Ventilation system and control method

Abstract

The invention provides a ventilation system and a control method, the control method comprises: determining current reference data of an air supply area to obtain first reference data, the reference data comprising personnel density and an activity intensity coefficient, the personnel density being used for reflecting the density of personnel in an environmental space where the air supply area is located, and the activity intensity coefficient being used for reflecting the activity intensity coefficient; the activity intensity coefficient is determined based on the type of personnel activity and used for reflecting the intensity of personnel activity in the environment space where the air supply area is located; the fresh air volume needed by the air supply area is determined based on the first reference data, and the target fresh air volume is obtained; and ventilation equipment is controlled according to the target fresh air volume, so that fresh air corresponding to the target fresh air volume is provided for the air supply area. The accuracy of fresh air volume calculation can be improved, and balance of air quality and energy consumption is achieved.

Description

Technical Field

[0001] The present application relates to the technical field of ventilation, and particularly relates to a ventilation system and a control method. Background Art

[0002] A ventilation system is a device or engineering system that maintains indoor air quality by introducing fresh air (i.e., fresh wind) and exhausting polluted air (exhaust air), and is widely used in multiple fields such as residential buildings, commercial buildings, and industrial sites.

[0003] Currently, the ventilation system usually determines the required fresh air volume based on the number of people in the room and the set per capita fresh air volume standard. However, factors such as the density of people in the room will affect the fresh air volume required by people. The method of directly calculating the fresh air volume according to the per capita fresh air volume standard is difficult to adapt to the dynamic changes in the room, and it is easy to have a serious mismatch between the supply of fresh air volume and the actual demand, which may lead to problems such as poor indoor air quality or energy waste. Summary of the Invention

[0004] The present application provides a ventilation system and a control method, which can improve the accuracy of fresh air volume calculation and achieve a balance between air quality and energy consumption.

[0005] In a first aspect, some embodiments of the present application provide a ventilation system, including: a ventilation device and a controller;

[0006] The ventilation device is used to provide fresh air to the air supply area;

[0007] The controller is configured to:

[0008] Determine the current reference data of the air supply area to obtain a first reference data, where the reference data includes the personnel density and the activity intensity coefficient. The personnel density is used to reflect the density of people in the environmental space where the air supply area is located, and the activity intensity coefficient is determined based on the type of personnel activities and is used to reflect the intensity of personnel activities in the environmental space where the air supply area is located;

[0009] Based on the first reference data, determine the required fresh air volume of the air supply area to obtain a target fresh air volume;

[0010] Control the ventilation device according to the target fresh air volume to provide fresh air corresponding to the target fresh air volume to the air supply area.

[0011] In the embodiments of the present application, since the personnel density in the reference data can reflect the density of personnel in the environmental space where the air supply area is located, and the activity intensity coefficient is determined based on the type of personnel activities and can better reflect the intensity of personnel activities in the environmental space where the air supply area is located, and the density of personnel and the intensity of personnel activities will have a greater impact on the generation amount of air pollutants in the air supply area and the demand of personnel for fresh air. Therefore, based on this first reference data, it is possible to better analyze the fresh air volume required for the air supply area in combination with the actual situation of personnel in the environmental space where the air supply area is located, thereby improving the accuracy of the calculated target fresh air volume, and the obtained target fresh air volume matches the current actual demand of the air supply area. Furthermore, by supplying the corresponding fresh air to the air supply area according to the target fresh air volume, it is possible to effectively control energy consumption while ensuring the air quality of the environmental space where the air supply area is located, and achieve the balance between air quality and energy efficiency.

[0012] In some embodiments, a ventilation system is provided. Based on the first reference data, the fresh air volume required for the air supply area is determined to obtain a target fresh air volume, including the following process: determining an initial fresh air volume according to the first reference data; determining a fresh air volume adjustment coefficient according to the first reference data and the second reference data, where the second reference data includes the historical reference data of the air supply area; and determining the target fresh air volume according to the initial fresh air volume and the fresh air volume adjustment coefficient.

[0013] In the above technical solution, the second reference data reflecting the historical fresh air volume demand is introduced, and the weighted coefficient of the initial fresh air volume is calculated by combining the first reference data and the second reference data of the air supply area. After obtaining the fresh air volume adjustment coefficient, the final target fresh air volume is accurately calculated by combining the initial fresh air volume that meets the current fresh air volume demand and the fresh air volume adjustment coefficient, which can fully consider the influence of the current actual fresh air volume demand and the historical fresh air volume demand, thereby improving the accuracy and precision of fresh air volume calculation while improving the real-time performance and stability of fresh air volume adjustment.

[0014] In some embodiments, a ventilation system is provided. Determining a fresh air volume adjustment coefficient according to the first reference data and the second reference data includes the following process: calculating the change rate of each data in the first reference data based on the first reference data and the second reference data; and determining the fresh air volume adjustment coefficient according to the change rate of each data.

[0015] In the above technical solution, after calculating the change rate of each data according to the current reference data and the historical reference data of the air supply area respectively, and then comprehensively determining the fresh air volume adjustment coefficient according to the change rate of each data, while considering the data fluctuation of the reference data, fully considering the influence degree of the fluctuation and change of different data in the reference data on the fresh air volume demand, and improving the accuracy and calculation precision of the obtained fresh air volume adjustment coefficient.

[0016] In some embodiments, a ventilation system is provided. Based on the first reference data and the second reference data, calculating the change rate of each data in the first reference data includes the following process: determining the change rate of each data according to the first reference data, the second reference data, and a pre-designed calculation rule corresponding to the data, where the pre-designed calculation rules corresponding to different data are different.

[0017] In the above technical solution, the change rate of each data is calculated according to different pre-designed calculation rules, so that the calculated change rate can more accurately reflect the dynamic characteristics of the corresponding data and improve the reliability of the obtained change rate.

[0018] In some embodiments, a ventilation system is provided. Determining the fresh air volume adjustment coefficient according to the change rate of each data includes the following process: determining the fresh air volume adjustment coefficient according to the change rate of each data and a predetermined fuzzy rule.

[0019] In the above technical solution, determining the final fresh air volume adjustment coefficient according to the change rate of each data and a predetermined fuzzy rule can better handle uncertainty and ambiguity, and can adapt to different environmental conditions and scenarios, etc. It can improve the flexibility and adaptability of the calculation of the fresh air volume adjustment coefficient, thereby helping to improve the accuracy and robustness of the calculation of the fresh air volume adjustment coefficient.

[0020] In some embodiments, a ventilation system is provided. Determining the current reference data of the air supply area to obtain the first reference data includes the following process: acquiring a monitoring image corresponding to the air supply area; determining the personnel density based on the monitoring image, and determining the activity intensity coefficient based on the monitoring image.

[0021] In the above technical solution, only the monitoring image of the air supply area needs to be collected, and the current personnel density and activity intensity coefficient of the air supply area are determined according to the collected monitoring image, without the need to additionally install various sensors for detection, which can better reduce the cost of the ventilation system.

[0022] In some embodiments, a ventilation system is provided. Determining the activity intensity coefficient based on the monitoring image includes the following process: determining the activity intensity of each person in the air supply area based on the monitoring image; determining the activity intensity coefficient according to the activity intensity of each person and the weighting coefficient corresponding to each person.

[0023] In the above technical solution, after determining the activity intensity of each person in the air supply area, through a weighted calculation method, the activity intensity coefficient of the entire air supply area is calculated according to the activity intensity of each person, rather than simple linear superposition, which can improve the accuracy and reliability of the obtained activity intensity coefficient.

[0024] In some embodiments, a ventilation system is provided. The activity intensity coefficient is determined according to the activity intensity of each person and the weighted coefficient corresponding to each person, including the following process: determining the weighted coefficient corresponding to each person based on the distance between the person and the air outlet of the ventilation device.

[0025] In the above technical solution, when calculating the weighted coefficient corresponding to each person in the air supply area, the weighted coefficient corresponding to the person is calculated according to the distance between the person and the air outlet of the ventilation device. When calculating the overall intensity of personnel activities in the air supply area to reflect the demand for fresh air volume, the demand for fresh air volume of each person can be determined more accurately, which helps to improve the accuracy of the obtained activity intensity coefficient.

[0026] In some embodiments, a ventilation system is provided. The reference data further includes a space influence coefficient. Determining the current reference data of the air supply area to obtain the first reference data includes the following process: determining the space influence coefficient according to the spatial layout of the air supply area, and the space influence coefficient is used to reflect the influence degree of the spatial layout on the air flow field in the environmental space where the air supply area is located.

[0027] In the above technical solution, analyzing the influence degree of the spatial layout of the air supply area on the air flow field in the environmental space where the air supply area is located to obtain the space influence coefficient enables subsequent analysis of the required fresh air volume of the air supply area to comprehensively analyze the required fresh air volume of the air supply area in combination with the influence degree of the actual spatial layout on the air flow field, so as to ensure the air quality of the environmental space where the air supply area is located.

[0028] In a second aspect, some embodiments of the present application further provide a control method, and the method includes:

[0029] Determining the current reference data of the air supply area to obtain the first reference data, where the reference data includes the personnel density and the activity intensity coefficient. The personnel density is used to reflect the density of personnel in the environmental space where the air supply area is located, and the activity intensity coefficient is determined based on the type of personnel activities and is used to reflect the intensity of personnel activities in the environmental space where the air supply area is located;

[0030] Determining the required fresh air volume of the air supply area based on the first reference data to obtain the target fresh air volume;

[0031] Controlling the ventilation device according to the target fresh air volume to supply fresh air corresponding to the target fresh air volume to the air supply area.

[0032] In a third aspect, an embodiment of the present application provides a control device, including a module for executing the control method in the second aspect.

[0033] Fourthly, an embodiment of the present application provides a computer-readable storage medium. The computer storage medium stores a computer program, and when the computer program is executed by a processor, the steps of the control method described in the second aspect above are implemented.

[0034] Fifthly, an embodiment of the present application provides a computer program product. When the computer program product runs on a ventilation system, the ventilation system is enabled to execute the control method described in the second aspect above.

[0035] It can be understood that the beneficial effects of the second to fifth aspects above can be referred to the relevant descriptions in the first aspect, and will not be repeated here. BRIEF DESCRIPTION OF THE DRAWINGS

[0036] In order to more clearly illustrate the technical solutions in the embodiments of the present application or the prior art, the following will briefly introduce the drawings required for use in the description of the embodiments or the prior art. Obviously, the drawings in the following description are only some embodiments of the present application. For those of ordinary skill in the art, without creative efforts, other drawings can be obtained based on these drawings.

[0037] Figure 1 Schematic diagram of the operation scenario between the ventilation system and the control device provided by some embodiments of the present application;

[0038] Figure 2 Schematic diagram of the hardware configuration of the control device provided by some embodiments of the present application;

[0039] Figure 3 Block diagram of the structure of a ventilation system provided by some embodiments of the present application;

[0040] Figure 4 Sequence interaction diagram of a control method provided by some embodiments of the present application;

[0041] Figure 5 Flow schematic diagram of another control method provided by some embodiments of the present application;

[0042] Figure 6 Flow schematic diagram of another control method provided by some embodiments of the present application;

[0043] Figure 7 Flow schematic diagram of another control method provided by some embodiments of the present application;

[0044] Figure 8 Flow schematic diagram of another control method provided by some embodiments of the present application;

[0045] Figure 9 Block diagram of the structure of another control device provided by some embodiments of the present application. Detailed implementation manners

[0046] Embodiments will be described in detail below, and examples thereof are shown in the accompanying drawings. When the following description refers to the accompanying drawings, unless otherwise indicated, the same numbers in different drawings represent the same or similar elements. The implementation manners described in the following embodiments do not represent all implementation manners consistent with the present application. They are merely examples of systems and methods consistent with some aspects of the present application detailed in the claims.

[0047] It should be noted that the brief description of the terms in the present application is only for facilitating the understanding of the implementation manners described hereinafter, rather than intending to limit the implementation manners of the present application. Unless otherwise specified, these terms should be understood in their ordinary and common meanings.

[0048] The terms "first", "second", "third", etc. in the specification, claims and above-mentioned drawings of the present application are used to distinguish similar or like objects or entities, and do not necessarily mean to limit a specific order or sequence, unless otherwise noted. It should be understood that such terms can be interchanged under appropriate circumstances.

[0049] The terms "comprising" and "having" and any variations thereof are intended to cover but not exclude inclusion. For example, a product or device comprising a series of components does not necessarily have to be limited to all the components clearly listed, but may include other components not clearly listed or inherent to these products or devices.

[0050] The term "module" refers to any known or later-developed hardware, software, firmware, artificial intelligence, fuzzy logic, or a combination of hardware or / and software code that can perform functions related to that element.

[0051] Figure 1 It is a schematic diagram of an operation scenario between a ventilation system and a control device provided for some embodiments of the present application. As Figure 1 shown, the user can operate the ventilation system 200 through touch operations, a mobile terminal 300, and a control device 100. For example, the control device 100 can be a remote control, a stylus, or a handle, etc.

[0052] In some embodiments, the control device 100 can be a remote control or a smart home controller, etc. For example, when the control device is a remote control, the communication manner between the remote control and the ventilation system includes but is not limited to infrared protocol communication, Bluetooth protocol communication, or other short-distance communication manners, and the ventilation system 200 is controlled in a wireless or wired manner. The user can input user instructions through keys, voice input, or control panel input on the remote control to control the ventilation system 200.

[0053] In some embodiments, a mobile terminal 300 (such as a tablet computer, a computer, a mobile phone, etc.) can also be used to control the ventilation system 200. For example, an application running on the mobile terminal 300 is used to control the ventilation system 200.

[0054] In some embodiments, the ventilation system may not receive instructions by using the above-mentioned mobile terminal 300 or control device 100, but receive user control through means such as buttons provided on the ventilation system. As Figure 1 It is also shown in that the ventilation system 200 also performs data communication with the server 400 through various communication methods. The ventilation system 200 is allowed to communicate and connect through a local area network (LAN), a wireless local area network (WLAN), and other networks.

[0055] Figure 2 For some embodiments provided by this application Figure 1 The hardware configuration block diagram of the control device in. As Figure 2 As shown, the control device 100 may include: a controller 110, a communication interface 130, a user input / output interface, a memory, and a power supply.

[0056] The control device 100 is configured to control the ventilation system 200, and can receive user input operation instructions, and convert the operation instructions into instructions recognizable and responsive by the ventilation system 200, playing an intermediary role in the interaction between the user and the ventilation system 200.

[0057] In some embodiments, the control device 100 can be an intelligent device. For example: The control device 100 can install various applications for controlling the ventilation system 200 according to user needs.

[0058] In some embodiments, as Figure 1 As shown, after the mobile terminal 300 or other intelligent electronic devices install the application for controlling the ventilation system 200, they can perform functions similar to those of the control device 100.

[0059] The controller 110 includes a processor 112, a RAM 113, a ROM 114, a communication interface 130, and a communication bus. The controller 110 is used to control the operation and operation of the control device 100, as well as the communication and cooperation between internal components and the data processing functions inside and outside.

[0060] Under the control of the controller 110, the communication interface 130 realizes the communication of control signals and data signals with the ventilation system 200. The communication interface 130 may include at least one of a WiFi chip 131, a Bluetooth module 132, an NFC module 133, and other near-field communication modules.

[0061] User input / output interface 140, where the input interface includes at least one of a microphone 141, a touchpad 142, a sensor 143, a button 144, and other input interfaces.

[0062] In some embodiments, the control device 100 includes at least one of a communication interface 130 and an input / output interface 140. The communication interface 130 in the control device 100, such as modules for WiFi, Bluetooth, NFC, etc., can encode user input instructions through the WiFi protocol, or the Bluetooth protocol, or the NFC protocol and send them to the ventilation system 200.

[0063] A memory 190, for storing various operating programs, data, and applications for driving and controlling the control device 100 under the control of a controller. The memory 190 can store various control signal instructions input by a user.

[0064] A power supply 180, for providing operating power support for each component of the control device 100 under the control of a controller.

[0065] Figure 3 Shows a Figure 1 structural block diagram of the ventilation system 200 provided by some embodiments of the present application. For the sake of convenience of description, only the parts related to the embodiments of the present application are shown.

[0066] Refer to Figure 3 , the above-mentioned ventilation system includes a ventilation device 310 and a controller 320. Among them,

[0067] The ventilation device 310 is used to supply fresh air to the air supply area;

[0068] The controller 320 is configured to:

[0069] Determine the current reference data of the above-mentioned air supply area to obtain first reference data. The above-mentioned reference data includes a personnel density and an activity intensity coefficient. The personnel density is used to reflect the density of personnel in the environmental space where the air supply area is located. The activity intensity coefficient is determined based on the type of personnel activities and is used to reflect the intensity of personnel activities in the environmental space where the air supply area is located.

[0070] Based on the above first reference data, determine the required fresh air volume of the above-mentioned air supply area to obtain a target fresh air volume.

[0071] Control the above-mentioned ventilation device 310 according to the above target fresh air volume to supply fresh air corresponding to the above target fresh air volume to the above-mentioned air supply area.

[0072] Figure 4 Shows a timing interaction diagram of a control method provided by some embodiments of the present application. As Figure 4 shown, the controller is configured to perform the following steps:

[0073] S401. Determine the current reference data of the above-mentioned air supply area to obtain the first reference data. The above-mentioned reference data includes the personnel density and the activity intensity coefficient. The above-mentioned personnel density is used to reflect the density of personnel in the environmental space where the above-mentioned air supply area is located. The above-mentioned activity intensity coefficient is determined based on the type of personnel activities and is used to reflect the intensity of personnel activities in the environmental space where the above-mentioned air supply area is located.

[0074] In the embodiments of the present application, considering the influence of low-frequency and high-intensity activities such as strength training activities, the activity intensity coefficient can be determined according to the type of personnel activities in the environmental space where the air supply area is located, so as to quantify the overall intensity of personnel activities in the environmental space where the air supply area is located, and can better ensure the accuracy of the finally determined activity intensity coefficient.

[0075] In some embodiments, the activity intensity coefficient can be determined based on the type of personnel actions in the environmental space where the air supply area is located, that is, further considering the influence caused by the differences in the specific actions of personnel during activities, so as to improve the accuracy of the determined activity intensity coefficient.

[0076] In other embodiments, the activity intensity coefficient can be jointly determined by combining multiple factors including the type of personnel activities, the type of actions, and the limb activity frequency in the environmental space where the air supply area is located. For example, the activity intensity coefficient is jointly determined by combining the type of personnel actions and their limb activity frequency in the environmental space where the air supply area is located, so as to fully consider the differences in personnel activity intensity caused by different activity frequencies when different personnel perform the same type of activities, and further improve the accuracy of the determined activity intensity coefficient.

[0077] In some embodiments, the reference data further includes one or more of data such as the basic fresh air volume, temperature, humidity, space layout, and environmental type. The above-mentioned environmental type can be a type determined according to the use of the environmental space where the air supply area is located, such as a bathroom, a kitchen, a meeting room, and a gymnasium, etc. The above-mentioned basic fresh air volume generally refers to the minimum fresh air volume that needs to be provided to the air supply area to meet corresponding standards or requirements such as the minimum per capita fresh air volume standard or indoor hygiene requirements, and its unit can be cubic meters per hour (m 3 / h).

[0078] In some embodiments, the current reference data of the air supply area can be determined by means of image detection, sensor detection, sound analysis, etc. Among them, different methods can be used to obtain different data in the reference data, and specific settings can be made according to the actual application scenario.

[0079] In the embodiments of the present application, due to the total amount of air pollutants generated by the influence of the density of people in the environmental space where the air supply area is located, and the intensity of people's activities has a great influence on the generation rate of air pollutants. Therefore, obtaining the current population density and activity intensity coefficient of the air supply area as the first reference data enables subsequent analysis of the actual demand for fresh air volume in the environmental space where the air supply area is located from different perspectives such as pollutant generation amount and generation rate, which is conducive to improving the accuracy of subsequent fresh air volume calculation.

[0080] S402. Determine the fresh air volume required for the air supply area based on the above first reference data to obtain the target fresh air volume.

[0081] Optionally, when determining the fresh air volume required for the air supply area based on the first reference data, the fresh air volume required for the air supply area can be determined by means such as grade division or intelligent algorithm.

[0082] For example, the load grade to which the first reference data belongs can be determined according to the first reference data and the set load grade division rule, and the fresh air volume corresponding to the load grade to which the first reference data belongs can be used as the target fresh air volume.

[0083] It should be noted that when determining the target fresh air volume based on the first reference data, the determined target fresh air volume can meet corresponding standards or requirements such as the minimum fresh air volume per capita standard or indoor hygiene requirements.

[0084] In some embodiments, the controller can determine the target fresh air volume based on the first reference data when detecting a trigger for a fresh air volume control instruction, or the controller can obtain the first reference data again when detecting a trigger for a fresh air volume control instruction, and determine the target fresh air volume according to the first reference data, which can be specifically set according to actual application requirements.

[0085] The above fresh air volume control instruction is used to instruct the controller to determine a target fresh air volume that matches the fresh air volume demand of the air supply area, so as to supply fresh air corresponding to the target fresh air volume to the air supply area. The fresh air volume control instruction can be triggered according to set trigger conditions. For example, the controller can trigger the fresh air volume control instruction when detecting that the difference between the first reference data and the second reference data (the second reference data includes historical reference data, such as the reference data one minute ago) is greater than or equal to a difference threshold (such as 10%). Or, the fresh air volume control instruction can also be triggered by an instruction input by the user through a control device, etc. For example, Figure 4 Taking the example where the user inputs a fresh air volume control instruction through a control device for illustration. The embodiments of the present application do not specifically limit the triggering method of the fresh air volume control instruction.

[0086] In the embodiments of the present application, since the first reference data reflects the density of people and the intensity of people's activities in the environmental space where the air supply area is located, based on this first reference data, the current actual demand of the air supply area can be accurately analyzed, and a target fresh air volume with relatively high accuracy can be obtained.

[0087] S403. Control the ventilation equipment according to the above target fresh air volume to supply fresh air corresponding to the target fresh air volume to the above air supply area.

[0088] Optionally, when the controller controls the ventilation equipment according to the target fresh air volume, it can supply fresh air corresponding to the target fresh air volume to the air supply area by controlling the air valve or fan of the ventilation equipment, etc., and can be specifically set according to the actual application scenario.

[0089] As an example, when controlling the ventilation equipment according to the target fresh air volume, the target rotation speed corresponding to the target fresh air volume can be calculated, and then the fresh air fan of the ventilation equipment can be controlled according to the target rotation speed. The target rotation speed is the rotation speed that the fresh air fan needs to reach when the ventilation system supplies fresh air corresponding to the target fresh air volume through the fresh air fan. Controlling the fresh air fan of the ventilation equipment according to the target rotation speed can quickly and accurately control the ventilation equipment to supply fresh air corresponding to the target fresh air volume to the air supply area by adjusting the rotation speed of the fresh air fan, so as to maintain the air quality of the environmental space where the air supply area is located.

[0090] In the embodiments of the present application, since the population density has a great influence on the generation amount of air pollutants in the environmental space, and the intensity of people's activities has a great influence on the generation rate of air pollutants in the environmental space, and the determined first reference data can reflect the current population density and the intensity of people's activities in the environmental space where the air supply area is located, therefore, according to this first reference data, the actual situation of air pollutants and the fresh air volume demand in the environmental space where the air supply area is located can be better analyzed, thereby improving the accuracy of the calculated target fresh air volume, and the obtained target fresh air volume matches the actual demand. Supplying corresponding fresh air to the air supply area according to the target fresh air volume can effectively control energy consumption while ensuring the air quality of the environmental space where the air supply area is located, and achieve the balance of air quality and energy efficiency.

[0091] Figure 5 shows a schematic flowchart of a control method provided by some embodiments. As Figure 5 shown, when the controller executes to determine the current reference data of the above air supply area and obtains the first reference data, it is configured to execute the following steps:

[0092] Determine the initial fresh air volume according to the above first reference data.

[0093] Optionally, when determining the initial fresh air volume according to the first reference data, the initial fresh air volume can be determined by means of grade division or intelligent algorithms, etc.

[0094] In some embodiments, the first reference data can be used as the input of a trained prediction model. The trained prediction model can analyze the fresh air volume demand of the air supply area based on the first reference data, so as to obtain the required initial fresh air volume.

[0095] In some embodiments, when training the constructed prediction model to obtain a trained prediction model, reference data can be obtained by collecting historical operation data of the ventilation system or through simulation and other means, so as to construct training samples according to the obtained reference data. After the training samples are constructed, the constructed training samples can be divided according to a set ratio (such as 8:2) to obtain a training sample set and a test sample set, so as to better evaluate the generalization ability of the model and prevent overfitting and other situations, and ensure the training effect of the prediction model.

[0096] In some embodiments, to reduce the influence of the dimensions of different data on the training effect of the prediction model, the training samples can be normalized before training. It should be understood that in addition to the personnel density and activity intensity coefficient, the reference data in the training samples can also include data such as temperature or humidity. Optionally, when normalizing each data in the reference data of the training samples, it can be to normalize the first target data in the reference data. The first target data can be set by the user or determined by an intelligent algorithm according to information such as the dimensions of each data in the reference data. For example, the first target data can include the personnel density and temperature.

[0097] When normalizing the training samples, after the training sample set and the test sample set are divided, the training samples in the training sample set and the test sample set can be normalized respectively, so as to avoid cross-contamination of data between the training sample set and the test sample set. Furthermore, during training and testing, the performance of the prediction model in actual application can be more realistically simulated, which is beneficial to improving the generalization ability of the prediction model.

[0098] Alternatively, all the constructed training samples can be normalized first, and then the normalized training samples can be divided according to a set ratio to obtain a training sample set and a test sample set.

[0099] In some embodiments, when normalizing the training samples, considering that the distribution of some data (such as personnel density) in different training samples may have large differences, simple normalization may cause the data to be over-amplified or over-shrunk. Therefore, when normalizing the data in the training samples, different data can be normalized differently.

[0100] For example, the first target data set in the training samples can be normalized based on logarithmic transformation, and the data other than the first target data in the training samples can be directly normalized based on the maximum and minimum values. By means of logarithmic transformation, the distribution of the first target data can be made more uniform, thereby enhancing the adaptability of the prediction model to data of different magnitudes.

[0101] In some embodiments, the target data after normalization can be expressed in the following form:

[0102]

[0103] where x norm represents the first target data after normalization, x represents the first target data before normalization, ln() represents the logarithmic function with the constant e as the base, and x max represents the maximum value among all the target data before normalization, and x min represents the minimum value among all the target data after normalization. It should be noted that in the embodiments of the present application, to avoid the situation where the data is meaningless when the first target data is 0, and at the same time make the distribution of the first target data smoother, a translation process (such as adding 1) can be performed on the first target data in the logarithmic function.

[0104] It should be understood that the maximum and minimum values among all the first target data are determined according to the data set being processed currently.

[0105] For example, assuming that the population density in each training sample of the training sample set needs to be normalized, the maximum and minimum values of the population density are determined according to the population density in each training sample of the training sample. Assuming that in each training sample of the training sample set, the minimum value of the population density is 0.1 person per square meter and the maximum value is 5 people per square meter; assuming that the population density in training sample M is 2 people per square meter, then the population density after normalization of training sample M is 0.44 people per square meter (calculated according to ).

[0106] In some embodiments, the constructed prediction model can be trained based on the Support Vector Machine (SVM) algorithm, that is, the optimization objective and training process of the prediction model are defined based on the Support Vector Machine algorithm to find the optimal decision boundary, so that the error between the predicted value and the actual value is as small as possible within the set tolerance range, and at the same time, the complexity of the prediction model is kept as low as possible to achieve regression.

[0107] Optionally, the training formula of the prediction model can be expressed in the following form:

[0108]

[0109] Among them, w represents the weight vector in the prediction model, which determines the contribution degree of each input feature to the output; C represents the penalty parameter, which is used to control the penalty degree for training samples with errors exceeding the tolerable range. The larger the C value, the more severe the penalty for errors. The smaller the C value, the higher the tolerance of the model for errors; ξ i and represent slack variables, which are used to reflect the errors of the predicted values that the prediction model can tolerate.

[0110] λ represents a hyperparameter, which is used to balance the sensitivity of the prediction model to errors. The larger the λ value, the more sensitive the prediction model is to the penalty for errors, thus paying more attention to the errors of each sample and helping to improve the accuracy of the model; n represents the number of training samples, represents the predicted value of the training sample predicted by the model, that is, the predicted fresh air volume. y represents the actual value of the training sample (i.e., the label value of the training sample), and x represents the input data of the prediction model, that is, the training sample; x i represents the i-th training sample, that is, the predicted value of the i-th training sample, and y i that is, the actual value of the i-th training sample; σ represents a parameter related to the data distribution, which affects the action intensity of the exponential form error penalty term. It determines the attenuation speed of the exponential function, thus affecting the degree of attention of the prediction model to training samples with different error sizes.

[0111] exp(-γ‖x i -x j ‖ 2 ) represents the radial basis kernel function, which can better handle nonlinear problems. γ is used to control the width of the kernel function. The larger the γ, the stronger the locality of the function. In the embodiments of the present application, the value of γ can be

[0112] s.t. represents the constraint conditions of the optimization problem, which are used to limit the value range of the optimization variables; b represents the bias term, which is used to adjust the predicted value of the prediction model so that the prediction model can better fit the data; φ(x) represents the mapping function that maps the input data to a high-dimensional feature space. By mapping, the data can be better separated, which helps to solve problems such as linear inseparability of data in the low-dimensional space.

[0113] During the training process, when using a training sample set to train a prediction model, the parameters of the prediction model (such as C, ε, γ, λ, α, and σ, etc.) can be finely adjusted through methods such as cross-validation, so that the model achieves the best performance on the training sample set. Optionally, k-fold cross-validation can be used for cross-validation, and the value of k is usually greater than 1 (such as 5 or 10). During the k-fold cross-validation process, the training sample set can be divided into k non-overlapping subsets. Each time, the model is trained with k - 1 subsets, and the remaining one subset is used for validation. This is repeated k times, and the average performance metric is taken as the evaluation result of the prediction model.

[0114] Determine a fresh air volume adjustment coefficient according to the above first reference data and second reference data, where the second reference data includes the historical reference data of the above-mentioned air supply area.

[0115] Determine the above-mentioned target fresh air volume according to the above-mentioned initial fresh air volume and the above-mentioned fresh air volume adjustment coefficient.

[0116] It should be understood that the reference data included in the second reference data can be the reference data of the air supply area determined in the previous n (n > 0) times, or all the historical reference data of the air supply area, or the reference data of the air supply area within a preset time period in the past. Specifically, it can be set according to actual application requirements.

[0117] In the embodiments of the present application, the preset time period can be a time period determined according to the current moment, and the duration of the preset time period can be determined according to user input or calculated by an intelligent algorithm. For example, the preset time period can be the time period corresponding to one hour before and after the current moment. Assuming that the first reference data is the reference data of the air supply area at 9:00, that is, the current moment is 9:00, then the preset time period can be from 8:00 to 10:00, and the obtained second reference data can include the reference data of the air supply area within the preset time period (that is, from 8:00 to 10:00) in the past 30 days.

[0118] Affected by multi-dimensional factors such as environmental and human behavior uncertainties, the fresh air volume required by the air supply area may change dynamically. Therefore, to improve the accuracy of the calculated fresh air volume and the stability of fresh air volume adjustment, the fresh air volume currently required by the air supply area can be comprehensively calculated by combining historical reference data and current reference data, which can not only quickly meet the current fresh air volume demand of the air supply area, but also predict future fresh air volume demands in combination with historical fresh air volume demands, and adjust the fresh air volume in advance at the current moment, thereby reducing the lag response of fresh air volume adjustment, etc., achieving a balance between real-time response and stability, and improving the accuracy of fresh air volume control.

[0119] Among them, when calculating the target fresh air volume, the initial fresh air volume that meets the current actual demand can be calculated first according to the first reference data, and the change of the reference data can be analyzed based on the current reference data (i.e., the first reference data) and the historical reference data (i.e., the second reference data), so as to calculate an appropriate fresh air volume adjustment coefficient according to the change of the reference data, and perform a weighted calculation on the initial fresh air volume according to the fresh air volume adjustment coefficient to obtain the required target fresh air volume.

[0120] Optionally, when determining the fresh air volume adjustment coefficient according to the first reference data and the second reference data, the degree of change of the reference data (which can be expressed as data change rate, fluctuation degree or similarity, etc.) can be calculated by means of difference or sliding window, etc., and then the fresh air volume adjustment coefficient can be calculated according to the degree of change; alternatively, the first reference data and the second reference data can be used as the input of a trained analysis model, and during the training process of the analysis model, the complex relationship between the change of the reference data and the fresh air volume adjustment coefficient can be well learned. Therefore, the analysis model can quickly and accurately analyze and obtain the required fresh air volume adjustment coefficient according to the first reference data and the second reference data; or, the current load (such as heat load or personnel load, etc.) of the air supply area can be calculated according to the first reference data, and the historical load of the air supply area can be calculated according to the second reference data, so as to calculate the degree of change of the load of the air supply area according to the current load and the historical load, and then determine the current fresh air volume adjustment coefficient according to the corresponding relationship between the degree of change of the load of the air supply area and the fresh air volume adjustment coefficient. The embodiments of the present application do not specifically limit the method for determining the fresh air volume adjustment coefficient according to the first reference data and the second reference data.

[0121] In some embodiments, a trained prediction model can be used to predict the current expected reference data of the air supply area according to the second reference data, so as to calculate the fresh air volume adjustment coefficient based on the difference degree between the expected reference data and the first reference data.

[0122] In the embodiments of the present application, the initial fresh air volume that matches the current actual demand is calculated according to the current reference data of the air supply area, and the weighted coefficient of the initial fresh air volume is calculated by combining the current reference data and the historical reference data of the air supply area. After obtaining the fresh air volume adjustment coefficient, the final target fresh air volume is accurately calculated by combining the initial fresh air volume and the fresh air volume adjustment coefficient, so as to improve the real-time performance and stability of fresh air volume adjustment while improving the accuracy and precision of fresh air volume calculation.

[0123] Figure 6 The flowchart of a control method provided by some embodiments is shown. As Figure 6 shown, when the controller executes to determine the fresh air volume adjustment coefficient according to the above first reference data and second reference data, it is configured to execute the following steps:

[0124] Based on the above first reference data and the above second reference data, calculate the change rate of each data in the first reference data.

[0125] Determine the above fresh air volume adjustment coefficient according to the change rate of each of the above data.

[0126] Since the change rate of data can reflect the dynamic characteristics and potential laws of the data, and thus can better reflect its future trends and stability, etc., therefore, the change rate of the reference data in the air supply area can be analyzed based on the first reference data and the second reference data, and the fresh air volume adjustment coefficient can be determined according to its change rate, which can fully consider the change trend of the reference data, improve the accuracy of the obtained fresh air volume adjustment coefficient, and help improve the performance of the target fresh air volume calculated subsequently in adapting to data fluctuations.

[0127] Among them, considering that each data in the first reference data is of different types and reflects the actual conditions of different factors in the environmental space where the air supply area is located, and there are usually certain differences in the influence degrees of different factors and their change situations on the actual demand for fresh air volume. Therefore, to improve the accuracy of the calculated fresh air volume adjustment coefficient, for each data in the first reference data, the change rate of each data can be calculated according to the first reference data and the second reference data respectively, and then combined with the influence degree of each data on the fresh air volume demand and the change rate of each data, the fresh air volume adjustment coefficient can be calculated.

[0128] It should be understood that when calculating the change rate of each data in the first reference data, the change rate can be directly calculated based on the real-time value of the data in the first reference data and the historical value of the data in the second reference data. Or, the current expected value of the data can also be predicted according to the historical value of the data in the second reference data, and the change rate of the data can be calculated according to its expected value and the real-time value in the first reference data. The specific calculation method of the data change rate can be set according to actual application requirements, and the embodiments of the present application do not make specific limitations on this.

[0129] For example, when the reference data includes the personnel density and the activity intensity coefficient, for the personnel density, the change rate of the personnel density can be calculated according to the difference between the real-time value of the personnel density in the first reference data and the historical value of the personnel density in the second reference data; for the activity intensity coefficient, the change rate of the activity intensity coefficient can be calculated according to the difference between the real-time value of the activity intensity coefficient in the first reference data and the historical value of the activity intensity coefficient in the second reference data. Furthermore, the fresh air volume adjustment coefficient is calculated according to the change rate of the personnel density and the change rate of the activity intensity coefficient.

[0130] In some other embodiments, based on the first reference data and the second reference data, the change rate of the second target data set in the first reference data may be calculated, and then the fresh air volume adjustment coefficient may be determined according to the change rate of the second target data. The second target data may be obtained according to user input or setting, or may be analyzed through intelligent algorithms such as large models.

[0131] For example, assuming that the set second target data may include personnel density, temperature, and humidity, when calculating the fresh air volume adjustment coefficient, the change rates of the personnel density, temperature, and humidity may be calculated respectively according to the first reference data and the second reference data, so as to calculate the required fresh air volume adjustment coefficient according to the change rates corresponding to the personnel density, temperature, and humidity.

[0132] It should be understood that when determining the fresh air volume adjustment coefficient according to the change rates of the respective data, weighted calculation may be performed according to the change rate of the data and the weighted coefficient of the data (assumed to be the first weighted coefficient, which may be determined according to information such as the influence degree of the data on the fresh air volume demand) to obtain the fresh air volume adjustment coefficient; alternatively, the change rates of the respective data may also be used as the input of the pre-trained model, and during the training process of the pre-trained model, the complex relationship between the change rates of the respective data and the fresh air volume adjustment coefficient can be well learned, and the required fresh air volume adjustment coefficient can be quickly and accurately calculated according to the change rates of the respective data through the pre-trained model.

[0133] In the embodiments of the present application, after calculating the change rates of the respective data according to the current reference data and the historical reference data in the air supply area, and then comprehensively determining the fresh air volume adjustment coefficient according to the change rates of the respective data, it is possible to fully consider the fluctuations and changes of different data in the reference data and the influence degree on the fresh air volume demand while considering the data fluctuations of the reference data, thereby improving the accuracy and calculation precision of the obtained fresh air volume adjustment coefficient.

[0134] Figure 7 The flowchart of a control method provided by some embodiments is shown. As Figure 7 shown, when the controller executes to determine the fresh air volume adjustment coefficient according to the change rates of the respective above-mentioned data, it is configured to execute the following steps:

[0135] Determine the change rates of the respective above-mentioned data according to the above-mentioned first reference data, the above-mentioned second reference data, and the pre-designed calculation rules corresponding to the data, wherein the above-mentioned pre-designed calculation rules corresponding to different data are different.

[0136] It should be understood that the pre-designed calculation rules corresponding to the respective data may be determined according to user settings or input, or may be analyzed and determined by intelligent algorithms such as large models according to the data characteristics of the respective data, and may be specifically set according to actual application requirements.

[0137] In the embodiments of the present application, since there may be certain differences in the dimensions or dynamic characteristics of different data, in order to calculate the change rates of different data more accurately, the change rates of each data can be calculated according to different pre-designed calculation rules, so that the calculated change rates can more accurately reflect the characteristics of the corresponding data and improve the reliability of the obtained change rates.

[0138] In some other embodiments, when the dynamic characteristics or dimensions of two kinds of data are similar (for example, the numerical ranges of the two kinds of data are in the same order of magnitude), the same pre-designed calculation rule can be used to calculate the change rates of these two kinds of data.

[0139] It should be noted that in the embodiments of the present application, the change rates of each data in the first reference data are calculated respectively. In some other embodiments, it may be to calculate the change rates of the set second target data in the first data, that is, the change rates of each second target data are calculated according to the pre-designed calculation rules corresponding to the first reference data, the second reference data, and the second target data, so as to determine the fresh air volume adjustment coefficient according to the change rates of each second target data.

[0140] In some embodiments, the reference data may include personnel density, activity intensity coefficient, temperature, and humidity. When calculating the change rates of each data, the change rate of the personnel density can be calculated based on logarithmic transformation, the change rate of the activity intensity coefficient can be calculated based on exponential transformation, the change rate of the temperature can be calculated based on the square difference, and the change rate of the humidity can be calculated based on the square root.

[0141] As an example, the first reference data is the reference data of the air supply area at the current moment, and the second reference data is the reference data of the air supply area at the previous moment, where the reference data includes personnel density, activity intensity coefficient, temperature, and humidity.

[0142] Among them, the change rate of the personnel density can be expressed in the following form:

[0143]

[0144] Among them, ΔP represents the change rate of the personnel density; P current represents the personnel density at the current moment, and P previous represents the personnel density at the previous moment; P max and P min respectively represent the maximum and minimum values of the personnel density in the data set, and this data set can include the reference data of the air supply area at the current moment and history. For example, the data set includes the reference data of the air supply area at the current moment and the past month.

[0145] In the above processing, the data range is compressed through logarithmic transformation to make its distribution more uniform, which helps to highlight the relative degree of change of the personnel density. The denominator ln(Pmax ) - ln(P min ) serves as a normalization factor, which helps to map the change rate of personnel density to a relatively stable interval, thereby improving the stability and reliability of the calculated change rate.

[0146] For example, assume that the personnel density P previous = 0.4 persons per square meter at the previous moment, and the personnel density P current = 0.5 persons per square meter at the current moment. The maximum value of personnel density P max = 5 persons per square meter in the dataset, and the minimum value is P min = 0.1 persons per square meter. Then the change rate of personnel density

[0147] The change rate of the activity intensity coefficient can be expressed in the following form:

[0148]

[0149] where ΔI represents the change rate of the activity intensity coefficient; I current represents the activity intensity coefficient at the current moment, and I previous represents the activity intensity coefficient at the previous moment; I max and I min represent the maximum and minimum values of the activity intensity coefficient in the dataset respectively. In the above processing, the range of data is expanded and the difference is amplified through exponential transformation, making the data distribution of the activity intensity coefficient more dispersed. At the same time, the denominator plays a normalization role, which helps to observe and analyze the change rate of the activity intensity coefficient, thereby improving the accuracy and precision of the calculated change rate.

[0150] The change rate of temperature can be expressed in the following form:

[0151]

[0152] where ΔT represents the change rate of temperature; T current represents the temperature at the current moment, and T previous represents the temperature at the previous moment; T max and T min represent the maximum and minimum values of temperature in the dataset respectively. In the above processing, the change rate of temperature is calculated in the form of the difference of squares to highlight the relative magnitude of temperature change. At the same time, the denominator plays a normalization role, which helps to improve the accuracy and precision of the calculated change rate.

[0153] For example, assume that the temperature T current = 27°C at the previous moment, and the temperature T previous= 25 °C, the maximum temperature T in the dataset max = 35 °C, the minimum temperature T min = 15 °C, then the temperature change rate

[0154] The change rate of humidity can be expressed in the following form:

[0155]

[0156] where, ΔH represents the change rate of humidity; H current represents the humidity at the current moment, H previous represents the humidity at the previous moment; H max and H min represent the maximum and minimum values of humidity in the dataset respectively. In the above processing, by calculating the humidity change rate in the way of square root, the range of data can be compressed, making the data distribution of humidity more uniform, so as to better reflect the characteristics of humidity change and help improve the accuracy of humidity change rate calculation.

[0157] For example, assume that the humidity H previous = 50% at the previous moment, and the humidity H current = 55% at the current moment, the maximum humidity H max = 80% in the dataset, and the minimum value represents H min = 30%, then the change rate of humidity

[0158] In some embodiments, when the controller executes to determine the fresh air volume adjustment coefficient according to the change rates of each of the said data, it is configured to execute the following steps:

[0159] Determine the fresh air volume adjustment coefficient according to the change rates of each of the above data and the predetermined fuzzy rules.

[0160] The fuzzy rule refers to the rule in fuzzy logic used to describe the fuzzy relationship between the input variable and the output variable. The fuzzy rule usually depends on the fuzzy set to define the fuzzy relationship between the input variable and the output variable. Through the fuzzy set, the fuzzy rule can map the fuzzy state of the input variable to the output variable.

[0161] Since the fuzzy rule can better handle complex and non-linear relationships and adapt to different environmental conditions and scenarios, etc., therefore, in the embodiments of the present application, when determining the fresh air volume adjustment coefficient according to the change rates of each data, the change rates of each data can be used as input variables, and the final fresh air volume adjustment coefficient can be determined according to the predetermined fuzzy rules and the change rates of each data.

[0162] It should be understood that each data is usually set with its corresponding fuzzy rule. When determining the fresh air volume adjustment coefficient, for each data, the change rate of the data can be used as the input variable, and the output variable corresponding to the data can be obtained by combining its corresponding fuzzy rule. Furthermore, the final fresh air volume adjustment coefficient is determined according to the output variables of each data.

[0163] It should be noted that when determining the fresh air volume adjustment coefficient according to the change rate of the data and the predetermined fuzzy rule, it can be based on the change rates of all data and their corresponding fuzzy rules to determine the fresh air volume adjustment coefficient, or it can be determined according to the change rate of the predetermined second target data and the fuzzy rule corresponding to the second target data.

[0164] For example, assuming that the second target data determined according to the user input includes the personnel density, temperature, and humidity, then the output variables corresponding to the personnel density, temperature, and humidity can be determined respectively according to the change rates of the personnel density, temperature, and humidity and their corresponding fuzzy rules.

[0165] As an example, the fuzzy relationship between the change rates (i.e., input variables) of each second target data and the output variables of each second target data can be represented by the following fuzzy set:

[0166]

[0167] After determining the output variables of each second target data through the above table, the final fuzzy result can be determined according to the corresponding fuzzy rule and the output variables of each second target data, and its fuzzy relationship can be represented by the following fuzzy set:

[0168] Classification (Level) Fresh air volume adjustment coefficient K Substantial reduction (-L2) K≤-0.5 Minor reduction (-L1) 0.5＜K≤0.7 Remain unchanged (L0) 0.7＜K＜1.3 Minor increase (L1) 1.3≤K＜1.5 Substantial increase (L2) K≥1.5

[0169] In this fuzzy set, if the change rate of the personnel density shows a "greatly positive change" and the change rate of the temperature is a "slightly positive change", it indicates that the personnel density increases rapidly and the temperature rises slightly. To ensure that the indoor air quality meets the standard, the fresh air volume adjustment coefficient can be set to "substantially increased"; if the change rate of the personnel density is a "tiny change", the change rate of the temperature is a "greatly negative change", and the change rate of the humidity is a "slightly positive change", it means that the personnel density is basically stable, the temperature drops significantly, and the humidity rises slightly. At this time, the fresh air volume adjustment coefficient can be set to "slightly decreased" because the change of indoor air is relatively small and a large amount of fresh air supply is not required.

[0170] After determining the final fuzzy result through the above fuzzy set, the fuzzy result can be converted into an accurate fresh air volume adjustment coefficient through a set conversion algorithm. Among them, the fresh air volume adjustment coefficient can be expressed in the following form:

[0171]

[0172] Among them, x i is an element in the universe of discourse, μ(x i ) is its membership degree, and β is a parameter used to adjust the defuzzification result.

[0173] In the embodiments of the present application, determining the final fresh air volume adjustment coefficient according to the change rate of each data and the predetermined fuzzy rules can better handle uncertainty and ambiguity, and can adapt to different environmental conditions and scenarios, etc., which can improve the flexibility and adaptability of the calculation of the fresh air volume adjustment coefficient, thereby contributing to improving the accuracy and robustness of the calculation of the fresh air volume adjustment coefficient.

[0174] Figure 8 The flowchart of the control method provided by some embodiments is shown. As Figure 8 shown, when the controller executes to determine the current reference data of the above-mentioned air supply area and obtains the first reference data, it is configured to execute the following steps:

[0175] Obtain the monitoring image corresponding to the above-mentioned air supply area.

[0176] Based on the above monitoring image, determine the above-mentioned personnel density, and based on the above monitoring image, determine the above-mentioned activity intensity coefficient.

[0177] In the embodiments of the present application, the monitoring image corresponding to the air supply area is collected through a camera, without the need to additionally install various sensors for detection, which can better reduce costs.

[0178] Since the monitoring image can clearly reflect the situation of the personnel in the environmental space where the air supply area is located, therefore, in the embodiments of the present application, the personnel density in the environmental space where the air supply area is located can be directly determined based on the monitoring image to obtain the required personnel density; and the intensity of the personnel activities in the environmental space where the air supply area is located can be directly determined based on the monitoring image to obtain the required activity intensity coefficient.

[0179] Among them, since the personnel activity intensity coefficient is determined based on the type of personnel activities, therefore, when determining the intensity of personnel activities based on the monitoring image, it can be to first determine the type of personnel activities according to the monitoring image, and then determine the final activity intensity coefficient according to the type of personnel activities. For example, determine the activity intensity coefficient according to the corresponding relationship between the predetermined activity type and the activity intensity.

[0180] Optionally, when determining the personnel density based on the monitoring image, the monitoring image can be detected through intelligent algorithms such as the human body detection algorithm to determine the current number of personnel in the environmental space where the air supply area is located. Furthermore, the current personnel density of the air supply area can be calculated by combining the area and the number of personnel of the air supply area.

[0181] In some embodiments, the monitoring image corresponding to the air supply area may include images collected by one or more cameras (i.e., the number of monitoring images may be one or more). When determining the personnel density in the air supply area, the area of the monitoring area corresponding to the monitoring image and the number of personnel may be determined first according to the monitoring image, so as to calculate the personnel density of each monitoring area according to the area and the number of personnel in each monitoring area. Furthermore, the personnel density of the entire air supply area may be calculated according to the personnel density of each monitoring area.

[0182] Optionally, when determining the area of the monitoring area corresponding to the monitoring image according to the monitoring image, the area of the monitoring area may be calculated in combination with information such as the installation position of the corresponding camera, the shooting angle, and the known environmental space size information.

[0183] As an example, assume that the camera corresponding to the monitoring image is installed at a corner of the ceiling in the environmental space where the air supply area is located. At this time, a perpendicular line can be drawn from the camera position to the ground, and the foot of the perpendicular is O. A plane rectangular coordinate system is established with O as the origin. Then, the area of the monitoring area corresponding to the monitoring image can be expressed in the following form:

[0184] A = X·Y

[0185] X = 2x·sina = 2h·tanθ·sinα

[0186] Y = 2y·sinβ = 2h·cotθ·sinβ

[0187] A = X·Y = 4h 2 ·tanθ·cotθ·sinα·sinβ = 4h 2 ·sinα·sinβ

[0188] Wherein, A represents the area of the monitoring area corresponding to the monitoring image, X represents the width of the monitoring area, Y represents the length (also referred to as the height) of the monitoring area, h represents the height of the ceiling in the environmental space where the air supply area is located (which can be equivalent to the installation height of the corresponding camera); θ represents the angle between the camera and the horizontal direction, 2α represents the horizontal viewing angle of the camera, 2β represents the vertical viewing angle of the camera; x represents the projection of the farthest distance that the camera can monitor in the horizontal direction in the horizontal direction, x = h·tanθ; y represents the projection of the farthest distance that the camera can monitor in the vertical direction in the vertical direction, y = h·tan(90° - θ) = h·cotθ.

[0189] As another example, the installation position of the camera corresponding to the monitoring image is not at a corner of the ceiling, but at a certain position on the ceiling. At this time, the area of the monitoring area corresponding to the monitoring image can be expressed in the following form:

[0190] A = X'·Y'

[0191] Among them, the width X' of the monitoring area can be expressed in the following form:

[0192]

[0193] Among them, a represents the distance between the camera on the ceiling and one side wall, and b represents the distance between the camera and the other side wall.

[0194] The length Y of the monitoring area ′ can be expressed in the following form:

[0195]

[0196] Among them, h1 represents the distance between the camera and the wall closer to the ceiling, and h2 represents the distance between the camera and the wall higher from the ceiling.

[0197] In the embodiments of the present application, only the monitoring images of the air supply area need to be collected, and the current personnel density and activity intensity coefficient of the air supply area are determined according to the collected monitoring images, without the need to additionally install various sensors for detection, which can better reduce the cost of the ventilation system.

[0198] In some other embodiments, the density of personnel in the environmental space where the air supply area is located can be determined by means of an infrared sensor to obtain the current personnel density of the air supply area. For example, the air supply area can be divided into multiple sub-areas, and pyroelectric infrared sensors are set in each sub-area. Since the infrared rays emitted by the human body can cause the pyroelectric effect inside the pyroelectric infrared sensor, thereby generating a change in the electrical signal, therefore, by counting the number of signal triggers of the pyroelectric infrared sensor, the number of personnel in the corresponding sub-area can be better determined, and thus the number of personnel in the air supply area can be obtained according to the number of personnel in each sub-area.

[0199] For example, assume that the air supply area is an office area, which is divided into 10 infrared sensing areas, each area has an area of 50 square meters. By counting the number of signal triggers of the infrared sensors in each infrared sensing area, the total number of people in the office area is calculated to be 250. Assuming that the number of people in each infrared sensing area is evenly distributed, the personnel density of this office area is persons per square meter.

[0200] In some embodiments, when the above-mentioned controller executes to determine the above-mentioned activity intensity coefficient based on the above-mentioned monitoring images, it is configured to execute the following steps:

[0201] Determine the activity intensity of each person in the above-mentioned air supply area based on the above-mentioned monitoring images.

[0202] The activity intensity coefficient is determined according to the activity intensity of each of the above-mentioned personnel and the weighting coefficient corresponding to each personnel.

[0203] In the embodiments of the present application, the activity intensity of a person can be determined according to the type of the person's activity. When determining the activity intensity of each person in the air supply area based on the monitoring image, the type of each person's activity can be first identified based on the monitoring image, so as to determine the activity intensity of each person according to the type of the person's activity.

[0204] In some embodiments, since the actions of some activities are usually dynamic, therefore, in order to more accurately identify the type of a person's activity, a plurality of monitoring images temporally continuous with the monitoring image at the current moment can be obtained, and the type of the person's activity is identified according to each temporally continuous monitoring image.

[0205] For example, the video stream within the first 5 seconds before the current moment can be obtained, 10 video frames in the video stream are extracted as monitoring images, 10 monitoring images temporally continuous with the monitoring image at the current moment are obtained, and in combination with the monitoring image at the current moment and the 10 obtained monitoring images, the types of activities of each person currently present in the environmental space where the air supply area is located are identified.

[0206] Optionally, when determining the activity intensity of a person according to the type of the person's activity, the activity intensity of the person can be determined according to the preset corresponding relationship between the activity type and the activity intensity.

[0207] For example, it is assumed that the activity intensity includes static, light activity, moderate activity, heavy activity, and vigorous activity. In the preset corresponding relationship, the activity types corresponding to static include types such as sleeping, standing still, and sitting still; the activity types corresponding to light activity include office work, reading, or eating, etc.; the activity types corresponding to moderate activity include walking, housework, and conversation, etc.; heavy activity includes running, dancing, and slow cycling, etc.; vigorous activity includes strength training and fast cycling, etc.

[0208] Among them, considering that the demand for fresh air volume by the activity intensity is not a simple linear superposition, in order to improve the accuracy of subsequent fresh air volume calculation, when calculating the overall activity intensity coefficient according to the activity intensity of each person, weighted calculation can be performed according to the activity intensity of each person and its corresponding weighting coefficient to obtain the final activity intensity coefficient, so that the activity intensity coefficient can more accurately reflect the intensity of the activities of the personnel in the environmental space where the air supply area is located, that is, more accurately reflect the overall demand for fresh air volume by the personnel in the environmental space where the air supply area is located under the influence of the activity intensity.

[0209] It should be understood that the weighting coefficient corresponding to each person (assumed to be called the second weighting coefficient) can be determined according to one or more pieces of information such as the activity type or position of the person, and the embodiments of the present application do not make specific limitations thereon.

[0210] For example, the weighted coefficient corresponding to a person can be dynamically calculated based on the activity type of the person, the population density within a set range of the location (such as within 2 meters around), and the distance between the location and the air supply outlet.

[0211] In the embodiments of the present application, since there are usually significant differences in the air pollutants generated by different types of activities and the required fresh air volume, etc., therefore, determining the activity intensity of each person in the air supply area according to the type of the person's activity can improve the accuracy of the obtained activity intensity. Furthermore, through the weighted calculation method, calculating the activity intensity coefficient of the entire air supply area based on the activity intensity of each person, rather than simple linear superposition, can improve the accuracy and reliability of the obtained activity intensity coefficient.

[0212] In some embodiments, before the above-mentioned controller executes to determine the above-mentioned activity intensity coefficient according to the activity intensity of each of the above-mentioned persons and the weighted coefficient corresponding to each person, it is further configured to execute the following steps:

[0213] Determine the weighted coefficient corresponding to each of the above-mentioned persons based on the distance between the person and the air outlet of the above-mentioned ventilation device.

[0214] It should be noted that the above-mentioned air outlet can be the air supply outlet of the ventilation device, can also be the air exhaust outlet of the ventilation device, or can also include both the air supply outlet and the air exhaust outlet at the same time.

[0215] In the embodiments of the present application, when a person's activity generates air pollutants, the distance between the person and the air outlet has a significant impact on the required fresh air volume in the room. For example, when the person's activity intensity is high and a large amount of air pollutants are generated, if the distance between the person and the air exhaust outlet is close, the air pollutants generated by the person will be more quickly discharged from the environmental space where the air supply area is located, reducing the corresponding demand for fresh air volume. Therefore, when calculating the weighted coefficient corresponding to each person in the air supply area, the weighted coefficient corresponding to the person can be calculated according to the distance between the person and the air outlet of the ventilation device, so that when calculating the overall intensity of the person's activity in the air supply area subsequently to reflect the demand for fresh air volume, the demand for fresh air volume of each person can be determined more accurately, which helps to improve the accuracy of the obtained activity intensity coefficient.

[0216] In some embodiments, the above-mentioned reference data further includes a space influence coefficient. When the above-mentioned controller executes to determine the current reference data of the above-mentioned air supply area and obtains the first reference data, it is configured to execute the following steps:

[0217] Determine the above-mentioned space influence coefficient according to the spatial layout of the above-mentioned air supply area, and the above-mentioned space influence coefficient is used to reflect the influence degree of the above-mentioned spatial layout on the air flow field in the environmental space where the above-mentioned air supply area is located.

[0218] The above spatial influence coefficient is used to reflect the influence degree of the above spatial layout on the airflow field in the environmental space where the above air supply area is located, that is, the spatial influence coefficient can reflect the influence degree of the spatial layout on the air circulation (also called ventilation effect) in the environmental space where the air supply area is located.

[0219] The airflow field refers to the distribution state of physical quantities such as the velocity, direction, pressure and temperature of air flow in the indoor space. The airflow field describes the movement law of air in three-dimensional space, which directly affects key factors such as indoor thermal comfort, air quality and pollutant diffusion.

[0220] It should be understood that the spatial layout of the air supply area includes but is not limited to partition structures, air outlet positions, equipment layouts, decoration materials and regional functions in the environmental space where the air supply area is located.

[0221] Optionally, the spatial layout of the air supply area can be determined according to the current monitoring image of the air supply area, or can be determined according to the layout file uploaded by the user. The embodiments of the present application do not make specific restrictions on this.

[0222] In the embodiments of the present application, since the distribution and characteristics of the airflow field will affect the accumulation of air pollutants and the delivery effect of fresh air, thereby affecting the demand for fresh air volume in the air supply area, therefore, analyzing the influence degree of the spatial layout of the air supply area on the airflow field in the environmental space where the air supply area is located to obtain the spatial influence coefficient, so that when analyzing the required fresh air volume of the air supply area later, it can comprehensively analyze the required fresh air volume of the air supply area in combination with the influence degree of the actual spatial layout on the airflow field, so as to ensure the air quality of the environmental space where the air supply area is located.

[0223] As described above in conjunction with Figures 4 to 8 , the control method of the embodiments of the present application has been described in detail. Next, the device embodiments of the present application will be described in conjunction with Figure 9 It should be understood that the control device in the embodiments of the present application can execute various control methods of the foregoing embodiments of the present application, that is, the specific working processes of the following various products can refer to the corresponding processes in the foregoing method embodiments.

[0224] Figure 9 The structural block diagram of a control device provided in some embodiments of the present application is shown. Referring to Figure 9 , the control device includes: a reference data acquisition module 910, a target fresh air volume calculation module 920 and a control module 930. Among them,

[0225] A reference data acquisition module 910 is configured to determine current reference data of the air supply area to obtain first reference data. The reference data includes a personnel density and an activity intensity coefficient. The personnel density is used to reflect the density of personnel in the environmental space where the air supply area is located. The activity intensity coefficient is determined based on the type of personnel activity and is used to reflect the intensity of personnel activity in the environmental space where the air supply area is located.

[0226] A target fresh air volume calculation module 920 is configured to determine the fresh air volume required for the air supply area based on the first reference data to obtain a target fresh air volume.

[0227] A control module 930 is configured to control a ventilation device according to the target fresh air volume to supply fresh air corresponding to the target fresh air volume to the air supply area.

[0228] Each unit module of the control device 900 can respectively execute the corresponding steps in the above method embodiments, so the unit modules will not be elaborated here. For details, please refer to the descriptions of the above corresponding steps.

[0229] An embodiment of the present application further provides a computer-readable storage medium. The computer-readable storage medium stores a computer program. When the computer program is executed by a controller, the steps in the above method embodiments can be implemented.

[0230] An embodiment of the present application provides a computer program product. When the computer program product runs on a controller, it enables the controller to execute the steps in the control methods described in the above embodiments.

[0231] When the integrated unit is implemented in the form of 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, to implement all or part of the processes in the above-described embodiment methods of this application, a computer program can be used to instruct relevant hardware to complete. The computer program can be stored in a computer-readable storage medium. When the computer program is executed by a processor, the steps of the above-described method embodiments can be implemented. Among them, the computer program includes computer program code, and the computer program code can be in the form of source code, object code, executable file, or some intermediate form, etc. The computer-readable medium can at least include: any entity or device that can carry the computer program code to the photographing device / electronic device, recording medium, computer memory, read-only memory (ROM), random access memory (RAM), electrical carrier signal, telecommunication signal, and software distribution medium. For example, a USB flash drive, a mobile hard disk, a magnetic disk, or an optical disc, etc. In some jurisdictions, according to legislation and patent practice, the computer-readable medium cannot be an electrical carrier signal and a telecommunication signal.

[0232] In the above embodiments, the descriptions of the various embodiments have their own focuses. For the parts not detailed or recorded in a certain embodiment, reference can be made to the relevant descriptions of other embodiments.

[0233] Those of ordinary skill in the art can realize that the units and algorithm steps of the examples described in combination with the embodiments disclosed herein can be implemented by electronic hardware, or by a combination of computer software and electronic hardware. Whether these functions are executed in a hardware or software manner depends on the specific application and design constraints of the technical solution. Professional technicians can use different methods to implement the described functions for each specific application, but such implementation should not be considered to exceed the scope of this application.

[0234] In the embodiments provided in this application, it should be understood that the disclosed device / network device and method can be implemented in other ways. For example, the device / network device embodiments described above are only illustrative. For example, the division of the modules or units is only a logical function division. In actual implementation, there can be other division methods. For example, multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the displayed or discussed coupling or direct coupling or communication connection to each other can be through some interfaces. The indirect coupling or communication connection of the device or unit can be in an electrical, mechanical, or other form.

[0235] The unit described as the separation component may or may not be physically separated. The component shown as a unit may or may not be a physical unit, that is, it may be located in one place or distributed across multiple network units. Some or all of the units can be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0236] The above embodiments are only used to illustrate the technical solutions of the present application, rather than limiting them; although the present application has been described in detail with reference to the foregoing embodiments, those of ordinary skill in the art should understand that: they can still modify the technical solutions described in the foregoing embodiments, or perform equivalent replacements on some of the technical features; and these modifications or replacements do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of the present application, and should all be included within the protection scope of the present application.

Claims

1. A ventilation system, characterized in that, Including: A ventilation device for providing fresh air to a supply air area; A controller configured to: Determine the current reference data of the supply air area to obtain first reference data, where the reference data includes the personnel density and the activity intensity coefficient. The personnel density is used to reflect the density of personnel in the environmental space where the supply air area is located, and the activity intensity coefficient is determined based on the type of personnel activities and is used to reflect the intensity of personnel activities in the environmental space where the supply air area is located; Determine the required fresh air volume of the supply air area based on the first reference data to obtain a target fresh air volume; Control the ventilation device according to the target fresh air volume to supply fresh air corresponding to the target fresh air volume to the supply air area.

2. The ventilation system according to claim 1, wherein When the controller executes determining the required fresh air volume of the supply air area based on the first reference data to obtain a target fresh air volume, it is configured to: Determine an initial fresh air volume according to the first reference data; Determine a fresh air volume adjustment coefficient according to the first reference data and second reference data, where the second reference data includes the historical reference data of the supply air area; Determine the target fresh air volume according to the initial fresh air volume and the fresh air volume adjustment coefficient.

3. The ventilation system according to claim 2, characterized in that, When the controller executes determining the fresh air volume adjustment coefficient according to the first reference data and second reference data, it is configured to: Based on the first reference data and the second reference data, calculate the change rate of each data in the first reference data; Determine the fresh air volume adjustment coefficient according to the change rate of each data.

4. The ventilation system according to claim 3, wherein When the controller executes calculating the change rate of each data in the first reference data based on the first reference data and the second reference data, it is configured to: Determine the change rate of each data according to the first reference data, the second reference data, and the pre-designed calculation rules corresponding to the data, where the pre-designed calculation rules corresponding to different data are different.

5. The ventilation system according to claim 3, characterized in that, When the controller executes determining the fresh air volume adjustment coefficient according to the change rate of each data, it is configured to: Determine the fresh air volume adjustment coefficient according to the change rate of each data and the predetermined fuzzy rules.

6. The ventilation system according to any one of claims 1 to 5, characterized in that, When the controller executes determining the current reference data of the supply air area to obtain first reference data, it is configured to: Obtain a monitoring image corresponding to the supply air area; Determine the personnel density based on the monitoring image, and determine the activity intensity coefficient based on the monitoring image.

7. The ventilation system according to claim 6, wherein, When the controller executes determining the activity intensity coefficient based on the monitoring image, it is configured to: Determine the activity intensity of each person in the supply air area based on the monitoring image; Determine the activity intensity coefficient according to the activity intensity of each person and the weighting coefficient corresponding to each person.

8. The ventilation system according to claim 7, characterized in that, Before the controller executes determining the activity intensity coefficient according to the activity intensity of each person and the weighting coefficient corresponding to each person, it is also configured to: Determine the weighting coefficient corresponding to each person based on the distance between the person and the air outlet of the ventilation device.

9. The ventilation system according to any one of claims 1 to 5, characterized in that, The reference data further includes a space influence coefficient. When the controller executes determining the current reference data of the supply air area to obtain first reference data, it is configured to: Determine the space influence coefficient according to the spatial layout of the air supply area, where the space influence coefficient is used to reflect the influence degree of the spatial layout on the air flow field in the environmental space where the air supply area is located.

10. A control method, characterized in that, Including: Determine the current reference data of the air supply area to obtain the first reference data, where the reference data includes the personnel density and the activity intensity coefficient. The personnel density is used to reflect the density of personnel in the environmental space where the air supply area is located, and the activity intensity coefficient is determined based on the type of personnel activities and is used to reflect the intensity of personnel activities in the environmental space where the air supply area is located; Determine the required fresh air volume of the air supply area based on the first reference data to obtain the target fresh air volume; Control the ventilation equipment according to the target fresh air volume to supply fresh air corresponding to the target fresh air volume to the air supply area.

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