CN117010831B - Building intelligent management system and method based on big data - Google Patents
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- F24F—AIR-CONDITIONING; AIR-HUMIDIFICATION; VENTILATION; USE OF AIR CURRENTS FOR SCREENING
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Abstract
The application discloses building intelligent management system and method based on big data, the system includes: the rainfall detection module is used for acquiring rainfall information and judging a raining state; the pre-detection module is used for identifying a first target user and judging a corresponding wetting state; the wetting confirmation module is used for analyzing and confirming to obtain second wetting information; the tracking module is used for marking the second target user and tracking the second target user in the monitoring video information; the avoidance zone determining module is used for setting a corresponding avoidance zone according to the position of the second target user; and the intelligent adjusting module is used for adjusting the air conditioner of the building area to avoid the avoidance area in response to the fact that the second target user is identified to enter the new building area. By timely and accurately identifying the rainy staff and adaptively adjusting the air conditioner, the air conditioner in the office building is more intelligent, and the user experience of the staff in working and working in rainy days is improved.
Description
Technical Field
The application relates to the technical field of intelligent building management, in particular to a building intelligent management system and method based on big data.
Background
Currently, when staff encounters rainy weather during commuting to work, even with rain gear, it is still unavoidable to get wet. At this time, if an employee enters an office place where the air conditioner is already turned on after being wetted, the employee is liable to get cold if the air conditioner lacks adaptive adjustment. And the reason is that the human body can have some physiological changes after rain, such as body temperature reduction, capillary contraction and the like, if the human body is immediately blown with air after being wetted, the temperature of the human body can be further reduced, and the capillary is more contracted, so that the normal metabolism and the immunity of the human body are affected, and the possibility of cold is further increased. Therefore, there is a need for an intelligent management system for buildings, which can timely and accurately identify rain workers and adaptively adjust air conditioners.
Disclosure of Invention
In order to overcome the defects and the shortcomings in the prior art, the application provides a building intelligent management system and method based on big data.
In order to achieve the above purpose, the present application adopts the following technical scheme:
according to one aspect of the present application, there is provided a building intelligent management system based on big data for an office building, the system comprising:
the rainfall detection module is used for acquiring rainfall information and judging a raining state;
the pre-detection module is used for identifying a first target user and judging a corresponding wetting state;
the wetting confirmation module is used for acquiring first wetting information and carrying out analysis and confirmation by combining the raining state and the wetting state to acquire second wetting information;
the tracking module is used for marking the first target user corresponding to the confirmed second wetting information as a second target user when the second target user is judged to be true, and tracking the second target user in the monitoring video information;
the avoidance area determining module is used for setting a corresponding avoidance area according to the position of the second target user;
and the intelligent adjusting module is used for adjusting the air conditioner of the building area to avoid the avoidance area in response to the fact that the second target user is identified to enter the new building area.
According to another aspect of the present application, there is provided a building intelligent management method based on big data, which is applied to the building intelligent management system based on big data and executed, the method including:
acquiring rainfall information and judging a raining state;
identifying a first target user and judging a corresponding wetting state;
acquiring first wetting information, and analyzing and confirming the first wetting information by combining the raining state and the wetting state to obtain second wetting information, wherein the first wetting information is obtained by sending the first target user;
marking a first target user corresponding to the confirmed second wetting information as a second target user when the second wetting information is judged to be true, and tracking the second target user in the monitoring video information;
setting a corresponding avoidance area according to the position of the second target user;
and in response to identifying that the second target user enters a new building area, adjusting air conditioners of the building area to avoid the avoidance area.
Preferably, in acquiring rainfall information and judging a raining state, specifically including: and analyzing and judging whether the rainfall information is in a raining state or not according to the rainfall threshold.
Preferably, in identifying the first target user and judging the corresponding wet state, the method specifically includes the following steps:
acquiring pre-detection image information;
identifying a first target user in the pre-detection image information;
when a first target user is successfully identified, intercepting an image area containing the first target user from the pre-detection image information to serve as an image to be processed;
inputting the image to be processed into a wet state identification model to obtain the wet state;
the wetting state is identification information corresponding to whether a first target user is wetted or not, and the wetting state identification model is obtained through machine learning training by using a plurality of groups of data, wherein each group of data in the plurality of groups of data comprises pictures to be identified containing different wetting conditions of a person target and identification information corresponding to whether the person target is wetted or not.
Preferably, the method for obtaining the first wetting information and combining the raining state and the wetting state to perform analysis and confirmation to obtain the second wetting information specifically includes:
responding to the obtained first wetting information when judging true:
judging whether the raining state is true in a preset historical time period and judging whether the wetting state is true, if so, determining to generate second wetting information, otherwise, sending first prompt information to the first target user, wherein the first prompt information is used for reminding the user to manually modify the first wetting information so as to ensure that the wetting condition is consistent with the actual condition;
and responding to the obtained first wetting information when judging false:
judging whether the raining state is true in a preset historical time period and judging whether the wetting state is true, if so, determining to generate second wetting information and simultaneously sending second prompt information to the first target user, wherein the second prompt information is used for prompting the first target user that the current wetting condition is automatically corrected according to the execution condition of the actual condition.
Preferably, the setting a corresponding avoidance area according to the location of the second target user specifically includes:
dividing coordinate points of each target building area in advance;
determining a coordinate point based on the position of the second target user;
and forming the avoidance region based on the coordinate points.
Preferably, in adjusting the air conditioner of the building area to avoid the avoidance area, the method specifically includes:
acquiring a target building area ID;
matching corresponding air conditioner preset information based on the target building area ID;
and carrying out self-adaptive selection from the preset information of the air conditioner based on the avoidance area so that a path from the air conditioner to the furthest blowing position avoids the avoidance area.
Preferably, in adaptively selecting from the preset information of the air conditioner based on the avoidance area, the method specifically includes:
determining a to-be-used combined parameter set based on a specified coordinate point, wherein the to-be-used combined parameter set comprises at least one combined parameter corresponding to the air conditioner preset information;
in response to the presence of at least two second target users located in the same target building area, screening repeated combination parameters from all combination parameter sets to be used so as to avoid all avoidance areas;
and responding to the condition that repeated combination parameters are failed to be screened from all combination parameter sets to be used, selecting default combination parameters for adjusting the air conditioner, wherein the default combination parameters are that all air outlets of the current target building area are set to be horizontally upwards so as to avoid all avoidance areas.
Preferably, the method further comprises:
and adjusting the air conditioner based on the user ID when only one second target user is located in the target building area, wherein the method specifically comprises the following steps of:
acquiring a user ID of the second target user;
determining temperature information from a user temperature demand table based on the user ID;
and adjusting the air conditioner based on the temperature information.
Preferably, the method further comprises:
setting an avoidance time limit for the second target user in response to identifying that the second target user enters a designated building area;
responding to the arrival of the avoidance time limit, and determining a coordinate point to be tracked according to the position of the second target user;
and acquiring a preset adjustment type of the second target user, and adjusting the air conditioner according to the preset adjustment type so as to enable the air conditioner to blow air aiming at the coordinate point to be tracked.
According to another aspect of the present application, there is provided a storage medium for storing program code for performing any one of the above-described big data based building intelligent management methods.
Compared with the prior art, the application has the following advantages and beneficial effects:
(1) According to the method and the system, the first target user is timely identified, the first wetting information is analyzed and confirmed by combining the raining state and the wetting state to obtain the second wetting information, the first target user which is judged to be true by the second wetting information is marked as the second target user, the condition that the second target user enters a new building area is tracked, the air conditioner in the building area is adaptively adjusted to avoid the avoidance area, namely, the raining staff is timely and accurately identified, and the air conditioner is adaptively adjusted, so that the air conditioner in an office building is more intelligent, the possibility that the raining staff suffers from cold is reduced, and the user experience of the staff in working in the weather of rainy days is improved.
(2) According to the method and the device, the first wetting information sent by the first target user is checked by combining the raining state and the judging result of the wetting state, the influence caused by unreliable data sent by misoperation of the user is reduced, the reliability of the wetting judging condition is increased, meanwhile, the accuracy of overall wetting analysis and judgment of the first target user is improved by timely reminding the user of mistakes, the final first wetting information is more in accordance with the actual wetting condition based on a secondary confirmation mechanism, and the situation that the error of the wetting judging condition is caused by misoperation of the user is avoided because the final second wetting information is more accurate.
(3) According to the method, the mapping table is formed by the combination parameters of the wind power and the wind direction of the air conditioner and the furthest blowing position, different areas are distinguished by utilizing the target building area ID, and then each coordinate point has corresponding air conditioner preset information to select, so that the combination parameters which accord with avoiding avoidance areas can be determined according to the target building area ID and the coordinate point, the air conditioner adjustment of the office building is more accurate and intelligent, and the raining staff is improved.
Drawings
FIG. 1 is a block diagram of a building intelligent management system based on big data in one embodiment;
FIG. 2 is a flow diagram of a building intelligent management method based on big data in one embodiment;
FIG. 3 is a flowchart illustrating steps for determining a wet state according to one embodiment;
FIG. 4 is a flowchart illustrating steps for analyzing and confirming second wetting information in one embodiment;
FIG. 5 is a flowchart illustrating steps for setting an avoidance area in one embodiment;
FIG. 6 is a flowchart illustrating steps for adjusting air conditioning of a building area in one embodiment;
FIG. 7 is a flowchart illustrating steps for adaptively selecting preset information of an air conditioner in one embodiment;
FIG. 8 is a flowchart illustrating steps for adjusting the temperature of an air conditioner to a temperature preferred by a user in one embodiment;
FIG. 9 is a flowchart showing steps in response processing performed when the avoidance time limit is released in one embodiment;
FIG. 10 is a block diagram of a terminal in one embodiment;
FIG. 11 is an internal block diagram of a computer device in one embodiment.
Detailed Description
In the description of the present disclosure, it is to be noted that embodiments of the present disclosure will be described in more detail below with reference to the accompanying drawings. While certain embodiments of the present disclosure have been shown in the accompanying drawings, it is to be understood that the present disclosure may be embodied in various forms and should not be construed as limited to the embodiments set forth herein, but are provided to provide a more thorough and complete understanding of the present disclosure. It should be understood that the drawings and embodiments of the present disclosure are for illustration purposes only and are not intended to limit the scope of the present disclosure. It should be understood that the various steps recited in the method embodiments of the present disclosure may be performed in and/or in parallel. Furthermore, method embodiments may include additional steps and/or omit performing the illustrated steps. The scope of the present disclosure is not limited in this respect.
The term "including" and variations thereof as used herein are intended to be open-ended, i.e., including, but not limited to. The term "based on" is based at least in part on. The term "one embodiment" means "at least one embodiment"; the term "another embodiment" means "at least one additional embodiment"; the term "some embodiments" means "at least some embodiments. Related definitions of other terms will be given in the description below.
It should be noted that the terms "first," "second," and the like in this disclosure are merely used to distinguish between different devices, modules, or units and are not used to define an order or interdependence of functions performed by the devices, modules, or units.
It should be noted that references to "a" and "an" in this disclosure are intended to be illustrative rather than limiting, and those of ordinary skill in the art will appreciate that "one or more" is intended to be construed as "one or more" unless the context clearly indicates otherwise.
The names of messages or information interacted between the various devices in the embodiments of the present disclosure are for illustrative purposes only and are not intended to limit the scope of such messages or information.
In order to make the objects, technical solutions and advantages of the present application more apparent, the present application will be further described in detail with reference to the accompanying drawings and examples. It should be understood that the specific embodiments described herein are for purposes of illustration only and are not intended to limit the present application.
Examples
As shown in fig. 1, in one embodiment, this embodiment provides a big data based building intelligent management system for an office building. Specifically, the system includes: the intelligent control system comprises a rainfall detection module, a pre-detection module, a wetting confirmation module, a tracking module, an avoidance area determination module and an intelligent regulation module;
in this embodiment, the rainfall detection module is configured to obtain rainfall information and determine a raining state;
the pre-detection module is used for identifying a first target user and judging a corresponding wetting state;
the wetting confirmation module is used for acquiring first wetting information and carrying out analysis and confirmation by combining the raining state and the wetting state to acquire second wetting information;
the tracking module is used for marking the first target user corresponding to the confirmed second wetting information as the second target user when the second target user is judged to be true, and tracking the second target user in the monitoring video information;
the avoidance zone determining module is used for setting a corresponding avoidance zone according to the position of the second target user;
and the intelligent adjusting module is used for adjusting the air conditioner of the building area to avoid the avoidance area in response to the fact that the second target user is identified to enter the new building area.
In this embodiment, the system may be operated in a cloud server, or may be operated in a designated server in a machine room in an office building.
In another embodiment, as shown in fig. 2, a building intelligent management method based on big data is provided, and the method is applied to the building intelligent management system based on big data in the above embodiment for execution. In practical application, the execution body of the embodiment is a processing device, and the processing device is specifically a server for data processing and analysis. Specifically, the method comprises the following steps:
s1, acquiring rainfall information and judging a raining state; the method specifically comprises the following steps: and analyzing and judging whether the rainfall information is in a rainy state according to the rainfall threshold value. In actual application, the rainfall information is collected through a rainfall sensor. In this embodiment, the number of the rain sensors may be at least three, an average rain value is obtained by averaging the rain information collected by all the rain sensors, and then, based on the rain threshold, whether the average rain value is in a raining state is determined, when the average rain value exceeds the rain threshold, the raining state is determined to be true, otherwise, the raining state is determined to be false.
In addition, this embodiment can also avoid rainfall information to acquire inaccurate unexpected condition through setting up at least three rainfall sensor, and when simultaneously two rainfall sensor's rainfall information phase difference is too big, additionally consider the rainfall information of third rainfall sensor, still can get average through two similar rainfall information, get rid of the biggest rainfall information of error for the raining state of final judgement is more accurate.
S2, identifying a first target user and judging a corresponding wetting state; in this embodiment, as shown in fig. 3, in identifying a first target user and determining a corresponding wetting state, the method specifically includes the following steps:
s21, acquiring pre-detection image information; wherein the pre-detected image information is an image containing a user who has just entered the building.
S22, identifying a first target user in the pre-detection image information; in actual application, a pre-stored face feature image set is preset before execution, face features are extracted from users just entering a building, the similarity between the pre-stored face feature image set and each pre-stored face feature is calculated, and the user identity corresponding to a result with the maximum similarity and greater than a first preset threshold value is matched to be a first target user; it should be further noted that each pre-stored face feature corresponds to one user ID one by one, so that the first target user belonging to the office building staff can be successfully and accurately identified.
S23, when the first target user is successfully identified, the pre-detection image information is intercepted to obtain an image area containing the first target user, and the image area is used as an image to be processed;
s24, inputting the image to be processed into a wet state identification model to obtain a wet state;
in actual application, the wetting state is identification information corresponding to whether a first target user is wetted or not, and the wetting state identification model is obtained through machine learning training by using a plurality of groups of data, wherein each group of data in the plurality of groups of data comprises pictures to be identified containing different wetting conditions of a person target and identification information corresponding to whether the person target is wetted or not.
S3, acquiring first wetting information, and analyzing and confirming the first wetting information by combining a raining state and a wetting state to obtain second wetting information, wherein the first wetting information is sent by a first target user, for example, the first wetting information can be sent by a mobile phone or can be sent by portable equipment such as an intelligent bracelet; in actual application, the first wetting information is a triggering condition for pre-judgment, whether the first wetting information is true or false is determined by manually selecting the information of whether the first target user is wet or not, and the first wetting information is sent to an execution main body to obtain the first wetting information.
In this embodiment, as shown in fig. 4, in acquiring the first wetting information and performing analysis and confirmation in combination with the raining state and the wetting state to obtain the second wetting information, the method specifically includes:
s31, responding to the acquired first wetting information, and when the first wetting information is judged to be true:
judging whether the raining state is true in a preset historical time period and judging whether the wetting state is true, if so, determining to generate second wetting information, otherwise, sending first prompt information to a first target user, wherein the first prompt information is used for reminding the user to manually modify the first wetting information so as to ensure that the wetting condition is consistent with the actual condition;
s32, responding to the obtained first wetting information, and when the first wetting information is judged to be false:
judging whether the raining state is true in a preset historical time period and judging whether the wetting state is true, if so, determining to generate second wetting information and simultaneously sending second prompt information to the first target user, otherwise, not performing subsequent processing because the second wetting information is not triggered, wherein the second prompt information is used for reminding the first target user that the current wetting condition is automatically corrected according to the actual condition.
It should be noted that, when the determination results of the raining state and the wetting state are both true, since the first wetting information is manually selected by the first target user, once the misoperation occurs, if the determination is performed only by the first wetting information, the accuracy is easily affected. Therefore, the first wetting information is checked by combining the raining state and the wetting state, the reliability of the wetting judgment condition is improved, a user can be reminded of mistakes in time, and after the second confirmation, the first wetting information is the wetting condition which is in line with the actual condition, so that the finally generated second wetting information is more accurate, and the situation that the wetting judgment condition is wrong due to misoperation of the user is avoided.
S4, marking the first target user corresponding to the confirmed second wetting information as a second target user when the second wetting information is judged to be true, and tracking the second target user in the monitoring video information;
in the present embodiment, the monitoring video information is a sequence of image frames, and may be, for example, a sequence of continuous image frames (i.e., a video stream) or a sequence of discrete image frames (i.e., an image data group sampled at a predetermined sampling time point), or the like.
S5, setting a corresponding avoidance area according to the position of the second target user;
in this embodiment, as shown in fig. 5, in setting a corresponding avoidance area according to a location where the second target user is located, the method specifically includes:
s51, dividing coordinate points of each target building area in advance;
in practical application, because different arrangements exist in each target building area, all coordinate points of each target building area need to be tested in advance, so that air conditioner preset information can be stored in advance, wherein the air conditioner preset information is specifically a mapping table of combination parameters of wind power and wind direction of an air conditioner and the position of farthest blowing.
Further, different areas are distinguished by using the target building area ID, corresponding air conditioner preset information is selected for each coordinate point, and therefore the combination parameters which accord with avoiding avoidance areas can be determined according to the target building area ID and the coordinate points.
S52, determining a coordinate point based on the position of the second target user;
s53, forming an avoidance region based on the coordinate points.
During practical application, the position of the second target user can be timely obtained by continuously tracking the second target user, the coordinate point is determined by judging whether the coverage area of the position and the coordinate point exceeds the preset area threshold value, so that the coordinate point of the second target user is timely updated, preset boundary frames are respectively constructed above the coordinate point and in the directions of the two sides of the coordinate point, and then an avoidance area is formed.
S6, in response to the fact that the second target user is identified to enter a new building area, adjusting air conditioners of the building area to avoid the avoidance area.
In this embodiment, as shown in fig. 6, in adjusting the air conditioner in the building area to avoid the avoidance area, the method specifically includes:
s61, acquiring a target building area ID;
s62, matching corresponding air conditioner preset information based on the ID of the target building area; in practical application, the target building area IDs are used for distinguishing different areas, and each target building area ID is provided with air conditioner preset information corresponding to the air conditioner preset information.
S63, carrying out self-adaptive selection from preset information of the air conditioner based on the avoidance area so that the path from the air conditioner to the furthest blowing position avoids the avoidance area.
Specifically, as shown in fig. 7, in the adaptive selection from the preset information of the air conditioner based on the avoidance area, the method includes:
s631, determining a to-be-used combined parameter set based on the designated coordinate point, wherein the to-be-used combined parameter set comprises at least one combined parameter in corresponding air conditioner preset information;
in practical application, because the preset information of the air conditioner is pre-stored, at least one combination parameter can be found for any coordinate point to adjust the air conditioner, namely, the air conditioner can avoid an avoidance area formed by the coordinate point by timely adjusting wind power and wind direction.
S632, in response to the fact that at least two second target users are located in the same target building area, repeated combination parameters are screened out of all combination parameter sets to be used, so that all avoidance areas are avoided.
In practical application, for the to-be-used combination parameter sets corresponding to two different coordinate points, the combination parameters for avoiding two avoidance areas simultaneously need to be found, namely, only the same combination parameters need to be found, and the wind power and the wind direction of the air conditioner after adjustment can avoid the two avoidance areas simultaneously.
S633, responding to the condition that repeated combination parameters are failed to be screened from all combination parameter sets to be used, selecting default combination parameters for adjusting the air conditioner, wherein the default combination parameters are that all air inlets of the current target building area are set to be horizontally upwards so as to avoid all avoidance areas.
When the method is applied practically, when the same combination parameters cannot be found, and then the wind power and the wind direction of the adjusted air conditioner cannot avoid two avoidance areas at the same time, a default combination parameter is reserved in advance, and all air openings of the current target building area are set to be horizontal upwards so as to cope with the abnormal situation, so that the reliability of the whole intelligent management is improved.
As shown in fig. 8, in another embodiment, there is further provided a building intelligent management method based on big data, where the building intelligent management method based on big data in any one of the above embodiments further includes:
s7, adjusting the air conditioner based on the user ID in response to the fact that only one second target user is located in the target building area; specifically, the method comprises the following steps:
s71, acquiring a user ID of the second target user; in practical application, the user ID is not only the unique identification of the face characteristics of the user, but also can be used for target identification in the video tracking process.
S72, determining temperature information from a user temperature demand table based on the user ID;
s73, adjusting the air conditioner based on the temperature information. In practical application, the user temperature demand table is input in advance for each user, each user ID can be matched with unique temperature information, and the user temperature demand table can be updated at any time. When only one second target user exists in the target building area, unique temperature information can be found through the user ID, and once the temperature of the user to preference is changed, the user can modify the favorite temperature through the mobile phone or the intelligent bracelet at any time, so that the intellectualization and humanization of the whole building of the office building are improved, and more comfortable temperature experience is provided for the user.
As shown in fig. 9, in another embodiment, there is further provided a building intelligent management method based on big data, where the building intelligent management method based on big data in any one of the above embodiments further includes:
s8, setting an avoidance time limit for the second target user in response to the fact that the second target user enters the appointed building area; in practical application, the avoidance time limit is the time for the second target user to obtain the self-adaptive air conditioner adjustment in the office building, for example, the avoidance time limit can be obtained by manually sending the second target user, or preset to be a time value of 30 minutes, 1 hour and the like.
S9, determining a coordinate point to be tracked according to the position of the second target user in response to the arrival of the avoidance time limit; in practical application, the coordinate points to be tracked are coordinate points when coordinate point division is performed on each target building area in advance, and because the time at the moment exceeds the avoidance time limit, the avoidance time limit is in a release state at the moment, the air conditioner is regulated to adaptively blow the coordinate points to be tracked, so that the blowing experience of staff and the intellectualization of air conditioner regulation are further improved.
S10, acquiring a preset adjustment type of a second target user, and adjusting the air conditioner according to the preset adjustment type so that the air conditioner blows air aiming at the coordinate point to be tracked. In practical application, the preset adjustment type can be any combination of wind power, wind direction and temperature of the air conditioner, namely, the preset adjustment type corresponds to the preference of the second target user for the air conditioner, the wind power, the wind direction and the temperature.
As shown in fig. 10, in another embodiment, there is provided a terminal including: at least one memory and at least one processor; wherein the at least one memory is configured to store program code, and the at least one processor is configured to invoke the program code stored in the at least one memory to perform any of the big data based building intelligent management methods of the above embodiments.
In another embodiment, a computer device is provided, which may be a server, and the internal structure thereof may be as shown in fig. 11. The computer device includes a processor, a memory, a network interface, and a database connected by a system bus. Wherein the processor of the computer device is configured to provide computing and control capabilities. The memory of the computer device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system, computer programs, and a database. The internal memory provides an environment for the operation of the operating system and computer programs in the non-volatile storage media. The database of the computer device is a physical layer for storing various databases. The network interface of the computer device is used for communicating with an external terminal through a network connection. The computer program, when executed by the processor, implements a building intelligent management method based on big data.
It will be appreciated by those skilled in the art that the structure shown in fig. 11 is merely a block diagram of a portion of the structure associated with the present application and is not limiting of the computer device to which the present application applies, and that a particular computer device may include more or fewer components than shown, or may combine some of the components, or have a different arrangement of components.
In another embodiment, a storage medium is provided for storing program code for performing the big data based building intelligent management method of any of the above embodiments.
The foregoing description is only of the preferred embodiments of the present disclosure and description of the principles of the technology being employed. It will be appreciated by persons skilled in the art that the scope of the disclosure referred to in this disclosure is not limited to the specific combinations of features described above, but also covers other embodiments which may be formed by any combination of features described above or equivalents thereof without departing from the spirit of the disclosure. Such as those described above, are mutually substituted with the technical features having similar functions disclosed in the present disclosure (but not limited thereto).
Moreover, although operations are depicted in a particular order, this should not be understood as requiring that such operations be performed in the particular order shown or in sequential order. In certain circumstances, multitasking and parallel processing may be advantageous. Likewise, while several specific implementation details are included in the above discussion, these should not be construed as limiting the scope of the present disclosure. Certain features that are described in the context of separate embodiments can also be implemented in combination in a single embodiment. Conversely, various features that are described in the context of a single embodiment can also be implemented in multiple embodiments separately or in any suitable subcombination.
The embodiments described above are preferred embodiments of the present application, but the embodiments of the present application are not limited to the embodiments described above, and any other changes, modifications, substitutions, combinations, and simplifications that do not depart from the spirit and principles of the present application should be made by equivalent substitution methods, and are included in the scope of the present application.
Claims (9)
1. A building intelligent management system based on big data for an office building, the system comprising:
the rainfall detection module is used for acquiring rainfall information and judging a raining state;
the pre-detection module is used for identifying a first target user and judging a corresponding wetting state;
the wetting confirmation module is used for acquiring first wetting information and carrying out analysis and confirmation by combining the raining state and the wetting state to acquire second wetting information;
the tracking module is used for marking the first target user corresponding to the confirmed second wetting information as a second target user when the second target user is judged to be true, and tracking the second target user in the monitoring video information;
the avoidance area determining module is used for setting a corresponding avoidance area according to the position of the second target user;
the intelligent adjusting module is used for adjusting air conditioners of the building area to avoid the avoidance area in response to the fact that the second target user is identified to enter a new building area;
a pre-stored face feature image set is preset before execution, face features are extracted from users just entering a building, similarity between the pre-stored face feature image set and each pre-stored face feature is calculated, and user identities corresponding to results with maximum similarity and greater than a first preset threshold value are matched to be the first target users;
the first wetting information is a triggering condition of front judgment, and whether the first wetting information is true or false is determined by manually selecting the information of whether the first target user wets or not;
the method for obtaining the first wetting information and combining the raining state and the wetting state to perform analysis and confirmation to obtain the second wetting information specifically comprises the following steps:
responding to the obtained first wetting information when judging true:
judging whether the raining state is true in a preset historical time period and judging whether the wetting state is true, if so, determining to generate second wetting information, otherwise, sending first prompt information to the first target user, wherein the first prompt information is used for reminding the user to manually modify the first wetting information so as to ensure that the wetting condition is consistent with the actual condition;
and responding to the obtained first wetting information when judging false:
judging whether the raining state is true in a preset historical time period and judging whether the wetting state is true, if so, determining to generate second wetting information and simultaneously sending second prompt information to the first target user, wherein the second prompt information is used for prompting the first target user that the current wetting condition is automatically corrected according to the execution condition of the actual condition.
2. A big data based building intelligent management method, which is applied to the big data based building intelligent management system of claim 1 for execution, the method comprising:
acquiring rainfall information and judging a raining state;
identifying a first target user and judging a corresponding wetting state;
acquiring first wetting information, and analyzing and confirming the first wetting information by combining the raining state and the wetting state to obtain second wetting information, wherein the first wetting information is obtained by sending the first target user;
marking a first target user corresponding to the confirmed second wetting information as a second target user when the second wetting information is judged to be true, and tracking the second target user in the monitoring video information;
setting a corresponding avoidance area according to the position of the second target user;
and in response to identifying that the second target user enters a new building area, adjusting air conditioners of the building area to avoid the avoidance area.
3. The building intelligent management method based on big data according to claim 2, wherein in acquiring rainfall information and judging a raining state, specifically comprising: and analyzing and judging whether the rainfall information is in a raining state or not according to the rainfall threshold.
4. The intelligent management method for buildings based on big data according to claim 2, wherein in identifying the first target user and judging the corresponding wetting state, it specifically comprises the following steps:
acquiring pre-detection image information;
identifying a first target user in the pre-detection image information;
when a first target user is successfully identified, intercepting an image area containing the first target user from the pre-detection image information to serve as an image to be processed;
inputting the image to be processed into a wet state identification model to obtain the wet state;
the wetting state is identification information corresponding to whether a first target user is wetted or not, and the wetting state identification model is obtained through machine learning training by using a plurality of groups of data, wherein each group of data in the plurality of groups of data comprises pictures to be identified containing different wetting conditions of a person target and identification information corresponding to whether the person target is wetted or not.
5. The building intelligent management method based on big data according to any one of claims 2 to 4, wherein in setting a corresponding avoidance zone according to a location where the second target user is located, specifically comprising:
dividing coordinate points of each target building area in advance;
determining a coordinate point based on the position of the second target user;
and forming the avoidance region based on the coordinate points.
6. The intelligent management method for buildings based on big data according to any one of claims 2 to 4, wherein in adjusting the air conditioner of the building area to avoid the avoidance zone, specifically comprising:
acquiring a target building area ID;
matching corresponding air conditioner preset information based on the target building area ID;
performing self-adaptive selection from the preset information of the air conditioner based on the avoidance area, so that a path from the air conditioner to the furthest blowing position avoids the avoidance area;
the preset information of the air conditioner is specifically a mapping table of the combination parameters of wind power and wind direction of the air conditioner and the position of the farthest blowing.
7. The building intelligent management method based on big data according to claim 6, wherein in adaptively selecting from the air conditioner preset information based on the avoidance zone, specifically comprising:
determining a to-be-used combined parameter set based on a specified coordinate point, wherein the to-be-used combined parameter set comprises at least one combined parameter corresponding to the air conditioner preset information;
in response to the presence of at least two second target users located in the same target building area, screening repeated combination parameters from all combination parameter sets to be used so as to avoid all avoidance areas;
and responding to the condition that repeated combination parameters are failed to be screened from all combination parameter sets to be used, selecting default combination parameters for adjusting the air conditioner, wherein the default combination parameters are that all air outlets of the current target building area are set to be horizontally upwards so as to avoid all avoidance areas.
8. The intelligent management method for buildings based on big data according to claim 6, further comprising:
and adjusting the air conditioner based on the user ID when only one second target user is located in the target building area, wherein the method specifically comprises the following steps of:
acquiring a user ID of the second target user;
determining temperature information from a user temperature demand table based on the user ID;
and adjusting the air conditioner based on the temperature information.
9. The intelligent management method for buildings based on big data according to claim 6, further comprising:
setting an avoidance time limit for the second target user in response to identifying that the second target user enters a designated building area;
responding to the arrival of the avoidance time limit, and determining a coordinate point to be tracked according to the position of the second target user;
and acquiring a preset adjustment type of the second target user, and adjusting the air conditioner according to the preset adjustment type so as to enable the air conditioner to blow air aiming at the coordinate point to be tracked.
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