Building energy-saving monitoring method and system
By establishing VR virtual buildings in buildings, collecting and visualizing energy-saving data, and combining user behavioral habit analysis, the delay problem of judging building energy-saving situations in the existing technology is solved, and more intuitive and timely monitoring and processing is achieved, and monitoring is improved. accuracy and user differentiated services.
Patent Information
- Application Number
- CN202411181502.9
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- Filing Date
- 2024-08-27
- Publication Date
- 2024-12-13
- Estimated Expiration
- Not applicable · inactive patent
AI Technical Summary
The existing technology is difficult to quickly and accurately judge the energy savings of buildings, cannot display monitoring data intuitively and timely, and cannot conduct differentiated monitoring for different users, resulting in delayed judgment.
By obtaining the construction drawings and material data of the building, establishing VR virtual buildings, collecting energy-saving data and visualizing it, obtaining user behavior data, analyzing daily habits, monitoring energy-saving data, judging exceptions, generating exception handling solutions, and in VR virtual buildings Perform simulation processing and adjustment.
It improves the intuitiveness of building energy-saving monitoring and the timeliness of abnormal handling, reduces the influence of human factors, improves the accuracy of user habitual judgment, and optimizes the effectiveness of abnormal cause analysis and processing solutions.
Smart Images
Figure CN119128236A_ABST
Abstract
Description
Technical Field
[0001] The present application relates to the field of data processing technology, and in particular to a building energy-saving monitoring method and system. Background Art
[0002] Building energy conservation refers to the use of energy-saving materials and technologies and strengthening energy management during the planning, design, construction and use of buildings, so as to reduce building energy consumption while ensuring building energy conservation and indoor environmental quality. The so-called energy-saving buildings currently refer to buildings that are designed and constructed in accordance with national and provincial building energy conservation standards, which can significantly improve indoor comfort and reduce energy consumption such as heating and air conditioning.
[0003] In the prior art, the monitoring of the energy consumption of a building is generally carried out by acquiring data from the building's electricity meter, water meter, and gas meter, etc., to monitor the energy consumption, and then filtering, calculating, comparing, and other operations are performed on the acquired relevant energy consumption data to analyze the energy saving of the building. However, through the above method, only the vague situation of the building's energy saving can be obtained, and the specific energy saving situation of the building cannot be determined. At the same time, when the building's energy saving is abnormal, the specific location and cause of the abnormality cannot be quickly and accurately determined. In addition, due to the personal factors of the users in the building, it is impossible to perform differentiated building energy saving monitoring for different users, and the monitored data cannot be displayed to the staff intuitively and in a timely manner, resulting in a delay in the staff's judgment on the building's energy saving situation.
[0004] To this end, we propose a building energy-saving monitoring method and system to solve the above problems. Summary of the invention
[0005] The purpose of the present invention is to provide a building energy-saving monitoring method and system to solve the problems raised in the above background technology.
[0006] In the first aspect, the present application provides a building energy-saving monitoring method, which adopts the following technical solution:
[0007] Acquire construction drawings and building material data of the building, and establish a VR virtual building based on the construction drawings and the building materials;
[0008] Collecting and processing energy-saving data in the building, generating visual actions, and placing the visual actions at corresponding positions in the VR virtual building;
[0009] Obtaining behavioral data of users in the building, analyzing the behavioral data, and determining and obtaining the daily habits of each user;
[0010] Monitor the energy-saving data, determine whether there is a problem with the energy-saving data, and analyze the problem in combination with the daily habits of the user to determine whether there is an abnormality;
[0011] If an abnormality occurs, the abnormal location and abnormal phenomenon of the abnormality are obtained, the cause of the abnormality is determined according to the abnormal location and abnormal phenomenon, and an abnormality handling plan is generated;
[0012] Based on the abnormal position and abnormal phenomenon, the position is marked in the VR virtual building and the simulation phenomenon is generated, and the simulation processing is performed according to the abnormal processing scheme to obtain the processing effect, and the abnormal processing scheme is adjusted based on the processing effect.
[0013] By adopting the above technical solution, the construction drawings and building material data of the building are obtained, a VR virtual building is generated, the energy-saving data in the building is collected, and the energy-saving data is visualized, visual actions are generated, and the visual actions are placed in the VR virtual building. The behavior data of users in the building is obtained, and the daily habits of users are judged. The energy-saving data is monitored to determine whether there is a problem, and combined with the daily habits of users, it is determined whether the problem is related to the user. If not, it is judged as abnormal, the abnormal location and abnormal phenomenon are obtained, and an abnormal handling plan is generated. Simulation processing is performed in the VR virtual building, the processing effect of the abnormal handling plan is judged, and the abnormal handling plan is adjusted. The intuitiveness of building energy-saving monitoring and the timeliness of handling when abnormalities occur are improved.
[0014] Preferably, the step of collecting and processing energy-saving data in the building, generating visual actions, and placing the visual actions at corresponding positions in the VR virtual building includes:
[0015] Collecting energy-saving data in a building, the energy-saving data including energy consumption data and environmental data, and determining the data quality of the energy consumption data and the environmental data;
[0016] If the data quality is lower than a preset threshold, the energy consumption data and / or the environmental data are pre-processed;
[0017] Establish a real-time visualization model, input energy consumption data and environmental data, and the visualization model generates corresponding visual actions according to the size of the input data;
[0018] The collection position of the energy-saving data is obtained, and based on the collection position, the visual action is placed at the collection position, and the visual action is dynamically adjusted according to the change of the energy-saving data.
[0019] By adopting the above technical solution, energy-saving data in buildings are collected, including environmental data and energy consumption data, the data quality of energy consumption data and environmental data is judged, low-quality data is preprocessed, a real-time visualization model is established, data is input, visual actions corresponding to the data are generated, and each visual action is placed in the corresponding collection position in the VR virtual building, and the visual action is dynamically adjusted according to the changes in energy-saving data. The visibility of energy-saving data in buildings is improved.
[0020] Preferably, the step of obtaining the behavior data of users in the building, analyzing the behavior data, and determining and obtaining the daily habits of each user includes:
[0021] Obtaining behavior data of users in the building, sorting the behavior data into strips to generate behavior data strings, obtaining the content of the behavior data strings, and gathering behavior data strings with the same or similar content to generate a behavior data set;
[0022] Based on the different behavior data sets, feature extraction is performed on the behavior data sets to obtain the user's behavior feature vector;
[0023] Acquiring the time points corresponding to the behavior feature vectors, and sorting the behavior feature vectors based on the time points to generate a feature vector sequence;
[0024] A time series analysis is performed on the feature vector sequence to determine and obtain the user's daily behavior pattern, and the user's daily habits are inferred based on the behavior pattern.
[0025] By adopting the above technical solution, the behavior data of users in the building is obtained, the behavior data is classified and sorted, and the behavior data with the same or similar content is concentrated to generate a behavior data set, and the features in the data set are extracted to obtain the user's behavior feature vector, and a feature vector sequence is generated according to the time point of each feature vector, and the user's daily behavior pattern is judged according to the feature vector sequence, and the user's daily habits are inferred according to the user's behavior pattern. The accuracy of judging the user's daily habits is improved.
[0026] Preferably, the step of monitoring the energy-saving data, determining whether there is a problem with the energy-saving data, and analyzing the problem in combination with the daily habits of the user to determine whether there is an abnormality includes:
[0027] Monitoring the energy-saving data, obtaining a data change curve of the energy-saving data, identifying the data change curve, and determining a local change trend of the data change curve;
[0028] Based on the data change curve, the overall trend of the data change curve is obtained, the local change trend is added to the overall trend, and whether there is a sudden change;
[0029] If yes, it is determined that there is a data problem with the energy-saving data, and based on the user's daily habits, the impact of the daily habits on the energy-saving data is determined;
[0030] Based on the data impact, obtaining a first feature of the data impact and a second feature of the sudden change, and determining the degree of consistency between the first feature and the second feature;
[0031] If the degree of agreement is lower than a preset threshold, it is determined that the user's daily habits are irrelevant to the data problem, and that the energy-saving data is abnormal.
[0032] By adopting the above technical solution, the energy-saving data is monitored, the data change curve of the energy-saving data is obtained, the data change curve is analyzed, the overall trend of the curve is judged, and the curve is identified at the same time to judge the local change trend of the data change curve. Combined with the overall trend, it is judged whether there is a sudden change in the data change curve. If so, combined with the user's daily habits, it is judged whether the sudden change is caused by the user's daily habits. If not, it is judged that the energy-saving data is abnormal. The impact of human factors on the energy-saving data monitoring process is reduced.
[0033] Preferably, if an abnormality occurs, the step of obtaining the abnormal position and abnormal phenomenon where the abnormality occurs, determining the cause of the abnormality according to the abnormal position and abnormal phenomenon, and generating an abnormality handling solution includes:
[0034] Acquire abnormal energy-saving data that generates an abnormality, and determine and acquire the abnormal location and abnormal phenomenon of the abnormal energy-saving data;
[0035] Based on the abnormal position, the construction drawings and the building material data, determining the building characteristics corresponding to the abnormal position;
[0036] Based on the abnormal phenomenon and in combination with the building characteristics, determining the degree of correlation between the abnormal phenomenon and the building characteristics;
[0037] It is determined whether the correlation degree exceeds a preset threshold value. If it exceeds, the abnormal cause of the abnormality is determined in combination with the building characteristics, and an abnormality handling plan is generated based on the abnormal cause and the building characteristics.
[0038] By adopting the above technical solution, the abnormal energy-saving data that generates the abnormality is obtained, the abnormal location and abnormal phenomenon of the abnormal energy-saving data are determined, the architectural characteristics of the building at the abnormal location are obtained, and the degree of correlation between the abnormal phenomenon and the architectural characteristics is determined, and whether the degree of correlation exceeds the threshold value is determined. If it exceeds, the abnormal cause of the abnormality is determined according to the architectural characteristics, and the architectural characteristics are combined to generate an abnormality handling plan. The effectiveness of the cause analysis in the process of generating the abnormality handling plan is improved.
[0039] Preferably, the step of marking the position in the VR virtual building and generating a simulated phenomenon based on the abnormal position and the abnormal phenomenon, performing simulated processing according to the abnormal processing scheme, obtaining the processing effect, and adjusting the abnormal processing scheme based on the processing effect includes:
[0040] Based on the abnormal position and the abnormal phenomenon, the position is marked in the VR virtual building, and the abnormal phenomenon is digitized to generate phenomenon data, and a simulated phenomenon is generated based on the phenomenon data;
[0041] Placing the simulated phenomenon at an abnormal position, and determining whether the apparent value of the simulated phenomenon exceeds a preset threshold value, and if not, adding special effects to the simulated phenomenon to increase the apparent value to the threshold value;
[0042] Based on the exception handling solution, use the exception handling solution to perform simulation processing, obtain the processing result of the simulation processing, and judge the processing effect;
[0043] It is determined whether the processing effect reaches the expected value. If not, the deficiencies in the abnormality processing scheme are determined and the deficiencies are corrected based on the processing effect.
[0044] By adopting the above technical solution, according to the abnormal location and abnormal phenomenon of abnormal energy-saving data, the location is marked in the VR virtual building, and the simulation phenomenon is generated at the same time. The obvious value of the simulation phenomenon is judged. If it is not obvious, special effects are added to make the obvious value of the simulation phenomenon reach the threshold. According to the abnormal processing plan, simulation processing is performed and the processing effect is obtained. If the effect does not reach the expected value, the deficiencies in the abnormal processing plan are judged and corrected. The visibility of the simulation processing of the abnormality and the integrity of the abnormal processing plan are improved.
[0045] Preferably, after the steps of marking the position in the VR virtual building and generating a simulated phenomenon based on the abnormal position and the abnormal phenomenon, performing a simulated processing according to the abnormal processing scheme, obtaining the processing effect, and adjusting the abnormal processing scheme based on the processing effect, the method further includes:
[0046] Obtaining the distribution of energy consumption in the building, generating an energy consumption distribution map, and arranging the energy consumption distribution map in a VR virtual building;
[0047] Obtain the beneficial effects and effective time periods of energy consumption at different locations in VR virtual buildings on users;
[0048] Determining whether the beneficial effect and the effective period meet the user's habitual needs according to the user's daily habits;
[0049] If the requirements are not met or are over met, a second distribution graph of energy consumption that does not meet the user's habitual needs is obtained, and the energy consumption plan is adjusted according to the user's habitual needs.
[0050] By adopting the above technical solution, the distribution of energy consumption of the building is obtained, and an energy consumption distribution map is generated. At the same time, it is arranged in the VR virtual building, and the beneficial effects and effective time periods of energy consumption at different locations in the VR virtual building on the user are judged. In combination with the user's daily habits, the beneficial effects and effective time periods are judged to meet the user's habitual needs. If the user's habitual needs are not met or the user's habitual needs are over met, a second distribution map of energy consumption of the user's habitual needs is obtained, and the energy consumption plan is adjusted according to the second distribution map. The waste of energy in the building is reduced.
[0051] In a second aspect, the present application provides a building energy-saving monitoring system, comprising:
[0052] Construction data acquisition module: used to obtain construction drawings and building material data of the building;
[0053] Energy-saving monitoring data module: used to obtain energy-saving data and monitor the energy-saving data in real time;
[0054] Behavior data acquisition module: used to obtain the behavior data of users in the building;
[0055] Behavior data analysis module: configured to be data-connected with the behavior data acquisition module, analyze the acquired behavior data, and determine the user's behavior habits;
[0056] Abnormal analysis module: configured to be data-connected with the energy-saving data monitoring module and the behavior data analysis module, and to determine whether the energy-saving data is abnormal based on the acquired energy-saving data and the user's behavior habits;
[0057] Virtual building module: configured to be data-connected with the construction data acquisition module and the abnormality analysis module, generate a VR virtual building according to the acquired construction drawings and building material data, and generate visual actions according to the energy-saving data provided in the abnormality analysis module, and display them in the VR virtual building;
[0058] Solution generation module: configured to be connected with the abnormal analysis module data, and to generate an abnormality handling solution according to the acquired abnormal energy-saving data;
[0059] Virtual processing module: configured to be data-connected with the virtual building module and the solution generation module, perform virtual processing in the VR virtual building according to the exception processing solution provided by the solution generation module, and correct the exception processing solution according to the processing effect.
[0060] Preferably, the virtual building module includes a virtual building generation unit and a data simulation unit;
[0061] The virtual building generation unit generates a VR virtual building according to the acquired construction drawings and building material data;
[0062] The data simulation unit generates visual actions according to the acquired energy-saving data, and places the visual actions in a VR virtual building.
[0063] Preferably, the virtual processing module includes a processing unit and a scheme correction unit;
[0064] The processing unit performs simulation processing in the VR virtual building according to the acquired exception processing solution, and obtains the processing effect;
[0065] The solution correction unit determines whether the processing effect reaches the expectation. If not, it determines the deficiencies in the abnormal processing solution and corrects the abnormal processing solution.
[0066] In summary, the present application includes at least one of the following beneficial technical effects:
[0067] 1. Obtain the construction drawings and building material data of the building, generate a VR virtual building, collect energy-saving data in the building, visualize the energy-saving data, generate visual actions, place the visual actions in the VR virtual building, obtain the behavior data of users in the building, judge the daily habits of users, monitor the energy-saving data, judge whether there is a problem, and combine the daily habits of users to judge whether the problem is related to the user. If not, it is judged as abnormal, obtain the abnormal location and abnormal phenomenon, and generate an abnormal handling plan, simulate the processing in the VR virtual building, judge the processing effect of the abnormal handling plan, and adjust the abnormal handling plan. It improves the intuitiveness of building energy-saving monitoring and the timeliness of handling when abnormalities occur.
[0068] 2. Monitor energy-saving data, obtain the data change curve of energy-saving data, analyze the data change curve, determine the overall trend of the curve, identify the curve, determine the local change trend of the data change curve, and determine whether there is a sudden change in the data change curve in combination with the overall trend. If so, determine whether the sudden change is caused by the user's daily habits in combination with the user's daily habits. If not, determine that the energy-saving data is abnormal. Reduce the impact of human factors on the energy-saving data monitoring process.
[0069] 3. Obtain the abnormal energy-saving data that generates the abnormality, determine the abnormal location and abnormal phenomenon of the abnormal energy-saving data, obtain the architectural characteristics of the building at the abnormal location, and determine the degree of correlation between the abnormal phenomenon and the architectural characteristics, and determine whether the correlation degree exceeds the threshold. If it exceeds, determine the abnormal cause of the abnormality based on the architectural characteristics, and generate an abnormality handling plan based on the architectural characteristics. This improves the effectiveness of cause analysis in the process of generating abnormality handling plans. BRIEF DESCRIPTION OF THE DRAWINGS
[0070] Figure 1 It is a flow chart of steps of a building energy-saving monitoring method of the present invention;
[0071] Figure 2 It is a sub-step flow chart of steps S200 and S300 in a building energy-saving monitoring method of the present invention;
[0072] Figure 3 It is a sub-step flow chart of steps S400 and S500 in a building energy-saving monitoring method of the present invention;
[0073] Figure 4 It is a flow chart of step S600 and sub-steps after S600 in a building energy-saving monitoring method of the present invention;
[0074] Figure 5 It is a module block diagram of a building energy-saving monitoring system of the present invention.
[0075] Explanation of the accompanying drawings: 1. Construction data acquisition module; 2. Energy-saving monitoring data module; 3. Behavior data acquisition module; 4. Behavior data analysis module; 5. Abnormal analysis module; 6. Virtual building module; 7. Solution generation module; 8. Virtual processing module. DETAILED DESCRIPTION
[0076] The following is combined with Figure 1-5 The present application is further described in detail, but the embodiments of the present invention are not limited thereto.
[0077] A building energy-saving monitoring method and system are disclosed in an embodiment of the present application.
[0078] In this embodiment, refer to Figure 1 , a building energy-saving monitoring method, comprising the following steps:
[0079] S100: Obtain construction drawings and building material data of the building, and establish a VR virtual building based on the construction drawings and building materials;
[0080] S200: collecting and processing energy-saving data in the building, generating visual actions, and placing the visual actions at corresponding positions in the VR virtual building;
[0081] S300: Obtaining behavior data of users in the building, analyzing the behavior data, and determining and obtaining the daily habits of each user;
[0082] S400: monitor the energy-saving data, determine whether there is a problem with the energy-saving data, and analyze the problem in combination with the user's daily habits to determine whether there is an abnormality;
[0083] S500: If an abnormality occurs, the abnormal location and abnormal phenomenon where the abnormality occurs are obtained, the abnormal cause is determined according to the abnormal location and abnormal phenomenon, and an abnormality handling plan is generated;
[0084] S600: Based on the abnormal position and abnormal phenomenon, the position is marked in the VR virtual building and a simulated phenomenon is generated, and simulation processing is performed according to the abnormal processing solution to obtain the processing effect, and the abnormal processing solution is adjusted based on the processing effect.
[0085] It should be pointed out that the above steps are only a preferred implementation order. In the specific implementation process, some steps can be replaced without affecting the overall implementation effect.
[0086] Reference Figure 2 In step S200, the steps of collecting and processing energy-saving data in the building, generating visual actions, and placing the visual actions at corresponding positions in the VR virtual building include:
[0087] S201: Collect energy-saving data in the building, the energy-saving data includes energy consumption data and environmental data, and determine the data quality of the energy consumption data and environmental data;
[0088] S202: If the data quality is lower than a preset threshold, preprocessing the energy consumption data and / or environmental data;
[0089] S203: Establishing a real-time visualization model, inputting energy consumption data and environmental data, and the visualization model generates corresponding visual actions according to the size of the input data;
[0090] S204: Acquire the collection position of the energy-saving data, and based on the collection position, place the visual action at the collection position, and dynamically adjust the visual action according to the change of the energy-saving data.
[0091] During application, a certain building is a small six-story residential building. The energy-saving data of the residential building, including the electricity consumption data, water consumption data, air circulation data, temperature change data, etc., are obtained to determine whether the quality of the relevant data is lower than the preset threshold. If there are many places where the air circulation is unclear in the air circulation data, the air circulation data is preprocessed to remove the unclear data in the air circulation data, establish a visualization model, and input the above-mentioned electricity consumption data, water consumption data, air circulation data, and temperature change data into the visualization model to generate virtual visual actions. According to the collection locations of different data, the visual actions are placed at the corresponding positions in the VR virtual building.
[0092] Reference Figure 2 In step S300, the steps of obtaining the behavior data of users in the building, analyzing the behavior data, and determining and obtaining the daily habits of each user include:
[0093] S301: Obtaining behavior data of users in the building, sorting the behavior data into strips, generating behavior data strings, obtaining the content of the behavior data strings, and gathering behavior data strings with the same or similar content to generate a behavior data set;
[0094] S302: Based on different behavior data sets, extract features from the behavior data sets to obtain a user's behavior feature vector;
[0095] S303: Obtaining the time points corresponding to the behavior feature vectors, and sorting the behavior feature vectors based on the time points to generate a feature vector sequence;
[0096] S304: Perform time series analysis on the feature vector sequence, determine and obtain the user's daily behavior pattern, and infer the user's daily habits based on the behavior pattern.
[0097] During application, in a small residential building, the behavioral data of the residents in the building are obtained and analyzed to obtain the behavioral feature vectors of different residents, generate feature vector sequences, and judge the behavioral patterns of different users. It is found that in this residential building, some residents have very high electricity and water consumption, some residents have very low electricity and water consumption, and the remaining residents are relatively normal. In addition, some residents in the residential building like to take a walk after dinner in the evening. Based on the behavioral models of different users, the daily behavioral habits of different users are judged.
[0098] Reference Figure 3In step S400, the energy-saving data is monitored, and it is determined whether there is a problem with the energy-saving data, and the problem is analyzed in combination with the user's daily habits to determine whether there is an abnormality, including:
[0099] S401: monitoring energy-saving data, obtaining a data change curve of the energy-saving data, identifying the data change curve, and determining a local change trend of the data change curve;
[0100] S402: Based on the data change curve, obtain the overall trend of the data change curve, add the local change trend to the overall trend, and determine whether there is a sudden change;
[0101] S403: If yes, determine whether there is a data problem in the energy-saving data, and determine the impact of the daily habits on the energy-saving data based on the daily habits of the user;
[0102] S404: Based on the data influence, a first feature of the data influence and a second feature of the sudden change are obtained, and a degree of consistency between the first feature and the second feature is determined;
[0103] S405: If the degree of agreement is lower than a preset threshold, it is determined that the user's daily habits are irrelevant to the data problem, and the energy-saving data is determined to be abnormal.
[0104] During application, in a small residential building, the energy-saving data of the building was monitored, and a change curve of the energy-saving data was generated. The overall trend and local change trend of the curve were obtained. After combined analysis, it was found that there was an abnormal temperature change data. In winter, the temperature in the residential building should be maintained in a more comfortable temperature range, but it was found that the temperature in one of the households was significantly lower. However, based on the daily habits of the residents of this household, it was found that the residents of this household should be more afraid of cold, so it was judged that there was an abnormality in the insulation or heating device in the room.
[0105] Reference Figure 3 In step S500, if an abnormality occurs, the abnormal location and abnormal phenomenon where the abnormality occurs are obtained, the abnormal cause is determined according to the abnormal location and abnormal phenomenon, and the abnormality handling solution is generated, including:
[0106] S501: Acquire abnormal energy-saving data that generates an abnormality, and determine and acquire the abnormal location and abnormal phenomenon of the abnormal energy-saving data;
[0107] S502: Based on the abnormal position, the construction drawings and the building material data, determine the building characteristics corresponding to the abnormal position;
[0108] S503: Based on the abnormal phenomenon and in combination with the building characteristics, determining the degree of correlation between the abnormal phenomenon and the building characteristics;
[0109] S504: Determine whether the correlation degree exceeds a preset threshold. If so, determine the cause of the abnormality in combination with the building characteristics, and generate an abnormality handling plan based on the cause of the abnormality and the building characteristics.
[0110] During application, it was discovered that the temperature in a small residential building was abnormal in winter. It was determined that the abnormality occurred in a single room of a certain household. According to the construction drawings and building material data of the small residential building, it was found that due to insufficient funds, thermal insulation materials were not installed on the outer wall of the room, causing the indoor temperature to dissipate to the outdoors. Based on this cause, an abnormality handling plan was generated to add thermal insulation materials to the outer wall of the room.
[0111] Reference Figure 4 In step S600, based on the abnormal position and abnormal phenomenon, the position is marked in the VR virtual building and a simulated phenomenon is generated, and a simulated process is performed according to the abnormal processing scheme to obtain the processing effect, and the abnormal processing scheme is adjusted based on the processing effect, including:
[0112] S601: Based on the abnormal position and the abnormal phenomenon, the position is marked in the VR virtual building, and the abnormal phenomenon is digitized to generate phenomenon data, and a simulated phenomenon is generated based on the phenomenon data;
[0113] S602: placing the simulated phenomenon at the abnormal position, and determining whether the apparent value of the simulated phenomenon exceeds a preset threshold. If not, adding special effects to the simulated phenomenon to increase the apparent value to the threshold;
[0114] S603: Based on the exception handling solution, use the exception handling solution to perform simulation processing, obtain the processing result of the simulation processing, and judge the processing effect;
[0115] S604: Determine whether the processing effect reaches the expected value. If not, determine the deficiencies in the abnormality processing solution and correct the deficiencies based on the processing effect.
[0116] During application, it was found that the temperature in a small residential building was abnormal in winter, and an abnormality handling plan was generated. In the VR virtual building of the small residential building, the abnormality handling plan was used to simulate the exterior wall reinforcement of the room with abnormal temperature, and the temperature change in the room after the simulation was obtained. After a period of time, the temperature in the room increased, but it was still in a low state. The shortcomings of the plan were analyzed, and it was determined that the heating device was not checked in the plan. Based on the shortcoming, the abnormality handling plan was corrected and the heating device in the room was checked.
[0117] Reference Figure 4After step S600, based on the abnormal position and abnormal phenomenon, the position is marked in the VR virtual building and a simulated phenomenon is generated, and a simulated process is performed according to the abnormal processing scheme to obtain the processing effect, and the abnormal processing scheme is adjusted based on the processing effect, and the steps also include:
[0118] S611: Obtaining the distribution of energy consumption in the building, generating an energy consumption distribution map, and arranging the energy consumption distribution map in the VR virtual building;
[0119] S612: Obtaining beneficial effects and effective time periods of energy consumption at different locations in the VR virtual building on the user;
[0120] S613: judging whether the beneficial effect and effective period meet the user's habitual needs according to the user's daily habits;
[0121] S614: If the requirements are not met or are over met, a second distribution graph of energy consumption that does not meet the user's habitual needs is obtained, and the energy consumption plan is adjusted according to the user's habitual needs.
[0122] During application, in a small residential building, the distribution of energy consumption in the building is obtained, and the same arrangement is made in the VR virtual building to determine the beneficial effects and effective time periods of the energy consumption distribution for users. It is found that there are problems with the lighting design in the corridor. At night, the lights in the corridor are always on to provide light for residents to walk. According to the habits of the residents in the residential building, it is determined that there will be no pedestrians in the corridor after twelve o'clock in the evening, but the lights are still kept on, which is a waste of electricity. Based on the daily habits of the residents, a second distribution map of energy consumption is generated, and the energy consumption plan is adjusted according to the second distribution map and the user's habitual needs.
[0123] Reference Figure 5 The embodiment of the present invention provides a building energy-saving monitoring system, using any one of the above-mentioned building energy-saving monitoring methods, including:
[0124] Construction data acquisition module 1: used to obtain construction drawings and building material data of the building;
[0125] Energy-saving monitoring data module 2: used to obtain energy-saving data and monitor the energy-saving data in real time;
[0126] Behavior data acquisition module 3: used to obtain the behavior data of users in the building;
[0127] Behavior data analysis module 4: configured to be data-connected with behavior data acquisition module 3, analyze the acquired behavior data, and determine the user's behavior habits;
[0128] Abnormal analysis module 5: configured to be data-connected with energy-saving data monitoring module and behavior data analysis module 4, and to judge whether the energy-saving data is abnormal according to the acquired energy-saving data and the user's behavior habits;
[0129] Virtual building module 6: configured to be data-connected with construction data acquisition module 1 and abnormality analysis module 5, generate a VR virtual building according to the acquired construction drawings and building material data, and generate visual actions according to the energy-saving data provided in abnormality analysis module 5, and display them in the VR virtual building;
[0130] Solution generation module 7: configured to be data-connected with the abnormality analysis module 5, and to generate an abnormality handling solution according to the acquired abnormal energy-saving data;
[0131] Virtual processing module 8: configured to be data-connected with virtual building module 6 and solution generation module 7, perform virtual processing in VR virtual building according to the exception processing solution provided by solution generation module 7, and correct the exception processing solution according to the processing effect.
[0132] The above are all preferred embodiments of the present application, and the protection scope of the present application is not limited thereto. Therefore, any equivalent changes made according to the structure, shape, and principle of the present application should be included in the protection scope of the present application.
Claims
1. A building energy-saving monitoring method, It is characterized in that The following steps are involved: Acquire construction drawings and building material data of the building, and establish a VR virtual building based on the construction drawings and the building materials; Collecting and processing energy-saving data in the building, generating visual actions, and placing the visual actions at corresponding positions in the VR virtual building; Obtaining behavioral data of users in the building, analyzing the behavioral data, and determining and obtaining the daily habits of each user; Monitor the energy-saving data, determine whether there is a problem with the energy-saving data, and analyze the problem in combination with the daily habits of the user to determine whether there is an abnormality; If an abnormality occurs, the abnormal location and abnormal phenomenon of the abnormality are obtained, the cause of the abnormality is determined according to the abnormal location and abnormal phenomenon, and an abnormality handling plan is generated; Based on the abnormal position and abnormal phenomenon, the position is marked in the VR virtual building and the simulation phenomenon is generated, and the simulation processing is performed according to the abnormal processing scheme to obtain the processing effect, and the abnormal processing scheme is adjusted based on the processing effect.
2. A building energy-saving monitoring method according to claim 1, It is characterized in that The step of collecting and processing energy-saving data in the building, generating visual actions, and placing the visual actions at corresponding positions in the VR virtual building includes: Collecting energy-saving data in a building, the energy-saving data including energy consumption data and environmental data, and determining the data quality of the energy consumption data and the environmental data; If the data quality is lower than a preset threshold, the energy consumption data and / or the environmental data are pre-processed; Establish a real-time visualization model, input energy consumption data and environmental data, and the visualization model generates corresponding visual actions according to the size of the input data; The collection position of the energy-saving data is obtained, and based on the collection position, the visual action is placed at the collection position, and the visual action is dynamically adjusted according to the change of the energy-saving data.
3. According to the building energy-saving monitoring method described in claim 1, It is characterized in that The steps of obtaining the behavior data of users in the building, analyzing the behavior data, and determining and obtaining the daily habits of each user include: Acquire user data in the building, organize the behavior data into strips, generate a behavior data string, acquire the content of the behavior data string, collect the behavior data strings with the same or similar content, and generate a behavior data set; Based on the different behavior data sets, feature extraction is performed on the behavior data sets to obtain the user's behavior feature vector; Acquiring the time points corresponding to the behavior feature vectors, and sorting the behavior feature vectors based on the time points to generate a feature vector sequence; A time series analysis is performed on the feature vector sequence to determine and obtain the user's daily behavior pattern, and the user's daily habits are inferred based on the behavior pattern.
4. A building energy-saving monitoring method according to claim 1, It is characterized in that The step of monitoring the energy-saving data, determining whether there is a problem with the energy-saving data, and analyzing the problem in combination with the daily habits of the user to determine whether there is an abnormality includes: Monitoring the energy-saving data, obtaining a data change curve of the energy-saving data, identifying the data change curve, and determining a local change trend of the data change curve; Based on the data change curve, the overall trend of the data change curve is obtained, the local change trend is added to the overall trend, and whether there is a sudden change; If yes, it is determined that there is a data problem with the energy-saving data, and based on the user's daily habits, the impact of the daily habits on the energy-saving data is determined; Based on the data impact, obtaining a first feature of the data impact and a second feature of the sudden change, and determining the degree of consistency between the first feature and the second feature; If the degree of agreement is lower than a preset threshold, it is determined that the user's daily habits are irrelevant to the data problem, and that the energy-saving data is abnormal.
5. A building energy-saving monitoring method according to claim 1, It is characterized in that If an abnormality occurs, the abnormal position and abnormal phenomenon of the abnormality are obtained, the cause of the abnormality is determined according to the abnormal position and abnormal phenomenon, and the step of generating an abnormality handling solution includes: Acquire abnormal energy-saving data that generates an abnormality, and determine and acquire the abnormal location and abnormal phenomenon of the abnormal energy-saving data; Based on the abnormal position, the construction drawings and the building material data, determining the building characteristics corresponding to the abnormal position; Based on the abnormal phenomenon and in combination with the building characteristics, determining the degree of correlation between the abnormal phenomenon and the building characteristics; It is determined whether the correlation degree exceeds a preset threshold value. If it exceeds, the abnormal cause of the abnormality is determined in combination with the building characteristics, and an abnormality handling plan is generated based on the abnormal cause and the building characteristics.
6. A building energy-saving monitoring method according to claim 1, It is characterized in that The steps of marking the position in the VR virtual building and generating a simulated phenomenon based on the abnormal position and the abnormal phenomenon, performing a simulated process according to the abnormal processing scheme, obtaining a processing effect, and adjusting the abnormal processing scheme based on the processing effect include: Based on the abnormal position and the abnormal phenomenon, the position is marked in the VR virtual building, and the abnormal phenomenon is digitized to generate phenomenon data, and a simulated phenomenon is generated based on the phenomenon data; Placing the simulated phenomenon at an abnormal position, and determining whether the apparent value of the simulated phenomenon exceeds a preset threshold value, and if not, adding special effects to the simulated phenomenon to increase the apparent value to the threshold value; Based on the exception handling solution, use the exception handling solution to perform simulation processing, obtain the processing result of the simulation processing, and judge the processing effect; It is determined whether the processing effect reaches the expected value. If not, the deficiencies in the abnormality processing scheme are determined and the deficiencies are corrected based on the processing effect.
7. A building energy-saving monitoring method according to claim 1, It is characterized in that After the steps of marking the position in the VR virtual building and generating a simulated phenomenon based on the abnormal position and the abnormal phenomenon, performing a simulated process according to the abnormal processing scheme, obtaining a processing effect, and adjusting the abnormal processing scheme based on the processing effect, the method further includes: Obtaining the distribution of energy consumption in the building, generating an energy consumption distribution map, and arranging the energy consumption distribution map in a VR virtual building; Obtain the beneficial effects and effective time periods of energy consumption at different locations in VR virtual buildings on users; Determining whether the beneficial effect and the effective period meet the user's habitual needs according to the user's daily habits; If the requirements are not met or are over met, a second distribution graph of energy consumption that does not meet the user's habitual needs is obtained, and the energy consumption plan is adjusted according to the user's habitual needs.
8. A building energy-saving monitoring system, using any one of the building energy-saving monitoring methods as claimed in claims 1 to 7, It is characterized in that include: Construction data acquisition module (1): used to obtain construction drawings and building material data of the building; Energy-saving monitoring data module (2): used to obtain energy-saving data and monitor the energy-saving data in real time; Behavior data acquisition module (3): used to acquire behavior data of users in the building; Behavior data analysis module (4): configured to be data-connected to the behavior data acquisition module (3), analyze the acquired behavior data, and determine the user's behavior habits; An abnormality analysis module (5): configured to be data-connected to the energy-saving data monitoring module and the behavior data analysis module (4), and to determine whether the energy-saving data is abnormal based on the acquired energy-saving data and the user's behavior habits; A virtual building module (6): configured to be data-connected with the construction data acquisition module (1) and the abnormality analysis module (5), to generate a VR virtual building according to the acquired construction drawings and building material data, and to generate visual actions according to the energy-saving data provided in the abnormality analysis module (5), and to display them in the VR virtual building; A solution generation module (7): configured to be data-connected with the abnormality analysis module (5) and to generate an abnormality processing solution based on the acquired abnormal energy-saving data; A virtual processing module (8) is configured to be data-connected to the virtual building module (6) and the solution generation module (7), to perform virtual processing in the VR virtual building according to the abnormal processing solution provided by the solution generation module (7), and to correct the abnormal processing solution according to the processing effect.
9. A building energy-saving monitoring system according to claim 8, It is characterized in that include: The virtual building module (6) comprises a virtual building generation unit and a data simulation unit; The virtual building generation unit generates a VR virtual building according to the acquired construction drawings and building material data; The data simulation unit generates visual actions according to the acquired energy-saving data, and places the visual actions in a VR virtual building.
10. A building energy-saving monitoring system according to claim 8, It is characterized in that include: The virtual processing module (8) comprises a processing unit and a scheme correction unit; The processing unit performs simulation processing in the VR virtual building according to the acquired exception processing solution, and obtains the processing effect; The solution correction unit determines whether the processing effect reaches the expectation. If not, it determines the deficiencies in the abnormal processing solution and corrects the abnormal processing solution.