Cloud platform-based green building adaptive control system and method

Abstract

The application belongs to the technical field of noise reduction control. A green building adaptive control system and method based on a cloud platform are provided. The method comprises: receiving a noise reduction request signal from a first indoor area of a target building, obtaining noise detection signals of a plurality of noise detectors of the target building according to the noise reduction request signal; determining the position information of a noise source according to the noise detection signals, generating a first noise reduction scheme for the first indoor area and a second noise reduction scheme for a second indoor area having a correlation with the first indoor area according to the position information of the noise source; adjusting the first state of sound insulation facilities in the first indoor area based on the first noise reduction scheme, and adjusting the second state of sound insulation facilities in the second indoor area based on the second noise reduction scheme, so that the noise level in the first indoor area is reduced to below the preset noise level. The application can automatically perform noise reduction processing on the indoor area in the building, thereby improving the living and office environment.

Description

TECHNICAL FIELD

[0001] The present application relates to the field of noise reduction control, in particular to a green building adaptive control system and method based on a cloud platform. BACKGROUND

[0002] Under the background of accelerating urbanization and the development of intelligent buildings, the construction scale of intelligent buildings (or green buildings) is increasing. However, noise has become one of the key factors affecting the quality of life, work and study of personnel in intelligent buildings. Traditional noise control methods for intelligent buildings are mostly passive and fixed, such as installing fixed sound insulation materials or sound insulation facilities, which cannot be dynamically adjusted according to real-time noise conditions and personnel activities, environmental changes and other factors in the building.

[0003] Therefore, how to effectively reduce the noise level in the building to provide users with a quieter and healthier living and working environment is a technical problem that needs to be solved at present. SUMMARY

[0004] To this end, the present application provides a green building adaptive control method, system, electronic device, computer storage medium and computer program product based on a cloud platform to solve the above technical problems.

[0005] The present application provides a green building adaptive control method based on a cloud platform, the method comprising: receiving a noise reduction request signal from a first indoor area of a target building, and obtaining noise detection signals of a plurality of noise detectors of the target building according to the noise reduction request signal; determining the position information of the noise source according to the noise detection signal, and generating a first noise reduction scheme for the first indoor area and a second noise reduction scheme for a second indoor area having a correlation with the first indoor area according to the position information of the noise source; adjusting the first state of the sound insulation facility in the first indoor area based on the first noise reduction scheme, and adjusting the second state of the sound insulation facility in the second indoor area based on the second noise reduction scheme, so that the noise level in the first indoor area is reduced to below the preset noise level.

[0006] Optionally, before the noise detection signals of the plurality of noise detectors of the target building are obtained according to the noise reduction request signal, the method further comprises: analyzing a plurality of sets of noise monitoring data of the target building by using a machine learning model, and analyzing the future trend of the noise pattern of the target building according to the noise monitoring data, i.e. predicting the expected maximum noise level of the target building in the current period; when the expected maximum noise level exceeds the noise level threshold, a trigger instruction is generated, which is used to respond to the noise reduction request signal.

[0007] Optionally, the method further comprises: determining a facing relationship between the first indoor area and the noise source according to the position information of the noise source and an indoor area distribution map of each floor of the target building, the facing relationship including front-facing, barrier-communicating facing, and back non-communicating facing; wherein the indoor area distribution map at least includes relative position relationships between a communicating area of the first indoor area and other indoor areas and an external environment of the target building, the communicating area including a door area and a window area; if the facing relationship between the first indoor area and the noise source is front-facing or back non-communicating facing, generating a first noise reduction scheme for the first indoor area according to the position information of the noise source, including: generating a first noise reduction scheme for the first indoor area according to a first noise intensity in the first indoor area.

[0008] Optionally, if the facing relationship between the first indoor area and the noise source is barrier-communicating facing, generating a second noise reduction scheme for a second indoor area having relevance with the first indoor area according to the position information of the noise source, including: generating a first noise reduction scheme for the first indoor area according to a first noise intensity in the first indoor area, executing the first noise reduction scheme, and monitoring whether the first noise reduction scheme reduces the noise level in the first indoor area to below a preset noise level; if yes, not generating a second noise reduction scheme for the second indoor area having relevance with the first indoor area; if no, generating a second noise reduction scheme for the second indoor area according to a second noise intensity in the second indoor area, executing the second noise reduction scheme, and monitoring whether the second noise reduction scheme reduces the noise level in the first indoor area to below the preset noise level; if no, continuing to generate a third noise reduction scheme for the second indoor area in a step-by-step enhancement manner until the third noise reduction scheme reduces the noise level in the first indoor area to below the preset noise level.

[0009] Optionally, the continuing to generate a third noise reduction scheme for the second indoor area in a step-by-step enhancement manner includes: extracting a use type of the second indoor area from the indoor area distribution map, the use type including a personnel presence area and a personnel non-presence area; determining a level span amplitude according to the use type; and continuing to generate a third noise reduction scheme for the second indoor area according to the level span amplitude; wherein the level span amplitude is used to adjust a span amplitude of a noise reduction level of the third noise reduction scheme relative to a noise reduction level of a previously generated noise reduction scheme, and the level span amplitude corresponding to the personnel presence area is lower than the level span amplitude corresponding to the personnel non-presence area.

[0010] The application further discloses a green building self-adaptive control system based on a cloud platform, which comprises a receiving module, a noise reduction scheme generation module and a noise reduction scheme execution module; the receiving module is used for receiving a noise reduction request signal from a first indoor area of a target building, and obtaining noise detection signals of a plurality of noise detectors of the target building according to the noise reduction request signal; the noise reduction scheme generation module is used for determining orientation information of a noise source according to the noise detection signals, and generating a first noise reduction scheme for the first indoor area and a second noise reduction scheme for a second indoor area having relevance with the first indoor area according to the orientation information of the noise source; and the noise reduction scheme execution module is used for adjusting a first state of sound insulation facilities in the first indoor area based on the first noise reduction scheme, and adjusting a second state of sound insulation facilities in the second indoor area based on the second noise reduction scheme, so that the noise level in the first indoor area is reduced to below a preset noise level.

[0011] Optionally, the receiving module is further used for analyzing a plurality of groups of noise monitoring data of the target building by using a machine learning model, and obtaining a future trend of a noise mode of the target building according to the noise monitoring data, i.e., predicting an expected maximum noise level of the target building in a current period; and when the expected maximum noise level exceeds a noise level threshold, a trigger instruction is generated, which is used for responding to the noise reduction request signal.

[0012] Optionally, the noise reduction scheme generation module is specifically used for determining a facing relationship between the first indoor area and the noise source according to the orientation information of the noise source and an indoor area distribution map of each floor of the target building, wherein the facing relationship comprises front-facing, barrier-communicating facing and back non-communicating facing; the indoor area distribution map at least comprises relative position relationships of a communicating area of the first indoor area, an external environment of the target building and other indoor areas, and the communicating area comprises a door area and a window area; if the facing relationship between the first indoor area and the noise source is front-facing or back non-communicating facing, the first noise reduction scheme for the first indoor area is generated according to the orientation information of the noise source, which comprises generating the first noise reduction scheme for the first indoor area according to a first noise intensity in the first indoor area.

[0013] Optionally, the noise reduction scheme generation module is further configured to: generate a first noise reduction scheme for the first indoor area according to a first noise intensity in the first indoor area, execute the first noise reduction scheme, and monitor whether the first noise reduction scheme reduces the noise level in the first indoor area to below a preset noise level; if yes, not generate a second noise reduction scheme for a second indoor area having a correlation with the first indoor area; if no, generate a second noise reduction scheme for the second indoor area according to a second noise intensity in the second indoor area, execute the second noise reduction scheme, and monitor whether the second noise reduction scheme reduces the noise level in the first indoor area to below the preset noise level; if no, continue to generate a third noise reduction scheme for the second indoor area in a stepwise enhancement manner until the third noise reduction scheme reduces the noise level in the first indoor area to below the preset noise level.

[0014] Optionally, the noise reduction scheme generation module is further configured to: obtain a use type of the second indoor area from an indoor area distribution map, the use type including a personnel presence area and a personnel non-presence area; determine a level span amplitude according to the use type, and continue to generate a third noise reduction scheme for the second indoor area according to the level span amplitude; wherein the level span amplitude is used to adjust a span amplitude of a noise reduction level of the third noise reduction scheme relative to a noise reduction level of a previously generated noise reduction scheme, and the level span amplitude corresponding to the personnel presence area is lower than the level span amplitude corresponding to the personnel non-presence area.

[0015] The application further discloses an electronic device, comprising: at least one processor, a memory, and a computer program stored in the memory, wherein the computer program runs on the at least one processor to implement the method according to any one of the preceding.

[0016] The application further discloses a computer storage medium, wherein the computer readable storage medium stores a computer program, and the computer program runs on a processor to implement the method according to any one of the preceding.

[0017] The application further discloses a computer program product, wherein the computer program contains a plurality of computer codes, and the computer codes run on a processor to implement the method according to any one of the preceding.

[0018] Distinguish from the passive and fixed noise control means (such as installing fixed sound insulation material) in the traditional intelligent building, on the one hand, the noise reduction scheme can be dynamically generated and executed according to the real-time noise situation, environmental changes and other factors, and the adaptive control of the noise in the building is realized;On the other hand, the scheme can also dynamically determine the indoor area that should be regulated according to the position information of the noise source, and then achieve better noise regulation level. BRIEF DESCRIPTION OF DRAWINGS

[0019] In order to more clearly illustrate the technical solutions of the embodiments of the present application, the drawings needed in the embodiments will be briefly introduced as follows, and it should be understood that the following drawings only show some embodiments of the present application, and therefore should not be regarded as a limitation on the scope, and for those skilled in the art, other related drawings can also be obtained without creative labor on the basis of these drawings.

[0020] Figure 1 is a flow diagram of a green building adaptive control method based on a cloud platform disclosed by the embodiments of the present application.

[0021] Figure 2 is a schematic diagram of the indoor area distribution of a certain floor of a target building disclosed by the embodiments of the present application.

[0022] Figure 3 is a structural schematic diagram of a green building adaptive control system based on a cloud platform disclosed by the embodiments of the present application. DETAILED DESCRIPTION

[0023] The embodiments of the present application will be described below by specific specific embodiments, and those skilled in the art can easily understand other advantages and effects of the present application from the contents disclosed in the specification. Obviously, the described embodiments are part of the embodiments of the present application, not all the embodiments. Based on the embodiments in the present application, all other embodiments obtained by those skilled in the art without creative labor are within the scope of protection of the present application.

[0024] In addition, the technical features involved in different embodiments of the present application described below can be combined with each other as long as there is no conflict between them.

[0025] As Figure 1 shown, the embodiments of the present application disclose a green building adaptive control method based on a cloud platform, the method comprises: S101, receiving a noise reduction request signal from a first indoor area of a target building, and obtaining noise detection signals of a plurality of noise detectors of the target building according to the noise reduction request signal.

[0026] In the key area of the intelligent building, high-precision noise detectors are deployed, and based on these noise detectors, the noise data outside and inside the intelligent building (i.e., green building) can be collected in real time; at the same time, the obtained noise detection signals can be uploaded to the cloud platform through a wireless communication protocol, for example, the cloud platform connects various noise detectors using Wi-Fi, Zigbee or other low-power wide-area network technologies, and combines the use of encryption technology and access control strategies to protect the integrity and privacy of noise data during transmission.

[0027] When the noise control equipment (such as the controller of the smart home system) in the first indoor area (such as an office, a resident room, etc.) of a target building detects that the noise level exceeds the threshold, or the personnel in the first indoor area feel noise interference, a noise reduction request signal is generated automatically or manually (for example, pressing a special noise reduction button, sending a request through a mobile phone APP or voice, etc.) by the controller, and the noise reduction request signal is transmitted to the cloud platform. After the cloud platform receives the above request signal, it obtains the noise data detected by the multiple noise detectors distributed in the target building.

[0028] For example, in the 10th floor 01 office (i.e., the first indoor area) of a smart office building, the employees are conducting an important video conference, but the surrounding noise affects the conference effect, and the employees send a noise reduction request signal through the smart control panel in the office. After the cloud platform receives the signal, it immediately obtains the noise detection signals detected by the noise detectors distributed in the corridors, elevator rooms, other offices, etc. of the office building.

[0029] S102, determining the position information of the noise source according to the noise detection signal, and generating a first noise reduction scheme for the first indoor area and a second noise reduction scheme for a second indoor area having relevance with the first indoor area according to the position information of the noise source.

[0030] After the cloud platform receives the noise detection signals from the multiple noise detectors, it analyzes and calculates the specific position of the noise source (such as from the next room, the corridor, the outdoor, etc.) based on at least the intensity of each noise detection signal. Generally speaking, the closer the noise detector is to the noise source, the greater the first noise intensity detected by the noise detector. By comparing the first noise intensity detected by each noise detector, the position information of the noise source can be preliminarily determined, and then a corresponding noise reduction scheme can be developed according to the position information of the noise source.

[0031] If the noise source is directly related to the first indoor area, for example, the noise source is located outside the door or window of the first indoor area, such as workers are conducting construction in the corridor outside the window of the first indoor area, or a lawn mower is trimming the lawn outside the window of the first indoor area, etc. At this time, a first noise reduction scheme for the first indoor area is generated.

[0032] If the noise source indirectly affects the noise level in the first indoor area through a second indoor area, i.e., the second indoor area is associated with the first indoor area, a second noise reduction scheme for the second indoor area associated with the first indoor area is generated, so that the noise level in the first indoor area can be indirectly reduced by adjusting the state of the sound insulation facility in the second indoor area. The second indoor area is, for example, a neighboring room of the first indoor area. In addition, the above-mentioned first noise reduction scheme and the second noise reduction scheme can be generated and used at the same time.

[0033] S103, adjusting the first state of the sound insulation facility in the first indoor area based on the first noise reduction scheme and adjusting the second state of the sound insulation facility in the second indoor area based on the second noise reduction scheme, so that the noise level in the first indoor area is reduced to below the preset noise level.

[0034] The cloud platform feeds back the generated noise reduction scheme to the smart home system controller in the first indoor area and / or the second indoor area, to trigger the state adjustment of the sound insulation facility in the corresponding indoor area. If it is the first noise reduction scheme, the state of the sound insulation facility in the first indoor area is adjusted; if it is the second noise reduction scheme, the state of the sound insulation facility in the second indoor area is adjusted. Through such adjustment, the ultimate goal is to reduce the noise level in the first indoor area to below the pre-set acceptable noise level, to provide a quieter environment for the user.

[0035] The above-mentioned sound insulation facility includes, but is not limited to, an electric curtain, a smart door and window, and other special sound insulation facilities (such as auxiliary door and window facilities using sound insulation panels) in the corresponding indoor area, for example, by controlling the electric curtain, the smart door and window to be closed, or controlling the auxiliary door and window facilities using sound insulation panels to be closed, or using active noise reduction equipment (generating white noise sound waves with opposite phase to noise sound waves, so that the two are superimposed, thereby reducing the amplitude of noise), to improve the sound insulation and noise reduction capability of the corresponding indoor area, and thus reduce the noise level in the corresponding indoor area.

[0036] Unlike the passive and fixed noise control means (such as fixed sound insulation materials) in traditional smart buildings, on the one hand, the noise reduction scheme of the present application can be dynamically generated and executed according to real-time noise conditions, environmental changes and other factors, realizing adaptive control of the noise in the building; on the other hand, the scheme of the present application can also dynamically determine the indoor area that should be regulated according to the position information of the noise source, and thus achieve a better noise regulation level.

[0037] Optionally, before the noise detection signals of the noise detectors in the target building are acquired according to the noise reduction request signal, the method further comprises: analyzing a plurality of sets of noise monitoring data of the target building by using a machine learning model, and analyzing a future trend of a noise pattern of the target building according to the noise monitoring data, i.e., predicting an expected maximum noise level of the target building in a current time period; when the expected maximum noise level exceeds a noise level threshold, a trigger instruction is generated, and the trigger instruction is used to respond to the noise reduction request signal.

[0038] In this embodiment, a plurality of noise detectors in the target building continuously collect a large amount of noise monitoring data, which contains first noise intensity, frequency, and other information at different times and different points. A machine learning model is constructed based on algorithms such as CNN and Transformer, and a plurality of sets of noise monitoring data at different times are used to train the model, so that the model can learn the noise variation law at different time periods (such as morning office peak period, noon rest time, evening off work time, etc.) and the noise characteristics of different areas (such as rooms near elevators, rooms near roads, etc.) each day. For example, the target building is a large residential complex, and the noise detectors collect noise data for several months at various resident rooms, community roads, and activity areas. The machine learning model analyzes these data and finds that the noise in the resident rooms near the community gate increases from 7:00 to 9:00 am each day, because there are frequent vehicle entries and exits at the community gate. After 10:00 pm, the noise in the public activity area in the community significantly decreases.

[0039] The trained machine learning model can analyze a plurality of sets of noise monitoring data collected recently (such as in the past week, in the morning of the current day, etc.) to obtain an expected maximum noise level of the target building in a current time period (such as 10:00-11:00). If the expected maximum noise level exceeds a noise level threshold, it indicates that there may be an uncomfortable noise level in the indoor area of the target building in the current time period. At this time, the cloud platform controls enters a response mode for the target building, i.e., responds to the noise reduction request signal of the target building.

[0040] Optionally, the method further comprises: determining the facing relationship between the first indoor area and the noise source according to the position information of the noise source and an indoor area distribution map of each floor of the target building, the facing relationship including front-facing, barrier-communicating facing, and back non-communicating facing; wherein the indoor area distribution map at least includes the relative position relationship between the communicating area of the first indoor area and the external environment of the target building and other indoor areas, and the communicating area includes a door area and a window area; if the facing relationship between the first indoor area and the noise source is front-facing or back non-communicating facing, generating a first noise reduction scheme for the first indoor area according to the position information of the noise source, including: generating a first noise reduction scheme for the first indoor area according to the first noise intensity in the first indoor area.

[0041] In this embodiment, after the position information of the noise source is determined, the detailed indoor area distribution map of each floor of the target building is combined for further analysis. The indoor area distribution map contains rich information, at least including the relative position relationship between the communicating area (such as the door area and the window area) of the first indoor area and the external environment of the target building and other indoor areas. By comparing and analyzing the position of the noise source with these information, the facing relationship between the first indoor area and the noise source can be determined, that is, it is judged whether the first indoor area is front-facing the noise source (for example, the noise source is in front of the first indoor area, and there is no other obstacle in between, at this time Figure 2 the office 201 and 203 in FIG. 1 are the first indoor area), or barrier-communicating facing the noise source (for example, there is a second indoor area between the noise source and the first indoor area, which has a communicating area with the first indoor area, at this time Figure 2 the office 202 in FIG. 1 is the first indoor area, and the office 201 is the second indoor area), or back non-communicating facing the noise source (for example, there is a second indoor area between the noise source and the first indoor area, which does not have a communicating area with the first indoor area, at this time Figure 2 the office 204 in FIG. 1 is the first indoor area, and the office 203 is the second indoor area). The determination of the facing relationship is very important for subsequent development of appropriate noise reduction schemes, because different facing relationships require different noise reduction measures.

[0042] According to different above-mentioned face-to-face relationships, different noise reduction schemes are adopted, for example, when the face-to-face relationship is front face-to-face and back non-communicating face-to-face, a noise reduction scheme corresponding to a corresponding noise reduction level can be generated based on the first noise intensity detected by the noise detector in the first indoor area. For example, the low-level noise reduction scheme is to close the curtain or window, the medium-level noise reduction scheme is to control the auxiliary door and window facility closed using the mute board, and the high-level noise reduction scheme is to control the active noise reduction equipment to start. Of course, the high-level noise reduction scheme can also optionally include at least part of the noise reduction means of the low-level noise reduction scheme, which will not be described in detail; and the noise reduction level of the noise reduction scheme can also be other grading methods, which will not be limited. Obviously, the greater the noise intensity, the higher the corresponding noise reduction scheme is taken, and the corresponding relationship between the noise intensity (or the segmented range of the noise intensity) and the corresponding noise reduction scheme can be established in advance.

[0043] Optionally, when the face-to-face relationship between the first indoor area and the noise source is blocking communication face-to-face, a second noise reduction scheme for a second indoor area associated with the first indoor area is generated according to the orientation information of the noise source, including: generating a first noise reduction scheme for the first indoor area according to the first noise intensity in the first indoor area, executing the first noise reduction scheme, and monitoring whether the first noise reduction scheme makes the noise level in the first indoor area below the preset noise level; if yes, the second noise reduction scheme for the second indoor area associated with the first indoor area is not generated; if no, a second noise reduction scheme for the second indoor area is generated according to the second noise intensity in the second indoor area, the second noise reduction scheme is executed, and it is monitored whether the second noise reduction scheme makes the noise level in the first indoor area below the preset noise level; if no, a third noise reduction scheme for the second indoor area is continuously generated in a step-by-step enhancement manner until the third noise reduction scheme makes the noise level in the first indoor area below the preset noise level.

[0044] In this embodiment, when the face-to-face relationship between the first indoor area and the noise source is blocking communication face-to-face, the noise level in the first indoor area is affected by the open state of the door and window outside itself, and is also affected by the second indoor area, that is, the noise is conducted to the first indoor area through the communicating second indoor area, thereby causing the noise level in the first indoor area to rise.

[0045] In this case, the application first generates a first noise reduction scheme for the first indoor area according to the first noise intensity in the first indoor area in the aforementioned manner, and then executes the first noise reduction scheme, such as closing the windows of the first indoor area, adjusting the sound-absorbing equipment in the room, and the like. At the same time, it is also monitored whether the noise level in the first indoor area is reduced to below the preset noise level, so as to monitor and evaluate the noise reduction effect of the first noise reduction scheme.

[0046] For example, the first indoor area is Figure 2 the office 202 in the target building, and the noise source is located outside the target building (for example, the work of a lawn mower, a cutting machine, etc.). The cloud platform detects that the first noise intensity of the office 202 is 60 decibels, and the preset noise level is 45 decibels, and the generated first noise reduction scheme is, for example, to close the windows of the office 202 and turn on the active noise reduction equipment (such as using the highest level of noise reduction scheme). Then the cloud platform sends the first noise reduction scheme to the relevant equipment in the office 202 for execution, and continuously monitors the change of the noise level in the office 202. If it is found through monitoring that the noise level in the office 202 is successfully reduced to the preset 45 decibels, it means that only the noise reduction measures for the office 202 have achieved the purpose of noise reduction, and at this time the system does not need to generate a noise reduction scheme for the office 201 which has the above-mentioned association with the office 202, so as to avoid unnecessary operation and waste of resources.

[0047] If it is found through monitoring that the noise level in the first indoor area does not decrease to below the preset noise level, it is necessary to further determine a second noise reduction scheme for the second indoor area, that is, to detect the noise intensity of the second indoor area and generate a second noise reduction scheme accordingly, and the generation method is basically the same as that of the first noise reduction scheme. Similar to the foregoing, the generated second noise reduction scheme for the second indoor area is also executed, and it is monitored whether the second noise reduction scheme makes the noise level in the first indoor area decrease to below the preset noise level.

[0048] For example, after the execution of the first noise reduction scheme, the noise level of the office 202 is 55 decibels, which is still higher than the preset 45 decibels. At this time, the system detects the noise level of the office 201 which is in a state of communication with the office 202, for example, 70 decibels. At this time, the corresponding second noise reduction scheme is generated, and then the system executes this second noise reduction scheme and continues to monitor the noise level of the office 202. If the noise level of the office 202 is lower than the preset 45 decibels, it indicates that the office 201 has been adjusted to make the noise of the office 202 reach the appropriate condition.

[0049] If the monitoring finds that the noise level of the office 202 has not been reduced below the preset noise level, the noise reduction regulation of the office 201 is insufficient, at this time, a stronger third noise reduction scheme for the office 201 can be generated and executed in a step-by-step enhancement manner until the third noise reduction scheme reduces the noise level in the office 202 to below 45 decibels.

[0050] The present application directly regulates the first indoor area to preliminarily reduce the noise level in the first indoor area, and directly regulates the second indoor area which has a communication relationship with the first indoor area to further indirectly reduce the noise level in the first indoor area. The combination of the two can ensure that the noise level in the first indoor area meets the requirements.

[0051] It should be noted that if the noise level in the office 202 has not been reduced to the preset noise level after the highest noise reduction level is used for the office 201, the above scheme can be ended, and corresponding prompt information can be output at the same time, that is, prompting the relevant personnel that the sound intensity of the noise source is too high and cannot completely eliminate its influence.

[0052] Optionally, the third noise reduction scheme for the second indoor area is generated in a step-by-step enhancement manner, including: obtaining the use type of the second indoor area from the indoor area distribution map, the use type including a personnel existing area and a personnel non-existing area; determining a level span range according to the use type, and generating a third noise reduction scheme for the second indoor area according to the level span range; wherein the level span range is used to adjust the span range of the noise reduction level of the third noise reduction scheme compared with the noise reduction level of the previously generated noise reduction scheme, and the level span range corresponding to the personnel existing area is lower than the level span range corresponding to the personnel non-existing area.

[0053] In this embodiment, in the process of indirectly reducing the noise level in the first indoor area to below the preset noise level by regulating the second indoor area, it is also necessary to minimize the interference to the second indoor area. For example, when there are people working in the second indoor area, if more noise reduction devices are regulated, although it is more helpful to indirectly reduce the noise level in the first indoor area, too many devices in the starting operation state will obviously have a greater impact on the work of these people, such as causing these people to be unable to concentrate, the closing of doors and windows leading to poor indoor ventilation, etc. At this time, the third noise reduction scheme can be set to span fewer noise reduction levels at a time, for example, the second noise reduction scheme earlier is a level 1 noise reduction scheme, and the third noise reduction scheme at this time is a level 2 noise reduction scheme. When there are no people working in the second indoor area, such as corridors, warehouses, etc., the above-mentioned impact is not possible. At this time, the third noise reduction scheme can be set to span higher noise reduction levels at a time, for example, the second noise reduction scheme earlier is a level 1 noise reduction scheme, and the third noise reduction scheme at this time is a level 3 noise reduction scheme.

[0054] The embodiment of the present application also discloses a green building adaptive control system based on a cloud platform, which comprises a receiving module, a noise reduction scheme generation module and a noise reduction scheme execution module. The receiving module is used for receiving a noise reduction request signal from a first indoor area of a target building, and obtaining noise detection signals of a plurality of noise detectors of the target building according to the noise reduction request signal. The noise reduction scheme generation module is used for determining position information of a noise source according to the noise detection signals, and generating a first noise reduction scheme for the first indoor area and a second noise reduction scheme for a second indoor area having correlation with the first indoor area according to the position information of the noise source. The noise reduction scheme execution module is used for adjusting a first state of sound insulation facilities in the first indoor area based on the first noise reduction scheme, and adjusting a second state of sound insulation facilities in the second indoor area based on the second noise reduction scheme, so that the noise level in the first indoor area is reduced to below a preset noise level.

[0055] The embodiment of the present application also discloses an electronic device, which comprises at least one processor, a memory and a computer program stored in the memory, wherein the computer program runs on the at least one processor to implement the method according to any one of the preceding embodiments.

[0056] The embodiment of the present application also discloses a computer storage medium, wherein the computer readable storage medium stores a computer program, and the computer program runs on a processor to implement the method according to any one of the preceding embodiments.

[0057] The embodiment of the present application also discloses a computer program product applied to the system according to any one of the preceding embodiments, wherein the computer program comprises a plurality of computer codes, and the computer codes run on a processor to implement the method according to any one of the preceding embodiments.

[0058] Those skilled in the art can understand that all or part of the processes in the above-mentioned embodiment methods can be completed by instructing the relevant hardware through a computer program. The computer program can be stored in a non-volatile computer readable storage medium, and when executed, can include the processes of the above-mentioned embodiment methods. Any reference to memory, storage, database or other medium used in the embodiments provided in the present application can include at least one of non-volatile and volatile memory. Non-volatile memory can include read-only memory (ROM), magnetic tape, floppy disk, flash memory or optical memory, etc. Volatile memory can include random access memory (RAM) or external cache memory. As an illustration but not limitation, RAM can be in various forms, such as static random access memory (SRAM) or dynamic random access memory (DRAM), etc.

[0059] Any combination of the technical features of the above embodiments can be made. In order to make the description simple, all possible combinations of the technical features in the above embodiments are not described, however, as long as the combination of the technical features does not exist contradictory, it should be considered as the scope of the present application.

[0060] The above embodiments only express several implementation manners of the present application, and the description is more specific and detailed, but it should not be understood as a limitation on the scope of the patent. It should be pointed out that for those skilled in the art, without departing from the concept of the present application, some modifications and improvements can be made, which are all within the protection scope of the present application. Therefore, the protection scope of the patent of the present application should be subject to the appended claims.

Claims

1. A green building adaptive control method based on a cloud platform, characterized in that: The method comprises: receiving a noise reduction request signal from a first indoor area of ​​a target building, and acquiring noise detection signals of a plurality of noise detectors of the target building according to the noise reduction request signal; determining the orientation information of the noise source according to the noise detection signal, and generating a first noise reduction scheme for the first indoor area and a second noise reduction scheme for a second indoor area associated with the first indoor area according to the orientation information of the noise source; adjusting a first state of the sound insulation facilities in the first indoor area based on the first noise reduction scheme, and adjusting a second state of the sound insulation facilities in the second indoor area based on the second noise reduction scheme, so that the noise level in the first indoor area is reduced to below a preset noise level; The method further comprises: Determine, based on the orientation information of the noise source and a distribution map of indoor areas on each floor of the target building, a facing relationship between the first indoor area and the noise source, wherein the facing relationship includes front facing, blocked connected facing, and back non-connected facing; wherein the indoor area distribution map includes at least the relative positional relationship between a connected area of ​​the first indoor area and the external environment and other indoor areas of the target building, wherein the connected area includes a door area and a window area; If the first indoor area and the noise source are facing each other head-on or facing each other back-on, generating a first noise reduction scheme for the first indoor area according to the orientation information of the noise source, including: generating a first noise reduction scheme for the first indoor area according to a first noise intensity in the first indoor area; When the first indoor area and the noise source are in a blocking-connection facing relationship, generating a second noise reduction scheme for a second indoor area associated with the first indoor area according to the orientation information of the noise source, including: generating a first noise reduction scheme for the first indoor area according to a first noise intensity in the first indoor area, executing the first noise reduction scheme, and monitoring whether the first noise reduction scheme reduces the noise level in the first indoor area to below a preset noise level; If yes, then do not generate a second noise reduction solution for a second indoor area associated with the first indoor area; If not, a second noise reduction scheme for the second indoor area is generated according to the second noise intensity in the second indoor area, the second noise reduction scheme is executed, and it is monitored whether the second noise reduction scheme reduces the noise level in the first indoor area to below the preset noise level; if not, a third noise reduction scheme for the second indoor area is continuously generated in a step-by-step enhancement manner until the third noise reduction scheme reduces the noise level in the first indoor area to below the preset noise level.

2. The cloud platform-based green building adaptive control method according to claim 1, characterized in that: Before acquiring noise detection signals of a plurality of noise detectors of the target building according to the noise reduction request signal, the method further includes: Analyzing multiple sets of noise monitoring data from a target building using a machine learning model, and deriving future trends in noise patterns of the target building based on the noise monitoring data, i.e., predicting the expected maximum noise level of the target building during the current time period; When the expected maximum noise level exceeds a noise level threshold, a trigger instruction is generated, where the trigger instruction is used to respond to the noise reduction request signal.

3. The cloud platform-based green building adaptive control method according to claim 1, characterized in that: The stepwise enhancement method of continuously generating a third noise reduction solution for the second indoor area includes: Extracting a usage type of the second indoor area from the indoor area distribution map, the usage type including a human presence area and a human non-presence area; A level span is determined based on the usage type, and a third noise reduction scheme for the second indoor area is continuously generated according to the level span; wherein the level span is used to adjust the noise reduction level of the third noise reduction scheme compared with the noise reduction level of the noise reduction scheme generated last time, and the level span corresponding to the area where people exist is lower than the level span corresponding to the area where people do not exist.

4. A green building adaptive control system based on a cloud platform, characterized in that: The system includes a receiving module, a noise reduction scheme generating module, and a noise reduction scheme executing module; The receiving module is configured to receive a noise reduction request signal from a first indoor area of ​​a target building, and obtain noise detection signals of a plurality of noise detectors of the target building according to the noise reduction request signal; The noise reduction scheme generating module is configured to determine the orientation information of the noise source according to the noise detection signal, and generate a first noise reduction scheme for the first indoor area and a second noise reduction scheme for a second indoor area associated with the first indoor area according to the orientation information of the noise source; the noise reduction scheme execution module being configured to adjust a first state of the sound insulation facilities in the first indoor area based on the first noise reduction scheme, and to adjust a second state of the sound insulation facilities in the second indoor area based on the second noise reduction scheme, so that the noise level in the first indoor area is reduced to below a preset noise level; The noise reduction scheme generation module is specifically used to: Determine, based on the orientation information of the noise source and a distribution map of indoor areas on each floor of the target building, a facing relationship between the first indoor area and the noise source, wherein the facing relationship includes front facing, blocked connected facing, and back non-connected facing; wherein the indoor area distribution map includes at least the relative positional relationship between a connected area of ​​the first indoor area and the external environment and other indoor areas of the target building, wherein the connected area includes a door area and a window area; If the first indoor area and the noise source are facing each other head-on or facing each other back-on, generating a first noise reduction scheme for the first indoor area according to the orientation information of the noise source, including: generating a first noise reduction scheme for the first indoor area according to a first noise intensity in the first indoor area; Optionally, the noise reduction solution generating module is further configured to: generating a first noise reduction scheme for the first indoor area according to a first noise intensity in the first indoor area, executing the first noise reduction scheme, and monitoring whether the first noise reduction scheme reduces the noise level in the first indoor area to below a preset noise level; If yes, then do not generate a second noise reduction solution for a second indoor area associated with the first indoor area; If not, a second noise reduction scheme for the second indoor area is generated according to the second noise intensity in the second indoor area, the second noise reduction scheme is executed, and it is monitored whether the second noise reduction scheme reduces the noise level in the first indoor area to below the preset noise level; if not, a third noise reduction scheme for the second indoor area is continuously generated in a step-by-step enhancement manner until the third noise reduction scheme reduces the noise level in the first indoor area to below the preset noise level.

5. The cloud platform-based green building adaptive control system according to claim 4, characterized in that: The receiving module is further configured to: Analyzing multiple sets of noise monitoring data from a target building using a machine learning model, and deriving future trends in noise patterns of the target building based on the noise monitoring data, i.e., predicting the expected maximum noise level of the target building during the current time period; When the expected maximum noise level exceeds a noise level threshold, a trigger instruction is generated, where the trigger instruction is used to respond to the noise reduction request signal.

6. An electronic device, characterized in that: include: At least one processor, a memory, and a computer program stored in the memory, wherein the computer program runs on the at least one processor to implement the method according to any one of claims 1 to 3.

7. A computer storage medium, characterized in that: The computer storage medium stores a computer program, and the computer program runs on a processor to implement the method according to any one of claims 1 to 3.

8. A computer program product, characterized in that: The computer program includes several computer codes, and the computer codes are executed on a processor to implement the method according to any one of claims 1 to 3.

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