the target building; acquiring personnel historical movement information of the target building; determining the type of each load based on the parameters information and historical operation information of the plurality of loads; determining the power supply mode of the load based on the type of each load; determine an elevator optimization operation scheme based on the elevator historical operation information of the target building; determine a lighting optimization operation scheme based on the personnel historical walking information of the target building.

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