Windowing behavior detection method and detection device, computer device, and storage medium

Abstract

The application provides a windowing behavior detection method and device, computer equipment and a storage medium, and belongs to the technical field of behavior detection. The method comprises the following steps: acquiring a target environment temperature of a target space region collected at a target collection time; acquiring a preset list for storing candidate collection data, wherein the candidate collection data comprises a candidate collection time and a candidate environment temperature; performing capacity detection on the preset list according to a total number of the candidate collection data in the preset list and a preset list capacity, and determining a list storage state of the preset list; recording the target collection time and the target environment temperature to the preset list according to the list storage state, and obtaining a target list; and performing windowing behavior detection on the target space region according to the list storage state and the target list, and obtaining a behavior detection result. The embodiments of the application can accurately detect whether an abnormal windowing behavior exists in the current space region, thereby effectively avoiding energy waste and unnecessary heat loss.

Description

Technical Field

[0001] This application relates to the field of behavior detection technology, and in particular to a method and device for detecting window opening behavior, a computer device, and a storage medium. Background Technology

[0002] Currently, window opening behavior detection refers to methods that use sensors or other technologies to monitor whether window opening behavior exists in a given space. For example, in an electric underfloor heating system, based on the results of window opening behavior detection, the system's operating mode can be automatically adjusted or heating can be stopped. When a user sets a temperature higher than the ambient temperature in the electric underfloor heating system, the heating source is activated. The heating source gradually raises the room temperature to the set temperature through heat exchange. However, when a user feels hot in the current space, they may choose to open a window to lower the indoor temperature, causing the room temperature to consistently fall below the set temperature, especially when there is a large temperature difference between the indoor and outdoor areas. Therefore, if the electric underfloor heating system continues to provide heat, this abnormal window opening behavior by the user will lead to energy waste and unnecessary heat loss. Summary of the Invention

[0003] The main objective of this application is to propose a window opening behavior detection method, detection device, computer equipment, and storage medium, which can accurately detect whether there is abnormal window opening behavior in the current space area, thereby effectively avoiding energy waste and unnecessary heat loss.

[0004] To achieve the above objectives, a first aspect of this application proposes a window opening behavior detection method, the method comprising:

[0005] Acquire the target ambient temperature of the target spatial region at the target acquisition time;

[0006] Obtain a preset list, which is used to store candidate acquisition data, including candidate acquisition time and candidate ambient temperature of the target space region acquired at the candidate acquisition time;

[0007] Based on the total number of candidate data to be collected in the preset list and the capacity of the preset list, the capacity of the preset list is checked to determine the list storage status of the preset list;

[0008] The target acquisition time and the target ambient temperature are recorded into a preset list according to the list storage status to obtain the target list;

[0009] Based on the storage state of the list and the target list, window opening behavior detection is performed on the target space region to obtain the behavior detection result.

[0010] In some embodiments, the step of recording the target acquisition time and the target ambient temperature into a preset list according to the list storage state to obtain a target list includes:

[0011] When the list storage state is saturated, the difference between the candidate acquisition time and the target acquisition time is calculated to obtain the acquisition time difference data.

[0012] The candidate data corresponding to the smallest acquisition time difference data is removed from the preset list to obtain a candidate list;

[0013] The target acquisition time and the target ambient temperature are recorded in the candidate list to obtain the target list.

[0014] In some embodiments, the step of performing windowing behavior detection on the target spatial region based on the list storage state and the target list to obtain behavior detection results includes:

[0015] When the list storage state is saturated, a target group is obtained from the target list. The target group includes first target data and second target data. The first target data and the second target data are adjacent data collected according to a preset collection time interval. The first target data includes a first collection time and a first ambient temperature collected in the target space area at the first collection time. The second target data includes a second collection time and a second ambient temperature collected in the target space area at the second collection time. The second collection time is later than the first collection time, and the time difference between the second collection time and the first collection time is the preset collection time interval.

[0016] Temperature judgment is performed on the first ambient temperature and the second ambient temperature in the same target group to obtain adjacent temperature judgment results;

[0017] Based on the adjacent temperature judgment results, the window opening behavior of the target space area is detected to obtain the behavior detection results.

[0018] In some embodiments, the adjacent temperature judgment result includes a first judgment sub-result, wherein the first judgment sub-result indicates that the first ambient temperature in the same target group in the target list is greater than or equal to the second ambient temperature;

[0019] The step of detecting window opening behavior in the target space area based on the adjacent temperature judgment result, and obtaining the behavior detection result, includes:

[0020] When the adjacent temperature judgment result is the first judgment sub-result, it is determined that the behavior detection result is that there is abnormal window opening behavior in the target space area;

[0021] If the adjacent temperature judgment result is not the first judgment sub-result, the window opening behavior detection is performed on the target space area according to the preset temperature difference threshold to obtain the behavior detection result.

[0022] In some embodiments, the step of detecting window opening behavior in the target space area based on a preset temperature difference threshold to obtain the behavior detection result includes:

[0023] The difference between the second ambient temperature and the first ambient temperature in the same target group is calculated to obtain adjacent temperature difference data.

[0024] The adjacent temperature difference data are summed according to the target list to obtain the total target temperature difference data;

[0025] When the total difference in the target temperature is less than or equal to the preset temperature difference threshold, the behavior detection result is determined to be that the abnormal window opening behavior exists in the target space area.

[0026] In some embodiments, the preset list is used to record data collected within a preset heating time interval. Before performing window opening behavior detection on the target space area according to a preset temperature difference threshold and obtaining the behavior detection result, the method includes: obtaining the preset temperature difference threshold, specifically including:

[0027] The historical heating temperature data of the target ambient temperature is obtained. The historical heating temperature data refers to the temperature data obtained after heating the target ambient temperature for the preset heating time interval without any abnormal window opening behavior.

[0028] The difference between the historical heating temperature data and the target ambient temperature is calculated to obtain historical heating temperature difference data.

[0029] The preset temperature difference threshold is obtained by averaging the historical heating temperature difference data.

[0030] In some embodiments, the step of performing capacity detection on the preset list based on the total number of candidate collected data in the preset list and the capacity of the preset list, and determining the list storage status of the preset list, includes:

[0031] When the total number of candidate data collected in the preset list is equal to the capacity of the preset list, the list storage state is determined to be the list saturation state.

[0032] When the total number of candidate data collected in the preset list is less than the capacity of the preset list, the list storage state is determined to be an unsaturated state.

[0033] To achieve the above objectives, a second aspect of this application provides a window opening behavior detection device, the device comprising:

[0034] The temperature acquisition module is used to acquire the target ambient temperature of the target spatial area at the target acquisition time;

[0035] The list acquisition module is used to acquire a preset list, which is used to store candidate acquisition data. The candidate acquisition data includes candidate acquisition time and candidate ambient temperature of the target space region acquired at the candidate acquisition time.

[0036] The capacity detection module is used to perform capacity detection on the preset list based on the total number of candidate data to be collected and the capacity of the preset list, and to determine the list storage status of the preset list.

[0037] The list recording module is used to record the target acquisition time and the target ambient temperature into a preset list according to the list storage status, thereby obtaining a target list;

[0038] The behavior detection module is used to perform window opening behavior detection on the target space region based on the list storage state and the target list, and obtain the behavior detection result.

[0039] To achieve the above objectives, a third aspect of this application provides a computer device, comprising:

[0040] At least one memory;

[0041] At least one processor;

[0042] At least one computer program;

[0043] The at least one computer program is stored in the at least one memory, and the at least one processor executes the at least one computer program to implement the window opening behavior detection method described in the first aspect above.

[0044] To achieve the above objectives, a fourth aspect of the present application provides a storage medium, which is a computer-readable storage medium storing a computer program for causing a computer to execute the window opening behavior detection method described in the first aspect.

[0045] This application discloses a window-opening behavior detection method, detection device, computer equipment, and storage medium. The method involves acquiring the target ambient temperature of a target spatial area at a target acquisition time. A preset list is obtained to store candidate acquisition data, including candidate acquisition time and candidate ambient temperature acquired at that time. Then, based on the total number of candidate acquisition data in the preset list and the list's capacity, a capacity check is performed on the preset list to determine its storage state. The target acquisition time and target ambient temperature are recorded into the preset list according to the storage state, resulting in a target list. Finally, window-opening behavior detection is performed on the target spatial area based on the list's storage state and the target list to obtain the detection result. This application utilizes a preset list and capacity check to determine the initiation status of behavior detection on the target spatial area. By performing window-opening behavior detection on the target spatial area based on the list's storage state and the target list, it can automatically and accurately detect whether abnormal window-opening behavior exists in the current spatial area, thereby effectively avoiding energy waste and unnecessary heat loss. Attached Figure Description

[0046] Figure 1 This is a first flowchart of the window opening behavior detection method provided in the embodiments of this application;

[0047] Figure 2 This is a schematic diagram of the structure of the preset list provided in the embodiments of this application;

[0048] Figure 3 yes Figure 1 The flowchart of step S140 in the middle;

[0049] Figure 4 yes Figure 3 The flowchart of step S360 in the process;

[0050] Figure 5 This is a schematic diagram of the structure of the target list provided in the embodiments of this application;

[0051] Figure 6 yes Figure 1 The flowchart of step S150 in the middle;

[0052] Figure 7 yes Figure 6 The flowchart of step S630 in the process;

[0053] Figure 8 yes Figure 7 The flowchart of step S720 in the process;

[0054] Figure 9 This is a second flowchart of the window opening behavior detection method provided in the embodiments of this application;

[0055] Figure 10 This is a flowchart of the window opening behavior detection method provided in the embodiments of this application;

[0056] Figure 11 This is a schematic diagram of the window opening behavior detection device provided in the embodiments of this application;

[0057] Figure 12 This is a schematic diagram of the hardware structure of the computer device provided in the embodiments of this application. Detailed Implementation

[0058] To make the objectives, technical solutions, and advantages of this application clearer, the following detailed description is provided in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative and not intended to limit the scope of this application.

[0059] It should be noted that although functional modules are divided in the device schematic diagram and a logical order is shown in the flowchart, in some cases, the steps shown or described may be performed in a different order than the module division in the device or the order in the flowchart. The terms "first," "second," etc., in the specification, claims, and the aforementioned drawings are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence.

[0060] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this application belongs. The terminology used herein is for the purpose of describing embodiments of this application only and is not intended to limit this application.

[0061] Currently, window opening behavior detection refers to methods that use sensors or other technologies to monitor whether window opening behavior exists in a given space. For example, in an electric underfloor heating system, based on the results of window opening behavior detection, the system's operating mode can be automatically adjusted or heating can be stopped. When a user sets a temperature higher than the ambient temperature in the electric underfloor heating system, the heating source is activated. The heating source gradually raises the room temperature to the set temperature through heat exchange. However, when a user feels hot in the current space, they may choose to open a window to lower the indoor temperature, causing the room temperature to consistently fall below the set temperature, especially when there is a large temperature difference between the indoor and outdoor areas. Therefore, if the electric underfloor heating system continues to provide heat, this abnormal window opening behavior by the user will lead to energy waste and unnecessary heat loss.

[0062] Based on this, embodiments of this application provide a method and device for detecting window opening behavior, a computer device, and a storage medium, which can accurately detect whether there is abnormal window opening behavior in the current space area, thereby effectively avoiding energy waste and unnecessary heat loss.

[0063] The window opening behavior detection method provided in this application can be applied to a terminal, a server, or software running on either a terminal or a server. In some embodiments, the terminal can be a smartphone, tablet, laptop, desktop computer, etc.; the server can be configured as an independent physical server, a server cluster or distributed system composed of multiple physical servers, or a cloud server providing basic cloud computing services such as cloud services, cloud databases, cloud computing, cloud functions, cloud storage, network services, cloud communication, middleware services, domain name services, security services, content delivery networks (CDN), and big data and artificial intelligence platforms; the software can be an application implementing the window opening behavior detection method, but is not limited to the above forms.

[0064] This application can be used in a wide variety of general-purpose or special-purpose computer system environments or configurations. Examples include: personal computers, server computers, handheld or portable devices, tablet devices, multiprocessor systems, microprocessor-based systems, set-top boxes, programmable consumer electronics, network personal computers (PCs), minicomputers, mainframe computers, and distributed computing environments including any of the above systems or devices. This application can be described in the general context of computer-executable instructions executed by a computer, such as program modules. Generally, program modules include routines, programs, objects, components, data structures, etc., that perform specific tasks or implement specific abstract data types. This application can also be practiced in distributed computing environments where tasks are performed by remote processing devices connected via a communication network. In distributed computing environments, program modules can reside in local and remote computer storage media, including storage devices.

[0065] Please see Figure 1 , Figure 1 This is an optional flowchart of the window opening behavior detection method provided in the embodiments of this application. Figure 1 The method described below may specifically include, but is not limited to, steps S110 to S150. Figure 1 These five steps will be explained in detail.

[0066] Step S110: Obtain the target ambient temperature collected in the target spatial region at the target acquisition time;

[0067] Step S120: Obtain a preset list. The preset list is used to store candidate acquisition data. The candidate acquisition data includes candidate acquisition time and candidate ambient temperature of the target space region acquired at the candidate acquisition time.

[0068] Step S130: Based on the total number of candidate data to be collected in the preset list and the capacity of the preset list, perform capacity detection on the preset list to determine the list storage status of the preset list;

[0069] Step S140: Record the target acquisition time and target ambient temperature into a preset list according to the list storage status to obtain the target list;

[0070] Step S150: Perform window opening behavior detection on the target space region based on the list storage state and the target list to obtain the behavior detection result.

[0071] It should be noted that this application first obtains the target ambient temperature collected from the target spatial region at the target collection time. A preset list is then obtained to store candidate collection data. This candidate collection data includes candidate collection time and candidate ambient temperature collected from the target spatial region at the candidate collection time. Then, based on the total number of candidate collection data in the preset list and the capacity of the preset list, a capacity check is performed on the preset list to determine its storage status. The target collection time and target ambient temperature are recorded into the preset list according to the storage status, resulting in a target list. Afterwards, window opening behavior detection is performed on the target spatial region based on the list storage status and the target list to obtain the behavior detection results. This application utilizes the preset list and capacity check of the preset list to determine the initiation status of behavior detection on the target spatial region, and performs window opening behavior detection on the target spatial region based on the list storage status and the target list. This can automatically and accurately detect whether there is abnormal window opening behavior in the current spatial region, thereby effectively avoiding energy waste and unnecessary heat loss.

[0072] It should be noted that the window opening behavior detection method in this application embodiment can be set in the electric underfloor heating device or in the server that controls the electric underfloor heating device, and no specific limitation is made here.

[0073] In step S110 of some embodiments, the target acquisition time refers to the time at which the current temperature is acquired. The target space area refers to the area where the target object's temperature is acquired at the acquisition time. The target ambient temperature refers to the temperature acquired in the target space area at the target acquisition time. For example, in an electric underfloor heating system, when the target object is set to a temperature higher than the indoor ambient temperature in the control system, the heating source is activated. The heating source gradually raises the room temperature to the set temperature through heat exchange. Therefore, the room at this time is the target space area, and the temperature sensor in the electric underfloor heating system will detect the target ambient temperature of the room at the target acquisition time.

[0074] In step S120 of some embodiments, the preset list refers to a data structure used to store candidate acquisition data. The candidate acquisition data includes candidate acquisition times and candidate ambient temperatures acquired from the target spatial region at the candidate acquisition times. A candidate acquisition time refers to a time earlier than the target acquisition time. For example, if the target acquisition time is 10:20 AM, the candidate acquisition times could be 10:05 AM, 10:15 AM, etc.

[0075] It should be noted that temperature data is collected according to a preset collection time interval, meaning the time difference between the candidate collection time and the target collection time is an integer multiple of the preset collection time interval. The preset collection time interval is determined based on the total preset recording duration corresponding to the preset list and the length of the preset list. For example, if the total preset recording duration is 10 minutes (600 seconds), the preset list is used to store data collected within 10 minutes. If the length of the preset list is 60 (60 represents the number of data items the preset list can store), then the preset collection time interval is 600 / 60 = 10 seconds. Therefore, temperature data is collected from the target spatial area every 10 seconds, resulting in one candidate collection data point, which is then stored in the preset list.

[0076] It should be noted that, for example, in an electric underfloor heating system, the preset data acquisition time interval can refer to the time interval at which the temperature sensor acquires temperature data from the target space area. For example... Figure 2 As shown, the preset total recording duration T1 corresponding to preset list 210 is 10 minutes (i.e., 600 seconds), and the length n of the preset list is 6. Each list unit 211 in preset list 210 is used to record one piece of collected data. At this time, the preset collection time interval T2 = T1 / (n-1) = 600 / (6-1) = 120 seconds (i.e., 2 minutes). For example, the ambient temperature collected by the temperature sensor at time t1 is S1, the ambient temperature collected at time t1+T2 is S2, the ambient temperature collected at time t1+2T2 is S3, the ambient temperature collected at time t1+3T2 is S4, the ambient temperature collected at time t1+5T2 is S5, and the ambient temperature collected at time t1+6T2 is S6.

[0077] The advantages of the above embodiments are that the data in the preset list is stored in the order it was added, thus maintaining the sequential nature of the data. This is particularly effective for data containing time information, allowing for easy retrieval and analysis of data in chronological order. Furthermore, the length of the preset list can be flexibly adjusted according to actual needs and specific application scenarios, making the list highly suitable for datasets that require frequent updates and modifications, thereby improving the efficiency of updating collected data.

[0078] In step S130 of some embodiments, the total number of candidate acquisition data refers to the number of acquisition data already stored in the current preset list. That is, before the target acquisition time and target ambient temperature are stored in the preset list, the number of acquisition data recorded in the preset list is counted to obtain the total number of candidate acquisition data.

[0079] The preset list capacity refers to the length of the preset list in the above embodiments. The total number of candidate data to be collected in the preset list is less than or equal to the preset list capacity.

[0080] In one embodiment, when the total number of candidate data points in the preset list equals the preset list capacity, the list storage state is determined to be saturated. For example, before storing the target acquisition time and target ambient temperature in the preset list, if the number of data points recorded in the preset list is counted and the total number of candidate data points is 6, and the preset list capacity is 6, then the list storage state is determined to be saturated.

[0081] In another embodiment, when the total number of candidate data to be collected in the preset list is less than the capacity of the preset list, the list storage state is determined to be unsaturated. For example, before storing the target collection time and target ambient temperature in the preset list, if the number of data to be collected in the preset list is counted and the total number of candidate data to be collected is 4, and the capacity of the preset list is 6, then the list storage state is determined to be unsaturated.

[0082] The advantage of the above embodiments is that by using a preset list of fixed length, the storage state of the preset list is determined by comparing the total number of candidate data to be collected in the preset list with the capacity of the preset list, which can avoid the impact of the continuous growth of the list on the efficiency of window opening behavior detection.

[0083] In step S140 of some embodiments, the target list refers to a data structure after the current target acquisition time and target ambient temperature are recorded in a preset list, and the preset list is updated. Therefore, the target list at this time contains all or part of the candidate acquisition data and the target acquisition data (i.e., including the target acquisition time and target ambient temperature).

[0084] Please see Figure 3 , Figure 3 This is an optional flowchart of step S140 provided in the embodiments of this application. In some embodiments of this application, step S140 may specifically include, but is not limited to, steps S310 to S330, as described below. Figure 3 These three steps will be explained in detail.

[0085] Step S310: When the list storage state is saturated, calculate the difference between the candidate acquisition time and the target acquisition time to obtain the acquisition time difference data.

[0086] Step S320: Delete the candidate acquisition data corresponding to the smallest acquisition time difference data from the preset list to obtain the candidate list;

[0087] Step S330: Record the target acquisition time and target ambient temperature into the candidate list to obtain the target list.

[0088] In steps S310 to S330 of some embodiments, when the list storage state is saturated, it means that the current preset list has no extra list units to store the target acquisition data. Therefore, according to the first-in, first-out principle, the target acquisition data is first stored in the preset list. Specifically, since the preset list contains multiple candidate acquisition data, the difference between the acquisition time and the target acquisition time for each candidate acquisition data is calculated to obtain the acquisition time difference data corresponding to each candidate acquisition data. Then, the candidate acquisition data corresponding to the smallest acquisition time difference data is deleted from the preset list to obtain the candidate list. That is, it is equivalent to deleting the candidate acquisition data that entered the preset list first. Afterwards, the target acquisition time and the target ambient temperature are recorded in the candidate list to obtain the target list.

[0089] For example, such as Figure 4As shown, for example, the preset list capacity is 6, and the total preset record duration corresponding to the preset list is 10 minutes (i.e., 600 seconds). In this case, the preset acquisition time interval = 600 / (6-1) = 120 seconds (i.e., 2 minutes). The current preset list records 6 candidate acquisition data points collected by the temperature sensor on the same day, specifically: an ambient temperature of 21 degrees Celsius collected at 10:00:00, an ambient temperature of 23 degrees Celsius collected at 10:02:00, an ambient temperature of 22 degrees Celsius collected at 10:04:00, an ambient temperature of 25 degrees Celsius collected at 10:06:00, an ambient temperature of 27 degrees Celsius collected at 10:08:00, and an ambient temperature of 29 degrees Celsius collected at 10:10:00. Therefore, the current preset list is in a list saturation state. The current target acquisition time is 10:12:00. The difference between each candidate acquisition time and the target acquisition time is calculated, resulting in acquisition time differences of 12 minutes, 10 minutes, 8 minutes, 6 minutes, 4 minutes, and 2 minutes respectively. Therefore, the data corresponding to an ambient temperature of 21 degrees Celsius acquired at 10:00:00 will be deleted. The target acquisition time of 10:12:00 and the corresponding target ambient temperature of 30 degrees Celsius will be recorded in the candidate list, and the new candidate list will be used as the target list.

[0090] It should be noted that when the list storage state is not saturated, the target acquisition time and target ambient temperature are directly recorded in the candidate list, and the new candidate list is used as the target list. For example... Figure 5 As shown, for example, the preset list capacity is 6, and the total preset recording duration corresponding to the preset list is 10 minutes (i.e., 600 seconds). In this case, the preset acquisition time interval = 600 / (6-1) = 120 seconds (i.e., 2 minutes). The current preset list records four candidate acquisition data points collected by the temperature sensor on the same day, specifically: an ambient temperature of 21 degrees Celsius collected at 10:00:00, an ambient temperature of 23 degrees Celsius collected at 10:02:00, an ambient temperature of 22 degrees Celsius collected at 10:04:00, and an ambient temperature of 25 degrees Celsius collected at 10:06:00. Therefore, the current preset list is in an unsaturated state. The current target acquisition time 10:08:00 and the corresponding target ambient temperature of 28 degrees Celsius are recorded in the candidate list, and the new candidate list is used as the target list.

[0091] It should be noted that when a new target data is collected again, steps S120 to S150 are repeated.

[0092] In step S150 of some embodiments, in order to improve the accuracy of window opening behavior detection, this application performs window opening behavior detection on the target space region based on the list storage state and the target list to obtain behavior detection results. The behavior detection results are used to indicate whether an object in the current target space region has performed window opening behavior, thereby affecting scenarios such as the heating speed of an electric underfloor heating system.

[0093] Please see Figure 6 , Figure 6 This is an optional flowchart of step S150 provided in the embodiments of this application. In some embodiments of this application, step S150 may specifically include, but is not limited to, steps S610 to S630, as described below. Figure 6 These three steps will be explained in detail.

[0094] Step S610: When the list storage state is list saturation state, obtain the target group from the target list. The target group includes first target data and second target data. The first target data and second target data are adjacent data collected according to a preset collection time interval. The first target data includes the first collection time and the first ambient temperature collected in the target space area at the first collection time. The second target data includes the second collection time and the second ambient temperature collected in the target space area at the second collection time.

[0095] Step S620: Perform temperature judgment on the first ambient temperature and the second ambient temperature in the same target group to obtain the adjacent temperature judgment results;

[0096] Step S630: Detect window opening behavior in the target space area based on the adjacent temperature judgment results, and obtain the behavior detection results.

[0097] In step S610 of some embodiments, to improve the accuracy of window opening behavior detection, this embodiment performs window opening behavior detection when the list storage state is saturated, and does not perform window opening behavior detection when the list storage state is unsaturated. A target group refers to a group of data consisting of any two adjacent data points randomly selected from the target list. For example, the target list includes collected data 1, collected data 2, collected data 3, and collected data 4. The target group in this case includes (collected data 1, collected data 2), (collected data 2, collected data 3), and (collected data 3, collected data 4).

[0098] It should be noted that the second acquisition time of the second target data is later than the first acquisition time of the first target data, and the time difference between the second acquisition time and the first acquisition time is a preset acquisition time interval.

[0099] In steps S620 and S630 of some embodiments, since the temperature in the target space area continuously rises when there is no window opening behavior in the heating scenario of electric underfloor heating, a temperature judgment is performed on the first and second ambient temperatures in the same target group to obtain adjacent temperature judgment results. The adjacent temperature judgment results are used to represent the overall change between adjacent ambient temperatures in a preset list. Then, window opening behavior detection is performed on the target space area based on the adjacent temperature judgment results to obtain behavior detection results.

[0100] The advantage of the above embodiments is that by judging the ambient temperature of adjacent areas in the target list, the overall temperature change trend can be obtained more clearly and simply, thereby enabling more efficient detection of abnormal window opening behavior in the current space area to avoid energy waste and unnecessary heat loss.

[0101] Please see Figure 7 , Figure 7 This is an optional flowchart of step S630 provided in the embodiments of this application. In some embodiments of this application, the adjacent temperature judgment result includes a first judgment sub-result, which indicates that the first ambient temperature in the same target group in the target list is greater than or equal to the second ambient temperature. Therefore, step S630 may specifically include, but is not limited to, steps S710 to S720. The following is a detailed explanation... Figure 7 These two steps will be explained in detail.

[0102] Step S710: When the adjacent temperature judgment result is the first judgment sub-result, the behavior detection result is determined to be that there is abnormal window opening behavior in the target space area;

[0103] Step S720: When the adjacent temperature judgment result is not the first judgment sub-result, the window opening behavior detection is performed on the target space area according to the preset temperature difference threshold to obtain the behavior detection result.

[0104] In step S710 of some embodiments, the first judgment sub-result refers to the result after temperature judgment for each target group. When adjacent temperature judgment results are the first judgment sub-results, the behavior detection result is determined to be abnormal window opening behavior in the target space region. For example, the target list contains six ambient temperatures obtained from morning to night according to the collection time: 31 degrees Celsius (°C), 29°C, 28°C, 27°C, 26°C, and 26°C. The target groups obtained from the target list include: (31°C, 29°C), (29°C, 28°C), (28°C, 27°C), (27°C, 26°C), and (26°C, 26°C). Comparing the two ambient temperatures in each target group, we get 31>29>28>27>26=26, indicating that the first ambient temperature in the same target group in the target list is greater than or equal to the second ambient temperature. Therefore, the trend of the ambient temperature is decreasing, and the behavior detection result can be determined to be abnormal window opening behavior in the target space region.

[0105] It should be noted that when the window opening behavior detection method of this application is applied to a terminal, the temperature sensor transmits the collected temperature to the terminal. The terminal automatically determines that the user has engaged in abnormal window opening behavior, then shuts off the heating and simultaneously alerts the user through human-computer interaction on the device terminal. If the terminal is connected to the internet, the abnormal window opening alert is sent via an app.

[0106] This application utilizes software to automatically detect abnormal window-opening behavior by users, eliminating the need for additional hardware costs. When the system detects abnormal window-opening behavior, it stops heating to prevent energy waste; simultaneously, it alerts the user via a human-machine interface or remote app to the abnormal window-opening behavior when heating is stopped. Therefore, it can automatically and accurately detect abnormal window-opening behavior in the current space, effectively preventing energy waste and unnecessary heat loss.

[0107] In step S720 of some embodiments, the adjacent temperature judgment result further includes a second judgment sub-result. The second judgment sub-result indicates that not all first ambient temperatures in the same target group in the target list are greater than or equal to the second ambient temperature. When the adjacent temperature judgment result is not the first judgment sub-result, it is the second judgment sub-result. For example, the target list contains six ambient temperatures obtained from morning to night according to the collection time: 21 degrees Celsius (°C), 23°C, 22°C, 25°C, 27°C, and 24°C. The target groups obtained from the target list include: (21°C, 23°C), (23°C, 22°C), (22°C, 25°C), (25°C, 27°C), and (27°C, 24°C). The two ambient temperatures in each target group are compared to obtain 21°C. <23> 22<25 <27> 24 indicates that the first ambient temperature in the same target group in the target list is not always greater than or equal to the second ambient temperature. Therefore, it is necessary to further detect window opening behavior in the target space area based on a preset temperature difference threshold to obtain the behavior detection results.

[0108] The advantage of the above embodiments is that, firstly, by judging the temperature of adjacent ambient temperatures, it is determined whether the ambient temperature of the previous sampled temperature is less than or equal to the ambient temperature of the next sampled temperature. When it is impossible to determine whether there is abnormal window opening behavior based on the temperature judgment results of adjacent ambient temperatures, window opening behavior detection is then performed on the target space area according to the preset temperature difference threshold. From a simple detection method to a more accurate detection condition, the efficiency of abnormal window opening behavior can be effectively improved.

[0109] Please see Figure 8 , Figure 8 This is an optional flowchart of step S720 provided in the embodiments of this application. In some embodiments of this application, step S720 may specifically include, but is not limited to, steps S810 to S830, as described below. Figure 8 These three steps will be explained in detail.

[0110] Step S810: Calculate the difference between the second ambient temperature and the first ambient temperature in the same target group to obtain adjacent temperature difference data;

[0111] Step S820: Summing adjacent temperature difference data according to the target list to obtain the total target temperature difference data;

[0112] Step S830: When the total difference in the target temperature is less than or equal to the preset temperature difference threshold, the behavior detection result is determined to be abnormal window opening behavior in the target space area.

[0113] In step S810 of some embodiments, when the adjacent temperature judgment result is not the first judgment sub-result, the difference between the second ambient temperature and the first ambient temperature in the same target group is calculated to obtain adjacent temperature difference data. For example, the target list contains six ambient temperatures obtained from early to late according to the collection time: 21 degrees Celsius (°C), 23°C, 22°C, 25°C, 27°C, and 24°C. The target groups obtained from the target list include: (21°C, 23°C), (23°C, 22°C), (22°C, 25°C), (25°C, 27°C), and (27°C, 24°C). The difference between the two ambient temperatures in each target group is calculated, that is, the ambient temperature collected at the later time point is subtracted from the ambient temperature collected at the previous time point. Then, the adjacent temperature difference data obtained for multiple target groups are as follows: 23-21=2°C, 22-23=-1°C, 25-22=3°C, 27-25=2°C, and 24-27=-3°C.

[0114] In step S820 of some embodiments, the total target temperature difference data refers to the sum of the adjacent temperature difference data corresponding to all target groups in the target list. For example, in the embodiment of step S810, the total target temperature difference data obtained by taking the adjacent temperature difference data of multiple target groups is 2 + (-1) + 3 + 2 + (-3) = 3℃.

[0115] In step S830 of some embodiments, since the temperature sensor may also malfunction during a single data acquisition, but one or a few malfunctions do not necessarily indicate the existence of abnormal window opening behavior, a preset temperature difference threshold is used to provide a measurement error range to avoid incorrect judgment of window opening behavior due to temperature sensor malfunctions. When the total target temperature difference is less than or equal to the preset temperature difference threshold, the behavior detection result is determined to indicate the existence of abnormal window opening behavior in the target space region. For example, if the preset temperature difference threshold is 0.5℃ and the total target temperature difference is 0.2℃, 0.2℃ < 0.5℃, therefore, the behavior detection result is determined to indicate the existence of abnormal window opening behavior in the target space region.

[0116] It should be noted that when the total target temperature difference is greater than the preset temperature difference threshold, the behavior detection result is determined to be that there is no abnormal window opening behavior in the target space area. For example, if the preset temperature difference threshold is 0.5℃ and the total target temperature difference is 2℃, 0.2℃ > 0.5℃. Therefore, this situation does not necessarily indicate window opening, but only that an anomaly may have occurred during a certain temperature acquisition. In this case, the behavior detection result is determined to be that there is no abnormal window opening behavior in the target space area.

[0117] In one embodiment, the climate compensation coefficient varies depending on the ambient temperature. The lower the ambient temperature, the faster it rises when heated at a fixed power. Therefore, the preset temperature difference threshold can be adaptively adjusted according to different ambient temperatures to improve the accuracy of window opening behavior detection.

[0118] Please see Figure 9 , Figure 9 This is another optional flowchart of the window opening behavior detection method provided in the embodiments of this application. In some embodiments of this application, a preset list is used to record data collected within a preset heating time interval. Before step S720, the window opening behavior detection method of this application further includes: obtaining a preset temperature difference threshold. This step may specifically include, but is not limited to, steps S910 to S930. The following is in conjunction with... Figure 9 These three steps will be explained in detail.

[0119] Step S910: Obtain historical heating temperature data of the target ambient temperature. Historical heating temperature data refers to the temperature data obtained after heating the target ambient temperature for a preset heating time interval without any abnormal window opening behavior.

[0120] Step S920: Calculate the difference between the historical heating temperature data and the target ambient temperature to obtain the historical heating temperature difference data;

[0121] Step S930: Calculate the average of historical heating temperature difference data to obtain a preset temperature difference threshold.

[0122] In step S910 of some embodiments, historical heating temperature data refers to temperature heating data collected from the target space area over a historical time period. It also refers to the temperature data obtained after heating the target ambient temperature for a preset heating time interval without any abnormal window opening behavior (i.e., the temperature under ideal conditions). The preset heating time interval is the total preset recording duration corresponding to the preset list. For example, if the total preset recording duration corresponding to the preset list is 10 minutes, and the target ambient temperature is 23°C, heating 23°C for 10 minutes over a historical time period yields historical heating temperature data of 30°C.

[0123] In steps S920 and S930 of some embodiments, if there is a window opening, the target ambient temperature may not reach the historical heating temperature data after heating for 10 minutes. Therefore, the difference between the historical heating temperature data and the target ambient temperature is calculated to obtain historical heating temperature difference data, and the average value of the historical heating temperature difference data is used as a preset temperature difference threshold. For example, if the total duration of the preset records corresponding to the preset list is 10 minutes, the target ambient temperature is 23°C, and the historical heating temperature data obtained after heating 23°C for 10 minutes is 30°C, the difference between the historical heating temperature data and the target ambient temperature is 30°C - 23°C = 7°C, 7°C / 2 = 3.5°C. In this case, the preset temperature difference threshold is 3.5°C.

[0124] It should be noted that the adaptive adjustment of the preset temperature difference threshold in this application is based on the user's past heating experience to correct the preset temperature difference threshold. For example, if a user needs 5℃ for a heating temperature difference ΔSTm to heat from 10℃ to 15℃, it usually takes 30 minutes. However, if a window is open, it may not be possible to heat to 15℃, or it may take an hour. Therefore, based on a 30-minute heating time, the actual temperature rise may only reach 12℃, and in this case, the preset temperature difference threshold is set to 5 / 2 = 2.5℃.

[0125] In one embodiment, when recording historical heating temperature data over a historical time period, a collection time can be recorded every 0.5℃. For example, when heating from -30℃ to 40℃, a total of 140 recording points are recorded. Each recording point records the time required to raise the temperature by 0.5℃ from the current ambient temperature. If the current ambient temperature is ST, and the required heating temperature is ΔT, then the heating time t is recorded for every 0.5℃ increase. Therefore, the index of the recorded temperature is 2*ST, 2*ST+1, ..., 2*ST+2*ΔT. That is, for example, if the current ambient temperature ST is 10℃ and ΔT is 0.5℃, the index of ST in the record list is 2*10 = 20, the next index is 21, corresponding to an ambient temperature of 10.5℃; the next index is 22, corresponding to an ambient temperature of 11℃.

[0126] It should be noted that adaptive adjustment of the preset temperature difference threshold requires the collection of historical time period data to determine historical heating temperature data. The historical time period can be three days or one week prior to the current time, etc., without specific limitations. During the window opening behavior detection process within the historical time period, the preset temperature difference threshold can be a pre-set data value, such as 1.5℃, 2℃, etc., without specific limitations.

[0127] For example, Figure 10 This paper presents a complete flowchart of the window opening behavior detection method provided in the embodiments of this application in a practical application. Specifically, it may include, but is not limited to, steps S1010 to S10100, which are described below in conjunction with… Figure 10 These ten steps will be explained in detail.

[0128] Step S1010: The temperature sensor collects a target ambient temperature and a target collection time at preset collection time intervals;

[0129] Step S1020: Define a preset list, which records the ambient temperature collected within a preset total recording time.

[0130] Step S1030: In response to the received target ambient temperature, determine whether the total number of candidate data collected in the preset list is equal to the preset list capacity. If yes, proceed to step S1040; otherwise, proceed to step S1050.

[0131] Step S1040: Remove the candidate data with the longest acquisition time for the current target and add the target acquisition temperature to the preset list to obtain the target list; then, execute step S1060.

[0132] Step S1050: Directly add the target temperature to the preset list, and then execute step S1010 again;

[0133] Step S1060: Determine whether the collected temperatures in the target list show a gradually increasing trend. If yes, repeat step S1030; otherwise, proceed to step S1070.

[0134] Step S1070: Calculate the difference between the second ambient temperature and the first ambient temperature in the same target group in the target list to obtain adjacent temperature difference data, and sum the adjacent temperature difference data according to the target list to obtain the total target temperature difference data;

[0135] Step S1080: Determine whether the target total temperature difference threshold is less than or equal to the preset temperature difference threshold. If yes, proceed to step S1090; otherwise, proceed to step S1030.

[0136] Step S1090: Determine that the behavior detection result indicates the presence of abnormal window opening behavior in the target space area;

[0137] Step S10100: Turn off the heating and simultaneously notify the user via human-machine interaction on the device terminal. If the terminal is connected to the internet, an abnormal window opening notification message will be sent via the APP.

[0138] This application utilizes software to automatically detect abnormal window-opening behavior by users, eliminating the need for additional hardware costs. When the system detects abnormal window-opening behavior, it stops heating to prevent energy waste. Simultaneously, it alerts the user via a human-machine interface or remote app to the abnormal window-opening behavior upon termination of heating. Furthermore, a preset temperature difference threshold can be adaptively adjusted based on ambient temperature to improve the accuracy of window-opening behavior detection. Therefore, this application utilizes a preset list and its capacity detection to determine the initiation status of behavior detection in the target space area. Based on the list's storage status and the target list, it performs window-opening behavior detection in the target space area, automatically and accurately detecting the presence of abnormal window-opening behavior in the current space area, thereby effectively avoiding energy waste and unnecessary heat loss.

[0139] Please see Figure 11 , Figure 11This is a schematic diagram of the window opening behavior detection device provided in the embodiments of this application. The device can implement the window opening behavior detection method of the above embodiments. The device includes a temperature acquisition module 1110, a list acquisition module 1120, a capacity detection module 1130, a list recording module 1140, and a detection module 1150.

[0140] Temperature acquisition module 1110 is used to acquire the target ambient temperature of the target spatial region at the target acquisition time.

[0141] The list acquisition module 1120 is used to acquire a preset list, which is used to store candidate acquisition data. The candidate acquisition data includes candidate acquisition time and candidate ambient temperature of the target space region acquired at the candidate acquisition time.

[0142] The capacity detection module 1130 is used to perform capacity detection on the preset list based on the total number of candidate data to be collected and the capacity of the preset list, and to determine the list storage status of the preset list.

[0143] The list recording module 1140 is used to record the target acquisition time and target ambient temperature into a preset list according to the list storage status, so as to obtain a target list;

[0144] The behavior detection module 1150 is used to perform window opening behavior detection on the target space region based on the list storage state and the target list, and obtain the behavior detection results.

[0145] It should be noted that the window opening behavior detection device in this application embodiment is used to implement the window opening behavior detection method in the above embodiment. The window opening behavior detection device in this application embodiment corresponds to the aforementioned window opening behavior detection method. For the specific processing procedure, please refer to the aforementioned window opening behavior detection method, which will not be repeated here.

[0146] This application also provides a computer device comprising: at least one memory, at least one processor, and at least one computer program. The at least one computer program is stored in the at least one memory, and the at least one processor executes the at least one computer program to implement any of the window opening behavior detection methods described in the above embodiments. This computer device can be any smart terminal, including tablet computers, in-vehicle computers, etc.

[0147] Please see Figure 12 , Figure 12 The illustration shows the hardware structure of a computer device according to another embodiment, the computer device comprising:

[0148] The processor 1210 can be implemented using a general-purpose central processing unit (CPU), microprocessor, application specific integrated circuit (ASIC), or one or more integrated circuits, and is used to execute relevant programs to implement the technical solutions provided in the embodiments of this application.

[0149] The memory 1220 can be implemented as a read-only memory (ROM), static storage device, dynamic storage device, or random access memory (RAM). The memory 1220 can store the operating system and other applications. When the technical solutions provided in the embodiments of this specification are implemented through software or firmware, the relevant program code is stored in the memory 1220 and called and executed by the processor 1210 using the window opening behavior detection method of the embodiments of this application.

[0150] The input / output interface 1230 is used to implement information input and output.

[0151] The communication interface 1240 is used to enable communication and interaction between this device and other devices. Communication can be achieved through wired means (such as USB, network cable, etc.) or wireless means (such as mobile network, WIFI, Bluetooth, etc.).

[0152] Bus 1250 transmits information between various components of the device (e.g., processor 1210, memory 1220, input / output interface 1230, and communication interface 1240);

[0153] The processor 1210, memory 1220, input / output interface 1230 and communication interface 1240 are connected to each other within the device via bus 1250.

[0154] This application also provides a storage medium, which is a computer-readable storage medium storing a computer program for causing a computer to execute the window opening behavior detection method described in the above embodiments.

[0155] Memory, as a non-transitory computer-readable storage medium, can be used to store non-transitory software programs and non-transitory computer-executable programs. Furthermore, memory may include high-speed random access memory, and may also include non-transitory memory, such as at least one disk storage device, flash memory device, or other non-transitory solid-state storage device. In some embodiments, memory may optionally include memory remotely located relative to the processor, and these remote memories can be connected to the processor via a network. Examples of such networks include, but are not limited to, the Internet, intranets, local area networks, mobile communication networks, and combinations thereof.

[0156] The embodiments described in this application are for the purpose of more clearly illustrating the technical solutions of the embodiments of this application, and do not constitute a limitation on the technical solutions provided by the embodiments of this application. As those skilled in the art will know, with the evolution of technology and the emergence of new application scenarios, the technical solutions provided by the embodiments of this application are also applicable to similar technical problems.

[0157] Those skilled in the art will understand that the technical solutions shown in the figures do not constitute a limitation on the embodiments of this application, and may include more or fewer steps than shown, or combine certain steps, or different steps.

[0158] The device embodiments described above are merely illustrative. The units described as separate components may or may not be physically separate; that is, they may be located in one place or distributed across multiple network units. Some or all of the modules can be selected to achieve the purpose of this embodiment according to actual needs.

[0159] Those skilled in the art will understand that all or some of the steps in the methods disclosed above, as well as the functional modules / units in the systems and devices, can be implemented as software, firmware, hardware, or suitable combinations thereof.

[0160] The terms “first,” “second,” “third,” “fourth,” etc. (if present) in the specification and accompanying drawings of this application are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence. It should be understood that such data can be interchanged where appropriate so that the embodiments of this application described herein can be implemented in orders other than those illustrated or described herein. Furthermore, the terms “comprising” and “having,” and any variations thereof, are intended to cover non-exclusive inclusion; for example, a process, method, system, product, or apparatus that comprises a series of steps or units is not necessarily limited to those steps or units explicitly listed, but may include other steps or units not explicitly listed or inherent to such processes, methods, products, or apparatus.

[0161] It should be understood that in this application, "at least one (item)" means one or more, and "more than" means two or more. "And / or" is used to describe the relationship between related objects, indicating that three relationships can exist. For example, "A and / or B" can represent three cases: only A exists, only B exists, and both A and B exist simultaneously, where A and B can be singular or plural. The character " / " generally indicates that the preceding and following related objects are in an "or" relationship. "At least one (item) of the following" or similar expressions refer to any combination of these items, including any combination of single or plural items. For example, at least one (item) of a, b, or c can represent: a, b, c, "a and b", "a and c", "b and c", or "a and b and c", where a, b, and c can be single or multiple.

[0162] In the several embodiments provided in this application, it should be understood that the disclosed apparatus and methods can be implemented in other ways. For example, the apparatus embodiments described above are merely illustrative; for instance, the division of the units described above is only a logical functional division, and in actual implementation, there may be other division methods. For example, multiple units or components may be combined or integrated into another system, or some features may be ignored or not executed. Furthermore, the coupling or direct coupling or communication connection shown or discussed may be through some interfaces; the indirect coupling or communication connection between apparatuses or units may be electrical, mechanical, or other forms.

[0163] The units described above as separate components may or may not be physically separate. The components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the units can be selected to achieve the purpose of this embodiment according to actual needs.

[0164] Furthermore, the functional units in the various embodiments of this application can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit. The integrated unit can be implemented in hardware or as a software functional unit.

[0165] If the integrated unit is implemented as a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of this application, in essence, or the part that contributes to the prior art, or all or part of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes multiple instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods of the various embodiments of this application. The aforementioned storage medium includes various media capable of storing programs, such as USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.

[0166] The preferred embodiments of the present application have been described above with reference to the accompanying drawings, but this does not limit the scope of the claims of the present application. Any modifications, equivalent substitutions, and improvements made by those skilled in the art without departing from the scope and substance of the embodiments of the present application shall be within the scope of the claims of the present application.

Claims

1. A method for detecting window opening behavior, characterized in that, The method includes: Acquire the target ambient temperature of the target spatial region at the target acquisition time; Obtain a preset list, which is used to store candidate acquisition data, including candidate acquisition time and candidate ambient temperature of the target space region acquired at the candidate acquisition time; Based on the total number of candidate data to be collected in the preset list and the capacity of the preset list, the capacity of the preset list is checked to determine the list storage status of the preset list; According to the list storage state, the target acquisition time and the target ambient temperature are recorded into a preset list to obtain a target list; wherein, when the list storage state is saturated, the difference between the candidate acquisition time and the target acquisition time is calculated to obtain acquisition time difference data; the candidate acquisition data corresponding to the smallest acquisition time difference data is deleted from the preset list to obtain a candidate list; the target acquisition time and the target ambient temperature are recorded into the candidate list to obtain the target list, so that the target list stores the latest acquired data; When the list storage state is saturated, a target group is obtained from the target list. A target group refers to a group of data consisting of any two adjacent data randomly selected from the target list. The target group includes first target data and second target data, wherein the first target data and the second target data are adjacent data collected according to a preset collection time interval. The first target data includes a first collection time and a first ambient temperature collected in the target space area at the first collection time. The second target data includes a second collection time and a second ambient temperature collected in the target space area at the second collection time. The second collection time is later than the first collection time, and the time difference between the second collection time and the first collection time is the preset collection time interval. Temperature judgment is performed on the first ambient temperature and the second ambient temperature in the same target group to obtain an adjacent temperature judgment result. The adjacent temperature judgment result includes a first judgment sub-result, which indicates that the first ambient temperature in the same target group in the target list is greater than or equal to the second ambient temperature. When the adjacent temperature judgment result is the first judgment sub-result, the behavior detection result is determined to be abnormal window opening behavior in the target space area; when the adjacent temperature judgment result is not the first judgment sub-result, the difference between the second ambient temperature and the first ambient temperature in the same target group is calculated to obtain adjacent temperature difference data; the adjacent temperature difference data is summed according to the target list to obtain the total target temperature difference data; when the total target temperature difference data is less than or equal to a preset temperature difference threshold, the behavior detection result is determined to be abnormal window opening behavior in the target space area.

2. The method according to claim 1, characterized in that, The preset list is used to record data collected within a preset heating time interval. Before calculating the difference between the second ambient temperature and the first ambient temperature in the same target group to obtain adjacent temperature difference data, the method includes: obtaining the preset temperature difference threshold, specifically including: The historical heating temperature data of the target ambient temperature is obtained. The historical heating temperature data refers to the temperature data obtained after heating the target ambient temperature for the preset heating time interval without any abnormal window opening behavior. The difference between the historical heating temperature data and the target ambient temperature is calculated to obtain historical heating temperature difference data. The preset temperature difference threshold is obtained by averaging the historical heating temperature difference data.

3. The method according to claim 1, characterized in that, The step of performing capacity detection on the preset list based on the total number of candidate data to be collected and the capacity of the preset list, and determining the list storage status of the preset list, includes: When the total number of candidate data collected in the preset list is equal to the capacity of the preset list, the list storage state is determined to be the list saturation state. When the total number of candidate data collected in the preset list is less than the capacity of the preset list, the list storage state is determined to be an unsaturated state.

4. A window opening behavior detection device, characterized in that, The device includes: The temperature acquisition module is used to acquire the target ambient temperature of the target spatial area at the target acquisition time; The list acquisition module is used to acquire a preset list, which is used to store candidate acquisition data. The candidate acquisition data includes candidate acquisition time and candidate ambient temperature of the target space region acquired at the candidate acquisition time. The capacity detection module is used to perform capacity detection on the preset list based on the total number of candidate data to be collected and the capacity of the preset list, and to determine the list storage status of the preset list. The list recording module is used to record the target acquisition time and the target ambient temperature into a preset list according to the list storage state to obtain a target list; wherein, when the list storage state is a list saturation state, the difference between the candidate acquisition time and the target acquisition time is calculated to obtain acquisition time difference data; the candidate acquisition data corresponding to the smallest acquisition time difference data is deleted from the preset list to obtain a candidate list; the target acquisition time and the target ambient temperature are recorded into the candidate list to obtain the target list, so that the target list stores the latest acquired data; A behavior detection module is used to retrieve a target group from the target list when the list storage state is saturated. A target group refers to a set of data consisting of any two adjacent data points randomly selected from the target list. The target group includes first target data and second target data, wherein the first target data and the second target data are adjacent data collected according to a preset collection time interval. The first target data includes a first collection time and a first ambient temperature collected in the target spatial region at the first collection time. The second target data includes a second collection time and a second ambient temperature collected in the target spatial region at the second collection time. The second collection time is later than the first collection time, and the time difference between the second collection time and the first collection time is the preset collection time interval. For the first ambient temperature and... The second ambient temperature is used to determine the adjacent temperature, which includes a first sub-determined result. The first sub-determined result indicates that the first ambient temperature in the same target group in the target list is greater than or equal to the second ambient temperature. When the adjacent temperature determination result is the first sub-determined result, the behavior detection result is determined to be abnormal window opening behavior in the target space area. When the adjacent temperature determination result is not the first sub-determined result, the difference between the second ambient temperature and the first ambient temperature in the same target group is calculated to obtain adjacent temperature difference data. The adjacent temperature difference data is summed according to the target list to obtain the total target temperature difference data. When the total target temperature difference data is less than or equal to a preset temperature difference threshold, the behavior detection result is determined to be abnormal window opening behavior in the target space area.

5. A computer device, characterized in that, include: At least one memory; At least one processor; At least one computer program; The at least one computer program is stored in the at least one memory, and the at least one processor executes the at least one computer program to perform: A method for detecting window opening behavior as described in any one of claims 1 to 3.

6. A storage medium, said storage medium being a computer-readable storage medium, characterized in that, The computer-readable storage medium stores a computer program that is used to cause the computer to execute: A method for detecting window opening behavior as described in any one of claims 1 to 3.

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