Intelligent energy management control method and platform based on perception network

Abstract

The application discloses a smart energy management control method and platform based on a perception network, and relates to the technical field of energy management.The method comprises the following steps: collecting the energy flow topology of a target building and extracting a first energy flow chain; collecting first energy supply data through a first sensor network and collecting first energy consumption data through a second sensor network; collecting real-time environmental conditions and real-time passenger flow information of the target building, and analyzing the scene adaptation index of the first energy consumption data; analyzing the energy consumption adaptation index of the first energy consumption data and the first energy supply data; and optimizing energy management control in combination with the scene adaptation index and the energy consumption adaptation index.The technical problems of lack of dynamic perception and scene-based adaptive regulation and control capability in the prior art, resulting in supply-demand mismatch and low energy efficiency, are solved, and the technical effects of realizing precise energy management and control based on multi-source perception data and scene driving and improving the dynamic adaptability of energy supply and demand and energy utilization efficiency are achieved.

Description

Technical Field

[0001] This application relates to the field of energy management technology, specifically to a smart energy management and control method and platform based on sensing networks. Background Technology

[0002] With the continuous growth of building energy consumption, energy management has become a crucial part of modern building operations. Traditional energy management struggles to cope with the dynamic changes in building energy consumption behavior, the complexity of multi-energy coupling, and the real-time fluctuations in user demand. This is especially true in large commercial buildings, industrial parks, or multi-purpose venues, where diverse energy flows and highly heterogeneous energy-consuming equipment can lead to problems such as low energy efficiency and supply-demand mismatch. Furthermore, existing IoT sensor networks are limited to data collection and visualization, lacking in-depth perception and integrated analysis of external conditions such as energy consumption scenarios, environmental factors, and human activity. For example, the energy demand of the same building may vary significantly at different times, with different human density, or under different ambient temperatures, making it impossible to achieve adaptive control based on multi-dimensional information. In addition, there is a lack of dynamic correlation analysis between energy supply data and energy consumption data, and the matching degree between energy consumption behavior and building scenarios is not included in the optimization objectives. This results in energy allocation strategies lacking scenario awareness capabilities, thereby affecting the accuracy of energy optimization control and energy utilization efficiency.

[0003] Therefore, current technologies suffer from a lack of dynamic perception and scenario-based adaptive control capabilities in energy management, leading to supply-demand mismatch and low energy efficiency. Summary of the Invention

[0004] This application provides a smart energy management and control method and platform based on sensing networks, which solves the technical problems in the existing technology of energy management lacking dynamic sensing and scenario-based adaptive regulation capabilities, resulting in supply and demand mismatch and low energy efficiency. It achieves the technical effect of realizing precise energy management and control based on multi-source sensing data and scenario-driven, and improving the dynamic adaptability of energy supply and demand and energy utilization efficiency.

[0005] This application provides a smart energy management and control method based on a sensing network, comprising: collecting the energy flow topology of a target building and extracting a first energy flow chain in which energy is supplied by a first energy supply node; collecting first energy supply data through a first sensor network connected to the first energy supply node, and collecting first energy consumption data through a second sensor network on energy-consuming devices in the first energy flow chain; collecting real-time environmental conditions and real-time pedestrian flow information of the target building, and analyzing the scenario adaptation index of the first energy consumption data; analyzing the energy consumption adaptation index of the first energy consumption data and the first energy supply data; and optimizing energy management and control by combining the scenario adaptation index and the energy consumption adaptation index.

[0006] In a possible implementation, the smart energy management and control method based on the sensing network further performs the following processing: establishing a standard scenario-energy consumption adaptation template library for the target building; retrieving the corresponding scenario-energy consumption adaptation template from the standard scenario-energy consumption adaptation template library based on the real-time environmental conditions and real-time pedestrian flow information; performing scenario adaptation analysis on the first energy consumption data using the scenario-energy consumption adaptation template to generate the scenario adaptation index.

[0007] In a possible implementation, the smart energy management and control method based on the sensing network further performs the following processing: extracting standard adapted energy consumption data from the scenario-energy consumption adaptation template; comparing the standard adapted energy consumption data with the first energy consumption data, performing scenario adaptation analysis based on the comparison differences, and generating the scenario adaptation index.

[0008] In a possible implementation, the smart energy management and control method based on the sensing network also performs the following processing: the standard scenario-energy consumption adaptation template library configures the energy consumption demand under the standard scenario-equipment on-state mapping specified by the target building.

[0009] In a possible implementation, the smart energy management and control method based on the sensing network further performs the following processing: extracting each first energy-consuming device in the first energy flow chain; analyzing each energy transmission loss from the first energy supply node to each first energy-consuming device; using each energy transmission loss as adaptation compensation, analyzing the adaptation degree between the first energy consumption data and the first energy supply data, and generating the energy consumption adaptation index.

[0010] In a possible implementation, the intelligent energy management and control method based on a sensing network further performs the following processing: determining whether both the scenario adaptation index and the energy consumption adaptation index meet preset adaptation thresholds; if the determination result is that the scenario adaptation index meets the thresholds but the energy consumption adaptation index does not, real-time collection of equipment operation data and equipment output status of each first energy-consuming device; performing energy consumption anomaly analysis based on the equipment operation data and equipment output status of each first energy-consuming device to locate energy consumption anomaly devices and anomaly states; generating anomaly warning signals based on the energy consumption anomaly devices and anomaly states and sending them to operation and maintenance personnel for optimization of energy management and control.

[0011] In a possible implementation, the smart energy management and control method based on the sensing network further performs the following processing: if the judgment result is that the scene adaptation index is not met but the energy consumption adaptation index is met; based on the equipment operation data of each first energy-consuming device, real-time environmental conditions, and real-time pedestrian flow information, and with the standard scene-device on-state mapping specified by the target building as a reference, identify the scene adaptation anomaly area; and use the standard scene-device on-state mapping to regulate the operation of the energy-consuming devices corresponding to the scene adaptation anomaly area.

[0012] In a possible implementation, the smart energy management and control method based on the sensing network also performs the following processing: if the judgment result is that neither the scenario adaptation index nor the energy consumption adaptation index is met, an abnormal warning signal is generated and sent to the operation and maintenance management personnel, and the abnormal area of ​​scenario adaptation is regulated and optimized to generate a regulation plan; the regulation plan is first frozen, and after receiving the operation and maintenance feedback signal of the abnormal warning signal, the scenario adaptation optimization regulation is performed according to the regulation plan.

[0013] In a possible implementation, the smart energy management and control method based on the sensing network further performs the following processing: based on the building information model of the target building, constructing a topology structure including energy supply nodes, energy conversion equipment, transmission and distribution networks and energy-consuming terminals, and establishing the energy flow topology; tracing all energy-consuming devices downstream of the first energy supply node from the energy flow topology, determining the connection relationship, and generating the first energy flow chain.

[0014] This application also provides a smart energy management and control platform based on a sensing network, comprising: an energy flow topology acquisition module for acquiring the energy flow topology of a target building and extracting a first energy flow chain supplied by a first energy supply node; a data acquisition module for acquiring first energy supply data through a first sensor network connected to the first energy supply node and acquiring first energy consumption data through a second sensor network on energy-consuming devices in the first energy flow chain; a scene adaptation index analysis module for acquiring real-time environmental conditions and real-time pedestrian flow information of the target building and analyzing scene adaptation indexes of the first energy consumption data; an energy consumption adaptation index analysis module for analyzing energy consumption adaptation indexes of the first energy consumption data and the first energy supply data; and an energy management and control optimization module for optimizing energy management and control by combining the scene adaptation indexes and the energy consumption adaptation indexes.

[0015] This application proposes a smart energy management and control method and platform based on a sensing network. The method involves collecting the energy flow topology of a target building and extracting the first energy flow chain. It also involves collecting first energy supply data through a first sensor network and first energy consumption data through a second sensor network. Furthermore, it collects real-time environmental conditions and pedestrian flow information of the target building and analyzes the scenario adaptation indicators of the first energy consumption data. Finally, it analyzes the energy consumption adaptation indicators of the first energy consumption data and the first energy supply data. Finally, it optimizes energy management and control by combining the scenario adaptation indicators and the energy consumption adaptation indicators. This addresses the technical problems in existing technologies where energy management lacks dynamic sensing and scenario-based adaptive control capabilities, leading to supply-demand mismatch and low energy efficiency. The method achieves precise energy management and control based on multi-source sensing data and scenario-driven approaches, improving the dynamic adaptability of energy supply and demand and energy utilization efficiency. Attached Figure Description

[0016] To more clearly illustrate the technical solutions of the embodiments of this disclosure, the accompanying drawings of the embodiments of this disclosure will be briefly described below. Flowcharts are used in this application to illustrate the operations performed by the platform according to the embodiments of this application. It should be understood that the preceding or following operations are not necessarily performed precisely in sequence. Instead, various steps can be processed in reverse order or simultaneously as needed. Furthermore, other operations can be added to these processes, or one or more steps can be removed from these processes.

[0017] Figure 1 This is a schematic diagram of the process of a smart energy management and control method based on a sensing network, provided in an embodiment of this application.

[0018] Figure 2 This is a schematic diagram of the structure of a smart energy management and control platform based on a sensing network, provided in an embodiment of this application.

[0019] Figure labeling: Energy flow topology acquisition module 10, data acquisition module 20, scenario adaptation index analysis module 30, energy consumption adaptation index analysis module 40, energy management and control optimization module 50. Detailed Implementation

[0020] The above description is only an overview of the technical solution of this application. In order to better understand the technical means of this application and to implement it in accordance with the contents of the specification, and to make the above and other objects, features and advantages of this application more obvious and understandable, specific embodiments of this application are given below.

[0021] To make the objectives, technical solutions, and advantages of this application clearer, the following detailed description of this application will be provided in conjunction with the accompanying drawings. The described embodiments should not be considered as limitations on this application. All other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this application.

[0022] In the following description, references to "some embodiments" describe a subset of all possible embodiments. However, it is understood that "some embodiments" can be the same or different subsets of all possible embodiments and can be combined with each other without conflict. The terms "first" and "second" are used merely to distinguish similar objects and do not represent a specific ordering of objects. The terms "comprising" and "having," and any variations thereof, are intended to cover non-exclusive inclusion. For example, a process, method, platform, product, or server that includes a series of steps or units is not necessarily limited to those steps or units explicitly listed, but may include other steps or modules not explicitly listed or inherent to these processes, methods, products, or devices. Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this application belongs. The terminology used herein is for the purpose of describing embodiments of this application only.

[0023] This application provides a smart energy management and control method based on a sensing network, such as... Figure 1 As shown, the method includes: Step S100: Collect the energy flow topology of the target building and extract the first energy flow chain that provides energy from the first energy supply node.

[0024] Step S100 further includes step S110, constructing a topology structure including energy supply nodes, energy conversion equipment, transmission and distribution networks and energy-consuming terminals based on the building information model of the target building, and establishing the energy flow topology; step S120, tracing all energy-consuming devices downstream of the first energy supply node from the energy flow topology, determining the connection relationship, and generating the first energy flow chain.

[0025] Preferably, a topology structure of equipment components such as energy supply nodes, energy conversion equipment, transmission and distribution networks, and energy-consuming terminals is constructed based on the building information model (BIM) of the target building. The BIM is a digital 3D model of the target building, to which an energy logic relationship network is added. This involves labeling the equipment components with energy logic attributes and their energy flow relationships, forming an energy flow topology. For example, an energy logic link is established from the municipal power grid access point to the low-voltage distribution cabinet, then to the distribution box on a certain floor, and finally to the indoor lighting circuit. Downstream tracing analysis is then performed on the energy flow topology. Specifically, an energy supply node is randomly selected as the energy flow topology, and the downstream paths of this energy supply node are traversed to obtain all energy-consuming devices that directly or indirectly obtain energy from this energy supply node. The connection relationships are determined, and finally, the first energy flow chain is generated, which may be a structured tree list to ensure accurate energy efficiency analysis.

[0026] Step S200: Collect first energy supply data through the first sensor network connected to the first energy supply node, and collect first energy consumption data through the second sensor network of the energy-consuming equipment in the first energy flow chain.

[0027] Preferably, the first energy supply data is collected through a first sensor network connected to the first energy supply node. The first sensor network may include various sensor devices. For example, if the energy supply node provides electricity, the first sensor network includes smart meters and power quality analyzers; if the energy supply node provides heat or cooling, the first sensor network includes flow meters, temperature sensors, and pressure sensors; if the energy supply node is a distributed energy source, the first sensor network includes inverter data acquisition devices. The first energy supply data is then obtained, reflecting the total amount and status of energy generated by the first energy supply node, such as total electricity and instantaneous power, total heating / cooling capacity and instantaneous heating / cooling load, supply voltage / current, etc. The first energy consumption data is then collected through a second sensor network. The second sensor network consists of sensor devices installed on multiple energy-consuming devices at the end of the first energy flow chain. For example, for an air conditioning unit, a secondary smart meter is installed on its power supply line; for a fan coil unit, a power metering unit is integrated on its control panel; for a lighting circuit, a circuit metering meter is installed in the lighting switch box, etc. The first energy consumption data reflects the energy consumed by each energy-consuming device in the first energy flow chain, such as the power consumption of a single device, the operating power of the device, and the operating status of the device.

[0028] Step S300: Collect real-time environmental conditions and real-time pedestrian flow information of the target building, and analyze the scenario adaptation index of the first energy consumption data.

[0029] Step S300 further includes step S310, establishing a standard scenario-energy consumption adaptation template library for the target building; step S320, retrieving the corresponding scenario-energy consumption adaptation template from the standard scenario-energy consumption adaptation template library based on the real-time environmental conditions and real-time pedestrian flow information; step S330, performing scenario adaptation analysis on the first energy consumption data using the scenario-energy consumption adaptation template to generate the scenario adaptation index.

[0030] Furthermore, step S310 also includes configuring the energy consumption requirements of the standard scenario-energy adaptation template library under the standard scenario-device on-state mapping specified by the target building.

[0031] Preferably, different standardized usage states of the target building are obtained, described by a combination of multiple parameters, mainly including time period, environmental conditions, and pedestrian load information, to obtain multiple standard scenarios specified for the target building, such as a high-temperature full-load scenario in summer on weekdays and a moderate-load scenario in winter during holidays. For each standard scenario, a reasonable operating plan for energy-consuming equipment and its expected energy consumption level are preset, including configuring the on-state of energy-consuming equipment, and calculating the corresponding expected energy consumption range based on the on-state of equipment, historical energy consumption data, and rated power of energy-consuming equipment, thereby configuring energy consumption requirements. Then, the standard scenarios specified for the target building are matched and mapped with the energy consumption requirements mapped under the on-state of equipment to construct a standard scenario-energy-consuming adaptation template library for the target building.

[0032] Preferably, real-time environmental conditions such as outdoor temperature, humidity, and illuminance of the target building are collected. At the same time, real-time pedestrian flow information is estimated through cameras, Wi-Fi probes, or access control systems. Then, the current real-time environmental conditions and real-time pedestrian flow information are matched with templates in the standard scenario-energy consumption adaptation template library to identify and determine the scenario-energy consumption adaptation template that best matches the current scenario. Next, scenario adaptation analysis of the first energy consumption data is performed using the scenario-energy consumption adaptation template. That is, the real-time collected first energy consumption data is compared with the energy consumption data provided by the scenario-energy consumption adaptation template to generate a quantitative evaluation result, which is used to represent the degree of matching between actual energy consumption and expected energy consumption in the scenario and to identify the matching level, thereby realizing intelligent energy efficiency management.

[0033] Furthermore, step S330 also includes step S331, extracting standard adaptation energy data from the scenario-energy adaptation template; step S332, comparing the standard adaptation energy data with the first energy data, completing scenario adaptation analysis based on the comparison differences, and generating the scenario adaptation index.

[0034] Preferably, standard energy consumption data is extracted from the scenario-energy consumption adaptation template, representing the ideal energy consumption range under the current standard scenario. This data is then compared with the first energy consumption data, and the difference rate is calculated as (actual energy consumption - expected energy consumption) / expected energy consumption × 100%. This generates a comparison difference, for example, a difference within ±5% indicates good adaptation, a difference between ±5% and 15% indicates slight deviation, and a difference exceeding ±15% indicates serious deviation. The scenario adaptation analysis is then completed based on the comparison difference, that is, the result of the comparison difference is output as a scenario adaptation indicator and the adaptation level is identified according to preset rules, thereby achieving intelligent and precise energy efficiency management.

[0035] Step S400: Analyze the energy consumption matching index of the first energy consumption data and the first energy supply data.

[0036] Step S400 further includes step S410, extracting each first energy-consuming device in the first energy flow chain; step S420, analyzing each energy transmission loss from the first energy supply node to each first energy-consuming device; and step S430, using each energy transmission loss as adaptation compensation, analyzing the adaptation degree between the first energy consumption data and the first energy supply data, and generating the energy consumption adaptation index.

[0037] Preferably, all end-user energy devices (i.e., each first-user energy device) are obtained from the first energy flow chain. Then, the energy transmission losses from the first energy supply node to each first-user energy device are analyzed. Specifically, the energy loss during the transmission process from the first energy supply node to each first-user energy device in the first energy flow chain is estimated based on the rated parameters and physical formulas of the devices. This may include energy loss due to resistance, friction, heat dissipation, etc., in the transmission equipment such as cables, transformers, pipes, pumps / fans, etc., thus obtaining each energy transmission loss. Finally, each energy transmission loss is used as an adaptation compensation to analyze the adaptation degree between the first energy consumption data and the first energy supply data. Here, the first energy supply data ≈ the sum of each first-user energy data + the sum of each energy transmission loss. The measured energy consumption data of all end-user devices is summed with each energy transmission loss to obtain the theoretical energy supply data. This theoretical energy supply data is compared with the measured first energy supply data to calculate the adaptation degree, quantify the efficiency of supply and demand matching, and serve as the final energy consumption adaptation index and label the adaptation level, thereby ensuring accurate energy management.

[0038] Step S500: Optimize energy management and control by combining the scenario adaptation index and the energy consumption adaptation index.

[0039] Step S500 further includes step S510, determining whether the scenario adaptation index and the energy consumption adaptation index both meet the preset adaptation threshold; step S520, if the determination result is that the scenario adaptation index meets the threshold but the energy consumption adaptation index does not, real-time collection of the device operation data and device output status of each first energy-consuming device; step S530, performing energy consumption anomaly analysis based on the device operation data and device output status of each first energy-consuming device, locating the energy consumption anomaly device and the anomaly status; step S540, generating an anomaly warning signal based on the energy consumption anomaly device and the anomaly status and sending it to the operation and maintenance management personnel for optimization of energy management control.

[0040] Preferably, it determines whether both the scenario adaptation index and the energy consumption adaptation index meet the preset adaptation threshold. The preset adaptation threshold is a critical value configured based on historical energy supply data and energy consumption data analysis. It is used to determine whether the scenario adaptation index and the energy consumption adaptation index are reasonable. If the judgment result is that the scenario adaptation index meets the requirements but the energy consumption adaptation index does not, it means that based on the current time, environment, and flow of people, the on-state of the energy-consuming equipment and the overall energy consumption level in the building are reasonable. However, the supplied energy is much greater than the sum of the actual energy consumed by the terminal equipment and the theoretical transmission loss. That is, the total energy transmission efficiency from the energy supply head to the energy consumption end is too low. In this case, the equipment operation data of each primary energy-consuming equipment is collected in real time, such as running time, start-stop frequency, and input energy, to reflect the internal operating efficiency and status of the equipment. At the same time, the equipment output status is collected to reflect the actual workload or output capacity of the equipment. The health status of the energy-consuming equipment is diagnosed by combining the equipment operation data and the equipment output status.

[0041] Preferably, energy consumption anomaly analysis is then performed based on the equipment operation data and output status of each primary energy-consuming device. Specifically, the input energy contained in the equipment operation data is compared with its output status to calculate the real-time operating energy efficiency ratio of the equipment, i.e., the unit output energy consumption. Then, based on the rated value of unit output energy consumption, the energy consumption anomaly device is identified and located. Its operation data is then analyzed to identify the abnormal state and determine the cause of the anomaly. Then, an anomaly warning signal is generated based on the energy consumption anomaly device and the abnormal state. This may include an alarm prompt "Energy consumption adaptation index abnormal - equipment-level energy efficiency failure", the energy consumption anomaly device, the abnormal state, and suggested measures. Finally, the anomaly warning signal is sent to the operation and maintenance management personnel for optimization of energy management and control, which greatly improves operation and maintenance efficiency and energy management level.

[0042] Furthermore, step S500 also includes step S550: if the judgment result is that the scene adaptation index is not met but the energy consumption adaptation index is met; step S560: based on the equipment operation data, real-time environmental conditions and real-time pedestrian flow information of each first energy-consuming device, and with reference to the standard scene-device on-state mapping specified by the target building, identify the scene adaptation abnormal area; step S570: adjust the operation of the energy-consuming device corresponding to the scene adaptation abnormal area with reference to the standard scene-device on-state mapping.

[0043] Preferably, if the judgment result is that the scene adaptation index is not met but the energy consumption adaptation index is met, it indicates that the current energy consumption behavior is unreasonable, such as excessive energy consumption during unattended periods or insufficient energy consumption during high-demand periods, but the energy transmission efficiency from the energy supply end to the energy consumption end is normal, with no abnormal loss or waste; then, the equipment operation data, real-time environmental conditions, and real-time pedestrian flow information of each primary energy-consuming device are obtained in real time, and the scene adaptation abnormal area is identified with reference to the standard scene-device on-state mapping specified by the target building. That is, based on the matching and comparison of equipment operation data, real-time environmental conditions, and real-time pedestrian flow information with the standard scene-device on-state mapping specified by the target building, the devices or areas that do not conform to the mapping rules are identified, that is, energy-consuming devices or areas with excessive / insufficient energy consumption, and thus the scene adaptation abnormal area is determined; finally, the operation of the energy-consuming devices corresponding to the scene adaptation abnormal area is adjusted according to the standard scene-device on-state mapping, so that the abnormal energy-consuming devices are restored to the normal operating state.

[0044] Furthermore, step S500 also includes step S580: if the judgment result is that neither the scene adaptation index nor the energy consumption adaptation index is met, an abnormal warning signal is generated and sent to the operation and maintenance management personnel, and the scene adaptation abnormal area is regulated and optimized to generate a control plan; step S590: first freeze the control plan, and after receiving the operation and maintenance feedback signal of the abnormal warning signal, the scene adaptation optimization control is performed according to the control plan.

[0045] Preferably, if the judgment result shows that neither the scenario adaptation index nor the energy consumption adaptation index is met, it indicates that the energy consumption behavior is unreasonable and the energy transmission efficiency is low, resulting in serious waste or unknown losses. In this case, abnormal warning signals are generated and sent to the operation and maintenance management personnel to confirm that both scenario adaptation and energy consumption adaptation are abnormal. At the same time, based on the standard scenario-equipment on-state mapping specified for the target building, the abnormal area of ​​scenario adaptation is controlled and optimized to generate a corresponding control plan. Then, the control plan is first frozen. After receiving the operation and maintenance feedback signal confirming that there are no equipment safety hazards, the control plan is unfrozen and the scenario adaptation optimization control is performed to ensure the safety and reliability of energy management operation and maintenance, and improve the dynamic adaptability of energy supply and demand and energy utilization efficiency.

[0046] In the above text, refer to Figure 1 A smart energy management and control method based on a sensing network according to an embodiment of the present invention is described in detail. Next, reference will be made to... Figure 2 This invention describes a smart energy management and control platform based on a sensing network according to an embodiment of the present invention.

[0047] The intelligent energy management and control platform based on a sensing network, according to embodiments of the present invention, addresses the technical problems in existing energy management technologies, such as the lack of dynamic sensing and scenario-based adaptive control capabilities, leading to supply-demand mismatch and low energy efficiency. It achieves precise energy management based on multi-source sensing data and scenario-driven approaches, improving the dynamic adaptability of energy supply and demand and energy utilization efficiency. Figure 2 As shown, the smart energy management and control platform based on the sensing network includes: an energy flow topology acquisition module 10, a data acquisition module 20, a scenario adaptation index analysis module 30, an energy consumption adaptation index analysis module 40, and an energy management and control optimization module 50.

[0048] The energy flow topology acquisition module 10 is used to acquire the energy flow topology of the target building and extract the first energy flow chain provided by the first energy supply node; the data acquisition module 20 is used to acquire first energy supply data through the first sensor network connected to the first energy supply node and acquire first energy consumption data through the second sensor network of the energy-consuming equipment in the first energy flow chain; the scene adaptation index analysis module 30 is used to acquire the real-time environmental conditions and real-time pedestrian information of the target building and analyze the scene adaptation index of the first energy consumption data; the energy consumption adaptation index analysis module 40 is used to analyze the energy consumption adaptation index of the first energy consumption data and the first energy supply data; and the energy management and control optimization module 50 is used to optimize energy management and control by combining the scene adaptation index and the energy consumption adaptation index.

[0049] The specific configuration of the scene adaptation index analysis module 30 will be described in detail below. The scene adaptation index analysis module 30 further includes: establishing a standard scene-energy consumption adaptation template library for the target building; retrieving the corresponding scene-energy consumption adaptation template from the standard scene-energy consumption adaptation template library based on the real-time environmental conditions and real-time pedestrian flow information; performing scene adaptation analysis on the first energy consumption data using the scene-energy consumption adaptation template to generate the scene adaptation index.

[0050] The following will describe in detail the specific configuration of the scenario adaptation index analysis module 30. The scenario adaptation index analysis module 30 further includes: extracting standard adaptation energy consumption data from the scenario-energy consumption adaptation template; comparing the standard adaptation energy consumption data with the first energy consumption data; performing scenario adaptation analysis based on the comparison differences; and generating the scenario adaptation index.

[0051] The following will describe the specific configuration of the scenario adaptation index analysis module 30 in detail. The scenario adaptation index analysis module 30 further includes: the energy consumption demand configuration of the standard scenario-energy consumption adaptation template library under the standard scenario-equipment on-state mapping specified by the target building.

[0052] The specific configuration of the energy consumption adaptation index analysis module 40 will be described in detail below. The energy consumption adaptation index analysis module 40 further includes: extracting each first energy-consuming device in the first energy flow chain; analyzing each energy transmission loss from the first energy supply node to each first energy-consuming device; using each energy transmission loss as adaptation compensation, analyzing the adaptation degree between the first energy consumption data and the first energy supply data, and generating the energy consumption adaptation index.

[0053] The specific configuration of the energy management and control optimization module 50 will be described in detail below. The energy management and control optimization module 50 further includes: determining whether both the scenario adaptation index and the energy consumption adaptation index meet preset adaptation thresholds; if the determination result is that the scenario adaptation index meets the thresholds but the energy consumption adaptation index does not, real-time collection of equipment operation data and equipment output status of each first energy-consuming device; performing energy consumption anomaly analysis based on the equipment operation data and equipment output status of each first energy-consuming device to locate energy consumption anomaly devices and anomaly states; and generating anomaly warning signals based on the energy consumption anomaly devices and anomaly states and sending them to operation and maintenance personnel for energy management and control optimization.

[0054] The specific configuration of the energy management and control optimization module 50 will be described in detail below. The energy management and control optimization module 50 further includes: if the judgment result is that the scene adaptation index is not met, but the energy consumption adaptation index is met; based on the equipment operation data of each first energy-consuming device, real-time environmental conditions, and real-time pedestrian flow information, and with reference to the standard scene-device on-state mapping specified by the target building, identifying scene adaptation anomaly areas; and using the standard scene-device on-state mapping to regulate the operation of the energy-consuming devices corresponding to the scene adaptation anomaly areas.

[0055] The specific configuration of the energy management and control optimization module 50 will be described in detail below. The energy management and control optimization module 50 further includes: if the judgment result indicates that neither the scenario adaptation index nor the energy consumption adaptation index is met, generating abnormal warning signals and sending them to the operation and maintenance personnel, and performing scene adaptation anomaly area control optimization to generate a control plan; first freezing the control plan, and then, after receiving the operation and maintenance feedback signal of the abnormal warning signal, performing scene adaptation optimization control according to the control plan.

[0056] The specific configuration of the energy flow topology acquisition module 10 will be described in detail below. The energy flow topology acquisition module 10 further includes: constructing a topology structure including energy supply nodes, energy conversion equipment, transmission and distribution networks, and energy-consuming terminals based on the building information model of the target building, and establishing the energy flow topology; tracing all energy-consuming devices downstream of the first energy supply node from the energy flow topology, determining the connection relationship, and generating the first energy flow chain.

[0057] The smart energy management and control platform based on a sensing network provided in this invention can execute the smart energy management and control method based on a sensing network provided in any embodiment of this invention, and has the corresponding functional modules and beneficial effects of the method.

[0058] Although this application makes various references to certain modules in the platform according to the embodiments of this application, any number of different modules can be used and run on user terminals and / or servers. The various units and modules included are only divided according to functional logic, but are not limited to the above division, as long as the corresponding functions can be achieved; in addition, the specific names of each functional unit are only for easy distinction between each other and are not used to limit the scope of protection of this invention.

[0059] The specific embodiments described above do not constitute a limitation on the scope of protection of this application. Those skilled in the art should understand that various modifications, combinations, and substitutions can be made according to design requirements and other factors. Any modifications, equivalent substitutions, and improvements made within the spirit and principles of this application should be included within the scope of protection of this application.

Claims

1. A smart energy management and control method based on a sensing network, characterized in that, include: Collect the energy flow topology of the target building and extract the first energy flow chain that provides energy from the first energy supply node; First energy supply data is collected through a first sensor network connected to the first energy supply node, and first energy consumption data is collected through a second sensor network on the energy-consuming equipment in the first energy flow chain. Collect real-time environmental conditions and real-time pedestrian flow information of the target building, and analyze the scenario adaptation index of the first energy consumption data; Analyze the energy consumption compatibility index of the first energy consumption data and the first energy supply data; Optimize energy management and control by combining the scenario adaptation index and the energy consumption adaptation index; Optimization of energy management and control by combining the scenario adaptation indicators and the energy consumption adaptation indicators includes: Determine whether both the scenario adaptation index and the energy consumption adaptation index meet the preset adaptation threshold; If the judgment result is that neither the scenario adaptation index nor the energy consumption adaptation index is met, an abnormal warning signal is generated and sent to the operation and maintenance management personnel, and the abnormal area of ​​scenario adaptation is regulated and optimized, and a regulation plan is generated. First, freeze the aforementioned control scheme. After receiving the operation and maintenance feedback signal of the abnormal warning signal, then use the control scheme to perform scenario adaptation and optimization control. Collect real-time environmental conditions and real-time pedestrian flow information of the target building, and analyze the scenario adaptation indicators of the first energy consumption data, including: Establish a standard scenario-energy adaptation template library for the target building; Based on the real-time environmental conditions and real-time pedestrian flow information, the corresponding scenario-energy-adaptation template is retrieved from the standard scenario-energy-adaptation template library. Using the scenario-energy consumption adaptation template, perform scenario adaptation analysis on the first energy consumption data to generate the scenario adaptation index; The analysis of energy consumption matching indicators between the first energy consumption data and the first energy supply data includes: Extract the first energy flow direction to each first energy-consuming device in the first energy flow chain; Analyze the energy transmission losses from the first energy supply node to each of the first energy-consuming devices; Using the various energy transmission losses as adaptation compensation, the compatibility between the first energy consumption data and the first energy supply data is analyzed to generate the energy consumption adaptation index.

2. The intelligent energy management and control method based on a sensing network as described in claim 1, characterized in that, Using the scenario-energy consumption adaptation template, scenario adaptation analysis is performed on the first energy consumption data to generate the scenario adaptation indicators, including: In the scenario described, standard adapted energy consumption data is extracted from the energy consumption adaptation template. The standard-adapted energy consumption data is compared with the first energy consumption data. Based on the comparison differences, a scenario adaptation analysis is performed, and the scenario adaptation index is generated.

3. The intelligent energy management and control method based on a sensing network as described in claim 2, characterized in that, The standard scenario-energy adaptation template library configures energy consumption requirements under the standard scenario-equipment on-state mapping specified by the target building.

4. The intelligent energy management and control method based on a sensing network as described in claim 1, characterized in that, Optimization of energy management and control by combining the scenario adaptation indicators and the energy consumption adaptation indicators includes: If the judgment result is that the scenario adaptation index is met but the energy consumption adaptation index is not met, the equipment operation data and equipment output status of each primary energy-consuming device are collected in real time. Energy consumption anomaly analysis is performed based on the equipment operation data and equipment output status of each primary energy-consuming device to locate the energy consumption anomaly device and abnormal status. Anomaly warning signals are generated based on the energy-consuming devices and abnormal states and sent to the operation and maintenance personnel for optimization of energy management and control.

5. The intelligent energy management and control method based on a sensing network as described in claim 4, characterized in that, After determining whether both the scenario adaptation index and the energy consumption adaptation index meet the preset adaptation threshold, the process includes: If the judgment result is that the scenario adaptation index is not met, but the energy consumption adaptation index is met; Based on the equipment operation data of each primary energy-consuming device, real-time environmental conditions, and real-time pedestrian flow information, and with reference to the standard scene-equipment on-state mapping specified for the target building, areas with abnormal scene adaptation are identified. The operation and control of energy-consuming devices corresponding to the abnormal adaptation areas of the scenario are adjusted by using a standard scenario-device on-state mapping.

6. The intelligent energy management and control method based on a sensing network as described in claim 1, characterized in that, Collect the energy flow topology of the target building and extract the first energy flow chain supplied by the first energy-supplying node, including: Based on the building information model of the target building, a topology including energy supply nodes, energy conversion equipment, transmission and distribution networks and energy consumption terminals is constructed, and the energy flow topology is established. From the energy flow topology, trace all energy-consuming devices downstream of the first energy supply node, determine the connection relationships, and generate the first energy flow chain.

7. A smart energy management and control platform based on a sensing network, characterized in that, The platform is used to implement the smart energy management and control method based on a sensing network as described in any one of claims 1 to 6, and the platform comprises: The energy flow topology acquisition module is used to acquire the energy flow topology of the target building and extract the first energy flow chain that is powered by the first energy supply node. The data acquisition module is used to acquire first energy supply data through the first sensor network connected to the first energy supply node, and to acquire first energy consumption data through the second sensor network of the energy-consuming equipment in the first energy flow chain; The scenario adaptation index analysis module is used to collect the real-time environmental conditions and real-time pedestrian flow information of the target building and analyze the scenario adaptation index of the first energy consumption data. The energy consumption adaptation index analysis module is used to analyze the energy consumption adaptation index of the first energy consumption data and the first energy supply data. The energy management and control optimization module is used to optimize energy management and control by combining the scenario adaptation index and the energy consumption adaptation index.

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