Intelligent energy management method and system based on internet of things big data

Abstract

The application relates to the technical field of energy management, in particular to a smart energy management method and system based on Internet of Things big data. The method comprises the following steps: acquiring power application information and power consumption data information of power consumption equipment, differentiating power consumption areas of the power consumption equipment based on the power application information and equipment position information, obtaining power consumption area information of the power consumption equipment, generating a local power grid model corresponding to the power consumption equipment according to the power consumption area information and electric energy transmission information, and performing regional power consumption simulation detection on power consumption state data according to the local power grid model to obtain the best power supply line in the power consumption area information. The application not only ensures the stability and efficiency of power supply, but also greatly reduces energy waste and realizes optimal allocation of power supply resources.

Description

Technical Field

[0001] This application relates to the technical field of energy management, and in particular to smart energy management methods and systems based on Internet of Things big data. Background Technology

[0002] With the application of technologies such as cloud computing, big data, and the Internet of Things in buildings, the deployment mode of building power infrastructure has changed, and new changes are needed in building construction as well. In the construction of new buildings, the power distribution network is a crucial link.

[0003] As the fundamental energy source for all facilities, electricity, like other production processes within those facilities, requires its distribution network to be monitored and managed. Distribution management involves connecting intelligent power and energy devices with communication capabilities to software systems that collect data, visualize status, and provide analysis and reporting functions, thereby enabling continuous monitoring and comprehensive management of the distribution system.

[0004] In current buildings, because each floor has different electrical equipment and power demands, the design of power supply facilities and lines is always aimed at meeting the power needs of the equipment on each floor to ensure the normal operation of the electrical equipment on each floor. However, this also means that the power supply line corresponding to each piece of equipment on each floor becomes the only power supply line used by that equipment, but it is not necessarily the most energy-efficient power supply line. Therefore, how to optimize the building power supply system to both meet the needs of the equipment on each floor and improve the overall energy efficiency has become an urgent problem to be solved. Summary of the Invention

[0005] To address at least one of the aforementioned technical problems, this application provides a smart energy management method and system based on Internet of Things big data.

[0006] Firstly, this application provides a smart energy management method based on Internet of Things big data, employing the following technical solution:

[0007] A smart energy management method based on IoT big data includes:

[0008] The system acquires power application information and power consumption data of electrical equipment. The power application information includes power supply information of power supply equipment in different areas of the building during a historical period and power transmission information of different power supply lines connected to the power supply equipment. The electrical equipment refers to the electrical equipment in the building connected to the corresponding power supply line during the historical period. The power consumption data is used to represent the equipment location information of the electrical equipment and the power consumption status data of the electrical equipment during the historical period.

[0009] Based on the power application information and the device location information, the power consumption area of ​​the electrical equipment is differentiated to obtain the power consumption area information of the electrical equipment;

[0010] A local power grid model corresponding to the electrical equipment is generated based on the electricity consumption area information and the power transmission information.

[0011] Based on the local power grid model, the power consumption status data is used to perform regional power consumption simulation detection to obtain the optimal power supply line in the power consumption area information.

[0012] By adopting the above technical solutions, comprehensive power application information and detailed power consumption data of electrical equipment within buildings can be captured and integrated. This information not only covers the historical supply capacity of power supply equipment and the transmission status of each power supply line, but also accurately records the specific location and dynamic power consumption status of electrical equipment. This comprehensive data collection lays a solid foundation for subsequent power consumption analysis and optimization. Based on the above rich data sources, regional segmentation of electrical equipment is further realized. By intelligently analyzing the correlation between power application information and equipment location information, the system can accurately classify electrical equipment into its respective power consumption area. This step effectively improves the precision and targeting of power consumption management. Subsequently, the system uses power consumption area information and power transmission information to construct a highly realistic local power grid model. This model not only reflects the physical connection structure of the power grid, but also incorporates the actual distribution of electrical equipment and its power consumption characteristics, providing an intuitive reference for subsequent power supply optimization. Finally, based on this accurate local power grid model, the power consumption status of each power consumption area under different power supply lines can be simulated and detected. Through scientific calculation and comparison, the optimal power supply line can be accurately identified. This not only ensures the stability and efficiency of power supply, but also greatly reduces energy waste and achieves optimal allocation of power supply resources.

[0013] In a preferred embodiment, this application can be further configured as follows: the step of differentiating the power consumption area of ​​the electrical device based on the power application information and the device location information to obtain the power consumption area information of the electrical device includes:

[0014] Based on the power application information, determine the power supply area location and power supply coverage of different power supply devices;

[0015] Based on the location of the power supply area and the location information of the equipment, a first vector set of different power supply devices and electrical devices is determined.

[0016] Based on the first vector set, it is determined whether the electrical device is located within the power supply coverage area. If the electrical device is located within the power supply coverage area, the power supply coverage area and the power supply equipment corresponding to the power supply coverage area are summarized to obtain the power consumption area information of the electrical device.

[0017] In a preferred embodiment, this application can be further configured as follows: generating a local power grid model corresponding to the electrical equipment based on the power consumption area information and the power transmission information includes:

[0018] The power transmission information determines the line length and environmental information of the power supply line connected to the power supply equipment.

[0019] The power supply line's group node information is determined based on the line length information and the surrounding environment information;

[0020] Based on the node positions in the power group node information and the positions in the equipment position information, a second vector set between the power supply line and the electrical equipment is determined.

[0021] Based on the second vector set, the power transmission association between the electrical equipment and different node positions in the group power node information is constructed to obtain the power supply line grid of the electrical equipment;

[0022] An initial local model is constructed based on the power supply coverage range in the power consumption area information, and the power supply area location in the power consumption area information and the location in the device location information are marked in the initial local model according to a preset ratio to obtain a marked local model.

[0023] The power supply line grid is integrated into the marked local model according to the correspondence of the same electrical equipment, and the initial position node of each power supply line in the power supply line grid is connected to the power supply equipment corresponding to the power supply line to obtain the local power grid model corresponding to the electrical equipment.

[0024] In a preferred embodiment, this application can be further configured as follows: determining the power supply node information of the power supply line based on the line length information and the environmental information includes:

[0025] Determine whether the line length information meets the preset line length. If it does, determine the environmental parameters of different nodes of the power supply line based on the environmental information. Then match the environmental parameters with the preset environmental coefficient standard to obtain the initial environmental coefficient of the initial node of the power supply line and the non-initial environmental coefficient corresponding to the non-initial node.

[0026] Calculate the environmental coefficient difference between the initial environmental coefficient and each of the non-initial environmental coefficients, and determine the first non-initial position node that meets the preset environmental coefficient difference requirement among the environmental coefficient differences. Define the first non-initial position node that meets the preset environmental coefficient difference requirement as the new initial position node, and define the position node between the new initial position node and the final position node among the non-initial environmental coefficients as the new non-initial position node.

[0027] The following steps are executed iteratively: Calculate the environmental coefficient difference between the new initial environmental coefficient and each new non-initial environmental coefficient, and determine the first non-initial position node in the environmental coefficient difference that meets the preset environmental coefficient difference requirement. Define the first non-initial position node that meets the preset environmental coefficient difference requirement as the new initial position node, and define the position node between the new initial position node and the final position node among the non-initial position nodes as the new non-initial position node, until there are no non-initial position nodes in the environmental coefficient difference that meet the preset environmental coefficient difference requirement.

[0028] The power supply line group node information is determined based on the initial position node and the new initial position node. The power supply node information includes the power transfer switch installed at the initial position node and the new initial position node, and the power transfer line connected to the power transfer switch.

[0029] In a preferred embodiment, this application may be further configured as follows: after performing regional electricity consumption simulation detection on the electricity consumption status data based on the local power grid model to obtain the optimal power supply line in the electricity consumption area information, the application further includes:

[0030] Obtain the actual power consumption information of the optimal power supply route, and calculate the power loss data of the power nodes in the optimal power supply route based on the actual power consumption information;

[0031] The calculated power loss value is matched with the preset power loss standard to determine whether the actual power consumption information meets the preset power loss standard.

[0032] If the actual power consumption information does not meet the preset power loss standard, then the local power grid model is subjected to power supply simulation analysis to obtain power grid layout instructions, and the layout of the power supply lines connected to the power-consuming equipment is controlled and adjusted.

[0033] In a preferred embodiment, this application can be further configured such that: the power supply simulation analysis of the local power grid model to obtain power grid layout instructions includes:

[0034] Based on the local power grid model, various power supply line combinations between the power supply equipment and the electrical equipment are determined;

[0035] Determine the changes in power consumption data corresponding to the power group node information of each of the various power supply line combinations under different equipment operating power conditions;

[0036] The changes in the electricity consumption data are accumulated according to the same power supply line combination to obtain the electricity consumption data corresponding to each power supply line combination;

[0037] The line scheduling length corresponding to each power supply line combination is determined based on the grouping node information of each power supply line combination;

[0038] The average energy consumption data of a certain power supply line combination is obtained by calculating the ratio between the power consumption data and the line scheduling length.

[0039] The target power supply line combination corresponding to the minimum average energy consumption data is determined, and the power transfer switch control sequence analysis is performed on the group power node information in the target power supply line combination to obtain the power grid layout command.

[0040] In a preferred embodiment, this application can be further configured such that: if the actual electricity consumption information does not conform to the preset energy loss standard, the following step further includes:

[0041] The power loss data of the power supply line segment corresponding to each group of power nodes in the optimal power supply line is determined based on the power loss data of the group of power nodes in the optimal power supply line.

[0042] The power loss data of the power supply line segment is calculated by comparing it with the length of the power supply line segment to obtain the power loss value of each power supply line segment.

[0043] Determine whether the power loss value of each power supply line segment meets the preset power loss standard. If it does not meet the standard, mark the line corresponding to the power supply line segment in the local power grid model to obtain the marked local power grid model.

[0044] Secondly, this application provides a smart energy management system based on Internet of Things big data, which adopts the following technical solution:

[0045] A smart energy management system based on Internet of Things (IoT) big data includes,

[0046] The information acquisition module is used to acquire power application information and power consumption data information of electrical equipment. The power application information is the power supply information of power supply equipment in different areas of the building during the historical period and the power transmission information of different power supply lines connected to the power supply equipment. The electrical equipment is the electrical equipment in the building connected to the corresponding power supply line during the historical period. The power consumption data information is used to represent the equipment location information of the electrical equipment and the power consumption status data of the electrical equipment during the historical period.

[0047] The region differentiation module is used to differentiate the power consumption region of the electrical equipment based on the power application information and the equipment location information, so as to obtain the power consumption region information of the electrical equipment.

[0048] The model generation module is used to generate a local power grid model corresponding to the electrical equipment based on the power consumption area information and the power transmission information.

[0049] The simulation detection module is used to perform regional power consumption simulation detection on the power consumption status data based on the local power grid model, and to obtain the optimal power supply line in the power consumption area information.

[0050] In one possible implementation, when the region differentiation module performs power consumption region differentiation on the electrical equipment based on the power application information and the equipment location information to obtain the power consumption region information of the electrical equipment, it is specifically used for:

[0051] Based on the power application information, determine the power supply area location and power supply coverage of different power supply devices;

[0052] Based on the location of the power supply area and the location information of the equipment, a first vector set of different power supply devices and electrical devices is determined.

[0053] Based on the first vector set, it is determined whether the electrical device is located within the power supply coverage area. If the electrical device is located within the power supply coverage area, the power supply coverage area and the power supply equipment corresponding to the power supply coverage area are summarized to obtain the power consumption area information of the electrical device.

[0054] In another possible implementation, when the model generation module generates a local power grid model corresponding to the electrical equipment based on the power consumption area information and the power transmission information, it is specifically used for:

[0055] The power transmission information determines the line length and environmental information of the power supply line connected to the power supply equipment.

[0056] The power supply line's group node information is determined based on the line length information and the surrounding environment information;

[0057] Based on the node positions in the power group node information and the positions in the equipment position information, a second vector set between the power supply line and the electrical equipment is determined.

[0058] Based on the second vector set, the power transmission association between the electrical equipment and different node positions in the group power node information is constructed to obtain the power supply line grid of the electrical equipment;

[0059] An initial local model is constructed based on the power supply coverage range in the power consumption area information, and the power supply area location in the power consumption area information and the location in the device location information are marked in the initial local model according to a preset ratio to obtain a marked local model.

[0060] The power supply line grid is integrated into the marked local model according to the correspondence of the same electrical equipment, and the initial position node of each power supply line in the power supply line grid is connected to the power supply equipment corresponding to the power supply line to obtain the local power grid model corresponding to the electrical equipment.

[0061] In another possible implementation, when the model generation module determines the group node information of the power supply line based on the line length information and the surrounding environment information, it is specifically used for:

[0062] Determine whether the line length information meets the preset line length. If it does, determine the environmental parameters of different nodes of the power supply line based on the environmental information. Then match the environmental parameters with the preset environmental coefficient standard to obtain the initial environmental coefficient of the initial node of the power supply line and the non-initial environmental coefficient corresponding to the non-initial node.

[0063] Calculate the environmental coefficient difference between the initial environmental coefficient and each of the non-initial environmental coefficients, and determine the first non-initial position node that meets the preset environmental coefficient difference requirement among the environmental coefficient differences. Define the first non-initial position node that meets the preset environmental coefficient difference requirement as the new initial position node, and define the position node between the new initial position node and the final position node among the non-initial environmental coefficients as the new non-initial position node.

[0064] The following steps are executed iteratively: Calculate the environmental coefficient difference between the new initial environmental coefficient and each new non-initial environmental coefficient, and determine the first non-initial position node in the environmental coefficient difference that meets the preset environmental coefficient difference requirement. Define the first non-initial position node that meets the preset environmental coefficient difference requirement as the new initial position node, and define the position node between the new initial position node and the final position node among the non-initial position nodes as the new non-initial position node, until there are no non-initial position nodes in the environmental coefficient difference that meet the preset environmental coefficient difference requirement.

[0065] The power supply line group node information is determined based on the initial position node and the new initial position node. The power supply node information includes the power transfer switch installed at the initial position node and the new initial position node, and the power transfer line connected to the power transfer switch.

[0066] In another possible implementation, the system further includes: an electricity consumption information module, an energy consumption matching module, and a power supply simulation module, wherein,

[0067] The electricity consumption information module is used to obtain the actual electricity consumption information of the optimal power supply route, and calculate the power loss data of the power nodes in the optimal power supply route based on the actual electricity consumption information.

[0068] The energy consumption matching module is used to match the calculated energy loss value with a preset energy loss standard to determine whether the actual power consumption information meets the preset energy loss standard.

[0069] The power supply simulation module is used to perform power supply simulation analysis on the local power grid model when the actual power consumption information does not meet the preset power loss standard, obtain power grid layout instructions, and control and adjust the line layout of the power supply lines connected to the power-consuming equipment.

[0070] In another possible implementation, when the power supply simulation module performs power supply simulation analysis on the local power grid model to obtain power grid layout instructions, it is specifically used for:

[0071] Based on the local power grid model, various power supply line combinations between the power supply equipment and the electrical equipment are determined;

[0072] Determine the changes in power consumption data corresponding to the power group node information of each of the various power supply line combinations under different equipment operating power conditions;

[0073] The changes in the electricity consumption data are accumulated according to the same power supply line combination to obtain the electricity consumption data corresponding to each power supply line combination;

[0074] The line scheduling length corresponding to each power supply line combination is determined based on the grouping node information of each power supply line combination;

[0075] The average energy consumption data of a certain power supply line combination is obtained by calculating the ratio between the power consumption data and the line scheduling length.

[0076] The target power supply line combination corresponding to the minimum average energy consumption data is determined, and the power transfer switch control sequence analysis is performed on the group power node information in the target power supply line combination to obtain the power grid layout command.

[0077] In another possible implementation, the system further includes: a loss determination module, a ratio calculation module, and a line marking module, wherein,

[0078] The loss determination module is used to determine the power loss data of the power supply line segment corresponding to each group of nodes in the optimal power supply line based on the power loss data of the group of nodes in the optimal power supply line.

[0079] The ratio calculation module is used to calculate the ratio between the power loss data of the power supply line segment and the length of the power supply line segment to obtain the power loss value of each power supply line segment.

[0080] The line marking module is used to determine whether the power loss value of each power supply line segment meets the preset power loss standard. If it does not meet the standard, the line corresponding to the power supply line segment in the local power grid model is marked to obtain the marked local power grid model.

[0081] Thirdly, this application provides an electronic device that adopts the following technical solution:

[0082] At least one processor;

[0083] Memory;

[0084] At least one application, wherein the at least one application is stored in memory and configured to be executed by at least one processor, the at least one application being configured to: execute the above-described smart energy management method based on Internet of Things big data.

[0085] Fourthly, this application provides a computer-readable storage medium, which adopts the following technical solution:

[0086] A computer-readable storage medium having a computer program stored thereon, which, when executed in a computer, causes the computer to perform the aforementioned smart energy management method based on Internet of Things big data.

[0087] In summary, this application includes at least one of the following beneficial technical effects:

[0088] When selecting power supply lines for each electrical device, the system comprehensively captures and integrates power application information within the building and detailed power consumption data of the devices. This information not only covers the historical power supply capacity of the power supply equipment and the transmission status of each power supply line, but also accurately records the specific location and dynamic power consumption status of the devices. This comprehensive data collection lays a solid foundation for subsequent power consumption analysis and optimization. Based on the aforementioned rich data sources, the system further achieves regional segmentation of electrical devices. By intelligently analyzing the correlation between power application information and device location information, the system can accurately classify electrical devices into their respective power consumption areas. This step effectively improves the precision and targeting of power consumption management. Subsequently, the system uses power consumption area information and power transmission information to construct a highly realistic local power grid model. This model not only reflects the physical connection structure of the power grid but also incorporates the actual distribution of electrical devices and their power consumption characteristics, providing an intuitive reference for subsequent power supply optimization. Finally, based on this accurate local power grid model, the system can simulate and detect the power consumption status of each power consumption area under different power supply lines, and accurately identify the optimal power supply line through scientific calculation and comparison. This not only ensures the stability and efficiency of power supply, but also greatly reduces energy waste and achieves optimal allocation of power supply resources. Attached Figure Description

[0089] Figure 1 A flowchart illustrating a smart energy management method based on Internet of Things big data, provided for an embodiment of this application;

[0090] Figure 2 A schematic diagram of the structure of a smart energy management system based on Internet of Things big data, provided for an embodiment of this application;

[0091] Figure 3 This is a schematic diagram of the structure of an electronic device provided in an embodiment of this application. Detailed Implementation

[0092] The following is in conjunction with the appendix Figure 1 To be continued Figure 3 This application will be described in further detail.

[0093] This specific embodiment is merely an explanation of this application and is not intended to limit it. After reading this specification, those skilled in the art can make modifications to this embodiment without contributing any inventive step, but such modifications are protected by patent law as long as they fall within the scope of the claims of this application.

[0094] To make the objectives, technical solutions, and advantages of the embodiments of this application clearer, the technical solutions of the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of this application, not all embodiments. Based on the embodiments of this application, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this application.

[0095] Furthermore, the term "and / or" in this article is merely a description of the relationship between related objects, indicating that three relationships can exist. For example, A and / or B can represent: A existing alone, A and B existing simultaneously, or B existing alone. Additionally, the character " / " in this article, unless otherwise specified, generally indicates that the preceding and following related objects have an "or" relationship.

[0096] The embodiments of this application will now be described in further detail with reference to the accompanying drawings.

[0097] This application provides a smart energy management method based on Internet of Things (IoT) big data, executed by an electronic device. This electronic device can be a server or a terminal device. The server can be a standalone physical server, a server cluster or distributed system composed of multiple physical servers, or a cloud server providing cloud computing services. The terminal device can be a smartphone, tablet, laptop, desktop computer, etc., but is not limited to these. The terminal device and the server can be directly or indirectly connected via wired or wireless communication. This application does not impose any limitations on this. Figure 1 As shown, the method includes:

[0098] Step S10: Obtain power application information and power consumption data of electrical equipment.

[0099] Among them, power application information refers to the power supply information of power supply equipment in different areas of the building during the historical period and the power transmission information of different power supply lines connected to the power supply equipment. Electrical equipment refers to the electrical equipment in the building connected to the corresponding power supply line during the historical period. Power consumption data information is used to represent the equipment location information of electrical equipment and the power consumption status data of electrical equipment during the historical period.

[0100] Specifically, power application information indicates the power supply status of power supply equipment (such as transformers and generators) deployed in different areas and floors within the building during a historical period. This includes key information such as the operating status, power supply volume, and power supply stability of the power supply equipment. Simultaneously, power application information also covers the power transmission status of different power supply lines connected to these power supply equipment, such as transmission efficiency, loss rate, line load, line length, and environmental parameters of the line location. Electrical equipment refers to various power-consuming devices connected to specific power supply lines within the building during the historical period, such as lighting fixtures, air conditioning equipment, and office computers. These devices obtain power from the power supply equipment through power supply lines to maintain their normal operation. Power consumption data information indicates the specific location information of these electrical devices (such as floor and room number) and their power consumption status data during the historical period. Power consumption status data includes electricity consumption, electricity consumption time, power consumption, and electricity consumption mode (such as standby, working, and hibernation). This data is crucial for analyzing building energy consumption and optimizing energy allocation.

[0101] In this application embodiment, Internet of Things (IoT) technology can be used to achieve remote data collection and transmission. By installing IoT sensors and communication modules on power supply equipment, power lines, and electrical devices, real-time data can be transmitted to a remote data center or cloud platform via a wireless network. Electronic devices can remotely access this data and perform online analysis and processing. Dedicated energy consumption monitoring equipment (such as data loggers and sensors) can also be deployed to collect data. These devices can be installed at key locations on power supply equipment, power lines, and electrical devices to record and transmit relevant data to a central processing unit or cloud server in real time. Data analysis software can then process and analyze this data to obtain the required power application information and electricity consumption data. Data can also be automatically collected through the building's internal energy management system (EMS). The EMS system can monitor the operating status of power supply equipment and the transmission status of power lines in real time, and obtain electricity consumption data from electrical devices through interfaces with devices such as smart meters. This data will be automatically stored in the system's database for subsequent analysis.

[0102] Step S11: Based on power application information and equipment location information, the power consumption area of ​​the electrical equipment is differentiated to obtain the power consumption area information of the electrical equipment.

[0103] Specifically, based on power application information, the power supply area location and power supply coverage of different power supply devices are determined. Based on the power supply area location and the location in the device location information, a first vector set of different power supply devices and electrical devices is determined. Based on the first vector set, it is determined whether the electrical device is located within the power supply coverage area. If the electrical device is located within the power supply coverage area, the power supply coverage area and the power supply devices corresponding to the power supply coverage area are summarized to obtain the power consumption area information of the electrical device.

[0104] In this embodiment of the application, a combination of Geographic Information System (GIS) and power system analysis software is employed. The GIS system is used to digitize the geographic information of buildings or power systems, including the spatial location information of power supply equipment, power lines, and electrical equipment. Then, the GIS data is imported into the power system analysis software, and combined with power application information, power supply area division and power supply coverage calculation are performed. Through the software's built-in algorithms and models, the optimized layout of power supply areas and the accurate calculation of power supply coverage can be automatically completed.

[0105] In this embodiment, the first vector set represents the set of locations and distances of different electrical devices relative to different power supply devices. In this application, to accurately obtain the location and distance between the electrical device and the power supply device, it is specifically represented as (Electrical Device A - Power Supply Device B) - (35° North by West, 4 meters). Additionally, the power consumption area information of the electrical device is used to represent the applicable power supply devices for the electrical device and the corresponding power supply coverage area of ​​the applicable power supply devices.

[0106] Step S12: Generate a local power grid model corresponding to the electrical equipment based on the power consumption area information and power transmission information.

[0107] Specifically, based on the power transmission information, the length and environmental information of the power supply lines connected to the power supply equipment are determined. Based on the line length and environmental information, the group node information of the power supply lines is determined. Based on the node positions in the group node information and the positions in the equipment position information, a second vector set between the power supply lines and the electrical equipment is determined. Based on the second vector set, power transmission associations are constructed between the electrical equipment and different node positions in the group node information to obtain the power supply line grid for the electrical equipment. An initial local model is constructed based on the power supply coverage area in the power consumption area information. The power supply area positions in the power consumption area information and the positions in the equipment position information are marked in the initial local model according to a preset ratio to obtain a marked local model. The power supply line grid is integrated into the marked local model according to the correspondence of the same electrical equipment. The initial position nodes of each power supply line in the power supply line grid are connected to the corresponding power supply equipment to obtain a local power grid model corresponding to the electrical equipment.

[0108] Specifically, it is determined whether the line length information meets the preset line length. If it does, the environmental parameters of different nodes of the power supply line are determined based on the environmental information. The environmental parameters are then matched with the preset environmental coefficient standard to obtain the initial environmental coefficient of the initial node of the power supply line and the non-initial environmental coefficient corresponding to the non-initial node. The environmental coefficient difference between the initial environmental coefficient and each non-initial environmental coefficient is calculated. The first non-initial node that meets the preset environmental coefficient difference requirement is determined. The first non-initial node that meets the preset environmental coefficient difference requirement is defined as the new initial node. The node between the new initial node and the final node in the non-initial environmental coefficient is defined as the new non-initial node.

[0109] In this embodiment, the preset line length is a pre-set segmentable line length, meaning that line segmentation can only be performed when the power supply line length meets the preset line length. Environmental parameters include temperature and humidity data of the power supply line's environment. The preset environmental coefficient standard is a standard implemented by personnel based on the varying degrees of impact of different environmental parameters on the power supply line in historical realities. For example, in high-temperature environments, the resistance of the power supply line increases, and the insulation capacity of the insulating material decreases, potentially causing the line to overheat, reduce dielectric strength, and even accelerate aging and increase the failure rate. Furthermore, high temperatures can affect the operating characteristics of electronic components (such as diodes and transistors), causing changes in their performance. In extremely low-temperature environments, the power supply line may become inelastic, increasing the risk of breakage. Especially in severe weather conditions such as strong winds, low temperatures can exacerbate line damage. Humidity leads to a decrease in the performance of the insulation material on the power supply line, and may even trigger electrochemical corrosion, thereby damaging conductors and components. This is precisely why power supply lines need to be segmented, because the environmental hazards to power supply lines vary depending on their location. The environmental coefficient in the preset environmental coefficient standard is used to assess the hazard coefficient of the current environment in which the power supply line is located.

[0110] The iterative execution steps are as follows: Calculate the environmental factor difference between the new initial environmental factor and each new non-initial environmental factor, and determine the first non-initial position node among the environmental factor differences that meets the preset environmental factor difference requirement. Define the first non-initial position node that meets the preset environmental factor difference requirement as the new initial position node. Define the position node between the new initial position node and the final position node among the non-initial position nodes as the new non-initial position node. Continue until there are no non-initial position nodes among the environmental factor differences that meet the preset environmental factor difference requirement. Then, determine the power supply line's group node information based on the initial position node and the new initial position node.

[0111] Among them, the group power node information includes the power transfer switches installed at the initial position node and the new initial position node, as well as the power transfer lines connected to the power transfer switches.

[0112] Specifically, the end of the power transfer route furthest from the power transfer switch is connected to the power transfer switch of the initial position node or new initial position node of other adjacent power supply lines within the coverage area of ​​the power supply equipment connected to the power supply line. For example, there are three power supply lines connected to power supply equipment A: power supply line A, power supply line B, and power supply line C. Power supply line A has an initial position node a and three new initial position nodes A1, A2, and A3; power supply line B has an initial position node b and two initial position nodes B1 and B2; and power supply line C has an initial position node c and one initial position node C1. Power supply line A is adjacent to power supply line B, and power supply line B is adjacent to power supply line C. Then, the power transfer route corresponding to initial position node a is connected to initial position node b, the power transfer route of A1 is connected to B1, the power transfer route of A2 is connected to B2, and the power transfer route of A3 is connected to the electrical equipment. Similarly, the power transfer route of initial position node b is connected to initial position node c, the power transfer route of B1 is connected to C1, and the power transfer route of B2 is connected to the electrical equipment. Based on the connection method of the power transfer routes mentioned above, a power supply line grid is formed for each electrical device corresponding to the applicable power supply equipment.

[0113] In this embodiment, the second vector set represents the relative orientation and relative distance between the initial position node and the new initial position node in the group node information of each power supply line connected to the power supply equipment and the location of the power-consuming equipment. The vectors in the second vector set are represented as (initial position node - power-consuming equipment A) (16° east of west, 6 meters) and (new initial position node A - power-consuming equipment A) (16.3 degrees east of west, 6.2 meters).

[0114] Step S13: Based on the local power grid model, perform regional power consumption simulation detection on the power consumption status data to obtain the optimal power supply line in the power consumption area information.

[0115] For the embodiments of this application, the optimal power supply line is the best route in the local power grid model that provides power to electrical equipment under ideal conditions, that is, the route with the best power utilization rate.

[0116] In this embodiment, when selecting power supply lines for each electrical device, comprehensive power application information within the building and detailed power consumption data of the device are captured and integrated. This information not only covers the historical supply capacity of the power supply equipment and the transmission status of each power supply line, but also accurately records the specific location and dynamic power consumption status of the device. This comprehensive data collection lays a solid foundation for subsequent power consumption analysis and optimization. Based on the aforementioned rich data sources, regional segmentation of electrical devices is further realized. By intelligently analyzing the correlation between power application information and device location information, the system can accurately classify electrical devices into their respective power consumption areas. This step effectively improves the precision and targeting of power consumption management. Subsequently, the system uses power consumption area information and power transmission information to construct a highly realistic local power grid model. This model not only reflects the physical connection structure of the power grid but also incorporates the actual distribution of electrical devices and their power consumption characteristics, providing an intuitive reference for subsequent power supply optimization. Finally, based on this accurate local power grid model, the power consumption status of each power consumption area under different power supply lines can be simulated and detected. Through scientific calculation and comparison, the optimal power supply line can be accurately identified. This not only ensures the stability and efficiency of power supply, but also greatly reduces energy waste and achieves optimal allocation of power supply resources.

[0117] Furthermore, based on the local power grid model, regional power consumption simulation detection is performed on the power consumption status data to obtain the optimal power supply line in the power consumption area information. This also includes: obtaining the actual power consumption information of the optimal power supply route, and calculating the power loss data of the power nodes in the optimal power supply route based on the actual power consumption information. The calculated power loss value is matched with the preset power loss standard to determine whether the actual power consumption information meets the preset power loss standard. If the actual power consumption information does not meet the preset power loss standard, a power supply simulation analysis is performed on the local power grid model to obtain power grid layout instructions and control and adjust the line layout of the power supply lines connected to the power consumption equipment.

[0118] Specifically, based on the local power grid model, various power supply line combinations between power supply equipment and electrical equipment are determined. Under different operating power conditions, the changes in power consumption data corresponding to the group node information of each power supply line combination are determined. These power consumption data changes are accumulated for the same power supply line combination to obtain the power consumption data corresponding to each power supply line combination. Based on the group node information of each power supply line combination, the corresponding line dispatch length is determined. The ratio of the power consumption data and the line dispatch length is calculated to obtain the average energy consumption data for each power supply line combination. The target power supply line combination corresponding to the minimum average energy consumption data is determined. Finally, the power transfer switch control sequence is analyzed for the group node information in the target power supply line combination to obtain the power grid layout instructions.

[0119] Furthermore, if the actual electricity consumption information does not meet the preset power loss standard, the process further includes: determining the power loss data of the power supply line segment corresponding to each group of nodes in the optimal power supply line based on the power loss data of the group of nodes in the optimal power supply line; calculating the ratio of the power loss data of the power supply line segment to the length of the power supply line segment to obtain the power loss value of each power supply line segment; determining whether the power loss value of each power supply line segment meets the preset power loss standard; if not, marking the line corresponding to the power supply line segment in the local power grid model to obtain the marked local power grid model.

[0120] The above embodiments introduce a smart energy management method based on IoT big data from the perspective of method and process. The following embodiments introduce a smart energy management system based on IoT big data from the perspective of virtual modules or virtual units. For details, please refer to the following embodiments.

[0121] This application provides a smart energy management system 20 based on Internet of Things big data, such as... Figure 2 As shown, Figure 2 This is a schematic diagram of a smart energy management system based on Internet of Things (IoT) big data, provided as an embodiment of this application. Specifically, the system 20 may include:

[0122] The information acquisition module 21 is used to acquire power application information and power consumption data information of electrical equipment. The power application information is the power supply information of power supply equipment in different areas of the building during the historical period and the power transmission information of different power supply lines connected to the power supply equipment. The electrical equipment is the electrical equipment in the building connected to the corresponding power supply line during the historical period. The power consumption data information is used to represent the equipment location information of the electrical equipment and the power consumption status data of the electrical equipment during the historical period.

[0123] The region differentiation module 22 is used to differentiate the power consumption region of the power-consuming equipment based on power application information and equipment location information, so as to obtain the power consumption region information of the power-consuming equipment.

[0124] Model generation module 23 is used to generate a local power grid model corresponding to the electrical equipment based on the power consumption area information and power transmission information;

[0125] The simulation detection module 24 is used to perform regional power consumption simulation detection on the power consumption status data based on the local power grid model, and to obtain the optimal power supply line in the power consumption area information.

[0126] In one possible implementation of this application embodiment, when the region differentiation module 22 differentiates the power consumption region of the power-consuming equipment based on power application information and equipment location information to obtain the power consumption region information of the power-consuming equipment, it is specifically used for:

[0127] Based on power application information, determine the power supply area location and power supply coverage of different power supply equipment;

[0128] Based on the location of the power supply area and the location information of the equipment, determine the first vector set of different power supply equipment and power consumption equipment;

[0129] The first vector set determines whether the electrical equipment is within the power supply coverage area. If the electrical equipment is within the power supply coverage area, the power supply coverage area and the power supply equipment corresponding to the power supply coverage area are summarized to obtain the power consumption area information of the electrical equipment.

[0130] In another possible implementation of this application embodiment, when the model generation module 23 generates a local power grid model corresponding to the electrical equipment based on the power consumption area information and power transmission information, it is specifically used for:

[0131] The power transmission information determines the length of the power supply line connected to the power supply equipment and its surrounding environment.

[0132] The power supply line's group node information is determined based on the line length information and the surrounding environment information;

[0133] The second vector set of power supply lines and electrical equipment is determined based on the node positions in the group power node information and the positions in the equipment position information.

[0134] Based on the second vector set, the power transmission association between different node positions in the information of electrical equipment and power group nodes is constructed to obtain the power supply line grid of electrical equipment.

[0135] An initial local model is constructed based on the power supply coverage in the power consumption area information, and the power supply area location in the power consumption area information and the location in the equipment location information are marked in the initial local model according to a preset ratio to obtain a marked local model.

[0136] The power supply line grid is integrated into the labeled local model according to the correspondence of the same electrical equipment, and the initial position node of each power supply line in the power supply line grid is connected to the power supply equipment corresponding to the power supply line to obtain the local power grid model corresponding to the electrical equipment.

[0137] In another possible implementation of this application embodiment, when the model generation module 23 determines the power supply line node information based on the line length information and the surrounding environment information, it is specifically used for:

[0138] Determine whether the line length information meets the preset line length. If it does, determine the environmental parameters of different nodes of the power supply line based on the environmental information, and match the environmental parameters with the preset environmental coefficient standard to obtain the initial environmental coefficient of the initial node of the power supply line and the non-initial environmental coefficient corresponding to the non-initial node.

[0139] Calculate the environmental coefficient difference between the initial environmental coefficient and each non-initial environmental coefficient, and determine the first non-initial position node that meets the preset environmental coefficient difference requirement. Define the first non-initial position node that meets the preset environmental coefficient difference requirement as the new initial position node, and define the position node between the new initial position node and the final position node among the non-initial position nodes as the new non-initial position node.

[0140] The iterative execution steps are as follows: Calculate the environmental coefficient difference between the new initial environmental coefficient and each new non-initial environmental coefficient, and determine the first non-initial position node in the environmental coefficient difference that meets the preset environmental coefficient difference requirement. Define the first non-initial position node that meets the preset environmental coefficient difference requirement as the new initial position node. Define the environmental coefficient between the new initial position node and the final environmental coefficient in the non-initial environmental coefficient as the new non-initial position node, until there are no non-initial position nodes in the environmental coefficient difference that meet the preset environmental coefficient difference requirement.

[0141] The power supply line grouping node information is determined based on the initial position node and the new initial position node. The power supply line grouping node information includes the power transfer switch installed at the initial position node and the new initial position node, as well as the power transfer line connected to the power transfer switch.

[0142] In another possible implementation of this application embodiment, system 20 further includes: an electricity consumption information module, an energy consumption matching module, and a power supply simulation module, wherein...

[0143] The electricity consumption information module is used to obtain the actual electricity consumption information of the optimal power supply route and calculate the power loss data of the power nodes in the optimal power supply route based on the actual electricity consumption information.

[0144] The energy consumption matching module is used to match the calculated energy loss value with the preset energy loss standard to determine whether the actual power consumption information meets the preset energy loss standard.

[0145] The power supply simulation module is used to perform power supply simulation analysis on the local power grid model when the actual power consumption information does not meet the preset power loss standard, obtain power grid layout instructions, and control and adjust the layout of the power supply lines connected to the power-consuming equipment.

[0146] In another possible implementation of this application embodiment, when the power supply simulation module performs power supply simulation analysis on the local power grid model to obtain power grid layout instructions, it is specifically used for:

[0147] Based on the local power grid model, various power supply line combinations between power supply equipment and power consumption equipment are determined;

[0148] Determine the changes in power consumption data corresponding to the group node information of each power supply line combination in various power supply line combinations under different equipment operating power conditions;

[0149] The changes in electricity consumption data are accumulated according to the same power supply line combination to obtain the electricity consumption data corresponding to each power supply line combination.

[0150] The line scheduling length corresponding to each power supply line combination is determined based on the grouping node information of each power supply line combination;

[0151] The average energy consumption data of a certain power supply line combination is obtained by calculating the ratio between the electricity consumption data and the line dispatch length.

[0152] The target power supply line combination corresponding to the minimum average energy consumption data is determined, and the power transfer switch control sequence is analyzed for the group power node information in the target power supply line combination to obtain the power grid layout instructions.

[0153] Another possible implementation of this application embodiment is that 20 further includes: a loss determination module, a ratio calculation module, and a line marking module, wherein...

[0154] The loss determination module is used to determine the power loss data of the power supply line segment corresponding to the information of each group of power nodes in the optimal power supply line based on the power loss data of the group of power nodes in the optimal power supply line.

[0155] The ratio calculation module is used to calculate the ratio between the power loss data of the power supply line segment and the length of the power supply line segment to obtain the power loss value of each power supply line segment.

[0156] The line marking module is used to determine whether the power loss value of each power supply line segment meets the preset power loss standard. If it does not meet the standard, the line corresponding to the power supply line segment in the local power grid model is marked to obtain the marked local power grid model.

[0157] Those skilled in the art will clearly understand that, for the sake of convenience and brevity, the specific working process of the smart energy management system 20 based on Internet of Things big data described above can be referred to the corresponding process in the aforementioned method embodiments, and will not be repeated here.

[0158] This application provides an electronic device, such as... Figure 3 As shown, Figure 3 This is a schematic diagram of the structure of an electronic device provided in an embodiment of this application. Figure 3 The illustrated electronic device 300 includes a processor 301 and a memory 303. The processor 301 and the memory 303 are connected, for example, via a bus 302. Optionally, the electronic device 300 may also include a transceiver 304. It should be noted that in practical applications, the transceiver 304 is not limited to one type, and the structure of this electronic device 300 does not constitute a limitation on the embodiments of this application.

[0159] Processor 301 may be a CPU (Central Processing Unit), a general-purpose processor, a DSP (Digital Signal Processor), an ASIC (Application Specific Integrated Circuit), an FPGA (Field Programmable Gate Array), or other programmable logic devices, transistor logic devices, hardware components, or any combination thereof. It can implement or execute the various exemplary logic blocks, modules, and circuits described in conjunction with the disclosure of this application. Processor 301 may also be a combination that implements computational functions, such as including one or more microprocessor combinations, a combination of a DSP and a microprocessor, etc.

[0160] Bus 302 may include a pathway for transmitting information between the aforementioned components. Bus 302 may be a PCI (Peripheral Component Interconnect) bus or an EISA (Extended Industry Standard Architecture) bus, etc. Bus 302 can be divided into address bus, data bus, control bus, etc. For ease of representation, Figure 3 The symbol is represented by a single thick line, but this does not mean that there is only one bus or one type of bus.

[0161] The memory 303 may be a ROM (Read Only Memory) or other type of static storage device capable of storing static information and instructions, RAM (Random Access Memory) or other type of dynamic storage device capable of storing information and instructions, or an EEPROM (Electrically Erasable Programmable Read Only Memory), CD-ROM (Compact Disc Read Only Memory) or other optical disc storage, optical disc storage (including compressed optical discs, laser discs, optical discs, digital universal optical discs, Blu-ray discs, etc.), magnetic disk storage media or other magnetic storage devices, or any other medium capable of carrying or storing desired program code in the form of instructions or data structures and accessible by a computer, but not limited thereto.

[0162] The memory 303 is used to store application code that executes the solution of this application, and its execution is controlled by the processor 301. The processor 301 is used to execute the application code stored in the memory 303 to implement the content shown in the foregoing method embodiments.

[0163] Electronic devices include, but are not limited to: mobile terminals such as mobile phones, laptops, digital radio receivers, PDAs (personal digital assistants), PADs (tablet computers), PMPs (portable multimedia players), and in-vehicle terminals (such as in-vehicle navigation terminals), as well as fixed terminals such as digital TVs and desktop computers. Servers can also be included. Figure 3 The electronic device shown is merely an example and should not impose any limitation on the functionality and scope of use of the embodiments of this application.

[0164] This application provides a computer-readable storage medium storing a computer program that, when run on a computer, enables the computer to execute the corresponding content in the aforementioned method embodiments.

[0165] It should be understood that although the steps in the flowcharts of the accompanying figures are shown sequentially as indicated by the arrows, these steps are not necessarily executed in the order indicated by the arrows. Unless explicitly stated herein, there is no strict order restriction on the execution of these steps, and they can be executed in other orders. Moreover, at least some steps in the flowcharts of the accompanying figures may include multiple sub-steps or multiple stages. These sub-steps or stages are not necessarily completed at the same time, but can be executed at different times, and their execution order is not necessarily sequential, but can be performed alternately or in turn with other steps or at least some of the sub-steps or stages of other steps.

[0166] The above are only some embodiments of this application. It should be noted that for those skilled in the art, several improvements and modifications can be made without departing from the principle of this application, and these improvements and modifications should also be considered within the scope of protection of this application.

Claims

1. A smart energy management method based on Internet of Things big data, characterized in that, include: The system acquires power application information and power consumption data of electrical equipment. The power application information includes power supply information of power supply equipment in different areas of the building during a historical period and power transmission information of different power supply lines connected to the power supply equipment. The electrical equipment refers to the electrical equipment in the building connected to the corresponding power supply line during the historical period. The power consumption data is used to represent the equipment location information of the electrical equipment and the power consumption status data of the electrical equipment during the historical period. Based on the power application information and the device location information, the power consumption area of ​​the electrical equipment is differentiated to obtain the power consumption area information of the electrical equipment; The step of differentiating the power consumption area of ​​the electrical equipment based on the power application information and the device location information to obtain the power consumption area information of the electrical equipment includes: Based on the power application information, determine the power supply area location and power supply coverage of different power supply devices; Based on the location of the power supply area and the location information of the equipment, a first vector set of different power supply devices and electrical devices is determined. Based on the first vector set, determine whether the electrical equipment is located within the power supply coverage area. If the electrical equipment is located within the power supply coverage area, then summarize the power supply coverage area and the power supply equipment corresponding to the power supply coverage area to obtain the power consumption area information of the electrical equipment. A local power grid model corresponding to the electrical equipment is generated based on the electricity consumption area information and the power transmission information. The step of generating a local power grid model corresponding to the electrical equipment based on the electricity consumption area information and the power transmission information includes: The power transmission information determines the line length and environmental information of the power supply line connected to the power supply equipment. The power supply line's group node information is determined based on the line length information and the surrounding environment information; Based on the node positions in the power group node information and the positions in the equipment position information, a second vector set between the power supply line and the electrical equipment is determined. Based on the second vector set, the power transmission association between the electrical equipment and different node positions in the group power node information is constructed to obtain the power supply line grid of the electrical equipment; An initial local model is constructed based on the power supply coverage range in the power consumption area information, and the power supply area location in the power consumption area information and the location in the device location information are marked in the initial local model according to a preset ratio to obtain a marked local model. The power supply line grid is integrated into the marked local model according to the correspondence of the same electrical equipment, and the initial position node of each power supply line in the power supply line grid is connected to the power supply equipment corresponding to the power supply line to obtain the local power grid model corresponding to the electrical equipment. Determining the power supply line's group node information based on the line length information and the surrounding environment information includes: Determine whether the line length information meets the preset line length. If it does, determine the environmental parameters of different nodes of the power supply line based on the environmental information. Then match the environmental parameters with the preset environmental coefficient standard to obtain the initial environmental coefficient of the initial node of the power supply line and the non-initial environmental coefficient corresponding to the non-initial node. Calculate the environmental coefficient difference between the initial environmental coefficient and each of the non-initial environmental coefficients, and determine the first non-initial position node that meets the preset environmental coefficient difference requirement among the environmental coefficient differences. Define the first non-initial position node that meets the preset environmental coefficient difference requirement as the new initial position node, and define the position node between the new initial position node and the final position node among the non-initial environmental coefficients as the new non-initial position node. The following steps are executed iteratively: Calculate the environmental coefficient difference between the new initial environmental coefficient and each new non-initial environmental coefficient, and determine the first non-initial position node in the environmental coefficient difference that meets the preset environmental coefficient difference requirement. Define the first non-initial position node that meets the preset environmental coefficient difference requirement as the new initial position node, and define the position node between the new initial position node and the final position node among the non-initial position nodes as the new non-initial position node, until there are no non-initial position nodes in the environmental coefficient difference that meet the preset environmental coefficient difference requirement. The power supply line group node information is determined based on the initial position node and the new initial position node. The power supply node information includes the power transfer switch installed at the initial position node and the new initial position node, and the power transfer line connected to the power transfer switch. Based on the local power grid model, the power consumption status data is used to perform regional power consumption simulation detection to obtain the optimal power supply line in the power consumption area information.

2. The smart energy management method based on Internet of Things big data according to claim 1, characterized in that, The step of performing regional electricity consumption simulation detection on the electricity consumption status data based on the local power grid model to obtain the optimal power supply line in the electricity consumption area information, further includes: Obtain the actual power consumption information of the optimal power supply line, and calculate the power loss data of the group nodes in the optimal power supply line based on the actual power consumption information; The calculated power loss value is matched with the preset power loss standard to determine whether the actual power consumption information meets the preset power loss standard. If the actual power consumption information does not meet the preset power loss standard, then the local power grid model is subjected to power supply simulation analysis to obtain power grid layout instructions, and the layout of the power supply lines connected to the power-consuming equipment is controlled and adjusted.

3. The smart energy management method based on Internet of Things big data according to claim 2, characterized in that, The process of performing power supply simulation analysis on the local power grid model to obtain power grid layout instructions includes: Based on the local power grid model, various power supply line combinations between the power supply equipment and the electrical equipment are determined; Determine the changes in power consumption data corresponding to the power group node information of each of the various power supply line combinations under different equipment operating power conditions; The changes in the electricity consumption data are accumulated according to the same power supply line combination to obtain the electricity consumption data corresponding to each power supply line combination; The line scheduling length corresponding to each power supply line combination is determined based on the grouping node information of each power supply line combination; The average energy consumption data of a certain power supply line combination is obtained by calculating the ratio between the power consumption data and the line scheduling length. The target power supply line combination corresponding to the minimum average energy consumption data is determined, and the power transfer switch control sequence analysis is performed on the group power node information in the target power supply line combination to obtain the power grid layout command.

4. The smart energy management method based on Internet of Things big data according to claim 2, characterized in that, If the actual electricity consumption information does not meet the preset power loss standard, the following steps are also included: The power loss data of the power supply line segment corresponding to each group of power nodes in the optimal power supply line is determined based on the power loss data of the group of power nodes in the optimal power supply line. The power loss data of the power supply line segment is calculated by comparing it with the length of the power supply line segment to obtain the power loss value of each power supply line segment. Determine whether the power loss value of each power supply line segment meets the preset power loss standard. If it does not meet the standard, mark the line corresponding to the power supply line segment in the local power grid model to obtain the marked local power grid model.

5. A smart energy management system based on Internet of Things big data, characterized in that, include: The information acquisition module is used to acquire power application information and power consumption data information of electrical equipment. The power application information is the power supply information of power supply equipment in different areas of the building during the historical period and the power transmission information of different power supply lines connected to the power supply equipment. The electrical equipment is the electrical equipment in the building connected to the corresponding power supply line during the historical period. The power consumption data information is used to represent the equipment location information of the electrical equipment and the power consumption status data of the electrical equipment during the historical period. The region differentiation module is used to differentiate the power consumption region of the electrical equipment based on the power application information and the equipment location information, so as to obtain the power consumption region information of the electrical equipment. When the region differentiation module performs power consumption region differentiation on the electrical equipment based on the power application information and the equipment location information to obtain the power consumption region information of the electrical equipment, it is specifically used for: Based on the power application information, determine the power supply area location and power supply coverage of different power supply devices; Based on the location of the power supply area and the location information of the equipment, a first vector set of different power supply devices and electrical devices is determined. Based on the first vector set, determine whether the electrical equipment is located within the power supply coverage area. If the electrical equipment is located within the power supply coverage area, then summarize the power supply coverage area and the power supply equipment corresponding to the power supply coverage area to obtain the power consumption area information of the electrical equipment. The model generation module is used to generate a local power grid model corresponding to the electrical equipment based on the power consumption area information and the power transmission information. When the model generation module generates a local power grid model corresponding to the electrical equipment based on the power consumption area information and the power transmission information, it is specifically used for: The power transmission information determines the line length and environmental information of the power supply line connected to the power supply equipment. The power supply line's group node information is determined based on the line length information and the surrounding environment information; Based on the node positions in the power group node information and the positions in the equipment position information, a second vector set between the power supply line and the electrical equipment is determined. Based on the second vector set, the power transmission association between the electrical equipment and different node positions in the group power node information is constructed to obtain the power supply line grid of the electrical equipment; An initial local model is constructed based on the power supply coverage range in the power consumption area information, and the power supply area location in the power consumption area information and the location in the device location information are marked in the initial local model according to a preset ratio to obtain a marked local model. The power supply line grid is integrated into the marked local model according to the correspondence of the same electrical equipment, and the initial position node of each power supply line in the power supply line grid is connected to the power supply equipment corresponding to the power supply line to obtain the local power grid model corresponding to the electrical equipment. When determining the power supply line node information based on the line length information and the surrounding environment information, the model generation module is specifically used for: Determine whether the line length information meets the preset line length. If it does, determine the environmental parameters of different nodes of the power supply line based on the environmental information. Then match the environmental parameters with the preset environmental coefficient standard to obtain the initial environmental coefficient of the initial node of the power supply line and the non-initial environmental coefficient corresponding to the non-initial node. Calculate the environmental coefficient difference between the initial environmental coefficient and each of the non-initial environmental coefficients, and determine the first non-initial position node that meets the preset environmental coefficient difference requirement among the environmental coefficient differences. Define the first non-initial position node that meets the preset environmental coefficient difference requirement as the new initial position node, and define the position node between the new initial position node and the final position node among the non-initial environmental coefficients as the new non-initial position node. The following steps are executed iteratively: Calculate the environmental coefficient difference between the new initial environmental coefficient and each new non-initial environmental coefficient, and determine the first non-initial position node in the environmental coefficient difference that meets the preset environmental coefficient difference requirement. Define the first non-initial position node that meets the preset environmental coefficient difference requirement as the new initial position node, and define the position node between the new initial position node and the final position node among the non-initial position nodes as the new non-initial position node, until there are no non-initial position nodes in the environmental coefficient difference that meet the preset environmental coefficient difference requirement. The power supply line group node information is determined based on the initial position node and the new initial position node. The power supply node information includes the power transfer switch installed at the initial position node and the new initial position node, and the power transfer line connected to the power transfer switch. The simulation detection module is used to perform regional power consumption simulation detection on the power consumption status data based on the local power grid model, and to obtain the optimal power supply line in the power consumption area information.

6. An electronic device, characterized in that, It includes: One or more processors; Memory; One or more applications, wherein the one or more applications are stored in the memory and configured to be executed by the one or more processors, the one or more applications being configured to: execute a smart energy management method based on Internet of Things big data according to any one of claims 1 to 4.

7. A computer-readable storage medium having a computer program stored thereon, characterized in that, When executed by the processor, the program implements the smart energy management method based on Internet of Things big data as described in any one of claims 1 to 4.

Images