A method and system for full-process energy consumption management of near-zero energy buildings based on BIM and IoT

Abstract

This invention belongs to the field of data processing technology. It discloses a method for full-process energy consumption management of near-zero energy buildings based on BIM and the Internet of Things. The method involves collecting and cleaning data to obtain building attribute information and high-quality sensor data. Spatial attribution calibration is performed on the high-quality sensor data to obtain an attribution spatial dataset. Based on the attribution spatial dataset, heat regions are identified, and local error analysis is conducted on these heat regions to obtain temperature interference correction data. Radiation deviation correction is applied to the temperature interference correction data to obtain photothermal correction data. Communication data stream detection is performed on the photothermal correction data to filter reliable communication data. Airflow direction attribution is adjusted on the reliable communication data to obtain an enhanced spatial dataset. A correction control strategy is generated based on the enhanced spatial dataset and sent to a preset energy consumption management terminal. This method improves the accuracy of energy consumption perception in the building environment and the response precision of the control strategy.

Description

Technical Field

[0001] This invention relates to the field of data processing technology, and more specifically, to a method for managing energy consumption throughout the entire process of near-zero energy buildings based on BIM and the Internet of Things. Background Technology

[0002] Against the backdrop of continuous advancements in environmental protection, building energy management has become an important means of urban emission reduction and energy optimization, with near-zero energy buildings serving as a key application direction for building energy-saving technologies. The development of BIM technology and the promotion of IoT technology have provided more convenient methods for controlling building energy consumption, giving buildings better environmental awareness and enabling refined energy-saving control. However, existing traditional management methods still pose certain challenges for accurate analysis and closed-loop control of building energy consumption.

[0003] In actual building model design and deployment, IoT-based sensors are often asymmetrically deployed, such as wall-mounted or ceiling-mounted. The collected sensor data signals are often limited by structural obstructions, resulting in data reflecting the surrounding environment rather than the area the sensor should be monitoring, leading to data misattribution. For example, a carbon dioxide sensor installed near a partition wall might incorrectly attribute its sampling results to the room itself, causing abnormal air conditioning response. Furthermore, existing traditional management methods lack thermal conductivity characteristics and spatial exposure assessment at the component level in BIM models, making it impossible to identify weak and continuous heat loss caused by components such as high-thermal-conductivity exterior walls and external pipes in energy consumption management. For example, some components located near… Heat loss from exposed or misaligned heat pipes in balconies, stairwells, or ventilation shafts, though not concentrated, can persist over time and cause significant data errors. Furthermore, the thermal response characteristics of window components under solar radiation vary significantly due to differences in actual orientation, but traditional management methods often overlook this difference, leading to accumulated errors in energy consumption prediction. Simultaneously, interruptions in data communication can cause abrupt changes in data, easily leading to misjudgments in energy consumption management. Moreover, in BIM models, near-zero energy buildings employ multi-duct designs, with airflow paths passing through multiple rooms. Traditional management methods struggle to trace airflow paths when temperature changes are detected, leading to misjudgments of the actual source of anomalies and resulting in blurred energy flow paths.

[0004] In view of this, the present invention proposes a method for full-process energy consumption management of near-zero energy buildings based on BIM and the Internet of Things to solve the above problems. Summary of the Invention

[0005] To overcome the aforementioned deficiencies of existing technologies and to achieve the above objectives, this invention provides the following technical solution: a method for full-process energy consumption management of near-zero energy buildings based on BIM and the Internet of Things, comprising:

[0006] S1. Collect building component information and sensor node physical data and perform data cleaning to obtain building attribute information and high-quality sensor data respectively;

[0007] S2. Based on building attribute information, spatial attribution calibration is performed on high-quality sensor data to obtain the attribution spatial dataset;

[0008] S3. Based on the spatial dataset, thermal regions are identified, and local error analysis is performed on the thermal regions to obtain temperature interference correction data;

[0009] S4. Perform radiation bias correction on the temperature interference correction data to obtain photothermal correction data; perform communication data stream detection on the photothermal correction data to screen reliable communication data;

[0010] S5. Adjust the airflow direction attribution of the trusted communication data to obtain an enhanced spatial dataset;

[0011] S6. Generate a modified control strategy based on the enhanced spatial dataset, and send the modified control strategy to the preset energy consumption management terminal.

[0012] Furthermore, the method for performing spatial attribution calibration includes:

[0013] Based on building attribute information, a node mapping space is constructed, with each spatial component as a node in the node mapping space; the functional direction vector of the spatial component is obtained, and the spatial angle between the functional direction vector of each spatial component and the surface normal of the spatial component is calculated. Based on the spatial angle, a weighted edge is established between each node, and a topology graph of the spatial component is constructed based on all nodes and weighted edges.

[0014] Obtain the sensor device installation coordinates of each node in a preset neighborhood space and project them onto the spatial component topology map; calculate the shortest projection path length and projection angle from the sensor device installation coordinates to other nodes in the preset neighborhood space and nodes in adjacent preset neighborhood spaces, and construct an association function based on the shortest projection path length and projection angle to calculate the association score; select the preset neighborhood space where the node with the association score is higher than the preset association score threshold is located as the home space of the sensor device installation coordinate; if there are multiple home spaces, use these home spaces as candidate home spaces, obtain the historical monitoring data of the sensor corresponding to the sensor device installation coordinate, and construct the historical data change vector; query the corresponding data of each node in the candidate home space in the high-quality sensor data, and construct the data change vector of the corresponding node; calculate the Euclidean distance between the historical data change vector and the node's data change vector, and select the nodes whose Euclidean distance is higher than the behavior consistency score to match with the sensor; integrate all the matched high-quality sensor data to obtain the home space dataset.

[0015] Furthermore, the method for determining the heat region includes:

[0016] Based on the topological connection relationship of each spatial component in the home space dataset, spatial components connected to the boundary area in each home space are identified; the material type of the spatial component is queried based on the building attribute information, and spatial components marked as high thermal conductivity materials are selected to form a set of high thermal conductivity components; the ratio of the exposed area to the surface area of ​​each high thermal conductivity component is calculated to obtain the exposed area ratio. If at least one side of the high thermal conductivity component is in direct contact with the non-enclosed space and the exposed area ratio is higher than the preset area ratio threshold, then the corresponding high thermal conductivity component is marked as an exposed component.

[0017] The installation height of the exposed component in the corresponding assigned space is detected, and the height ratio of the installation height to the height of the assigned space is calculated; the relative connection angle between the functional direction vector of the exposed component and the adjacent connecting component is calculated. If the relative connection angle is greater than a preset deviation angle threshold and the height ratio is higher than a preset height proportion threshold, the corresponding exposed component is identified as a heat component; the area where the heat component and the adjacent connecting component are located is marked as a heat area.

[0018] Furthermore, the method for performing local error analysis includes:

[0019] The area consisting of non-thermal components of the same type as thermal components and adjacent non-thermal connecting components is defined as a non-thermal region; real-time temperature data of spatial components in thermal regions in the assigned spatial dataset is obtained, and non-thermal temperature data of spatial components in the non-thermal region closest to the current thermal region within the same time period is collected. A control temperature baseline is constructed based on the real-time temperature data and non-thermal temperature data; the difference between each timestamp in the control temperature baseline is calculated, a temperature residual sequence is constructed based on all differences, and a sliding time window is constructed to traverse the temperature residual sequence.

[0020] The baseline derivative of non-thermal temperature data in the control temperature baseline corresponding to each sliding time window is calculated to obtain the normal temperature change rate. A local difference function is constructed based on the mean normal temperature change rate and the mean temperature residual within the same sliding time window. The temperature trend value of the corresponding sliding time window is calculated using the local difference function, and the value in each sliding time window in the temperature residual sequence is subtracted from the temperature trend value of the corresponding sliding time window to obtain the thermal adjustment value for each time stamp in the complete temperature residual sequence. The thermal adjustment value is used to correct the real-time temperature data of the thermal components to obtain temperature disturbance correction data.

[0021] Furthermore, the method for performing radiation deviation correction includes:

[0022] The system identifies exterior window components based on building attribute information and obtains the surface normal vector of each component. It divides the temperature interference correction data into time periods, extracts the thermal energy data for each exterior window component corresponding to each time period, and simultaneously detects the solar altitude angle and azimuth angle data corresponding to the same timestamp. A solar incidence vector is constructed based on these data. The cosine of the angle between the surface normal vector and the solar incidence vector is calculated as the illumination projection value per unit exterior window component. A weighted summation of the illumination projection value per unit exterior window component within each time period is performed on a pre-defined uniform solar heat gain coefficient, and the average value is taken to generate the local radiation coefficient for the corresponding exterior window component. The average value of the product of this local radiation coefficient and the thermal energy data for each timestamp within the corresponding exterior window component within the time period is calculated to obtain the solar thermal radiation for that time period. The solar thermal radiation for each time period is removed from the temperature interference correction data to obtain the solar thermal correction data.

[0023] Furthermore, the method for performing communication data stream detection includes:

[0024] Identify the timestamp of the corresponding data for each spatial component in the photothermal correction data, and calculate the time interval between adjacent timestamps; set a maximum sampling time interval, and if there are three or more consecutive time intervals that exceed the maximum sampling time interval, mark the time segment corresponding to the consecutive time intervals as a suspected communication packet loss period;

[0025] Obtain historical data sequences within a preset sliding window from the historical photothermal correction data, and calculate the average historical data change rate of this data sequence. Extract the corresponding data sequence for each spatial component in the photothermal correction data, and construct the same preset sliding window to calculate the difference between the maximum and minimum data change rates in the data sequence. If the difference in data change rates within the same preset sliding window is higher than a preset multiple of the average historical data change rate, and the same preset sliding window overlaps with a communication packet loss period, then the overlapping period is marked as an unreliable period. Perform a secondary screening on the corresponding data for the remaining periods, excluding the unreliable periods, to obtain reliable communication data.

[0026] Furthermore, the methods for performing secondary screening include:

[0027] Calculate the standard deviation of the time interval for the corresponding data in the remaining time periods, and identify the time periods with a standard deviation of the time interval below a preset first standard deviation threshold as high stability periods; calculate the total length of the time periods excluding high stability periods; if the ratio of the total length of the time period to the length of the remaining time periods excluding unreliable periods is lower than the preset data coverage ratio, then lower the preset first standard deviation threshold and perform a second screening, and repeat the adjustment of the preset first standard deviation threshold until the length ratio is greater than or equal to the preset data coverage ratio; integrate the data corresponding to all high stability periods into reliable communication data.

[0028] Furthermore, the method for adjusting the airflow direction includes:

[0029] The process involves: acquiring the vent types and connections of each ventilation component in any home space within trusted communication data; constructing an air propagation path map based on these vent types and connections; identifying the connection order of each ventilation component node in the air propagation path map; marking the upstream and downstream nodes of each ventilation component node based on their relative positions in the connection order; calculating the temperature change between adjacent ventilation component nodes; constructing a temperature change sequence for each ventilation component node; detecting the timestamp of the temperature change for each ventilation component node; identifying a downstream node as a flow direction deviation node if its timestamp is earlier than that of an upstream node and its temperature change exceeds a preset temperature change threshold; tracing back all upstream nodes connected to the flow direction deviation node; calculating the temperature change difference and connection distance between each upstream node and the flow direction deviation node; using the home space corresponding to the upstream node with the largest temperature change difference and the shortest connection distance as the main contribution space; marking the home space corresponding to the flow direction deviation node as a space to be corrected; updating the spatial assignment of the data corresponding to the space to be corrected to the main contribution space to obtain the enhanced space dataset.

[0030] Furthermore, the method for generating the modified control strategy includes:

[0031] The system enhances the matching of spatial datasets with historical control parameter ranges to identify the energy load status of each assigned space. Based on the preset standard energy consumption control template, the parameters related to the energy load status of each assigned space are adjusted, and the adjustment process is encoded into a multi-dimensional instruction sequence, which is the correction control strategy.

[0032] A near-zero energy building full-process energy consumption management system based on BIM and IoT, which is used to implement a method for full-process energy consumption management of near-zero energy buildings based on BIM and IoT, is characterized by including:

[0033] The data acquisition module is used to collect information on building components and physical data of sensor nodes and to perform data cleaning to obtain building attribute information and high-quality sensor data, respectively.

[0034] The spatial calibration module is used to perform spatial attribution calibration on high-quality sensor data based on building attribute information to obtain the attribution spatial dataset.

[0035] The heat discrimination module is used to discriminate heat regions based on the spatial dataset and perform local error analysis on the heat regions to obtain temperature interference correction data.

[0036] The photothermal correction module is used to correct radiation deviations in temperature interference correction data to obtain photothermal correction data; and to perform communication data stream detection on the photothermal correction data to screen reliable communication data.

[0037] The space enhancement module is used to adjust the airflow direction attribution of trusted communication data to obtain an enhanced space dataset.

[0038] The strategy generation module is used to generate modified control strategies based on the enhanced spatial dataset and send the modified control strategies to the preset energy consumption management terminal; the modules are connected to each other via wired and / or wireless means.

[0039] The technical effects and advantages of this invention's method for whole-process energy consumption management of near-zero energy buildings based on BIM and the Internet of Things are as follows:

[0040] By using a near-zero energy building model constructed with BIM technology as the data foundation, building attribute information and high-quality sensor data are extracted. The sensor data undergoes spatial attribution calibration, temperature interference correction, radiation deviation correction, traffic flow detection, and airflow adjustment, achieving data enhancement for full-process energy consumption management. This realizes a BIM- and IoT-based full-process energy consumption management method. Compared with existing experience, it improves the accuracy of energy consumption perception in the built environment and the responsiveness of control strategies. It enables energy consumption control based on the semantic information of actual components and heat transfer logic, improves the sensitivity of anomaly identification and avoids misjudgment and miscontrol, and enhances the rationality and stability of energy consumption scheduling. For near-zero energy buildings, it effectively reduces unnecessary energy redundancy, improves energy efficiency, and is more responsive, adaptable, and fault-tolerant in residential and office buildings with multiple rooms sharing air ducts or complex structural obstructions. Attached Figure Description

[0041] Figure 1 This is a schematic diagram of a near-zero energy building energy consumption management method based on BIM and IoT according to the present invention.

[0042] Figure 2 This is a schematic diagram of a near-zero energy building energy consumption management system based on BIM and IoT, according to the present invention. Detailed Implementation

[0043] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0044] Example 1

[0045] Please see Figure 1 As shown in this embodiment, a method for full-process energy consumption management of near-zero energy buildings based on BIM and IoT includes:

[0046] S1. Collect building component information and sensor node physical data and perform data cleaning to obtain building attribute information and high-quality sensor data respectively;

[0047] S2. Based on building attribute information, spatial attribution calibration is performed on high-quality sensor data to obtain the attribution spatial dataset;

[0048] S3. Based on the spatial dataset, thermal regions are identified, and local error analysis is performed on the thermal regions to obtain temperature interference correction data;

[0049] S4. Perform radiation bias correction on the temperature interference correction data to obtain photothermal correction data; perform communication data stream detection on the photothermal correction data to screen reliable communication data;

[0050] S5. Adjust the airflow direction attribution of the trusted communication data to obtain an enhanced spatial dataset;

[0051] S6. Generate a modified control strategy based on the enhanced spatial dataset, and send the modified control strategy to the preset energy consumption management terminal.

[0052] In this embodiment, building components refer to, for example, doors, windows, pipes, lighting fixtures, and other devices existing in a near-zero energy building model constructed using BIM technology. Building component information is the attribute data of each building component extracted from the near-zero energy building model, including its geometric shape parameters, construction material type, and spatial coordinate position. Sensor node physical data refers to the parameter information of each building component collected by sensors simulated in the near-zero energy building model, such as temperature and wind speed. Data cleaning is achieved by filtering and denoising the building component information and sensor node physical data and filling in missing values, resulting in higher quality building attribute information and higher quality sensor data.

[0053] Methods for performing space attribution calibration include:

[0054] Based on building attribute information, a node mapping space is constructed, with each spatial component serving as a node in the node mapping space. The node mapping space is a data space used to store various spatial components in the BIM near-zero energy building model, where each spatial component is regarded as a node. Meanwhile, spatial components refer to building components installed in various rooms in the BIM near-zero energy building model, such as doors, windows, and pipes.

[0055] The functional direction vectors of spatial components are obtained, and the spatial angle between the functional direction vector of each spatial component and the surface normal of the spatial component is calculated. Weighted edges are established between each node based on this spatial angle. A topology graph of the spatial components is constructed based on all nodes and weighted edges. The functional direction vector refers to the direction corresponding to the function possessed by the spatial component, such as the ventilation direction of a pipe or the lighting direction of a window. The functional direction vector corresponding to each spatial component is extracted from the design information of the BIM near-zero energy building model. The surface normal of the spatial component is perpendicular to the structural surface of the spatial component, and the resulting spatial angle is used as the weighted edge. The smaller this value, the more consistent the functional direction vector is with the geometric direction of the component, reflecting the functional expression strength of the spatial component. For example, a smaller angle between the geometric direction and the functional direction vector of a ventilation component indicates that the air propagation path is blocked to some extent, which is not conducive to ventilation.

[0056] In the topology diagram of spatial components, edges are formed by connecting the relative positions of each spatial component, and the difference in the spatial angle between different nodes is used as the weight of the edge. The smaller the weight, the more consistent the functional orientation between the two spatial components. Conversely, the two spatial components may not belong to the same functional orientation.

[0057] The sensor device installation coordinates of each node in the preset neighborhood space are obtained and projected onto the spatial component topology map. The preset neighborhood space of the corresponding node is constructed based on the design size information of each room in the BIM near-zero energy building model. Each preset neighborhood space corresponds to a room, and each preset neighborhood space includes multiple spatial components belonging to the same room. The sensor device is projected onto the spatial component topology map based on the specific installation position of the sensor device in the room.

[0058] Calculate the shortest projection path length and projection angle from the sensor device's installation coordinates to other nodes within the preset neighborhood space and nodes in adjacent preset neighborhood spaces. Construct an attribution association function based on the shortest projection path length and projection angle to calculate the association score. The shortest projection path length refers to the displacement distance from the projection point of the sensor device's installation coordinates to any node in the preset neighborhood space and adjacent preset neighborhood spaces. The projection angle refers to the three-dimensional spatial angle between the projection point and each node. The formula for calculating the attribution association function is: The above calculation formula is a dimensionless calculation, using only numerical values. This represents the correlation score between the sensor device's installation coordinates and any node in the preset neighborhood space and adjacent preset neighborhood spaces. Indicates the shortest projection path length; Represents the cosine value of the projected angle; and These represent the weights of the shortest projection path length and the projection angle, respectively. In this embodiment, the weights are empirically set based on the historical attribution association function. and The values ​​of the two weights are all within the range of . .

[0059] The preset neighborhood space containing nodes with correlation scores higher than a preset correlation score threshold is selected as the home space of the sensor device's installation coordinates. The preset correlation score threshold is set based on historical judgment experience, and the preset neighborhood space containing nodes with correlation scores higher than the preset correlation score threshold is selected as the home space of the sensor device. By selecting and retaining the spaces containing nodes with high perceptibility, the judgment is made from both distance and angle aspects to avoid the space that is closest to a single point but has no connection with the data sampling of the corresponding component being considered as the home space. Even if the node and the sensor are not in the same preset neighborhood space, if there is still a high correlation score in the adjacent preset neighborhood spaces, then from the perspective of sensor perception intensity, the corresponding preset neighborhood space can still be regarded as the home space of the sensor.

[0060] If multiple attribution spaces exist, these attribution spaces are used as candidate attribution spaces. Historical monitoring data of the sensor corresponding to the sensor installation coordinates are obtained, and a historical data change vector is constructed. The historical data change vector is constructed by calculating mathematical and statistical features such as the mean, standard deviation, and derivative of the historical monitoring data for subsequent operation judgment.

[0061] Query the corresponding data of each node in the candidate home space in the high-quality sensor data, and construct the data change vector of the corresponding node. This is done by extracting the corresponding data of each node in the candidate home space in the high-quality sensor data obtained in this data acquisition, and also by calculating mathematical statistical features to construct the data change vector of the corresponding node.

[0062] The Euclidean distance between the historical data change vector and the node's data change vector is calculated. Nodes with an Euclidean distance higher than the behavioral consistency score are matched with sensors. This process involves calculating the Euclidean distance between the historical data change vector and the node's data change vector, setting a behavioral consistency score based on relevant clustering algorithm theory, and matching nodes with a Euclidean distance higher than this behavioral consistency score to sensors. The node's corresponding home space is then mapped to the sensor. Therefore, a sensor may have multiple home spaces. All the matched high-quality sensor data are integrated to obtain the home space dataset.

[0063] Methods for identifying heat regions include:

[0064] Based on the topological connection relationship of each spatial component in the home space dataset, spatial components connected to the boundary area in each home space are identified. Specifically, based on the connection relationship of each spatial component in the home space dataset in the spatial component topology map, spatial components that are continuously extended and connected and belong to the boundary area are identified. The boundary area is a preset edge area in each room set based on the design information of BIM near-zero energy building.

[0065] Based on the building attribute information, the material type of the spatial component is queried, and the spatial components marked with high thermal conductivity are selected to form a set of high thermal conductivity components. In this embodiment, the set of spatial components marked with high thermal conductivity is constructed by extracting the material type of the spatial component corresponding to the building attribute information. This includes spatial components such as thermally conductive walls and pipes.

[0066] The ratio of exposed area to surface area of ​​each high thermal conductivity component is calculated to obtain the exposed area ratio. If at least one side of the high thermal conductivity component is in direct contact with an open space and the exposed area ratio is higher than a preset area ratio threshold, the corresponding high thermal conductivity component is marked as an exposed component. The exposed area of ​​each high thermal conductivity component is identified based on the design information of the BIM near-zero energy building model. The exposed area refers to the surface area of ​​the high thermal conductivity component in direct contact with the open space of the open space. The surface area refers to the area of ​​all complete surfaces of the high thermal conductivity component. A preset area ratio threshold is set based on historical area determination experience. Components in the above set of high thermal conductivity components with an exposed area ratio higher than the preset area ratio threshold are selected as exposed components. The exposed area ratio reflects the degree of passive heat exchange of the component.

[0067] The installation height of exposed components in their corresponding assigned spaces is detected, and the ratio of this installation height to the height of the assigned space is calculated. The height of the assigned space refers to the height from the floor to the ceiling in the assigned space corresponding to the exposed component. The installation height is obtained by querying the design information of the BIM near-zero energy building model.

[0068] The relative connection angle between the functional direction vectors of the exposed component and the adjacent connected component is calculated. If the relative connection angle is greater than a preset deviation angle threshold and the height ratio is higher than a preset height proportion threshold, the corresponding exposed component is identified as a heat-generating component. The calculation of the relative connection angle between the functional direction vectors of the exposed component and the adjacent connected component reflects whether there is a significant bend in the connection path between the exposed component and the surrounding connected components. Based on the design information of the BIM near-zero energy building model, preset deviation angle thresholds and preset height proportion thresholds are set. If both the relative connection angle is greater than the preset deviation angle threshold and the height ratio is higher than the preset height proportion threshold, the corresponding exposed component is identified as a heat-generating component.

[0069] The area containing the heat-generating component and its adjacent connecting components is marked as the heat-generating area, which refers to the area formed by the heat-generating component and its directly connected adjacent connecting components.

[0070] Methods for performing local error analysis include:

[0071] The area formed by non-heating components of the same type as the heat-generating components and adjacent non-heating connecting components is defined as a non-heating area. Non-heating components refer to components of the same type as heat-generating components, but whose exposed area ratio, relative connection angle, and height ratio cannot meet the heat-generating component standards. Non-heating areas refer to the area formed by non-heating components and adjacent non-heating connecting components directly connected to them.

[0072] Real-time temperature data of spatial components in the heat region of the assigned spatial dataset is obtained. Non-heat temperature data of spatial components in the non-heat region closest to the current heat region within the same time period is collected. A control temperature baseline is constructed based on the real-time temperature data and the non-heat temperature data. The non-heat temperature data is obtained by acquiring the relevant data of each spatial component in the non-heat region closest to the current heat region in the same time period through time consistency constraints. The change curves of real-time temperature data and non-heat temperature data are plotted to construct the control temperature baseline.

[0073] The difference at each time point in the baseline temperature is calculated. A temperature residual sequence is constructed based on all the differences. A sliding time window is constructed to traverse the temperature residual sequence. The temperature residual sequence is obtained by subtracting the real-time temperature data of the heat component from the non-heat component at each time point. Each temperature residual sequence corresponds to the temperature difference change between the heat component in a heat region and the non-heat component in any non-heat region. The sliding time window size is set based on historical experience to traverse the sequence.

[0074] The normal temperature change rate is obtained by calculating the baseline derivative of the non-thermal temperature data in the control temperature baseline corresponding to each sliding time window. The normal temperature change rate is obtained by differentiating the baseline of the non-thermal temperature data in the control temperature baseline corresponding to each time period of the sliding time window.

[0075] A local difference function is constructed based on the mean normal temperature change rate and the mean temperature residual within the same sliding time window. The formula for calculating the local difference function is as follows: The above calculation formula uses dimensionless calculation and only uses numerical values ​​for calculation. Indicates temperature trend value; This indicates the window size corresponding to the sliding time window; This represents the normal temperature change rate within the corresponding sliding time window period. This represents the average temperature residual over a period of time within the same sliding time window.

[0076] The temperature trend value of the corresponding sliding time window is calculated using the local difference function, and the difference between the value in each sliding time window in the temperature residual sequence and the temperature trend value of the corresponding sliding time window is obtained to obtain the heat adjustment value of each time point in the complete temperature residual sequence. The heat adjustment value of each time point in the entire complete temperature residual sequence is obtained by calculating the difference between the value of the temperature residual sequence and the temperature trend value in each sliding time window.

[0077] The real-time temperature data of the heating element is corrected using the heat adjustment value to obtain temperature interference correction data. The heat adjustment value includes both negative and positive numbers. The heat adjustment value is added to the real-time temperature data of the heating element, and the adjusted data is the temperature interference correction data.

[0078] Methods for radiation bias correction include:

[0079] The external window components are identified based on building attribute information, and the surface normal vector of each external window component is obtained. The external window components are identified based on the information fields of each spatial component in the building attribute information, and the surface normal vector of the component surface of each external window component is extracted for subsequent calculations.

[0080] The temperature interference correction data is divided into time periods. The thermal energy data of each external window component corresponding to each time period is extracted from the temperature interference correction data. At the same time, the solar altitude angle and azimuth angle data corresponding to the same timestamp are detected. The solar incidence vector is constructed based on the solar altitude angle and azimuth angle data. The timestamps of the temperature interference correction data are divided into time periods based on relevant meteorological knowledge. Each time period corresponds to a different solar radiation stage. The solar altitude angle and azimuth angle of each corresponding timestamp are detected. The solar altitude angle and azimuth angle are represented by trigonometric functions using mathematical methods and concatenated into a vector representation to obtain the solar incidence vector.

[0081] The cosine of the angle between the surface normal vector and the solar incident vector is calculated and used as the illumination projection value of a unit external window component. The illumination projection value of each external window component is obtained by calculating the cosine of the angle between the surface normal vector and the solar incident vector. The larger the value, the more perpendicular the sunlight shines on the external window component.

[0082] The local radiation coefficient of the corresponding window component is generated by weighted summation of the unit window component illumination projection value at each timestamp within a time period and the preset unified solar heat gain coefficient is calculated and averaged. The preset unified solar heat gain coefficient refers to the default value used in the building thermal energy simulation process, namely SHGC. The local radiation coefficient of the window component for the corresponding time period is obtained by multiplying the unit window component illumination projection value of all timestamps within a certain time period with the preset unified solar heat gain coefficient, summing and averaging.

[0083] The solar thermal radiation for that time period is obtained by averaging the product of the local emissivity and the thermal energy data of each time stamp within the time period of the corresponding external window component. The thermal energy data refers to the radiative heat data collected in real time in the temperature interference correction data. The solar thermal radiation for that time period is obtained by multiplying the local emissivity of the corresponding time period with the thermal energy data of each time stamp and taking the average value of the product within this time period. This value is used to reflect the heat increment caused by solar radiation within the corresponding time period.

[0084] The amount of solar thermal radiation in each time period is removed from the temperature interference correction data to obtain solar thermal correction data. By removing the amount of solar thermal radiation in each time period from the temperature interference correction data, the influence of heat generated by sunlight on the temperature control and acquisition data is eliminated.

[0085] Methods for detecting communication data streams include:

[0086] The timestamp of the corresponding data for each spatial component in the photothermal correction data is identified, and the time interval between adjacent timestamps is calculated. The time interval is obtained by calculating the difference between adjacent timestamps for each pair of data corresponding to each spatial component in the photothermal correction data, which is used as a basis for subsequent judgment.

[0087] A maximum sampling time interval is set. If there are three or more consecutive time intervals that are greater than the maximum sampling time interval, the time segment corresponding to the consecutive time intervals is marked as a suspected communication packet loss period. The maximum sampling time interval is set based on historical BIM near-zero energy building model design experience. In this embodiment, the occurrence of three or more consecutive time intervals that are greater than the maximum sampling time interval is used as the judgment criterion, and the corresponding time segment is marked as the communication interruption area, which is the communication packet loss period.

[0088] Obtain the historical data sequence of the preset sliding window in the historical photothermal correction data, and calculate the average historical data change rate of the historical data sequence. The historical data sequence includes the historical data corresponding to each spatial component in the historical photothermal correction data. Calculate the average historical data change rate of the historical data sequence corresponding to each spatial component. This value is used to reflect the typical changes in the corresponding dimension of the data.

[0089] Extract the data sequence consisting of the corresponding data of each spatial component in the photothermal correction data, and construct the same preset sliding window to calculate the difference between the maximum and minimum data change rates in the data sequence. The same sliding window is a sliding window of the same size as the preset sliding window in the above historical photothermal correction data. Calculate the difference between the extreme values ​​of the change rates of the corresponding data of each spatial component in the photothermal correction data within the sliding window to reflect the degree of data change within the sliding window period.

[0090] If the difference in the rate of change of data within the same preset sliding time window is higher than a preset multiple of the average rate of change of historical data, and the same preset sliding time window overlaps with a period of communication packet loss, then the overlapping period is marked as an unreliable period. If the extreme difference in the rate of change of data within the same preset sliding window is higher than a preset multiple of the average rate of change of historical data for the corresponding component, and also overlaps with a period of communication packet loss, then the overlapping period is considered to have discontinuous data, and is marked as an unreliable period. The preset multiple refers to a multiple set based on historical sampling experience, which serves as a boundary for identifying abnormal data jumps, preventing the system from identifying data mutations due to wide communication intervals.

[0091] The corresponding data for the remaining time periods, excluding the unreliable periods, are further filtered to obtain reliable communication data. In this embodiment, the unreliable periods are ultimately a very small number of periods, so removing the data from the unreliable periods will not affect the overall integrity of the data. The secondary filtering of the remaining time periods is to ensure that the filtered data has a high degree of consistency in terms of time granularity and fluctuation, thus obtaining reliable communication data.

[0092] The methods for secondary screening include:

[0093] Calculate the standard deviation of the time intervals of the corresponding data for the remaining time periods. Determine the time intervals with standard deviations of the time intervals that are lower than the preset first standard deviation threshold as high stability time periods. The preset first standard deviation threshold is set based on historical screening experience. Calculate the standard deviation of all time intervals of the corresponding data for each time period in the remaining time periods. The smaller the value, the smaller the overall distribution fluctuation of the time intervals between adjacent time periods, and the easier it is to consider that the communication is in a stable state. Therefore, the time periods that meet the conditions are set as high stability time periods.

[0094] The total length of time periods excluding highly stable periods is calculated. If the ratio of this total length to the length of all other time periods excluding unreliable periods is lower than the preset data coverage ratio, the preset first standard deviation threshold is lowered and a second screening is performed. This process is repeated until the time length ratio is greater than or equal to the preset data coverage ratio. The preset data coverage ratio is set based on historical screening experience. By calculating the ratio of the total length of the remaining time periods excluding highly stable periods to the length of the remaining time periods and comparing it with the preset data coverage ratio, insufficient data coverage due to screening is avoided. If the time length ratio is lower than the preset data coverage ratio, the preset first standard deviation threshold is gradually adjusted and a second screening is performed. This process is repeated until the time length ratio is greater than or equal to the preset data coverage ratio to avoid losing too much sample data.

[0095] All data corresponding to high stability periods are integrated into reliable communication data. In this embodiment, reliable communication data is obtained by integrating all high stability period data after elimination. For the data corresponding to the eliminated timestamps, a machine learning model trained on historical normal data is used to predict and fill the data.

[0096] Methods for adjusting airflow direction assignment include:

[0097] The system acquires the vent types and connection relationships of each ventilation component in any home space within the trusted communication data. Based on the vent types and connection relationships, it constructs an air propagation path diagram. The system uses design information from the BIM near-zero energy building model to identify the ventilation components from any home space within the trusted communication data, as well as the connection topology between vents and ducts. Vent types include, for example, supply air vents, return air vents, and exhaust air vents. The system uses the ducts in these ventilation components as directed edges to construct the air propagation path diagram.

[0098] Identify the connection order of each ventilation component node in the air propagation path diagram, and mark the upstream and downstream nodes of each ventilation component node based on the relative position of each ventilation component node in the connection order. For each ventilation component node, the node whose connection order precedes it is regarded as the upstream node, and the node whose connection order follows it is regarded as the downstream node.

[0099] The temperature change between adjacent ventilation component nodes is calculated, a temperature change sequence of ventilation components is constructed, and the timestamp of the temperature change of each ventilation component node is detected. The temperature change is sorted according to the connection order between several ventilation component nodes to form the temperature change sequence. At the same time, the timestamp corresponding to each temperature change in the temperature change sequence is extracted for subsequent judgment.

[0100] If the timestamp of the temperature change in the downstream node is earlier than that of the upstream node, and the amount of temperature change is higher than the preset temperature change threshold, then the downstream node is determined to be a flow deviation node. If the timestamp of the downstream node is earlier than that of the upstream node and the amount of temperature change is higher than the preset temperature change threshold set based on the BIM near-zero energy building design constraint information, it indicates that the information transmission at this time is inconsistent with the original design information, and there may be abnormalities such as signal errors or air backflow. Therefore, the downstream node in the relative position is regarded as a flow deviation node.

[0101] Tracing back all upstream nodes connected to the flow deviation node, the temperature change difference and connection distance between each upstream node and the flow deviation node are calculated. The domain space corresponding to the upstream node with the largest temperature change difference and the shortest connection distance is taken as the main contribution space. Tracing back all nodes connected to the flow deviation node refers to tracing back the sequence of upstream nodes that are continuously connected and reach the flow deviation node at their relative positions. By calculating the temperature change difference and connection distance between each upstream node in the above upstream node sequence and the flow deviation node at this time, the component node with the largest temperature influence and the shortest path is preferentially selected as the source point that plays a dominant role in this abnormal change. The domain space to which the source point belongs is taken as the main contribution space, which is used to represent the actual data source location that caused the flow deviation.

[0102] The space corresponding to the flow deviation node is marked as the space to be corrected. The spatial assignment of the data corresponding to the space to be corrected is updated to the main contribution space to obtain the enhanced space dataset. The original space of the node identified as the flow deviation node is marked as the space to be corrected because it is not detected as the actual source of the current temperature change. By updating the assignment labels of all its related data to the currently identified main contribution space, the related sensor data is migrated from the physical location to the assignment region of the air energy-dominant region, and finally the enhanced space dataset reflecting the real air heat transfer behavior is obtained.

[0103] The methods for generating modified control strategies include:

[0104] The enhanced spatial dataset is matched with historical control parameter ranges to identify the energy load status of each assigned space. The enhanced spatial dataset is high-quality energy consumption characteristic data after a full-process data processing workflow including spatial attribution calibration, temperature anomaly calibration, elimination of light and heat interference, and adjustment of airflow direction. It integrates multi-dimensional information such as spatial attribution, temperature, air propagation, and radiation. By matching this enhanced spatial dataset with historical control parameter ranges that have been regulated in the historical records, and combining it with the design information of the BIM near-zero energy building model itself, the energy load status of each dimension of the enhanced spatial dataset is identified.

[0105] Based on a preset standard energy consumption control template, the parameters related to the energy load status of each assigned space are adjusted, and the adjustment process is encoded into a multi-dimensional instruction sequence, which is the correction control strategy. Then, by retrieving the preset standard energy consumption control template for historical regulation, the parameters of the relevant space components in each assigned space are adjusted from the energy load status to the standard status. At the same time, the parameter regulation change process is encoded into an executable multi-dimensional instruction sequence, forming an implementable and deployable correction control strategy.

[0106] This embodiment uses a near-zero energy building model constructed with BIM technology as its data foundation, extracting building attribute information and high-quality sensor data. It then performs spatial attribution calibration, temperature interference correction, radiation deviation correction, traffic data flow detection, and airflow direction adjustment on the sensor data, achieving data enhancement for full-process energy consumption management. This realizes a BIM and IoT-based full-process energy consumption management method. Compared with existing experience, it improves the accuracy of energy consumption perception in the building environment and the responsiveness of control strategies. It can perform energy consumption control based on the semantic information of actual components and heat transfer logic, improves the sensitivity of anomaly identification and avoids misjudgment and miscontrol, and enhances the rationality and stability of energy consumption scheduling. For near-zero energy buildings, it effectively reduces unnecessary energy redundancy, improves energy efficiency, and is more responsive, adaptable, and fault-tolerant in residential and office buildings with multiple rooms sharing air ducts or complex structural obstructions.

[0107] Example 2

[0108] Please see Figure 2 As shown, for parts not described in detail in this embodiment, please refer to the description in Embodiment 1. A near-zero energy building energy consumption management system based on BIM and IoT is provided, comprising:

[0109] The data acquisition module is used to collect information on building components and physical data of sensor nodes and to perform data cleaning to obtain building attribute information and high-quality sensor data, respectively.

[0110] The spatial calibration module is used to perform spatial attribution calibration on high-quality sensor data based on building attribute information to obtain the attribution spatial dataset.

[0111] The heat discrimination module is used to discriminate heat regions based on the spatial dataset and perform local error analysis on the heat regions to obtain temperature interference correction data.

[0112] The photothermal correction module is used to correct radiation deviations in temperature interference correction data to obtain photothermal correction data; and to perform communication data stream detection on the photothermal correction data to screen reliable communication data.

[0113] The space enhancement module is used to adjust the airflow direction attribution of trusted communication data to obtain an enhanced space dataset.

[0114] The strategy generation module is used to generate modified control strategies based on the enhanced spatial dataset and send the modified control strategies to the preset energy consumption management terminal; the modules are connected to each other via wired and / or wireless means.

[0115] The above description is merely a preferred embodiment of the present invention and is not intended to limit the present invention. Although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art can still modify the technical solutions described in the foregoing embodiments or make equivalent substitutions for some of the technical features. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the protection scope of the present invention.

[0116] All formulas in this manual are dimensionless and calculated numerically. The formulas are derived from software simulations based on a large amount of collected data to obtain the most recent real-world results. The preset parameters and thresholds in the formulas are set by those skilled in the art according to the actual situation.

[0117] Although embodiments of the invention have been shown and described, those skilled in the art will understand that various changes, modifications, substitutions and alterations can be made to these embodiments without departing from the principles and spirit of the invention, the scope of which is defined by the claims and their equivalents.

Claims

1. A method for full-process energy consumption management of near-zero energy buildings based on BIM and IoT, characterized in that, include: S1. Collect building component information and sensor node physical data and perform data cleaning to obtain building attribute information and high-quality sensor data respectively; S2. Based on building attribute information, spatial attribution calibration is performed on high-quality sensor data to obtain the attribution spatial dataset; S3. Based on the spatial dataset, thermal regions are identified, and local error analysis is performed on the thermal regions to obtain temperature interference correction data; S4. Perform radiation bias correction on the temperature interference correction data to obtain photothermal correction data; Perform communication data stream detection on photothermal correction data to screen reliable communication data; S5. Adjust the airflow direction attribution for trusted communication data, including: The process involves: acquiring the vent types and connections of each ventilation component in any home space of the trusted communication data; constructing an air propagation path map based on the vent types and connections; identifying the connection order of each ventilation component node in the air propagation path map; marking the upstream and downstream nodes of each ventilation component node based on the relative position of each node in the connection order; calculating the temperature change between adjacent ventilation component nodes; constructing a temperature change sequence for each ventilation component node; detecting the timestamp of the temperature change in each ventilation component node; identifying the downstream node as a flow direction deviation node if the timestamp of the temperature change in the downstream node is earlier than that in the upstream node and the temperature change is higher than a preset temperature change threshold; tracing back all upstream nodes connected to the flow direction deviation node; calculating the temperature change difference and connection distance between each upstream node and the flow direction deviation node; using the home space corresponding to the upstream node with the largest temperature change difference and the shortest connection distance as the main contribution space; marking the home space corresponding to the flow direction deviation node as the space to be corrected; updating the spatial home of the data corresponding to the space to be corrected as the main contribution space to obtain the reinforcement space dataset. S6. Generate a modified control strategy based on the enhanced spatial dataset, and send the modified control strategy to the preset energy consumption management terminal.

2. The method for full-process energy consumption management of near-zero energy buildings based on BIM and IoT as described in claim 1, characterized in that, The methods for performing spatial attribution calibration include: Based on building attribute information, a node mapping space is constructed, with each spatial component as a node in the node mapping space; the functional direction vector of the spatial component is obtained, and the spatial angle between the functional direction vector of each spatial component and the surface normal of the spatial component is calculated. Based on the spatial angle, a weighted edge is established between each node, and a topology graph of the spatial component is constructed based on all nodes and weighted edges. Obtain the sensor device installation coordinates of each node in a preset neighborhood space and project them onto the spatial component topology map; calculate the shortest projection path length and projection angle from the sensor device installation coordinates to other nodes in the preset neighborhood space and nodes in adjacent preset neighborhood spaces, and construct an association function based on the shortest projection path length and projection angle to calculate the association score; select the preset neighborhood space where the node with the association score is higher than the preset association score threshold is located as the home space of the sensor device installation coordinate; if there are multiple home spaces, use these home spaces as candidate home spaces, obtain the historical monitoring data of the sensor corresponding to the sensor device installation coordinate, and construct the historical data change vector; query the corresponding data of each node in the candidate home space in the high-quality sensor data, and construct the data change vector of the corresponding node; calculate the Euclidean distance between the historical data change vector and the node's data change vector, and select the nodes whose Euclidean distance is higher than the behavior consistency score to match with the sensor; integrate all the matched high-quality sensor data to obtain the home space dataset.

3. The method for full-process energy consumption management of near-zero energy buildings based on BIM and IoT as described in claim 2, characterized in that, The methods for determining heat regions include: Based on the topological connection relationship of each spatial component in the home space dataset, spatial components connected to the boundary area in each home space are identified; the material type of the spatial component is queried based on the building attribute information, and spatial components marked as high thermal conductivity materials are selected to form a set of high thermal conductivity components; the ratio of the exposed area to the surface area of ​​each high thermal conductivity component is calculated to obtain the exposed area ratio. If at least one side of the high thermal conductivity component is in direct contact with the non-enclosed space and the exposed area ratio is higher than the preset area ratio threshold, then the corresponding high thermal conductivity component is marked as an exposed component. The installation height of the exposed component in the corresponding assigned space is detected, and the height ratio of the installation height to the height of the assigned space is calculated; the relative connection angle between the functional direction vector of the exposed component and the adjacent connecting component is calculated. If the relative connection angle is greater than a preset deviation angle threshold and the height ratio is higher than a preset height proportion threshold, the corresponding exposed component is identified as a heat component; the area where the heat component and the adjacent connecting component are located is marked as a heat area.

4. The method for full-process energy consumption management of near-zero energy buildings based on BIM and IoT as described in claim 3, characterized in that, The methods for performing local error analysis include: The area consisting of non-thermal components of the same type as thermal components and adjacent non-thermal connecting components is defined as a non-thermal region; real-time temperature data of spatial components in thermal regions in the assigned spatial dataset is obtained, and non-thermal temperature data of spatial components in the non-thermal region closest to the current thermal region within the same time period is collected. A control temperature baseline is constructed based on the real-time temperature data and non-thermal temperature data; the difference between each timestamp in the control temperature baseline is calculated, a temperature residual sequence is constructed based on all differences, and a sliding time window is constructed to traverse the temperature residual sequence. The baseline derivative of non-thermal temperature data in the control temperature baseline corresponding to each sliding time window is calculated to obtain the normal temperature change rate. A local difference function is constructed based on the mean normal temperature change rate and the mean temperature residual within the same sliding time window. The temperature trend value of the corresponding sliding time window is calculated using the local difference function, and the value in each sliding time window in the temperature residual sequence is subtracted from the temperature trend value of the corresponding sliding time window to obtain the thermal adjustment value for each time stamp in the complete temperature residual sequence. The thermal adjustment value is used to correct the real-time temperature data of the thermal components to obtain temperature disturbance correction data.

5. A method for full-process energy consumption management of near-zero energy buildings based on BIM and IoT as described in claim 4, characterized in that, The methods for performing radiation deviation correction include: The system identifies exterior window components based on building attribute information and obtains the surface normal vector of each component. It divides the temperature interference correction data into time periods, extracts the thermal energy data for each exterior window component corresponding to each time period, and simultaneously detects the solar altitude angle and azimuth angle data corresponding to the same timestamp. A solar incidence vector is constructed based on these data. The cosine of the angle between the surface normal vector and the solar incidence vector is calculated as the illumination projection value per unit exterior window component. A weighted summation of the illumination projection value per unit exterior window component within each time period is performed on a pre-defined uniform solar heat gain coefficient, and the average value is taken to generate the local radiation coefficient for the corresponding exterior window component. The average value of the product of this local radiation coefficient and the thermal energy data for each timestamp within the corresponding exterior window component within the time period is calculated to obtain the solar thermal radiation for that time period. The solar thermal radiation for each time period is removed from the temperature interference correction data to obtain the solar thermal correction data.

6. The method for full-process energy consumption management of near-zero energy buildings based on BIM and IoT as described in claim 5, characterized in that, The methods for detecting communication data streams include: Identify the timestamp of the corresponding data for each spatial component in the photothermal correction data, and calculate the time interval between adjacent timestamps; set a maximum sampling time interval, and if there are three or more consecutive time intervals that exceed the maximum sampling time interval, mark the time segment corresponding to the consecutive time intervals as a suspected communication packet loss period; Obtain historical data sequences from a preset sliding window in the historical photothermal correction data, and calculate the average historical data change rate of the historical data sequence; extract the corresponding data sequence of each spatial component in the photothermal correction data, and construct the same preset sliding window to calculate the difference between the maximum and minimum data change rates in the data sequence; if the difference in data change rates in the same preset sliding window is higher than a preset multiple of the average historical data change rate, and the same preset sliding window overlaps with the communication packet loss period, then the overlapping period is marked as an unreliable period; perform secondary filtering on the corresponding data of the remaining periods other than the unreliable periods to obtain reliable communication data.

7. A method for full-process energy consumption management of near-zero energy buildings based on BIM and IoT as described in claim 6, characterized in that, The methods for performing secondary screening include: Calculate the standard deviation of the time interval for the corresponding data in the remaining time periods, and identify the time periods with a standard deviation of the time interval below a preset first standard deviation threshold as high stability periods; calculate the total length of the time periods excluding high stability periods; if the ratio of the total length of the time period to the length of the remaining time periods excluding unreliable periods is lower than the preset data coverage ratio, then lower the preset first standard deviation threshold and perform a second screening, and repeat the adjustment of the preset first standard deviation threshold until the length ratio is greater than or equal to the preset data coverage ratio; integrate the data corresponding to all high stability periods into reliable communication data.

8. A method for full-process energy consumption management of near-zero energy buildings based on BIM and IoT as described in claim 7, characterized in that, The methods for generating the modified control strategy include: The system enhances the matching of spatial datasets with historical control parameter ranges to identify the energy load status of each assigned space. Based on the preset standard energy consumption control template, the parameters related to the energy load status of each assigned space are adjusted, and the adjustment process is encoded into a multi-dimensional instruction sequence, which is the correction control strategy.

9. A near-zero energy building whole-process energy consumption management system based on BIM and IoT, used to implement the near-zero energy building whole-process energy consumption management method based on BIM and IoT as described in any one of claims 1-8, characterized in that, include: The data acquisition module is used to collect information on building components and physical data of sensor nodes and to perform data cleaning to obtain building attribute information and high-quality sensor data, respectively. The spatial calibration module is used to perform spatial attribution calibration on high-quality sensor data based on building attribute information to obtain the attribution spatial dataset. The heat discrimination module is used to discriminate heat regions based on the spatial dataset and perform local error analysis on the heat regions to obtain temperature interference correction data. The photothermal correction module is used to correct radiation deviations in temperature interference correction data to obtain photothermal correction data. Perform communication data stream detection on photothermal correction data to screen reliable communication data; The space enhancement module is used to adjust the airflow direction attribution of trusted communication data to obtain an enhanced space dataset. The strategy generation module is used to generate modified control strategies based on the enhanced spatial dataset and send the modified control strategies to the preset energy consumption management terminal; the modules are connected to each other via wired and / or wireless means.

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