Near-zero-energy-consumption building whole-process energy consumption management and control method based on BIM and Internet of Things

Abstract

The invention belongs to the technical field of data processing, and discloses a near-zero energy consumption building whole-process energy consumption management and control method based on BIM and Internet of Things. Building attribute information and high-quality sensing data are acquired and obtained through data cleaning, and spatial affiliation calibration is performed on the high-quality sensing data to obtain an affiliation spatial data set; heat area discrimination is carried out based on the attribution space data set, local error analysis is carried out on a heat area, and temperature interference correction data are obtained; performing radiation deviation correction on the temperature interference correction data to obtain photo-thermal correction data; performing communication data flow detection on the photo-thermal correction data, and screening communication credible data; performing air flow direction attribution adjustment on the communication credible data to obtain an enhanced spatial data set; generating a correction control strategy based on the enhanced spatial data set, and sending the correction control strategy to a preset energy consumption management and control terminal; and the accuracy of energy consumption perception and the response precision of the regulation and control strategy in the building environment are improved.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of data processing, more particularly, the present application relates to a kind of near zero energy consumption building whole process energy consumption management and control method based on BIM and Internet of Things. BACKGROUND

[0002] Under the background of continuous environmental protection, building energy consumption management has become an important means of urban emission reduction and energy optimization, and near zero energy consumption building is a key application direction of building energy saving technology. With the development of BIM technology and the popularization of Internet of Things technology, it provides a more convenient method for building energy consumption management and control, and makes the building have better environmental perception ability, which provides the possibility for fine energy saving control. However, the existing traditional control method still has certain challenges for the accurate analysis and closed-loop control of building energy consumption.

[0003] In the design and deployment of actual building models, sensors based on Internet of Things are often asymmetrically laid on walls or ceilings. The collected sensor data signals are often limited by structural barriers, resulting in actual reflection of adjacent space environment data, rather than the data of the area that the sensor should reflect, and then data attribution error occurs. For example, a carbon dioxide sensor installed near a partition wall may be incorrectly attributed to the data of the room, resulting in abnormal air conditioning response. On the other hand, in the existing traditional control method, the BIM model lacks the expression of thermal conductivity characteristics and the judgment of spatial exposure at the component level, so that the weak and continuous heat loss caused by components such as high thermal conductivity external walls and external pipes cannot be identified in energy consumption management and control. For example, some heat-conducting pipes near balconies, staircases or ventilation shafts may have heat loss due to partial exposure or orientation deviation, which may not be concentrated but may exist for a long time, and may also cause significant data errors after a long time. In addition, the thermal response characteristics of window components under solar radiation may have significant deviations due to actual orientation differences, but the traditional control method may easily ignore this difference, resulting in cumulative energy consumption prediction errors. At the same time, data communication interruption may cause data changes to appear suddenly, which may easily lead to misjudgment of energy consumption management and control. Moreover, in the BIM model, near zero energy consumption buildings use multi-air duct design, and air propagation path passes through multiple rooms. The traditional control method may have difficulty in tracking the air propagation path when temperature changes are found, which may lead to misjudgment of the actual abnormal source point and may cause energy flow path to be blurred. In view of the above problems, the present application provides a near zero energy consumption building whole process energy consumption management and control method based on BIM and Internet of Things. SUMMARY

[0004] In order to overcome the above-mentioned defects of the prior art, in order to achieve the above-mentioned purpose, the present application provides the following technical scheme: a near zero energy consumption building whole process energy consumption management and control method based on BIM and Internet of Things, comprising: S1. Collecting building component information and sensor node physical data and performing data cleaning to obtain building attribute information and high-quality sensor data respectively; S2. Based on the building attribute information, calibrating the high-quality sensor data to obtain a spatial attribution data set; S3. Based on the attribution spatial data set, discriminating heat regions and performing local error analysis on the heat regions to obtain temperature interference correction data; S4. Correcting the temperature interference correction data for radiation bias to obtain light-heat correction data; detecting communication data flow on the light-heat correction data to screen communication credible data; S5. Adjusting the communication credible data for air flow attribution to obtain an enhanced spatial data set; S6. Generating a correction control strategy based on the enhanced spatial data set and sending the correction control strategy to a preset energy consumption management terminal.

[0005] Further, the space attribution calibration method comprises: Based on the building attribute information, constructing a node mapping space, taking each space component as a node of the node mapping space; obtaining a functional direction vector of the space component, calculating a spatial included angle between the functional direction vector of each space component and a surface normal of the space component, establishing a weight edge between each node based on the spatial included angle, and constructing a space component topology graph based on all nodes and weight edges; Obtaining a sensor device installation coordinate of each node in a preset neighborhood space and projecting it into the space component topology graph; calculating the shortest projection path length and projection included angle of the sensor device installation coordinate to other nodes in the preset neighborhood space and nodes in adjacent preset neighborhood spaces, constructing an attribution correlation function to calculate a correlation score based on the shortest projection path length and the projection included angle; screening a preset neighborhood space where a node with a correlation score higher than a preset correlation score threshold is located as an attribution space of the sensor device installation coordinate; if there are multiple attribution spaces, taking these attribution spaces as candidate attribution spaces, obtaining historical monitoring data of a sensor corresponding to the sensor device installation coordinate, and constructing a historical data change vector; querying corresponding data of each node in the candidate attribution space in the high-quality sensor data, and constructing a data change vector of the corresponding node; calculating the Euclidean distance between the historical data change vector and the data change vector of the node, screening nodes with a Euclidean distance higher than a behavior consistent score for matching with the sensor; and integrating all matched high-quality sensor data to obtain an attribution spatial data set.

[0006] Further, the heat region discrimination method comprises: Based on the topological connection relationship of each space component in the home space data set, the space component connected in the boundary region in each home space is identified; the material type of the space component is queried based on the building attribute information, and the space component marked as high thermal conductivity grade material is screened to form a high thermal conductivity component set; the ratio of the exposed area to the surface area of each high thermal conductivity component is calculated to obtain the exposed area ratio, and if at least one side of the high thermal conductivity component is directly in contact with the non-closed space and the exposed area ratio is higher than the preset area ratio threshold, the corresponding high thermal conductivity component is marked as an exposed component; The installation height of the exposed component in the corresponding home space is detected, and the height ratio of the installation height to the home space height is calculated; the relative connection angle of the functional direction vector of the exposed component and the adjacent connected component is calculated, and if the relative connection angle is greater than the preset deviation angle threshold and the height ratio is higher than the preset height ratio threshold, the corresponding exposed component is determined as a heat component; the region where the heat component and the adjacent connected component are located is marked as a heat region.

[0007] Further, the manner of performing local error analysis includes: The region composed of non-heat components of the same type as the heat component and adjacent non-heat connected components is set as a non-heat region; real-time temperature data of the space components of the heat region in the home space data set is obtained, non-heat temperature data of the space components in the non-heat region closest to the current heat region within the same period is collected, and a comparison temperature baseline is constructed based on the real-time temperature data and the non-heat temperature data; the difference value of each timestamp in the comparison temperature baseline is calculated, a temperature residual sequence is constructed based on all the difference values, and a sliding time window is constructed to traverse the temperature residual sequence; The baseline derivative belonging to the non-heat temperature data in the comparison 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 value of the normal temperature change rate and the mean value of the temperature residual in 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 of 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 heat adjustment value of each timestamp in the complete temperature residual sequence; the real-time temperature data of the heat component is corrected using the heat adjustment value to obtain temperature interference correction data.

[0008] Further, the manner of performing radiation deviation correction includes: The building attribute information is used to identify the outer window component, and a face normal vector of each outer window component is obtained; the temperature interference correction data is divided into time periods, and the thermal energy data of each outer window component in each time period in the temperature interference correction data is extracted, and the sun elevation angle and azimuth angle data corresponding to the same time stamp are detected, and a sun incident vector is constructed based on the sun elevation angle and azimuth angle data; the included angle cosine value of the face normal vector and the sun incident vector is calculated as a unit outer window component illumination projection value; the unit outer window component illumination projection value of each time stamp in the time period is used to perform weighted summation calculation on the preset uniform solar heat gain coefficient, and the average value is taken to generate a local radiation coefficient of the corresponding outer window component; the average value of the product of the local radiation coefficient and the thermal energy data of each time stamp in the time period of the corresponding outer window component is obtained as the light-heat radiation amount of the time period; and the light-heat radiation amount of each time period is removed from the temperature interference correction data to obtain light-heat correction data.

[0009] Further, the communication data stream detection manner comprises: The time stamps of the corresponding data of each space component in the light-heat correction data are identified, and the time interval between adjacent time stamps is calculated; a maximum sampling time interval is set, and if the time interval is greater than the maximum sampling time interval for three or more times, the time interval corresponding to the time period is marked as a suspected communication packet loss period; The historical data sequence of a preset sliding window in the historical light-heat correction data is obtained, and the average value of the historical data change rate of the historical data sequence is calculated; a data sequence composed of the corresponding data of each space component in the light-heat correction data is extracted, and the difference between the maximum data change rate and the minimum data change rate in the data sequence is calculated in the same preset sliding window; if the difference between the data change rates in the same preset sliding time window is higher than the preset multiple of the average value of the historical data change rate, and the same preset sliding time window overlaps with the communication packet loss period, the overlapping period is marked as an untrusted period; the corresponding data of the remaining periods except the untrusted period is subjected to secondary screening to obtain communication trusted data.

[0010] Further, the secondary screening manner comprises: The time interval standard deviation of the corresponding data of the remaining periods is calculated, and the period whose time interval standard deviation is lower than a preset first standard deviation threshold is determined as a high stability period; the total length of the periods except the high stability period is counted, and if the ratio of the total length of the periods to the time length of the remaining periods except the untrusted period is lower than a preset data coverage ratio, the preset first standard deviation threshold is reduced and the secondary screening is performed again, and the preset first standard deviation threshold is repeatedly adjusted until the time length ratio is greater than or equal to the preset data coverage ratio; the data corresponding to all high stability periods is integrated as communication trusted data.

[0011] Further, the manner of performing air flow attribution adjustment comprises: Obtain the air outlet type and connection relationship of each ventilation component in any one attribution space in the communication credible data, construct an air propagation path graph based on the air outlet type and connection relationship, identify the connection order of each ventilation component node in the air propagation path graph, mark the upstream node and downstream node of the ventilation component node based on the relative position of each ventilation component node in the connection order, calculate the temperature change between adjacent ventilation component nodes, construct a ventilation component temperature change sequence, and detect the timestamp of the temperature change of each ventilation component 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 the preset temperature change threshold, the downstream node is determined as a flow deviation node; backtrack all upstream nodes connected with the flow deviation node, calculate the temperature change difference and connection distance between each upstream node and the flow deviation node, and take the attribution space corresponding to the upstream node with the maximum temperature change difference and the shortest connection distance as the main contribution space; mark the attribution space corresponding to the flow deviation node as a to-be-modified space, update the space attribution of the data corresponding to the to-be-modified space to the main contribution space, and obtain an enhanced space data set.

[0012] Further, the manner of generating a modified control strategy comprises: Match the enhanced space data set with the historical control parameter interval, identify the energy load state of each attribution space, adjust the parameters related to the energy load state of each attribution space according to the preset standard energy consumption control template, and encode the adjustment process as a multi-dimensional instruction sequence, which is the modified control strategy.

[0013] A near-zero energy consumption building whole-process energy consumption control system based on BIM and Internet of Things, which is used to realize a near-zero energy consumption building whole-process energy consumption control method based on BIM and Internet of Things, characterized in that it comprises: A data acquisition module is configured to acquire building component information and sensor node physical data and perform data cleaning to obtain building attribute information and high-quality sensor data, respectively. A space calibration module is configured to calibrate the high-quality sensor data based on the building attribute information to obtain an attribution space data set. A heat discrimination module is configured to discriminate heat regions based on the attribution space data set, and perform local error analysis on the heat regions to obtain temperature interference correction data. A light-heat correction module is configured to correct the radiation deviation of the temperature interference correction data to obtain light-heat correction data, and detect communication data flow based on the light-heat correction data to screen communication credible data. A space enhancement module is configured to adjust the air flow attribution of the communication credible data to obtain an enhanced space data set. The strategy generation module is configured to generate a correction control strategy based on the reinforcement space data set and send the correction control strategy to a preset energy consumption management terminal.

[0014] The technical effect and advantages of the BIM and Internet of Things based whole-process energy consumption management method for near-zero energy consumption buildings are as follows: Based on the near-zero energy consumption building model constructed by the BIM technology, the building attribute information and high-quality sensing data are extracted, and the sensing type data are sequentially subjected to spatial attribution calibration, temperature interference correction, radiation deviation correction, traffic data flow detection and air flow adjustment, so that the data enhancement for the whole-process energy consumption management is realized, and a whole-process energy consumption management method based on the BIM and the Internet of Things is realized. Compared with the prior art, the accuracy of energy consumption perception and the response accuracy of the control strategy are improved under the building environment, the energy consumption control can be performed based on the actual component information semantics and heat transfer logic, the abnormal identification sensitivity is improved and misjudgment and miscontrol are avoided, and the rationality and stability of energy consumption scheduling are improved. BRIEF DESCRIPTION OF DRAWINGS

[0015] Figure 1 FIG. 1 is a schematic diagram of a BIM and Internet of Things based whole-process energy consumption management method for near-zero energy consumption buildings according to the present application; Figure 2 FIG. 2 is a schematic diagram of a BIM and Internet of Things based whole-process energy consumption management system for near-zero energy consumption buildings according to the present application. DETAILED DESCRIPTION

[0016] The technical solutions in the embodiments of the present application will be clearly and completely described below with reference to the drawings in the embodiments of the present application. Obviously, the described embodiments are only part of the embodiments of the present application, rather than all the embodiments. Based on the embodiments in the present application, all other embodiments obtained by those skilled in the art without creative labor fall within the scope of the present application.

[0017] Embodiment 1 Please refer to Figure 1 The BIM and Internet of Things based whole-process energy consumption management method for near-zero energy consumption buildings according to the present application includes the following steps: S1. Collecting building component information and sensing node physical data and performing data cleaning to obtain building attribute information and high-quality sensing data, respectively; S2. Based on the building attribute information, performing spatial attribution calibration on the high-quality sensing data to obtain attribution space data set; S3. Heat region discrimination is performed based on the home space data set, and local error analysis is performed on the heat region to obtain temperature interference correction data; S4. Radiative deviation correction is performed on the temperature interference correction data to obtain light-heat correction data; and communication data stream detection is performed on the light-heat correction data to screen communication credible data; S5. Air flow direction attribution adjustment is performed on the communication credible data to obtain the strengthened space data set; S6. A correction control strategy is generated based on the strengthened space data set, and the correction control strategy is sent to a preset energy consumption management terminal.

[0018] In the embodiment, the building component refers to, for example, doors and windows, pipes, lamps and other devices existing in the near-zero energy consumption building model constructed by using the BIM technology, and the building component information is the attribute data information of the building component itself, such as the geometric shape parameters, the construction material type and the spatial coordinate position of each building component extracted from the near-zero energy consumption building model; the sensor node physical data refers to the parameter information of each building component collected by the sensor simulated in the near-zero energy consumption building model, such as the temperature and the wind speed; the data cleaning is implemented by filtering and denoising and filling the missing values on the building component information and the sensor node physical data to obtain the building attribute information and the high-quality sensor data of higher quality.

[0019] The space attribution calibration method comprises the following steps: The node mapping space is constructed based on the building attribute information, and each space component is taken as a node of the node mapping space, wherein the node mapping space is a data space for storing each space component in the BIM near-zero energy consumption building model, and each space component is regarded as a node in the node mapping space; meanwhile, the space component refers to the building component installed in each room of the BIM near-zero energy consumption building model, such as doors and windows and pipes.

[0020] The functional direction vector of the space component is obtained, the spatial included angle between the functional direction vector of each space component and the surface normal of the space component is calculated, the weight edge between each node is established in combination with the spatial included angle, and the space component topology graph is constructed based on all nodes and weight edges, wherein the functional direction vector refers to the direction corresponding to the function of the space component, such as the ventilation direction of the pipe or the lighting direction of the window, and the functional direction vector of each space component is extracted from the design information of the BIM near-zero energy consumption building model; wherein the surface normal of the space component is perpendicular to the construction surface of the space component, and the formed spatial included angle is taken as the weight edge, and the smaller the value is, the more consistent the functional direction vector is with the geometric direction of the component, which reflects the functional expression strength of the space component, for example, the geometric direction of the ventilation component has a smaller included angle with the functional direction vector, which indicates that the air propagation path is blocked to a certain extent, which is not conducive to the ventilation function.

[0021] In the space component topology graph, edges are formed based on the relative positions between each space component, and the difference in the spatial angle between different nodes is taken as the weight of the edge. The smaller the weight, the more consistent the functional orientation between the two space components, and vice versa. The two space components may not belong to the same space component in the same functional orientation.

[0022] The sensor device installation coordinates of each node in the preset neighborhood space are obtained and projected into the space component topology graph. Each preset neighborhood space is constructed based on the design size information of each room in the BIM near-zero energy consumption building model, and each preset neighborhood space corresponds to a room. At the same time, a plurality of space components belonging to the same room are included in each preset neighborhood space. The specific installation position of the sensor device in the room is projected into the space component topology graph.

[0023] The shortest projection path length and the projection angle of the sensor device installation coordinates to the nodes in the preset neighborhood space and the nodes in the adjacent preset neighborhood space are calculated, and the correlation score is calculated based on the shortest projection path length and the projection angle. The shortest projection path length refers to the displacement distance from the projection point of the sensor device installation coordinates to any node in the preset neighborhood space and the adjacent preset neighborhood space. The projection angle refers to the three-dimensional spatial angle between the projection point and each node. The calculation formula of the correlation function is: ; wherein the above calculation formula is a dimensionless calculation, only numerical values are used for calculation, represents the correlation score of the sensor device installation coordinates and any node in the preset neighborhood space and the adjacent preset neighborhood space; represents the shortest projection path length; represents the cosine value of the projection angle; and respectively represent the weights of the two dimensions of the shortest projection path length and the projection angle. In this embodiment, the values of and are set based on the historical correlation function and The value range of the two weights is .

[0024] 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.

[0025] 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.

[0026] 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.

[0027] 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.

[0028] Methods for identifying 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. 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.

[0029] query the material type of the space component based on the building attribute information, and filter out the space components marked as high-thermal-conductivity-grade materials to form a high-thermal-conductivity-component set, wherein the material type of the space component corresponding to the building attribute information is extracted, and the space components marked as high-thermal-conductivity-grade materials are selected to form the set, which in this embodiment includes space components such as high-thermal-conductivity walls and pipes.

[0030] calculate the ratio of the exposed area to the surface area of each high-thermal-conductivity component to obtain an exposed area ratio, and if at least one side of the high-thermal-conductivity component is directly in contact with the non-enclosed space and the exposed area ratio is higher than a preset area ratio threshold, mark the corresponding high-thermal-conductivity component as an exposed component, wherein 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 non-enclosed space, and the surface area refers to the area of the complete surface of the high-thermal-conductivity component; the preset area ratio threshold is set based on historical area determination experience, and the components in the high-thermal-conductivity-component set whose exposed area ratio is higher than the preset area ratio threshold are filtered out as exposed components, wherein the exposed area ratio reflects the degree of passive heat exchange of the component.

[0031] detect the installation height of the exposed component in the corresponding attribution space, and calculate the height ratio of the installation height to the height of the attribution space, wherein the height of the attribution space refers to the height from the floor to the ceiling in the attribution space corresponding to the exposed component; the installation height is obtained by querying the design information of the BIM near-zero-energy building model.

[0032] calculate the relative connection angle of the functional direction vectors of the exposed component and the adjacent connecting component, and if the relative connection angle is greater than a preset deviation angle threshold and the height ratio is higher than a preset height ratio threshold, determine the corresponding exposed component as a heat component, wherein the relative connection angle of the functional direction vectors of the exposed component and the adjacent component connected thereto is calculated to reflect whether the connection path of the exposed component and the surrounding connecting component is obviously bent; the preset deviation angle threshold and the preset height ratio threshold are set based on the design information of the BIM near-zero-energy building model, and if both the relative connection angle is greater than the preset deviation angle threshold and the height ratio is higher than the preset height ratio threshold, the corresponding exposed component is determined as a heat component.

[0033] mark the area where the heat component and the adjacent connecting component are located as a heat area, wherein the heat area refers to the area formed by the heat component and the adjacent connecting component directly connected thereto.

[0034] The manner of performing local error analysis includes: The region formed by the same type of non-heat components as the heat components and the adjacent non-heat connecting components is set as a non-heat region, wherein the non-heat components refer to the components of the same type as the heat components but cannot meet the standards of the heat components in terms of the exposed area ratio, the relative connecting angle and the height ratio; the non-heat region refers to the region formed by the non-heat components and the adjacent non-heat connecting components directly connected with the components.

[0035] Real-time temperature data of the space components in the heat region in the home space data set is acquired, non-heat temperature data of the space components in the non-heat region closest to the current heat region in the same time period is collected, and a comparison temperature baseline is constructed based on the real-time temperature data and the non-heat temperature data, wherein the relevant data of each space component in the non-heat region closest to the current heat region in displacement distance in the same time period is obtained as the non-heat temperature data through time consistency constraint; and the change curves of the real-time temperature data and the non-heat temperature data are drawn respectively to construct the comparison temperature baseline.

[0036] The difference value of each time stamp in the comparison temperature baseline is calculated, a temperature residual sequence is constructed based on all the difference values, and a sliding time window is constructed to traverse the temperature residual sequence, wherein the temperature residual sequence is obtained by calculating the difference between the real-time temperature data of the heat components and the non-heat temperature data of the non-heat components for each time stamp; and each temperature residual sequence corresponds to the temperature difference change between the heat components of a heat region and the non-heat components of any non-heat region, and the size of the sliding time window is set based on historical experience to traverse the sequence.

[0037] The baseline derivative belonging to the non-heat temperature data in the comparison temperature baseline corresponding to each sliding time window is calculated to obtain the normal temperature change rate, wherein the normal temperature change rate is obtained by deriving the baseline of the non-heat temperature data in the comparison temperature baseline corresponding to the time period of each sliding time window.

[0038] A local difference function is constructed based on the mean value of the normal temperature change rate and the mean value of the temperature residual in the same sliding time window, wherein the calculation formula of the local difference function is: ; wherein the above calculation formula is a dimensionless calculation, and only numerical values are used for calculation; represents the temperature trend value; represents the window size of the corresponding sliding time window; represents the normal temperature change rate in the time period of the corresponding sliding time window; represents the mean value of the temperature residual in the time period of the same sliding time window.

[0039] The temperature trend value corresponding to the sliding time window is calculated by using the local difference function, and the value of each sliding time window in the temperature residual sequence is subtracted from the temperature trend value corresponding to the sliding time window to obtain the heat adjustment value of each timestamp in the complete temperature residual sequence, wherein the heat adjustment value of each timestamp in the complete temperature residual sequence is obtained by calculating the difference between the value of the temperature residual sequence and the temperature trend value in the period of each sliding time window.

[0040] The real-time temperature data of the heat component is corrected by using the heat adjustment value to obtain temperature interference correction data, wherein the heat adjustment value includes both negative and positive numbers, and the heat adjustment value is added to the real-time temperature data of the heat component. The adjusted data is the temperature interference correction data.

[0041] The radiation deviation correction method includes: The outer window component is identified based on the building attribute information, and the surface normal vector of each outer window component is obtained, wherein the outer window component is identified based on the information field of each space component in the building attribute information, and the surface normal vector of the component surface of each outer window component is extracted for subsequent calculation.

[0042] The temperature interference correction data is divided into time periods, the thermal energy data of each outer window component in each time period of the temperature interference correction data is extracted, and the sun incident vector is constructed based on the sun elevation angle and azimuth angle data, wherein the timestamps of the temperature interference correction data are divided into time periods based on the knowledge in the meteorological field, each time period corresponds to a different sun illumination stage; the sun elevation angle and azimuth angle corresponding to each timestamp are detected, the sun elevation angle and azimuth angle are represented by using the trigonometric function of the mathematical method, and are spliced into a vector representation to obtain the sun incident vector.

[0043] The cosine value of the included angle between the surface normal vector and the sun incident vector is calculated as the unit outer window component illumination projection value, wherein the illumination projection value of each outer window component is obtained by calculating the cosine value of the included angle between the surface normal vector and the sun incident vector, and the larger the value is, the more perpendicular the sun light is to the outer window component.

[0044] The preset uniform solar heat gain coefficient is weighted and summed by using the unit outer window component illumination projection value of each timestamp in the time period, and the average value is taken to generate the local radiation coefficient corresponding to the outer window component, wherein the preset uniform solar heat gain coefficient refers to the default value used in the building thermal energy simulation process, that is, SHGC; the product of the unit outer window component illumination projection value of all timestamps in a certain time period and the preset uniform solar heat gain coefficient is calculated, and the sum is taken to obtain the local radiation coefficient of the outer window component corresponding to the time period.

[0045] The average value of the product of the local radiation coefficient and the thermal energy data of each time stamp in the time period of the corresponding external window member is calculated to obtain the amount of light-heat radiation in the time period, wherein the thermal energy data refers to the real-time collected radiation heat data in the temperature interference correction data, and the product of the local radiation coefficient and the thermal energy data of each time stamp in the corresponding time period is multiplied to obtain the average value of the product in the time period, which is used to reflect the heat increment formed by solar radiation in the corresponding time period.

[0046] The light-heat radiation amount in each time period is removed from the temperature interference correction data to obtain light-heat correction data, wherein the influence of the heat generated by sunlight on the temperature regulation collection data is eliminated by removing the light-heat radiation amount in each time period from the temperature interference correction data.

[0047] The communication data stream detection method comprises the following steps: The time stamps of the corresponding data of each space member in the light-heat correction data are identified, and the time interval between adjacent time stamps is calculated, wherein the time interval between adjacent time stamps is calculated by calculating the difference between each pair of corresponding data of each space member in the light-heat correction data, which is used as a basis for subsequent judgment.

[0048] A maximum sampling time interval is set, and if there are three or more consecutive time intervals greater than the maximum sampling time interval, the time section corresponding to the consecutive time intervals is marked as a suspected communication packet loss period, wherein the maximum sampling time interval is set based on historical BIM near-zero energy building model design experience, and in this embodiment, three or more consecutive time intervals greater than the maximum sampling time interval are used as the basis for judgment, and the corresponding time section is marked as a communication interruption area, i.e. a communication packet loss period.

[0049] The historical data sequence of a preset sliding window in the historical light-heat correction data is obtained, and the average value of the historical data change rate of the historical data sequence is calculated, wherein the historical data sequence includes the historical data corresponding to each space member in the historical light-heat correction data, and the average value of the historical data change rate of the historical data sequence corresponding to each space member is calculated, which is used to reflect the typical change of the corresponding dimension data.

[0050] A data sequence is extracted from the corresponding data of each space member in the light-heat correction data, and the difference between the maximum data change rate and the minimum data change rate in the data sequence is calculated by constructing the same preset sliding window, wherein the same sliding window is a sliding window with the same size as the preset sliding window in the historical light-heat correction data, and the difference between the change rate extreme values of the corresponding data of each space member in the light-heat correction data in the sliding window is calculated, which reflects the degree of change of the data in the sliding window period.

[0051] If the difference of the data change rate in the same preset sliding time window is higher than the preset multiple of the average value of the historical data change rate, and the same preset sliding time window overlaps with the communication packet loss period, the overlapping period is marked as an untrusted period. If the extreme difference of the data change rate in the same preset sliding window is higher than the preset multiple of the average value of the historical data change rate of the corresponding component in the historical data, and at the same time overlaps with the communication packet loss period, it is considered that the period of the overlapping part exists data discontinuity, and is marked as an untrusted period. The preset multiple refers to the multiple set based on historical sampling experience, which is used as the boundary for identifying data jump anomalies to prevent the system from identifying data mutation due to wide communication intervals.

[0052] The corresponding data of the remaining periods except the untrusted period is subjected to secondary screening to obtain communication trusted data. In the embodiment, the untrusted period is ultimately a small number of periods, so excluding the data of the untrusted period will not affect the integrity of the overall data. The remaining periods are subjected to secondary screening to ensure that the screened data has high consistency in time granularity and fluctuation degree, thereby obtaining communication trusted data.

[0053] The secondary screening method includes: The time interval standard deviation of the corresponding data of the remaining periods is calculated, and the period with a time interval standard deviation lower than a preset first standard deviation threshold is determined as a high stability period. The preset first standard deviation threshold is set based on historical screening experience. The standard deviation of all time intervals of the corresponding data of each period in the remaining periods is calculated. The smaller the value, the smaller the overall distribution fluctuation of adjacent time intervals, and it is more likely to be considered in a communication stable state. Therefore, the period meeting the condition is set as a high stability period.

[0054] The total length of the periods except the high stability period is counted. If the ratio of the total length of the periods to the time length of the remaining periods except the untrusted period is lower than a preset data coverage ratio, the preset first standard deviation threshold is reduced and the secondary screening is performed again. The preset first standard deviation threshold is repeatedly adjusted 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 periods except the high stability period in the above remaining periods to the time length of the above remaining periods and comparing it with the preset data coverage ratio, the situation of insufficient data coverage caused by screening is avoided. If the time length ratio is lower than the preset data coverage ratio, the preset first standard deviation threshold is adjusted step by step and the secondary screening is performed again. The process is repeated until the time length ratio is greater than or equal to the preset data coverage ratio, so as not to lose too much sample data.

[0055] Integrate all high-stability period data into communication trusted data, in this embodiment, communication trusted data is obtained by integrating all high-stability period data after elimination, and the data corresponding to the eliminated time stamp is predicted and filled by using the machine learning model trained based on historical normal data as corpus.

[0056] The manner of air flow attribution adjustment includes: Obtain the air outlet type and connection relationship of each ventilation component in any attribution space in the communication trusted data, and construct an air propagation path graph based on the air outlet type and connection relationship, wherein the ventilation components in any attribution space in the communication trusted data and the connection topological relationship between the air outlet and the pipeline are identified based on the design information of the BIM near-zero energy consumption building model, and the air outlet type includes, for example, air supply outlet, return air outlet and exhaust air outlet, etc., and the pipeline in these ventilation components is used as a directed edge to construct the air propagation path graph.

[0057] Identify the connection order of each ventilation component node in the air propagation path graph, and mark the upstream node and the downstream node of the ventilation component node based on the relative position of each ventilation component node in the connection order, wherein for each ventilation component node, the node before the connection order is the upstream node, and the node after the connection order is the downstream node.

[0058] Calculate the temperature change amount between adjacent ventilation component nodes, construct a ventilation component temperature change sequence, and detect the time stamp of the temperature change of each ventilation component node, wherein the temperature change amount is sorted according to the connection order between several ventilation component nodes to form the temperature change sequence, and the time stamp corresponding to each temperature change amount in the temperature change sequence is extracted for subsequent judgment.

[0059] If the time stamp of the temperature change in the downstream node is earlier than that in the upstream node, and the temperature change amount is higher than the preset temperature change threshold, the downstream node is determined as a flow deviation node, wherein if the time stamp of the downstream node is earlier than that of the upstream node, and the temperature change amount is higher than the preset temperature change threshold set based on the BIM near-zero energy consumption building design constraint information, it means that the information transmission at this time does not conform to the original design information, and there may be signal errors or air backflow abnormalities, therefore the downstream node in the relative position is taken as the flow deviation node.

[0060] Backtracking all upstream nodes connected with the flow deviation node, respectively calculating the temperature change difference and connection distance between each upstream node and the flow deviation node, taking the attribution space of the upstream node corresponding to the maximum temperature change difference and the shortest connection distance as the main contribution space, wherein backtracking all nodes connected with the flow deviation node means backtracking all continuously connected upstream nodes in the relative position and reaching the flow deviation node; by respectively 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 maximum temperature influence and the shortest path is preferentially selected as the source point of the dominant effect on the abnormal change, and the attribution space to which the source point belongs is taken as the main contribution space for representing the actual data source position leading to the flow deviation.

[0061] The attribution space corresponding to the flow deviation node is marked as a to-be-corrected space, the space attribution of the data corresponding to the to-be-corrected space is updated to the main contribution space, and a reinforced space data set is obtained, wherein the original attribution space of the flow deviation node is marked as a to-be-corrected space because it is detected that it is not the actual source point of the current temperature change; by updating the attribution labels of all related data to the currently identified main contribution space, the migration of related sensing data from the physical location to the attribution area of the air energy dominant area is realized, and finally the reinforced space data set reflecting the real air heat transfer behavior is obtained.

[0062] The way of generating the correction control strategy includes: The reinforced space data set is matched with the historical control parameter interval to identify the energy load state of each attribution space, wherein the reinforced space data set is high-quality energy consumption characteristic data after the whole process of space attribution calibration, temperature anomaly calibration, light-heat interference exclusion and air flow adjustment, and fuses multi-dimensional information such as space attribution, temperature, air propagation and radiation; by matching the reinforced space data set with the historical control parameter interval in the historical record, and combining the design information of the BIM near-zero energy consumption building model itself, it is identified that each dimension data in the reinforced space data set is in what energy load state.

[0063] The energy load state related parameters of each attribution space are adjusted according to the preset standard energy consumption control template, and the adjustment process is coded as a multi-dimensional instruction sequence, that is, the correction control strategy, wherein the parameters of the related space components in each attribution space are adjusted from the energy load state to the standard state by calling the preset standard energy consumption control template for historical control, and the parameter adjustment process is coded as an executable multi-dimensional instruction sequence to form an implementable and deployable correction control strategy.

[0064] The embodiment takes the near zero energy consumption building model constructed by the BIM technology as a data basis, extracts building attribute information and high-quality sensing data, and sequentially performs space attribution calibration, temperature interference correction, radiation deviation correction, communication data flow detection and air flow direction adjustment on the sensing type data, realizes data enhancement for whole-process energy consumption control, and realizes a whole-process energy consumption control method based on BIM and the Internet of Things; compared with the existing experience, the accuracy of energy consumption perception and the response accuracy of control strategies are improved under the building environment, the energy consumption control can be performed based on actual component information semantics and heat transfer logic, the abnormal identification sensitivity is improved and misjudgment and miscontrol are avoided, and the rationality and stability of energy consumption scheduling are improved; for the near zero energy consumption building, unnecessary energy consumption redundancy is effectively reduced, and the energy use efficiency is improved, and the response adaptability and deployment fault tolerance are higher in the residential building, office building or complex structure shielding with multiple rooms sharing the air duct.

[0065] Embodiment 2 Please refer to Figure 2 The embodiment does not describe some parts in detail, and the embodiment 1 is described, and a whole-process energy consumption control system of a near zero energy consumption building based on BIM and the Internet of Things is provided, comprising: A data acquisition module is configured to acquire building component information and sensing node physical data and perform data cleaning to obtain building attribute information and high-quality sensing data, respectively. A space calibration module is configured to perform space attribution calibration on the high-quality sensing data based on the building attribute information to obtain attribution space data set. A heat discrimination module is configured to perform heat region discrimination based on the attribution space data set, and perform local error analysis on the heat region to obtain temperature interference correction data. A light-heat correction module is configured to perform radiation deviation correction on the temperature interference correction data to obtain light-heat correction data, and perform communication data flow detection on the light-heat correction data to screen communication credible data. A space strengthening module is configured to perform air flow direction attribution adjustment on the communication credible data to obtain strengthened space data set. A strategy generation module is configured to generate a correction control strategy based on the strengthened space data set, and send the correction control strategy to a preset energy consumption control terminal. The modules are connected by wired and / or wireless modes.

[0066] The above only describes the preferred embodiments of the present application and is not used to limit the present application, although the present application is described in detail with reference to the foregoing embodiments, the technical solutions recorded in the foregoing embodiments can be modified or some technical features can be replaced by equivalents for those skilled in the art. Any modification, equivalent replacement, improvement, etc. made within the spirit and principles of the present application shall be included in the protection scope of the present application.

[0067] The formula of the present specification is a value calculated by dimensionless, the formula is obtained by collecting a large amount of data to simulate a formula of the most recent real situation, and the preset parameters and threshold values in the formula are set by a person skilled in the art according to the actual situation.

[0068] Although the embodiments of the present application have been shown and described, those of ordinary skill in the art can understand that various changes, modifications, replacements and variations can be made to the embodiments without departing from the principles and purposes of the present application, and the scope of the present application 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 the photothermal correction data to screen reliable communication data; S5. Adjust the airflow direction attribution of the trusted communication data to obtain an enhanced spatial 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 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.

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 adjusting the airflow direction include: The process involves: acquiring the vent types and connections of each ventilation component in any home space within the 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.

9. A method for full-process energy consumption management of near-zero energy buildings based on BIM and IoT as described in claim 8, 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.

10. 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 1-9, 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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