An air conditioning unit control method, device, equipment and storage medium
By using environmental image recognition technology to obtain the cooling demand of air conditioning units, the problem of insufficient or wasted power resources during the nighttime cooling storage process of large air conditioning units is solved, and precise cooling demand control and energy-saving cooling are achieved.
Patent Information
- Application Number
- CN202310453900.0
- Authority / Receiving Office
- CN · China
- Patent Type
- Patents(China)
- Current Assignee / Owner
- Filing Date
- 2023-04-24
- Publication Date
- 2026-01-27
- Estimated Expiration
- 2043-04-24
AI Technical Summary
Large air conditioning units suffer from insufficient or wasted power resources during nighttime cooling storage, and cannot accurately control cooling demand, resulting in wasted cooling and increased costs.
By acquiring environmental images of the target area, identifying personnel, equipment, and job attribute parameters, generating environmental feature vectors, and using a cold air volume prediction model to precisely control ice storage capacity and cold air delivery, on-demand cooling can be achieved.
Precise control of ice storage capacity and cold air delivery avoids waste of electricity and cold air resources, improves refrigeration efficiency, and saves costs.
Smart Images
Figure CN116753601B_ABST
Abstract
Description
Technical Field
[0001] This application relates to the field of air conditioning technology, and in particular to an air conditioning unit control method, device, equipment and storage medium. Background Technology
[0002] Large air conditioning units are mostly used in large buildings such as hospitals, schools, shopping malls, hotels, office buildings, and factories. By using ice storage technology, the unit is turned on for cooling during off-peak hours at night, and the cold storage medium is made into ice or ice crystals. During peak hours of daytime power supply, the heat absorption effect of the melting process of the ice or ice crystals is utilized to achieve the cooling effect.
[0003] This technology enables off-peak electricity use and reduces costs at night, but it also presents other technical challenges. The process of forming ice crystals from the cold storage medium at night also requires significant electricity. Since the cooling demand from users during the day is not constant, nighttime cold storage may lead to resource shortages or substantial waste. Furthermore, the lack of an intelligent temperature field and steady-state temperature in the entire space environment results in the generated cool air being wasted in unnecessary spaces or at unnecessary times, leading to wasted electricity and additional costs. Summary of the Invention
[0004] This application provides a control method, device, equipment, and storage medium for an air conditioning unit, which can perform ice storage on demand, avoiding waste of power and cooling resources or insufficient cooling resources, thus saving costs. The technical solution is as follows:
[0005] On one hand, embodiments of this application provide an air conditioning unit control method, the method comprising:
[0006] Acquire environmental images within the target area;
[0007] Image recognition is performed on the environmental image to extract environmental feature parameters. The environmental feature parameters include at least one of personnel attribute parameters, equipment attribute parameters, and job attribute parameters. The environmental feature parameters are used to indicate at least one of heat dissipation level, air conditioning demand level, and air conditioning demand period.
[0008] Based on the environmental characteristic parameters, the cooling volume is predicted to obtain the cooling demand during the next air conditioning operating period.
[0009] According to the required cooling demand, the air conditioning equipment in the target area is controlled to store ice and deliver cooling air for the next air conditioning period.
[0010] Optionally, the step of performing image recognition on the environmental image and extracting environmental feature parameters includes:
[0011] Crowd recognition is performed on the environmental image to extract the personnel attribute parameters, which are used to indicate the degree of air conditioning demand and heat dissipation.
[0012] The environmental image is used to identify devices and extract device attribute parameters, which are used to indicate the degree of heat dissipation.
[0013] Based on the job label corresponding to the environmental image, the job attribute parameters are determined, which are used to indicate the degree of air conditioning demand and the time period of air conditioning demand.
[0014] Optionally, the step of performing crowd recognition on the environmental image and extracting personnel attribute parameters includes:
[0015] The environmental image is subjected to crowd recognition to obtain the gender ratio, crowd density and cooling distance within the target area. The gender ratio is the ratio of the number of people of different genders, the crowd density is the ratio of the number of people to the area of the crowd, and the cooling distance is the average distance between the cooling equipment and the center point of the crowd.
[0016] The area of the people in the environmental image is estimated to obtain their body surface area; the heat dissipation parameters of the crowd in the target area are determined based on the body surface area, body surface temperature and space temperature of each person, wherein the space temperature is the temperature at a preset distance from the person in the space.
[0017] Optionally, the step of performing device identification on the environmental image and extracting device attribute parameters includes:
[0018] The area of the heat-dissipating object in the environmental image is estimated to obtain the surface area of the heat-dissipating object;
[0019] The heat dissipation parameters of the objects in the target area are determined based on the surface area, surface temperature, and ambient temperature of each heat dissipation object, wherein the ambient temperature is the temperature at a preset distance from the heat dissipation object within the space.
[0020] Optionally, determining the job attribute parameters based on the job tag corresponding to the environmental image includes:
[0021] Based on the job labels, the job level is determined, and different job levels correspond to different levels of air conditioning demand.
[0022] Optionally, the step of predicting the cooling demand based on the environmental characteristic parameters to obtain the cooling demand for the next air conditioning operating period includes:
[0023] An environmental feature vector is generated based on the environmental feature parameters and the weights corresponding to each environmental feature parameter.
[0024] The environmental feature vector is input into the air conditioning volume prediction model to obtain the air conditioning demand. The air conditioning volume prediction model is used to match the air conditioning demand from the prediction table based on the environmental feature vector. The prediction table is obtained by training the air conditioning volume prediction model.
[0025] On the other hand, this application provides an air conditioning unit control device, the device comprising:
[0026] The acquisition module is used to acquire environmental images within the target area;
[0027] The recognition module is used to perform image recognition on the environmental image and extract environmental feature parameters. The environmental feature parameters include at least one of personnel attribute parameters, equipment attribute parameters, and job attribute parameters. The environmental feature parameters are used to indicate at least one of heat dissipation level, air conditioning demand level, and air conditioning demand period.
[0028] The prediction module is used to predict the cooling volume based on the environmental characteristic parameters to obtain the cooling demand during the next air conditioning operating period.
[0029] The control module is used to control the air conditioning equipment in the target area to store ice and deliver cold air during the next air conditioning operating period according to the cold air demand.
[0030] On the other hand, this application provides an electronic device including a memory and a processor, wherein the memory stores a computer program that, when executed by the processor, performs an air conditioning unit control method as described above.
[0031] On the other hand, this application provides an air conditioning unit, which includes the electronic equipment, cold storage equipment and air conditioning equipment described above. The cold storage equipment is used to make ice or ice crystals from the cold storage medium through ice cold storage technology. The air conditioning equipment is used to output cold air through the heat absorption effect of the melting process of ice or ice crystals. The electronic equipment is communicatively connected to the cold storage equipment and the air conditioning equipment and is used to control the cold storage capacity of the cold storage equipment and the cold air delivery volume of the air conditioning equipment.
[0032] On the other hand, this application provides a computer program product that runs on a processor of an electronic device, causing the electronic device to perform an air conditioning unit control method as described above.
[0033] The technical solution provided in this application includes at least the following beneficial effects:
[0034] This application provides an air conditioning unit control method, device, equipment, and storage medium that extracts environmental feature parameters from environmental images that reflect the degree of heat dissipation or cooling demand, and predicts the cooling demand during the next air conditioning operating period based on environmental feature scalars. It accurately controls the amount of ice crystals produced and the delivery of cooling air, enabling on-demand ice storage, avoiding waste of power and cooling resources or insufficient cooling resources, saving costs, and improving the unit's cooling efficiency. Attached Figure Description
[0035] To more clearly illustrate the technical solutions in the embodiments of this application, the accompanying drawings used in the description of the embodiments will be briefly introduced below.
[0036] Figure 1 This is a flowchart of a control method for an air conditioning unit provided in an exemplary embodiment of this application;
[0037] Figure 2 This is a flowchart of a control method for an air conditioning unit provided in another exemplary embodiment of this application;
[0038] Figure 3 This is a flowchart of a control method for an air conditioning unit provided in another exemplary embodiment of this application;
[0039] Figure 4 This is a structural block diagram of a control device for an air conditioning unit provided in an exemplary embodiment of this application;
[0040] Figure 5 This is a structural block diagram of an electronic device provided in an exemplary embodiment of this application. Detailed Implementation
[0041] To make the objectives, technical solutions, and advantages of this application clearer, the embodiments of this application will be described in further detail below with reference to the accompanying drawings. The described embodiments should not be regarded as limitations on this application. All other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this application.
[0042] In the following description, references are made to “some embodiments,” which describe a subset of all possible embodiments. However, it is understood that “some embodiments” may be the same subset or different subsets of all possible embodiments and may be combined with each other without conflict.
[0043] If the application documents contain similar descriptions such as "first, second, third", the following explanation shall be added: In the following description, the terms "first, second, third" are used only to distinguish similar objects and do not represent a specific order of objects. It is understood that "first, second, third" may be interchanged in a specific order or sequence where permitted, so that the embodiments of this application described herein can be implemented in an order other than that illustrated or described herein.
[0044] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this application belongs. The terminology used herein is for the purpose of describing embodiments of this application only and is not intended to limit this application.
[0045] To address the problems existing in related technologies, this application provides an air conditioning unit control method. This method is applied to an electronic device, such as a mobile terminal or computer. In some embodiments, the electronic device can be a controller for an air conditioning unit, which includes a controller, a cold storage device, and an air conditioning unit. The cold storage device is used to convert a cold storage medium into ice or ice crystals using ice storage technology. The air conditioning unit is used to output cold air through the heat absorption effect of the melting process of the ice or ice crystals. The controller is communicatively connected to the cold storage device and the air conditioning unit, and is used to control the cold storage capacity of the cold storage device and the cold air delivery rate of the air conditioning unit.
[0046] The functions implemented by the air conditioning unit control method provided in this application embodiment can be achieved by the processor of an electronic device calling program code, wherein the program code can be stored in a computer storage medium.
[0047] Please refer to Figure 1 The diagram illustrates a flowchart of a control method for an air conditioning unit provided in an exemplary embodiment of this application. The method includes the following steps:
[0048] Step 101: Obtain an environmental image within the target area.
[0049] The environmental images are obtained from video files captured by the camera. The electronic equipment uses these environmental images to analyze and predict air conditioning demand. The electronic equipment reads the video files captured by the camera and extracts images from them.
[0050] In one possible implementation, the video capture device parses the captured video into images and stores them in the corresponding image data folder. The electronic device iterates through all image files in the given image data folder and stores the paths of the image files.
[0051] To illustrate, cameras collect video footage of the areas where various air conditioning units are located within a building. Electronic devices acquire video footage of each area at a preset time each day (e.g., 7:00 PM) to obtain environmental images.
[0052] Optionally, the electronic device can extract parameters by reading each frame of the video in chronological order, or the electronic device can extract parameters by reading one frame of the video at a preset time interval, such as reading one frame of the video every 3 seconds.
[0053] Step 102: Perform image recognition on the environmental image and extract environmental feature parameters. The environmental feature parameters include at least one of personnel attribute parameters, equipment attribute parameters, and job attribute parameters. The environmental feature parameters are used to indicate at least one of heat dissipation level, air conditioning demand level, and air conditioning demand period.
[0054] Environmental images can reflect information such as the number of people, the number of air conditioning units, and working hours within a target area. Electronic devices can identify environmental images, extract information including but not limited to the aforementioned types that reflect the degree of heat dissipation and cooling demand, and parameterize them to obtain relevant parameters, namely environmental feature parameters.
[0055] The environmental characteristic parameters include at least one of the following: personnel attribute parameters, equipment attribute parameters, and job attribute parameters. Personnel attribute parameters describe the degree to which personnel within a region affect air conditioning demand, such as population density; higher density requires more air conditioning. Equipment attribute parameters describe the degree to which instruments and equipment within a region affect air conditioning demand, such as equipment heat dissipation; higher heat dissipation requires more air conditioning. Job attribute parameters describe the degree to which the type of job within a region affects air conditioning demand, such as the time of day during which a job directly affects the time of day when air conditioning demand is needed.
[0056] In one possible implementation, the electronic device loads and runs the You Only Look Once (YOLO) model, inputs environmental images into the YOLO model for image recognition, and generates environmental feature parameters based on the recognized targets.
[0057] Step 103: Based on environmental characteristic parameters, predict the cooling volume to obtain the cooling demand during the next air conditioning operating period.
[0058] Since environmental characteristic parameters can reflect information such as the degree of heat dissipation and the degree of cooling demand, and the cooling demand during two adjacent air conditioning operating periods is usually not much different, electronic devices can predict the cooling demand based on environmental characteristic parameters.
[0059] To illustrate, for buildings such as hospitals and hotels, electronic devices can predict the air conditioning demand for the next calendar day; for buildings such as schools, office buildings, and factories, electronic devices can predict the air conditioning demand for the next workday; and for places such as art galleries that are closed in the morning and open in the afternoon, electronic devices can predict the air conditioning demand for the afternoon opening period during the morning period.
[0060] In one possible implementation, technicians can pre-calculate environmental characteristic parameters and air conditioning demand for various environments, and create a Q-table to store status values and reward values. The status values correspond to environmental characteristic parameters, and the reward values correspond to specific real-time actions (including ice storage capacity, etc.). The electronic device stores this table, allowing it to query the corresponding air conditioning demand based on the extracted environmental characteristic parameters.
[0061] Step 104: Control the air conditioning equipment in the target area to store ice and deliver cold air for the next air conditioning operating period according to the demand for cold air.
[0062] The electronic equipment controls the air conditioning equipment in the target area to store ice based on the cooling demand during the next air conditioning operating period. In addition, the electronic equipment can also combine the image acquisition time to further determine the cooling demand for each sub-period (e.g., down to the hourly cooling demand) and control the air conditioning equipment in the target area to deliver cooling more precisely.
[0063] In another possible implementation, the electronic device can also predict the heating demand in the manner described above during the heating season and control the heating delivery.
[0064] In summary, the method provided in this application extracts environmental feature parameters from environmental images that reflect the degree of heat dissipation or the degree of cooling demand, and predicts the cooling demand during the next air conditioning operating period based on the environmental feature parameters. It accurately controls the amount of ice crystals produced and the delivery of cooling, enabling ice storage on demand, avoiding waste of power and cooling resources or insufficient cooling resources, saving costs, and improving the unit's cooling efficiency.
[0065] Please refer to Figure 2 The diagram illustrates a flowchart of a control method for an air conditioning unit provided in another exemplary embodiment of this application. The method includes the following steps:
[0066] Step 201: Obtain an environmental image within the target area.
[0067] The specific implementation of step 201 can be referred to step 101 above, and will not be repeated here in the embodiments of this application.
[0068] Step 202: Perform crowd recognition on the environmental image and extract personnel attribute parameters. The personnel attribute parameters are used to indicate the degree of air conditioning demand and heat dissipation.
[0069] Within the same space, the number and density of people both affect the demand for air conditioning. Electronic devices use a trained YOLO model to analyze crowd density, selecting all human figures within the image and calculating their attribute parameters.
[0070] In one possible implementation, step 202 specifically includes the following steps:
[0071] Crowd recognition is performed on environmental images to obtain the gender ratio, crowd density, and cooling distance within the target area. The gender ratio is the ratio of the number of people of different genders, the crowd density is the ratio of the number of people to the area of the crowd, and the cooling distance is the average distance between the cooling equipment and the center point of the crowd.
[0072] The area of people in the environmental image is estimated to obtain their body surface area; the heat dissipation parameters of the crowd in the target area are determined based on the body surface area, body surface temperature and space temperature of each person, where the space temperature is the temperature at a preset distance from the person in the space.
[0073] Personnel attribute parameters include, but are not limited to, gender ratio, population density, cooling distance, and population heat dissipation parameters. Higher population density results in greater and more concentrated heat dissipation, thus requiring more cooling capacity. Due to gender differences, men and women have different heat dissipation and optimal body surface temperatures; a higher male proportion leads to greater cooling demand, and vice versa. Therefore, varying male-to-female ratios within a building directly impact cooling and air delivery solutions. The distribution of air conditioning equipment within the building also affects cooling demand; greater distance from air conditioning units to people results in higher cooling requirements. Since the human body dissipates heat from the environment, this heat dissipation also affects ambient temperature; higher heat dissipation and ambient temperature lead to higher cooling demand. Therefore, population heat dissipation parameters are also related to cooling demand.
[0074] In a schematic representation, the electronic device performs crowd recognition on an environmental image, outputting the number of males (num_male) and females (num_female) in the image, obtaining the gender ratio K = num_male / num_female. Simultaneously, the algorithm can directly calculate the area s of the crowd in the current environmental image (ignoring outliers). The ratio of the crowd area in the image to the actual crowd area in the scene is denoted as f, a parameter that can be obtained through actual measurement. The crowd density P can then be expressed as P = (num_male + num_female) / s * f. The electronic device can also use a clustering algorithm to find the center point of the crowd in the environmental image, and then calculate the average distance H between each air conditioning unit in the target area and the center point of the crowd. The electronic device measures the surface temperature T of individuals using an infrared imager, estimates the surface area S of individuals based on the environmental image, and uses a temperature sensor to measure the temperature Th at a distance h from the individuals. Based on the above data, the electronic device can calculate the unit heat dissipation q = T * S * (T - Th) / h for each individual, obtaining the heat dissipation parameters q1, q2, q3... for each individual, and thus the heat dissipation parameter Q for the entire crowd. 人 = q1 + q2 + ... + qn.
[0075] Step 203: Perform device identification on the environmental image and extract device attribute parameters, which are used to indicate the degree of heat dissipation.
[0076] Heat dissipation from equipment and people is also an important factor affecting the spatial temperature field. Electronic devices estimate heat dissipation by identifying targets in an environmental image. In one possible implementation, step 203 specifically includes the following steps:
[0077] Step 203a: Estimate the area of the heat dissipation object in the environmental image to obtain the surface area of the heat dissipation object.
[0078] Step 203b: Determine the heat dissipation parameters of the objects in the target area based on the surface area, surface temperature and space temperature of each heat dissipation object. The space temperature is the temperature at a preset distance from the heat dissipation object in the space.
[0079] In a schematic representation, an electronic device measures the surface temperature T of an object using an infrared imager, estimates the object's surface area S based on an environmental image, and measures the temperature Th at a distance h from the object using a temperature sensor. The object includes, but is not limited to, air conditioning equipment, computer equipment, and other heat-dissipating devices. Based on this data, the electronic device can calculate the object's unit heat dissipation q = T*S*(T-Th) / h, obtaining the heat dissipation parameters q1, q2, q3… for each object, and ultimately the object's heat dissipation parameter Q. 物 = q1 + q2 + ... + qn.
[0080] Optionally, when predicting cooling demand later, the electronic device can combine human body heat dissipation parameters and object heat dissipation parameters into one item to obtain the total heat dissipation parameter for calculation, i.e., Q. 总 =Q 人 +Q 物 .
[0081] Step 204: Based on the job labels corresponding to the environmental images, determine the job attribute parameters. The job attribute parameters are used to indicate the degree of air conditioning demand and the time period of air conditioning demand.
[0082] When the target area includes a work area, job type is also an important factor to consider when predicting air conditioning demand. Workers in different job types have different levels of physical labor and different heat dissipation, and therefore different air conditioning demands.
[0083] In one possible implementation, step 204 specifically includes the following steps:
[0084] Based on job labels, the job's labor level is determined, and different job labor levels correspond to different levels of air conditioning demand.
[0085] Technicians pre-calculate the average physical labor time and job type (e.g., whether it involves carrying or technical operations) for each job type, and assign different job labor levels based on the degree of physical labor.
[0086] As an illustration, hotel positions include front desk and back office. The physical labor intensity of front desk is lower than that of back office, therefore the labor level corresponding to the job label "back office" is higher than that corresponding to the job label "front desk".
[0087] In addition, electronic devices can learn when to deliver cooling air and the amount of cooling air delivered at each time period based on the job's working hours, thereby achieving energy-saving and intelligent cooling control.
[0088] As an illustration, electronic devices can determine the working hours of a job based on factors such as the operating hours of the equipment, the time when personnel are on duty, and the time of personnel movement, thereby determining the time of air conditioning demand. For example, if the opening hours of an art museum exhibition hall are 10:00-16:00, the electronic devices will determine the working hours of the jobs within the exhibition hall area as 10:00-16:00.
[0089] Step 205: Generate an environmental feature vector based on the environmental feature parameters and the weights corresponding to each environmental feature parameter.
[0090] The electronic device fuses various environmental characteristic parameters to obtain an environmental feature vector. In one possible implementation, since different factors have varying degrees of influence on air conditioning demand, each environmental characteristic parameter in this embodiment is assigned a corresponding weight. The electronic device generates the environmental feature vector based on the environmental characteristic parameters and their corresponding weights.
[0091] Indicative, electronic devices are based on the total heat dissipation parameter Q. 总 And total heat dissipation parameter Q 总 The corresponding weights w_q, gender ratio K and its corresponding weight w_k, population density P and its corresponding weight w_p, cooling distance H and its corresponding weight w_h, job label L and its corresponding weight w_L are used to generate an environmental feature vector [w_q*Q]. 总 , w_k*K, w_p*P, w_h*H, w_L*L].
[0092] Step 206: Input the environmental feature vector into the air conditioning quantity prediction model to obtain the air conditioning demand during the next air conditioning operating period.
[0093] Among them, the air conditioning volume prediction model is used to match the air conditioning demand from the prediction table based on the environmental feature vector. The prediction table is obtained by training the air conditioning volume prediction model.
[0094] The electronic device combines environmental feature parameters from the above steps to generate a feature input vector, and then inputs the vector into a reinforcement learning model to predict the total required cooling volume. In one possible implementation, technicians can pre-calculate environmental feature parameters and cooling volume requirements for various environments and create a Q-Table to store state values and reward values. State values correspond to environmental feature parameters, and reward values correspond to specific real-time actions (including ice storage capacity, etc.). The electronic device stores this table, allowing it to query the corresponding cooling volume requirements based on the extracted environmental feature parameters. In the Q-Table, columns represent output actions, rows represent state values, and each Q-Table score represents the maximum expected reward the electronic device will receive when taking action in that state. The electronic device initializes the Q-Table at the beginning of model building and iteratively updates the table during the reinforcement learning algorithm training process until the model converges.
[0095] In one possible implementation, the reinforcement learning model consists of two neural network modules: an action network and an evaluation network. The action network determines the optimal action to be applied to the environment at the next moment based on the current state. With internal reinforcement signals from the evaluation network, the output nodes of the action network can perform random searches and increase the probability of selecting the optimal action, while the entire action network can be trained online. During the model training phase, the electronic device first initializes the Q-table, selects and executes actions through the action network, evaluates them using the evaluation network, updates the function and Q-table of the reinforcement learning model, and repeats the iteration until the model converges.
[0096] Optionally, the electronic device can also more accurately predict the demand for air conditioning in each time period based on the environmental feature parameters corresponding to the environmental image and the time period, so as to deliver air conditioning in a time-based and directional manner.
[0097] Step 207: Control the air conditioning equipment in the target area to store ice and deliver cold air for the next air conditioning operating period according to the demand for cold air.
[0098] The specific implementation of step 207 can be referred to step 104 above, and will not be repeated here in the embodiments of this application.
[0099] In this embodiment, environmental characteristic parameters such as population density, gender ratio, cooling distance, and job labels that can reflect the degree of cooling demand or heat dissipation are extracted from environmental images. Based on these environmental characteristic parameters, the cooling demand for the next air conditioning operating period is predicted. By quantitatively predicting the cooling demand through environmental characteristics, ice storage can be carried out on demand, avoiding the waste of power and cooling resources or the phenomenon of insufficient cooling resources. In addition, the appropriate amount of cooling can be delivered in a timely and directional manner to achieve energy saving and comfort, while also avoiding the waste of electricity and realizing intelligent control of large units.
[0100] In combination with the above embodiments, Figure 3 An air conditioning unit control flow is illustrated. First, after the electronic equipment starts running, a network model is loaded; then, a dataset is loaded, i.e., video files are read from a specific folder and images are extracted from them; the computer device performs object detection on the images and performs feature quantization based on the extracted data to generate vectors; the vectors are input into a reinforcement learning algorithm to obtain the corresponding reward value, and the required cooling volume is determined.
[0101] Figure 4 This is a structural block diagram of an air conditioning unit control device provided in an exemplary embodiment of this application. The device includes the following structure:
[0102] Acquisition module 401 is used to acquire environmental images within the target area;
[0103] The recognition module 402 is used to perform image recognition on the environmental image and extract environmental feature parameters. The environmental feature parameters include at least one of personnel attribute parameters, equipment attribute parameters, and job attribute parameters. The environmental feature parameters are used to indicate at least one of heat dissipation level, air conditioning demand level, and air conditioning demand period.
[0104] Prediction module 403 is used to predict the amount of cooling air based on the environmental characteristic parameters, and obtain the cooling air demand during the next air conditioning operating period;
[0105] The control module 404 is used to control the air conditioning equipment in the target area to store ice and deliver cold air during the next air conditioning opening period according to the cold air demand.
[0106] Optionally, the identification module 402 is further configured to:
[0107] Crowd recognition is performed on the environmental image to extract personnel attribute parameters, which are used to indicate the degree of air conditioning demand and heat dissipation.
[0108] The environmental image is used to identify devices and extract device attribute parameters, which are used to indicate the degree of heat dissipation.
[0109] Based on the job labels corresponding to the environmental images, job attribute parameters are determined, which are used to indicate the degree of air conditioning demand and the time period of air conditioning demand.
[0110] Optionally, the identification module 402 is further configured to:
[0111] The environmental image is subjected to crowd recognition to obtain the gender ratio, crowd density and cooling distance within the target area. The gender ratio is the ratio of the number of people of different genders, the crowd density is the ratio of the number of people to the area of the crowd, and the cooling distance is the average distance between the cooling equipment and the center point of the crowd.
[0112] The area of the people in the environmental image is estimated to obtain their body surface area; the heat dissipation parameters of the crowd in the target area are determined based on the body surface area, body surface temperature and space temperature of each person, wherein the space temperature is the temperature at a preset distance from the person in the space.
[0113] Optionally, the identification module 402 is further configured to:
[0114] The area of the heat-dissipating object in the environmental image is estimated to obtain the surface area of the heat-dissipating object;
[0115] The heat dissipation parameters of the objects in the target area are determined based on the surface area, surface temperature, and ambient temperature of each heat dissipation object, wherein the ambient temperature is the temperature at a preset distance from the heat dissipation object within the space.
[0116] Optionally, the identification module 402 is further configured to:
[0117] Based on the job labels, the job level is determined, and different job levels correspond to different levels of air conditioning demand.
[0118] Optionally, the prediction module 403 is further configured to:
[0119] An environmental feature vector is generated based on the environmental feature parameters and the weights corresponding to each environmental feature parameter.
[0120] The environmental feature vector is input into the air conditioning volume prediction model to obtain the air conditioning demand for the next day. The air conditioning volume prediction model is used to match the air conditioning demand for the next day from the prediction table based on the environmental feature vector. The prediction table is obtained by training the air conditioning volume prediction model.
[0121] It should be noted that, in the embodiments of this application, if the above-described control method for an air conditioning unit is implemented as a software functional module and sold or used as an independent product, it can also be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the embodiments of this application, or the part that contributes to the prior art, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause an electronic device (which may be a personal computer, server, or network device, etc.) to execute all or part of the methods described in the various embodiments of this application. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, portable hard drives, read-only memory (ROM), magnetic disks, or optical disks. Thus, the embodiments of this application are not limited to any specific hardware and software combination.
[0122] Accordingly, this application provides a storage medium storing a computer program thereon, characterized in that the computer program, when executed by a processor, implements the steps in the control method for an air conditioning unit provided in the above embodiments.
[0123] This application provides an electronic device; Figure 5 This is a schematic diagram of the composition structure of the electronic device provided in the embodiments of this application, such as... Figure 5As shown, the electronic device 500 includes: a processor 501, at least one communication bus 502, a user interface 503, at least one external communication interface 504, and a memory 505. The communication bus 502 is configured to enable communication between these components. The user interface 503 may include a display screen, and the external communication interface 504 may include standard wired and wireless interfaces. The processor 501 is configured to execute a program stored in the memory for a control method of an air conditioning unit, to implement the steps in the control method of an air conditioning unit provided in the above embodiment.
[0124] This application provides an air conditioning unit, which includes the electronic equipment, cold storage equipment, and air conditioning equipment described in the above embodiments. The cold storage equipment is used to form ice or ice crystals from the cold storage medium using ice storage technology. The air conditioning equipment is used to output cold air through the heat absorption effect of the melting process of ice or ice crystals. The electronic equipment is communicatively connected to the cold storage equipment and the air conditioning equipment and is used to control the cold storage capacity of the cold storage equipment and the cold air delivery volume of the air conditioning equipment.
[0125] It should be noted that the descriptions of the storage media, electronic devices, and air conditioning units described above are similar to the descriptions of the method embodiments described above, and have similar beneficial effects. For technical details not disclosed in the storage media and device embodiments of this application, please refer to the descriptions of the method embodiments of this application for understanding.
[0126] It should be understood that the phrase "one embodiment" or "an embodiment" throughout the specification means that a specific feature, structure, or characteristic related to the embodiment is included in at least one embodiment of this application. Therefore, "in one embodiment" or "in an embodiment" appearing throughout the specification does not necessarily refer to the same embodiment. Furthermore, these specific features, structures, or characteristics can be combined in any suitable manner in one or more embodiments. It should be understood that in the various embodiments of this application, the sequence numbers of the above-described processes do not imply a sequential order of execution; the execution order of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the embodiments of this application. The sequence numbers of the above-described embodiments are merely descriptive and do not represent the superiority or inferiority of the embodiments.
[0127] It should be noted that, in this document, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, object, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such a process, method, object, or apparatus. Unless otherwise specified, an element defined by the phrase "comprising one..." does not exclude the presence of other identical elements in the process, method, object, or apparatus that includes that element.
[0128] In the several embodiments provided in this application, it should be understood that the disclosed devices and methods can be implemented in other ways. The device embodiments described above are merely illustrative. For example, the division of units is only a logical functional division, and in actual implementation, there may be other division methods, such as: multiple units or components can be combined, or integrated into another system, or some features can be ignored or not executed. In addition, the coupling, direct coupling, or communication connection between the various components shown or discussed can be through some interfaces, and the indirect coupling or communication connection between devices or units can be electrical, mechanical, or other forms.
[0129] The units described above as separate components may or may not be physically separate. The components shown as units may or may not be physical units. They may be located in one place or distributed across multiple network units. Some or all of the units may be selected to achieve the purpose of this embodiment according to actual needs.
[0130] In addition, each functional unit in the various embodiments of this application can be integrated into one processing unit, or each unit can be a separate unit, or two or more units can be integrated into one unit; the integrated unit can be implemented in hardware or in the form of hardware plus software functional units.
[0131] Those skilled in the art will understand that all or part of the steps of the above method embodiments can be implemented by hardware related to program instructions. The aforementioned program can be stored in a computer-readable storage medium. When the program is executed, it performs the steps of the above method embodiments. The aforementioned storage medium includes various media that can store program code, such as mobile storage devices, read-only memory (ROM), magnetic disks, or optical disks.
[0132] Alternatively, if the integrated units described above are implemented as software functional modules and sold or used as independent products, they can also be stored in a computer-readable storage medium. Based on this understanding, the technical solutions of the embodiments of this application, or the parts that contribute to the prior art, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a controller to execute all or part of the methods described in the various embodiments of this application. The aforementioned storage medium includes various media capable of storing program code, such as mobile storage devices, ROMs, magnetic disks, or optical disks.
[0133] The above description is merely an embodiment of this application, but the scope of protection of this application is not limited thereto. Any variations or substitutions that can be easily conceived by those skilled in the art within the scope of the technology disclosed in this application should be included within the scope of protection of this application. Therefore, the scope of protection of this application should be determined by the scope of the claims.
Claims
1. A method for controlling an air conditioning unit, characterized in that, The method includes: Acquire environmental images within the target area; Image recognition is performed on the environmental image to extract environmental feature parameters. The environmental feature parameters include at least one of personnel attribute parameters, equipment attribute parameters, and job attribute parameters. The environmental feature parameters are used to indicate at least one of heat dissipation level, air conditioning demand level, and air conditioning demand period. Based on the environmental characteristic parameters, the cooling volume is predicted to obtain the cooling demand during the next air conditioning operating period. Control the air conditioning equipment in the target area to store ice and deliver cold air during the next air conditioning turn-on period according to the cold air demand. The step of predicting the cooling demand based on the environmental characteristic parameters to obtain the cooling demand during the next air conditioning operating period includes: An environmental feature vector is generated based on the environmental feature parameters and the weights corresponding to each environmental feature parameter. The environmental feature vector is input into the air conditioning volume prediction model to obtain the air conditioning demand. The air conditioning volume prediction model is used to match the air conditioning demand from the prediction table based on the environmental feature vector. The prediction table is obtained by training the air conditioning volume prediction model.
2. The method according to claim 1, characterized in that, The step of performing image recognition on the environmental image and extracting environmental feature parameters includes: Crowd recognition is performed on the environmental image to extract the personnel attribute parameters, which are used to indicate the degree of air conditioning demand and heat dissipation. The environmental image is used to identify devices and extract device attribute parameters, which are used to indicate the degree of heat dissipation. Based on the job label corresponding to the environmental image, the job attribute parameters are determined, which are used to indicate the degree of air conditioning demand and the time period of air conditioning demand.
3. The method according to claim 2, characterized in that, The step of performing crowd recognition on the environmental image and extracting personnel attribute parameters includes: The environmental image is subjected to crowd recognition to obtain the gender ratio, crowd density and cooling distance within the target area. The gender ratio is the ratio of the number of people of different genders, the crowd density is the ratio of the number of people to the area of the crowd, and the cooling distance is the average distance between the cooling equipment and the center point of the crowd. The area of the people in the environmental image is estimated to obtain their body surface area; the heat dissipation parameters of the crowd in the target area are determined based on the body surface area, body surface temperature and space temperature of each person, wherein the space temperature is the temperature at a preset distance from the person in the space.
4. The method according to claim 2, characterized in that, The step of performing device identification on the environmental image and extracting device attribute parameters includes: The area of the heat-dissipating object in the environmental image is estimated to obtain the surface area of the heat-dissipating object; The heat dissipation parameters of the objects in the target area are determined based on the surface area, surface temperature, and ambient temperature of each heat dissipation object, wherein the ambient temperature is the temperature at a preset distance from the heat dissipation object within the space.
5. The method according to claim 2, characterized in that, The step of determining job attribute parameters based on the job tag corresponding to the environmental image includes: Based on the job labels, the job level is determined, and different job levels correspond to different levels of air conditioning demand.
6. An air conditioning unit control device, characterized in that, The device includes: The acquisition module is used to acquire environmental images within the target area; The recognition module is used to perform image recognition on the environmental image and extract environmental feature parameters. The environmental feature parameters include at least one of personnel attribute parameters, equipment attribute parameters, and job attribute parameters. The environmental feature parameters are used to indicate at least one of heat dissipation level, air conditioning demand level, and air conditioning demand period. The prediction module is used to predict the cooling volume based on the environmental characteristic parameters to obtain the cooling demand during the next air conditioning operating period. The control module is used to control the air conditioning equipment in the target area to store ice and deliver cold air during the next air conditioning turn-on period according to the cold air demand. The step of predicting the cooling demand based on the environmental characteristic parameters to obtain the cooling demand during the next air conditioning operating period includes: An environmental feature vector is generated based on the environmental feature parameters and the weights corresponding to each environmental feature parameter. The environmental feature vector is input into the air conditioning volume prediction model to obtain the air conditioning demand. The air conditioning volume prediction model is used to match the air conditioning demand from the prediction table based on the environmental feature vector. The prediction table is obtained by training the air conditioning volume prediction model.
7. An electronic device, characterized in that, The electronic device includes a memory and a processor. The memory stores a computer program, which, when executed by the processor, performs an air conditioning unit control method as described in any one of claims 1 to 5.
8. An air conditioning unit, characterized in that, The air conditioning unit includes the electronic device, cold storage device, and air conditioning device as described in claim 7, wherein the cold storage device is used to form ice or ice crystals from the cold storage medium through ice cold storage technology, the air conditioning device is used to output cold air through the heat absorption effect of the melting process of ice or ice crystals, and the electronic device is communicatively connected to the cold storage device and the air conditioning device to control the cold storage capacity of the cold storage device and the cold air delivery capacity of the air conditioning device.
9. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores at least one instruction, at least one program, code set, or instruction set, wherein the at least one instruction, at least one program, code set, or instruction set is loaded and executed by a processor to implement an air conditioning unit control method as described in any one of claims 1 to 5.
10. A computer program product, characterized in that, The computer program product runs on the processor of the electronic device, causing the electronic device to perform an air conditioning unit control method as described in any one of claims 1 to 5.
Citation Information
Patent Citations
Central air conditioner control method of smart hospital and storage medium
CN115854506A