Smart energy management control method and platform based on sensing network

Abstract

The invention discloses a smart energy management control method and platform based on a sensing network, and relates to the technical field related to energy management, and the method comprises the steps: collecting the energy flow direction topology of a target building, and extracting a first energy flow direction chain; acquiring first energy supply data through the first sensor network, and acquiring first energy consumption data through the second sensor network; collecting real-time environment conditions and real-time people flow information of the target building, and analyzing a scene adaptation index of the first energy consumption data; analyzing energy consumption adaptation indexes 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 supply and demand mismatching and low energy efficiency caused by lack of dynamic perception and scene-based adaptive regulation and control capabilities in energy management in the prior art are solved, and the technical effects of realizing accurate energy management and control based on multi-source perception data and scene driving and improving the dynamic adaptability of energy supply and demand and the energy utilization efficiency are achieved.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of energy management, and particularly relates to a smart energy management control method and platform based on a perception network. BACKGROUND

[0002] With the continuous growth of building energy consumption, energy management has become an important part of modern building operation. Traditional energy management is difficult to cope with the dynamic changes of building energy consumption behavior, the complexity of multi-energy coupling and the real-time fluctuations of user demand. In particular, in large commercial buildings, industrial parks or comprehensive venues, the energy flow is diverse, and the energy consumption equipment is highly heterogeneous, which may lead to low energy utilization efficiency, supply-demand mismatch and other problems. Moreover, the existing Internet of Things sensor network is limited to data collection and visualization, lacking deep perception and fusion analysis of external conditions such as energy consumption scenarios, environmental factors and human flow activities. For example, the energy consumption demand of the same building may vary significantly at different times, different human flow densities or different environmental temperatures, and it is impossible to achieve adaptive regulation 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 of energy consumption behavior and building scenarios is not included in the optimization target, resulting in a lack of scenario perception ability of energy distribution strategy, which further affects the precision of energy optimization control and energy utilization efficiency.

[0003] Therefore, in the related art at present, there is a technical problem that energy management lacks dynamic perception and scenario-based adaptive regulation capability, leading to supply-demand mismatch and low energy efficiency. SUMMARY

[0004] The present application provides a smart energy management control method and platform based on a perception network, which solves the technical problem of lack of dynamic perception and scenario-based adaptive regulation capability in the prior art, leading to supply-demand mismatch and low energy efficiency, and achieves precise energy management and control based on multi-source perception data and scenario driving, and improves the dynamic adaptability of energy supply and demand and energy utilization efficiency.

[0005] The present application provides a smart energy management control method based on a perception network, comprising: collecting an energy flow topology of a target building, and extracting a first energy flow chain provided with energy 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 an energy consumption device in the first energy flow chain; collecting real-time environmental conditions and real-time human flow information of the target building, analyzing a scenario adaptation index of the first energy consumption data; analyzing an 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 scenario adaptation index and the energy consumption adaptation index.

[0006] In a possible implementation, the method further comprises the following processing: establishing a standard scene-energy consumption adaptation template library of the target building; based on the real-time environmental conditions and real-time passenger flow information, calling a corresponding scene-energy consumption adaptation template from the standard scene-energy consumption adaptation template library; performing scene adaptation analysis on the first energy consumption data based on the scene-energy consumption adaptation template, and generating the scene adaptation index.

[0007] In a possible implementation, the method further comprises the following processing: extracting standard adaptation energy consumption data from the scene-energy consumption adaptation template; comparing the standard adaptation energy consumption data with the first energy consumption data, and performing scene adaptation analysis according to the comparison difference, and generating the scene adaptation index.

[0008] In a possible implementation, the method further comprises the following processing: the standard scene-energy consumption adaptation template library is configured by the energy consumption demand of the target building under the standard scene-device opening state mapping.

[0009] In a possible implementation, the method further comprises the following processing: extracting each first energy consumption device in the first energy flow chain; analyzing each energy transmission loss from the first energy supply node to each first energy consumption device; taking the energy transmission loss as an adaptation compensation, analyzing the adaptation degree of the first energy consumption data and the first energy supply data, and generating the energy consumption adaptation index.

[0010] In a possible implementation, the method further comprises the following processing: determining whether the scene adaptation index and the energy consumption adaptation index both satisfy a preset adaptation threshold; if the determination result is that the scene adaptation index satisfies and the energy consumption adaptation index does not satisfy, collecting device operation data and device output state of each first energy consumption device in real time; performing energy consumption anomaly analysis based on the device operation data and the device output state of each first energy consumption device, locating an energy consumption anomaly device and an anomaly state; generating an anomaly warning signal based on the energy consumption anomaly device and the anomaly state, and sending the anomaly warning signal to an operation and maintenance control personnel for optimization of energy management control.

[0011] In a possible implementation, the method further comprises the following processing: if the determination result is that the scene adaptation index does not satisfy and the energy consumption adaptation index satisfies; based on the device operation data of each first energy consumption device, the real-time environmental conditions and the real-time passenger flow information, identifying a scene adaptation anomaly area with reference to the standard scene-device opening state mapping of the target building; performing operation regulation on an energy consumption device corresponding to the scene adaptation anomaly area based on the standard scene-device opening state mapping.

[0012] In a possible implementation, the method further performs the following processing: if the result of the judgment is that neither the scene adaptation index nor the energy consumption adaptation index is satisfied, an abnormal alarm signal is generated and sent to an operation and maintenance control personnel, and scene adaptation abnormal area regulation and optimization is performed to generate a regulation and control scheme; the regulation and control scheme is first frozen, and then, after receiving an operation and maintenance feedback signal of the abnormal alarm signal, the scene adaptation optimization regulation and control is performed according to the regulation and control scheme.

[0013] In a possible implementation, the method further performs the following processing: based on the building information model of the target building, a topology structure including a power supply node, an energy conversion device, a power transmission and distribution network and an energy consumption terminal is constructed, and the energy flow topology is established; all energy consumption devices downstream of the first power supply node are traced from the energy flow topology, and a connection relationship is determined to generate the first energy flow chain.

[0014] The application also provides a smart energy management and control platform based on a perception network, which includes: an energy flow topology acquisition module, configured to acquire an energy flow topology of a target building and extract a first energy flow chain provided with energy by a first power supply node; a data acquisition module, configured to acquire first power supply data through a first sensor network connected to the first power supply node and acquire first energy consumption data through a second sensor network on an energy consumption device in the first energy flow chain; a scene adaptation index analysis module, configured to acquire real-time environmental conditions and real-time flow information of the target building and analyze a scene adaptation index of the first energy consumption data; an energy consumption adaptation index analysis module, configured to analyze an energy consumption adaptation index of the first energy consumption data and the first power supply data; and an energy management and control optimization module, configured to optimize energy management and control in combination with the scene adaptation index and the energy consumption adaptation index.

[0015] The smart energy management and control method and platform based on a perception network provided in the application acquire an energy flow topology of a target building and extract a first energy flow chain; acquire first power supply data through a first sensor network and acquire first energy consumption data through a second sensor network; acquire real-time environmental conditions and real-time flow information of the target building and analyze a scene adaptation index of the first energy consumption data; analyze an energy consumption adaptation index of the first energy consumption data and the first power supply data; and optimize energy management and control in combination with the scene adaptation index and the energy consumption adaptation index. The technical problems of lack of dynamic perception and scene self-adaptive regulation and control capability in energy management in the prior art, leading to mismatch between supply and demand 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 dynamic adaptability of energy supply and demand and energy utilization efficiency are achieved. BRIEF DESCRIPTION OF DRAWINGS

[0016] In order to more clearly illustrate the technical solutions of the embodiments of the present disclosure, the drawings of the embodiments of the present disclosure will be briefly introduced below, and the flowcharts are used to illustrate the operations performed by the platform according to the embodiments of the present disclosure. It should be understood that the foregoing or the following operations are not necessarily performed in sequence. On the contrary, various steps can be processed in reverse order or simultaneously according to needs. Meanwhile, other operations can be added to these processes, or one or more steps of operations can be removed from these processes.

[0017] Figure 1 The flowchart of the intelligent energy management control method based on the perception network provided by the embodiments of the present application is shown.

[0018] Figure 2 The schematic diagram of the intelligent energy management control platform structure based on the perception network provided by the embodiments of the present application is shown.

[0019] Legend: energy flow topology collection module 10, data collection module 20, scene adaptation index analysis module 30, energy consumption adaptation index analysis module 40, energy management control optimization module 50. DETAILED DESCRIPTION

[0020] The above description is only a summary of the technical solutions of the present application, in order to more clearly understand the technical means of the present application, the content of the specification can be implemented, and in order to make the above and other purposes, features and advantages of the present application more obvious and easy to understand, the following specific embodiments of the present application are described.

[0021] In order to make the purpose, technical solutions and advantages of the present application more clear, the present application will be further described in detail below in combination with the drawings, and the described embodiments should not be regarded as limiting the present application, all other embodiments obtained by those skilled in the art without making creative labor belong to the scope of protection of the present application.

[0022] In the following description, "some embodiments" are referred to, which describe a subset of all possible embodiments, but it can be understood that "some embodiments" can be the same subset or different subsets of all possible embodiments, and can be combined with each other without conflict, and the term "first\second" referred to is only to distinguish similar objects, and does not represent a specific order for the objects. The terms "include" and "have" and any variations thereof are intended to cover non-exclusive inclusion, for example, a process, method, platform, product or server including a series of steps or units does not have to be limited to those steps or units clearly listed, but can include other steps or modules that are not clearly 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 understood by those skilled in the art to which the present application belongs. The terms used herein are only for the purpose of describing the embodiments of the present application.

[0023] The embodiments of the present application provide a smart energy management control method based on a perception network, as shown in the accompanying drawings, the method comprises: Figure 1 Step S100, collecting an energy flow topology of a target building, and extracting a first energy flow chain provided by a first energy supply node.

[0024] Step S100 further comprises steps S110 and S120. In step S110, based on a building information model of the target building, a topology structure including energy supply nodes, energy conversion devices, pipe networks and energy consumption terminals is constructed, and the energy flow topology is established. In step S120, all energy consumption devices downstream of the first energy supply node are traced from the energy flow topology, and a connection relationship is determined to generate the first energy flow chain.

[0025] Preferably, based on the building information model of the target building, a topology structure of device components such as energy supply nodes, energy conversion devices, pipe networks and energy consumption terminals is constructed. The building information model is a digital three-dimensional model of the target building, and an energy logic relationship network is added to it, that is, energy logic attributes and energy flow relationships of device components are labeled to form an energy flow topology, for example, an energy logic link from a municipal power grid access point to a low-voltage distribution cabinet, to a certain floor distribution box, and finally to an indoor lighting circuit. Then, downstream tracing analysis is performed in the energy flow topology. Specifically, a random energy supply node is selected as an energy flow topology, the downstream path of the energy supply node is traversed, all energy consumption devices that directly or indirectly obtain energy from the energy supply node are obtained, the connection relationship is determined, and finally the first energy flow chain is generated, which can be a structured tree list to ensure accurate energy efficiency analysis.

[0026] ​Step S200, collecting first energy supply data by a first sensor network connected to the first energy supply node, and collecting first energy consumption data by a second sensor network installed on the use equipment in the first energy flow chain.

[0027] Preferably, the first energy supply data is collected by a first sensor network connected to the first energy supply node, wherein the first sensor network can include various sensor devices, for example, the energy supply node provides electricity, and the first sensor network includes a smart meter and a power quality analyzer; the energy supply node provides heat or cold, and the first sensor network includes a flow meter, a temperature sensor, and a pressure sensor; the energy supply node is a distributed energy source, and the first sensor network includes an inverter data collector; thereby obtaining the first energy supply data reflecting the total amount and state of energy emitted by the first energy supply node, such as total power and instantaneous power, total heating / cooling and instantaneous heating / cooling load, power supply voltage / current, etc. The first energy consumption data is collected by a second sensor network installed on a plurality of use equipment 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, an electric power metering unit is integrated on its control panel; for a lighting circuit, a circuit metering electric meter is installed in the lighting switch box, etc. The first energy consumption data reflects the energy consumed by each use equipment in the first energy flow chain, such as the power consumption of a single device, the operating power of the device, and the operating state of the device, etc.

[0028] Step S300, collecting real-time environmental conditions and real-time flow information of the target building, and analyzing scene adaptation indicators of the first energy consumption data.

[0029] Step S300 further includes step S310, establishing a standard scene-energy adaptation template library of the target building; step S320, based on the real-time environmental conditions and real-time flow information, retrieving a corresponding scene-energy adaptation template from the standard scene-energy adaptation template library; step S330, performing scene adaptation analysis of the first energy consumption data based on the scene-energy adaptation template, and generating the scene adaptation indicators.

[0030] Further, step S310 further includes that the standard scene-energy adaptation template library is configured by energy consumption demand under a standard scene-equipment opening state mapping of the target building.

[0031] Preferably, different standardized use states of the target building are obtained, which are described by multiple parameter combinations, mainly including time period, environmental condition and human flow load information, and then multiple target building specified standard scenes are obtained, such as a high-load scene in summer high temperature on weekdays, a medium-load scene in winter during holidays, etc.; a reasonable energy-using device operation scheme and its expected energy consumption level are preset for each standard scene, including the configuration of the opening state of the energy-using device, and the corresponding expected energy consumption range is calculated according to the device opening state, historical energy consumption data and energy-using device rated power, and then the energy consumption demand is configured; then the target building specified standard scene and the energy consumption demand under the mapping of the device opening state are matched and mapped to construct a standard scene-energy-using adaptation template library of the target building.

[0032] Preferably, the real-time environmental conditions such as outdoor temperature, humidity, and illumination of the target building are collected, and the real-time human flow information is estimated through a camera, a Wi-Fi probe or an access control system, and then the current real-time environmental conditions and real-time human flow information are matched with the templates in the standard scene-energy-using adaptation template library to identify and determine the scene-energy-using adaptation template that best fits the current scene, and then the scene-energy-using adaptation template is used for scene adaptation analysis of the first energy-using data, that is, the real-time collected first energy-using data is compared with the energy-using data provided by the scene-energy-using adaptation template to generate a quantitative evaluation result for representing the matching degree of actual energy use and scene expected energy use and identifying the matching level, thereby realizing intelligent energy efficiency management.

[0033] Further, step S330 further comprises step S331 of extracting standard adaptation energy-using data from the scene-energy-using adaptation template; and step S332 of comparing the standard adaptation energy-using data with the first energy-using data to complete scene adaptation analysis according to the comparison difference and generate the scene adaptation index.

[0034] Preferably, the standard adaptation energy-using data is extracted from the scene-energy-using adaptation template, which represents the ideal energy consumption range under the current standard scene, and is compared with the first energy-using data to calculate (actual energy consumption - expected energy consumption) / expected energy consumption x 100% to obtain a difference rate, generate a comparison difference, for example, a difference within ±5% indicates good adaptation, a difference within ±5% to 15% indicates mild deviation, and a difference exceeding ±15% indicates serious deviation; then the scene adaptation analysis is completed according to the comparison difference, that is, the result of the comparison difference is output as the scene adaptation index and the adaptation level is identified according to the preset rule, thereby realizing intelligent and precise energy efficiency management.

[0035] Step S400, analyze the energy consumption adaptation index of the first energy-using 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 is judged whether the scene adaptation index and the energy consumption adaptation index both satisfy a preset adaptation threshold value, wherein the preset adaptation threshold value is a critical value configured according to historical energy supply data and energy consumption data analysis, and is used to judge whether the scene adaptation index and the energy consumption adaptation index are reasonable. If the judgment result is that the scene adaptation index satisfies and the energy consumption adaptation index does not satisfy, it is indicated that according to the current time, environment, and people flow, the opening state and overall energy consumption level of the energy consumption equipment in the building are reasonable, but the supplied energy is much greater than the sum of the actual consumed energy and the theoretical transmission loss of the terminal equipment, that is, the total energy transmission efficiency from the energy supply source to the energy consumption terminal is too low. Therefore, the device operation data of each first energy consumption equipment is collected in real time, such as the running time, start-stop frequency, input energy, and the like, to reflect the internal operation efficiency and state of the equipment. At the same time, the device output state is collected to reflect the actual workload or output capacity of the equipment, and the energy consumption equipment health degree is diagnosed in combination with the device operation data and the device output state.

[0041] Preferably, then, based on the device operation data and the device output state of each first energy consumption equipment, energy consumption anomaly analysis is performed. Specifically, the input energy included in the device operation data is compared with the output state, the real-time operation energy efficiency ratio of the equipment is calculated, that is, the energy consumption per unit output, and then the energy consumption anomaly equipment is identified and located according to the rated value of the energy consumption per unit output. The running data of the energy consumption anomaly equipment is analyzed to identify the abnormal state, and then the abnormal reason is determined. Then, an abnormal warning signal is generated according to the energy consumption anomaly equipment and the abnormal state, which can include an alarm prompt “energy consumption adaptation index abnormality-device level energy efficiency fault”, the energy consumption anomaly equipment, the abnormal state, and the recommended measures. Finally, the abnormal warning signal is sent to the operation and maintenance control personnel for optimization of energy management control, greatly improving the operation and maintenance efficiency and the energy management level.

[0042] Further, step S500 further includes step S550, if the judgment result is that the scene adaptation index does not satisfy and the energy consumption adaptation index satisfies; step S560, according to the device operation data of each first energy consumption equipment, real-time environmental conditions, and real-time people flow information, a standard scene-equipment opening state mapping of the target building is taken as a reference to identify a scene adaptation abnormal area; and step S570, the energy consumption equipment corresponding to the scene adaptation abnormal area is controlled according to the standard scene-equipment opening state mapping.

[0043] Preferably, if the judgment result is that the scene adaptation index does not meet the requirement and the energy consumption adaptation index meets the requirement, it indicates that the current energy use behavior is unreasonable, for example, the energy consumption is too high in the period of no one or the energy consumption is insufficient in the period of high demand, but the energy transmission efficiency from the energy supply end to the energy use end is normal, without abnormal loss or waste; then, the device operation data of each first energy use device, real-time environmental conditions and real-time people flow information are acquired in real time, and a standard scene-device opening state mapping specified by a target building is taken as a reference to identify a scene adaptation abnormal area, that is, the device operation data, real-time environmental conditions and real-time people flow information are matched and compared with the standard scene-device opening state mapping specified by the target building, to identify and determine the devices or areas that do not conform to the mapping rule, that is, the energy use devices or areas with excessive energy consumption or insufficient energy consumption, and then the scene adaptation abnormal area is determined; finally, the energy use devices corresponding to the scene adaptation abnormal area are controlled according to the standard scene-device opening state mapping, so that the abnormal energy use devices return to the normal operating state.

[0044] Further, step S500 further includes step S580, if the judgment result is that the scene adaptation index and the energy consumption adaptation index do not meet the requirement, respectively generate an abnormal alarm signal and send it to the operation and maintenance control personnel, and perform scene adaptation abnormal area control optimization to generate a control scheme; and step S590, first freeze the control scheme, and then perform scene adaptation optimization control according to the control scheme after receiving the operation and maintenance feedback signal of the abnormal alarm signal.

[0045] Preferably, if the judgment result is that the scene adaptation index and the energy consumption adaptation index do not meet the requirement, it indicates that the energy use behavior is unreasonable and the energy transmission efficiency is low, with serious waste or unknown loss, and then respectively generate an abnormal alarm signal and send it to the operation and maintenance control personnel to clearly indicate that the scene adaptation and the energy consumption adaptation are both abnormal, and perform scene adaptation abnormal area control optimization based on the standard scene-device opening state mapping specified by the target building to generate a corresponding control scheme; and then first freeze the control scheme, and then unfreeze the control scheme to perform scene adaptation optimization control after receiving the operation and maintenance feedback signal of the operation and maintenance personnel confirming that there is no device safety hazard, to ensure the safety and reliability of energy management and operation, and to improve the dynamic adaptation of energy supply and demand and the energy utilization efficiency.

[0046] In the foregoing, the method for intelligent energy management and control based on a perception network according to an embodiment of the present application is described in detail. Figure 1 The method for intelligent energy management and control based on a perception network according to an embodiment of the present application is described in detail. Figure 2 The intelligent energy management and control platform based on a perception network according to an embodiment of the present application is described.

[0047] The intelligent energy management control platform based on the perception network according to the embodiment of the present application is used for solving the technical problem that the energy management lacks dynamic perception and scene-based adaptive regulation and control in the prior art, leading to mismatch between supply and demand and low energy efficiency, and achieves the technical effect of realizing precise energy management and control based on multi-source perception data and scene driving, and improving dynamic adaptability of energy supply and demand and energy utilization efficiency. Figure 2 As shown in the figure, the intelligent energy management control platform based on the perception network comprises an energy flow topology collection module 10, a data collection module 20, a scene adaptation index analysis module 30, an energy consumption adaptation index analysis module 40, and an energy management control optimization module 50.

[0048] The energy flow topology collection module 10 is used for collecting the energy flow topology of a target building and extracting a first energy flow chain provided with energy by a first energy supply node; the data collection module 20 is used for 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 a use device in the first energy flow chain; the scene adaptation index analysis module 30 is used for collecting real-time environmental conditions and real-time flow information of the target building and analyzing a scene adaptation index of the first energy consumption data; the energy consumption adaptation index analysis module 40 is used for analyzing an energy consumption adaptation index of the first energy consumption data and the first energy supply data; and the energy management control optimization module 50 is used for optimizing energy management control in combination with the scene adaptation index and the energy consumption adaptation index.

[0049] Next, the specific configuration of the scene adaptation index analysis module 30 will be described in detail. The scene adaptation index analysis module 30 further comprises: establishing a standard scene-energy consumption adaptation template library of the target building; based on the real-time environmental conditions and real-time flow information, calling a corresponding scene-energy consumption adaptation template from the standard scene-energy consumption adaptation template library; performing scene adaptation analysis of the first energy consumption data by using the scene-energy consumption adaptation template, and generating the scene adaptation index.

[0050] Next, the specific configuration of the scene adaptation index analysis module 30 will be described in detail. The scene adaptation index analysis module 30 further comprises: extracting standard adaptation energy consumption data from the scene-energy consumption adaptation template; comparing the standard adaptation energy consumption data with the first energy consumption data, and generating the scene adaptation index according to the comparison difference.

[0051] Next, the specific configuration of the scene adaptation index analysis module 30 will be described in detail. The scene adaptation index analysis module 30 further comprises: the standard scene-energy consumption adaptation template library is configured by energy consumption demand under a standard scene-device opening state mapping of 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 comprises: extracting each first energy-consuming device in the first energy flow chain; analyzing the energy transmission loss of each first energy supply node to each first energy-consuming device; taking the energy transmission loss as an adaptation compensation, analyzing the adaptation degree of 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 control optimization module 50 will be described in detail below. The energy management control optimization module 50 further comprises: determining whether the scene adaptation index and the energy consumption adaptation index both satisfy a preset adaptation threshold; if the determination result is that the scene adaptation index satisfies and the energy consumption adaptation index does not satisfy, collecting device operation data and device output state of each first energy-consuming device in real time; performing energy consumption anomaly analysis based on the device operation data and the device output state of each first energy-consuming device, locating energy consumption anomaly devices and abnormal states; generating an abnormal alarm signal to an operation and maintenance control personnel for optimization of energy management control based on the energy consumption anomaly devices and abnormal states.

[0054] The specific configuration of the energy management control optimization module 50 will be described in detail below. The energy management control optimization module 50 further comprises: if the determination result is that the scene adaptation index does not satisfy and the energy consumption adaptation index satisfies; according to the device operation data of each first energy-consuming device, real-time environmental conditions and real-time passenger flow information, taking the standard scene-device opening state mapping specified by the target building as a reference, identifying a scene adaptation abnormal area; performing operation regulation and control on the energy-consuming devices corresponding to the scene adaptation abnormal area according to the standard scene-device opening state mapping.

[0055] The specific configuration of the energy management control optimization module 50 will be described in detail below. The energy management control optimization module 50 further comprises: if the determination result is that the scene adaptation index and the energy consumption adaptation index both do not satisfy, respectively generating an abnormal alarm signal to an operation and maintenance control personnel, and performing scene adaptation abnormal area regulation and optimization to generate a regulation and control scheme; first freezing the regulation and control scheme, and then performing scene adaptation optimization regulation and control according to the regulation and control scheme after receiving an operation feedback signal of the abnormal alarm signal.

[0056] The specific configuration of the energy flow topology collection module 10 will be described in detail below. The energy flow topology collection module 10 further comprises: based on the building information model of the target building, constructing a topology structure including energy supply nodes, energy conversion devices, transmission and distribution pipe 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.

[0057] The intelligent energy management control platform based on the perception network provided by the embodiments of the present application can execute the intelligent energy management control method based on the perception network provided by any of the embodiments of the present application, and has the corresponding function modules and beneficial effects of the execution method.

[0058] Although the present application makes various references to certain modules in the platform according to the embodiments of the present application, however, any number of different modules can be used and run on the user terminal and / or server, and the various units and modules are only divided according to the functional logic, but are not limited to the above division, as long as the corresponding functions can be realized; in addition, the specific names of the functional units are only for the convenience of mutual differentiation, and are not used to limit the protection scope of the present application.

[0059] The above specific embodiments do not constitute a limitation on the protection scope of the present 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 the present application should be included in the protection scope of the present application.

Claims

1. A method for intelligent energy management control based on a perception network, characterized in that, The method comprises the following steps: Collecting the energy flow topology of the target building, and extracting a first energy flow chain provided with energy 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 a use device in the first energy flow chain; Collecting real-time environmental conditions and real-time flow information of the target building, and analyzing scene adaptation indicators of the first energy consumption data; Analyzing energy consumption adaptation indicators of the first energy consumption data and the first energy supply data; Optimizing energy management control by combining the scene adaptation indicators and the energy consumption adaptation indicators. 2.The method of claim 1, wherein, Collecting real-time environmental conditions and real-time flow information of the target building, and analyzing scene adaptation indicators of the first energy consumption data, comprising: Establishing a standard scene-energy adaptation template library of the target building; Based on the real-time environmental conditions and real-time flow information, calling the corresponding scene-energy adaptation template from the standard scene-energy adaptation template library; Performing scene adaptation analysis of the first energy consumption data with the scene-energy adaptation template to generate the scene adaptation indicators. 3.The method of claim 2, wherein, Performing scene adaptation analysis of the first energy consumption data with the scene-energy adaptation template to generate the scene adaptation indicators, comprising: Extracting standard adaptation energy consumption data from the scene-energy adaptation template; Comparing the standard adaptation energy consumption data with the first energy consumption data, and completing scene adaptation analysis according to the comparison difference to generate the scene adaptation indicators. 4.The method of claim 3, wherein, The standard scene-energy adaptation template library is configured by the energy consumption demand under the standard scene-device opening state mapping of the target building. 5.The method of claim 1, wherein the method further comprises: Analyzing energy consumption adaptation indicators of the first energy consumption data and the first energy supply data, comprising: Extracting each first energy consumption device in the first energy flow chain; Analyzing each energy transmission loss from the first energy supply node to each first energy consumption device; Taking the energy transmission loss as an adaptation compensation, analyzing the adaptation degree of the first energy consumption data and the first energy supply data to generate the energy consumption adaptation indicators. 6.The method of claim 1, wherein the method further comprises: Optimizing energy management control by combining the scene adaptation indicators and the energy consumption adaptation indicators, comprising: Judging whether the scene adaptation indicators and the energy consumption adaptation indicators meet the preset adaptation threshold value; If the judgment result is that the scene adaptation indicators meet the preset adaptation threshold value and the energy consumption adaptation indicators do not meet the preset adaptation threshold value, collecting device operation data and device output state of each first energy consumption device in real time; Based on the device operation data and the device output state of each first energy consumption device, performing energy consumption anomaly analysis to locate energy consumption anomaly devices and anomaly states; Generating an abnormal alarm signal to the operation and maintenance control personnel for energy management control optimization with the energy consumption anomaly devices and anomaly states. 7.The method of claim 6, wherein, After judging whether the scene adaptation indicators and the energy consumption adaptation indicators meet the preset adaptation threshold value, comprising: If the judgment result is that the scene adaptation indicators do not meet the preset adaptation threshold value and the energy consumption adaptation indicators meet the preset adaptation threshold value; According to the device operation data, the real-time environmental conditions and the real-time flow information of each first energy consumption device, identifying scene adaptation abnormal areas with the standard scene-device opening state mapping of the target building as a reference. The standard scene-device open state mapping is used to adapt the abnormal area of the scene to the energy-using device. 8.The method of claim 7, wherein the method further comprises: If the result is that neither the scene adaptation index nor the energy consumption adaptation index is satisfied, an abnormal warning signal is generated and sent to the operation and maintenance control personnel, and scene adaptation abnormal area control optimization is performed to generate a control scheme; The control scheme is first frozen, and then after receiving the operation and maintenance feedback signal of the abnormal warning signal, the scene adaptation optimization control is performed according to the control scheme.

9. The intelligent energy management and control method based on a sensing network as described in claim 1, characterized in that, The energy flow topology of the target building is collected, and a first energy flow chain provided with energy by a first energy supply node is extracted, including: Based on the building information model of the target building, a topology structure including energy supply nodes, energy conversion devices, energy transmission and distribution networks, and energy-using terminals is constructed, and the energy flow topology is established; From the energy flow topology, all energy-using devices downstream of the first energy supply node are traced, and the connection relationship is determined to generate the first energy flow chain.

10. The intelligent energy management control platform based on the perception network, characterized in that, The platform is used to implement the intelligent energy management and control method based on the perception network according to any one of claims 1 to 9, and the platform comprises: An energy flow topology collection module is configured to collect the energy flow topology of the target building, and extract a first energy flow chain provided with energy by a first energy supply node; A data collection module is configured to collect first energy supply data through a first sensor network connected to the first energy supply node, and collect first energy-using data through a second sensor network on the energy-using device in the first energy flow chain; A scene adaptation index analysis module is configured to collect real-time environmental conditions and real-time flow information of the target building, and analyze the scene adaptation index of the first energy-using data; An energy consumption adaptation index analysis module is configured to analyze the energy consumption adaptation index of the first energy-using data and the first energy supply data; An energy management and control optimization module is configured to optimize energy management and control in combination with the scene adaptation index and the energy consumption adaptation index.

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