Air conditioning control device and air conditioning control program

Abstract

To provide an air-conditioning control device that can perform air-conditioning control reflecting feeling of a crewman, and an air-conditioning control program.SOLUTION: An air-conditioning control device comprises a processor, and the processor estimates feeling of a crewman when performing spot air-conditioning for the crewman in a cabin, and controls spot air-conditioning on the basis of the estimated result.SELECTED DRAWING: Figure 1

Description

Technical Field

[0001] The present disclosure relates to an air-conditioning control device and an air-conditioning control program.

Background Art

[0002] Patent Document 1 discloses a technique for performing spot air conditioning on a vehicle occupant when predetermined spot air conditioning conditions are satisfied.

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] In Patent Document 1, for example, it is also possible to create a learned model by machine learning and perform air-conditioning control using the learned model. However, the demand for vehicle air conditioning depends on how the occupant feels at that time, and control that does not reflect the occupant's feeling may not result in comfort. Therefore, there has been a demand for air-conditioning control that reflects the occupant's feeling.

[0005] The present disclosure has been made in view of the above, and an object thereof is to provide an air-conditioning control device and an air-conditioning control program capable of performing air-conditioning control that reflects the occupant's feeling.

Means for Solving the Problems

[0006] The air-conditioning control device according to the present disclosure includes a processor, and when the processor is performing spot air conditioning on an occupant in a vehicle interior, the processor estimates the emotion of the occupant and controls the spot air conditioning based on the estimation result.

[0007] The air-conditioning control program according to the present disclosure causes a processor to estimate the emotion of a passenger in a vehicle interior when spot air-conditioning is being performed on the passenger, and to control the spot air-conditioning based on the estimation result.

Effect of the Invention

[0008] According to the present disclosure, it is possible to perform air-conditioning control that reflects how the passenger feels by estimating whether the spot air-conditioning is comfortable for the passenger and controlling the spot air-conditioning based on the estimation result.

Brief Description of the Drawings

[0009]

Figure 1

Figure 2

Figure 3

Mode for Carrying Out the Invention

[0010] An air-conditioning control device and an air-conditioning control program according to an embodiment of the present disclosure will be described with reference to the drawings. Note that the constituent elements in the following embodiments include those that can be easily replaced by those skilled in the art and those that are substantially the same.

[0011] (Air-conditioning control device) The air-conditioning control device 1 is for controlling the air-conditioning device (air conditioner) of a vehicle. The air-conditioning control device 1 may be mounted on the vehicle, or may be realized by a server device or the like separate from the vehicle. In the present embodiment, the description will be made on the premise that the air-conditioning control device 1 is mounted on the vehicle. Further, the air-conditioning device has a cooling function and a heating function, but in the present embodiment, the description will be made on the premise of using the cooling function.

[0012] When the air-conditioning control device 1 is realized by a server device, the vehicle and the server device are connected by a network including, for example, an Internet line network, a mobile phone line network, etc. Then, the server device which is the air-conditioning control device 1 communicates via the communication unit (Data Communication Module: DCM) of the vehicle to remotely control the air-conditioning device of the vehicle.

[0013] As shown in FIG. 1, the air-conditioning control device 1 includes a control unit 10, a storage unit 20, and a sensor group 30. The control unit 10 includes a processor and a memory (main storage unit). Specifically, the processor is composed of a CPU (Central Processing Unit), a DSP (Digital Signal Processor), an FPGA (Field-Programmable Gate Array), a GPU (Graphics Processing Unit), etc. Also, the memory is composed of a RAM (Random Access Memory), a ROM (Read Only Memory), etc.

[0014] The control unit 10 loads the program stored in the storage unit 20 into the working area of the main storage unit and executes it, and controls each component etc. through the execution of the program to realize a function that meets a predetermined purpose. The control unit 10 functions as an air-conditioning control unit 11, a situation estimation unit 12, and an emotion estimation unit 13 through the execution of the program stored in the storage unit 20.

[0015] When a predetermined spot air-conditioning condition is satisfied, the air-conditioning control unit 11 performs spot air-conditioning on the passengers in the vehicle. Note that spot air-conditioning means locally sending cold air to the corresponding passengers. Also, examples of the spot air-conditioning condition include the temperature inside the vehicle compartment, etc.

[0016] The air-conditioning control unit 11 performs air-conditioning control (first air-conditioning control) on whether to perform spot air-conditioning as described above, and air-conditioning control (second air-conditioning control) based on the emotion estimation result by the emotion estimation unit 13 as shown in FIG. 2.

[0017] When the air conditioning control unit 11 controls the spot air conditioning based on the emotion estimation result by the emotion estimation unit 13, it adjusts the parameters of the spot air conditioning so that the repeatedly obtained emotion estimation results of the passengers vary from discomfort to pleasure. The parameters of this spot air conditioning are, for example, the target of the spot air conditioning, the air volume, the temperature, the wind direction, etc. Further, the air conditioning control unit 11 may control the spot air conditioning using the situation estimation result by the situation estimation unit 12 in addition to the emotion estimation result by the emotion estimation unit 13.

[0018] For example, when the air conditioning control unit 11 obtains from the emotion estimation unit 13 an emotion estimation result that the passenger who is performing the spot air conditioning is "comfortable", the air conditioning control unit 11 performs air conditioning control to continue the spot air conditioning. On the other hand, for example, when the air conditioning control unit 11 obtains from the emotion estimation unit 13 an emotion estimation result that the passenger who is performing the spot air conditioning is "uncomfortable", the air conditioning control unit 11 performs air conditioning control such as turning the spot air conditioning on and off, switching the target of the spot air conditioning, adjusting the air volume, adjusting the temperature, and adjusting the wind direction.

[0019] The air conditioning control unit 11 can perform the above air conditioning control (second air conditioning control) based on a pre-trained learned model by machine learning or a predetermined rule. When using a learned model, the input data is, for example, the numerical value (probability) of the comfort or discomfort of the passenger estimated by the emotion estimation unit 13. As this input data, the situation estimation result (situations of the following (1) to (9)) by the situation estimation unit 12 may be further used. And the output data is, for example, the timing of turning the spot air conditioning on and off, the switching destination of the target of the spot air conditioning, the air volume of the spot air conditioning, the temperature of the spot air conditioning, the wind direction of the spot air conditioning, etc. Note that the numerical value of comfort or discomfort indicates, for example, a numerical index indicating comfort or discomfort. Also, the probability of comfort or discomfort indicates the probability of a specific emotion indicating comfort or discomfort.

[0020] The method for constructing the learned model used in the air conditioning control unit 11 is not particularly limited, and for example, various machine learning methods such as deep learning using a neural network, support vector machine, decision tree, naive Bayes, k-nearest neighbor method, etc. can be used.

[0021] The situation estimation unit 12 estimates various situations related to the occupants and the vehicle. Examples of the situations estimated by the situation estimation unit 12 include the situation of the occupants, the situation of the passenger compartment, the situation around the occupants, and the like.

[0022] As shown in FIG. 2, the situation estimation unit 12 acquires, for example, image data of the occupants from the cameras among the sensor group 30. Then, the situation estimation unit 12 estimates the situation of the occupants based on the acquired image data. Examples of the situation of the occupants estimated by the situation estimation unit 12 in this case include the following.

[0023] (1) The shininess of the occupant's face due to sweat, moisture, etc. (2) The degree of cohesion of the occupant's hair due to sweat, moisture, etc. (3) Whether or not the occupant's bangs are covering the eyes (4) Whether or not the occupant's body, clothes, shoes, etc. are wet with rain (5) The materials of the occupant's clothes, shoes, etc.

[0024] Also, as shown in FIG. 2, the situation estimation unit 12 acquires, for example, image data of the interior of the vehicle from the cameras among the sensor group 30. Then, the situation estimation unit 12 estimates the situation of the interior of the vehicle based on the acquired image data. Examples of the situation of the interior of the vehicle estimated by the situation estimation unit 12 in this case include the following.

[0025] (6) The presence or absence and the position of the portion directly irradiated by direct sunlight in the vehicle interior (7) The presence or absence and the position of the portion overheated in the vehicle interior

[0026] Also, as shown in FIG. 2, the situation estimation unit 12 acquires, for example, sensor data of the interior of the vehicle from the infrared sensors among the sensor group 30. Then, the situation estimation unit 12 estimates the situation of the interior of the vehicle based on the acquired sensor data. Examples of the situation of the interior of the vehicle estimated by the situation estimation unit 12 in this case include the above (7).

[0027] Further, as shown in FIG. 2, the situation estimation unit 12 acquires sensor data around the vehicle from in-vehicle sensors among the sensor group 30, for example. Then, the situation estimation unit 12 estimates the situation around the occupant based on the acquired sensor data. Examples of the situation around the occupant estimated by the situation estimation unit 12 in this case include the following.

[0028] (8) Whether the surrounding air is dry (9) The surrounding air temperature

[0029] The situation estimation unit 12 can perform the above situation estimation based on a learned model pre-learned by machine learning. When using a learned model, the input data is, for example, occupant image data, in-vehicle image data, in-vehicle sensor data, and sensor data around the vehicle. And the output data is, for example, the situations such as the above (1) to (9).

[0030] The method for constructing the learned model used by the situation estimation unit 12 is not particularly limited, and various machine learning methods such as deep learning using a neural network, support vector machine, decision tree, naive Bayes, k-nearest neighbor method, etc. can be used.

[0031] The emotion estimation unit 13 estimates the emotion of the occupant when spot air conditioning is performed on the occupant in the vehicle interior. The emotion estimation unit 13 estimates the emotion of the occupant based on sensor data (image data, biometric data) acquired from the sensor group 30 that observes the state of the occupant. Further, the emotion estimation unit 13 estimates the emotion of the occupant from the acquired sensor data using a trained machine learning model. Further, the emotion estimation unit 13 may further estimate the emotion of the occupant using the situation estimation result by the situation estimation unit 12 in addition to the detection data (image data, biometric data) by the sensor group 30. Note that the emotion of the occupant estimated by the emotion estimation unit 13 specifically indicates the numerical value (probability) of the pleasure or displeasure of the occupant. Also, the estimation of the emotion of the occupant by the emotion estimation unit 13 is repeatedly executed at a predetermined control cycle.

[0032] As shown in FIG. 2, for example, the emotion estimation unit 13 acquires image data of the occupant from a camera among the sensor group 30. Then, the emotion estimation unit 13 estimates the emotion of the occupant based on the acquired image data. In this case, the emotion estimation unit 13 calculates a numerical value (probability) of the pleasure or displeasure of the occupant based on, for example, the expression of the occupant included in the image data.

[0033] Also, as shown in FIG. 2, for example, the emotion estimation unit 13 acquires biological data of the occupant from a biological sensor among the sensor group 30. Then, the emotion estimation unit 13 estimates the emotion of the occupant based on the acquired biological data. In this case, the emotion estimation unit 13 calculates a numerical value (probability) of the pleasure or displeasure of the occupant based on, for example, the body temperature, heartbeat, pulse, blood pressure, electroencephalogram, etc. of the occupant included in the biological data.

[0034] Note that the emotion estimation unit 13 may estimate the emotion of the occupant based on both the detection data (image data, biological data) by the sensor group 30 and the situation estimation result by the situation estimation unit 12 as shown in FIG. 2.

[0035] The emotion estimation unit 13 can perform the above emotion estimation based on a learned model pre-learned by machine learning. When using a learned model, the input data is, for example, image data of the occupant and biological data of the occupant. As this input data, the situation estimation result (situations (1) to (9) above) by the situation estimation unit 12 may be further used. And the output data is, for example, a numerical value (probability) of the pleasure or displeasure of the occupant.

[0036] The method for constructing the learned model used in the emotion estimation unit 13 is not particularly limited, and various machine learning methods such as deep learning using a neural network, support vector machine, decision tree, naive Bayes, k-nearest neighbor method, etc. can be used.

[0037] The storage unit 20 is realized by a recording medium such as an EPROM (Erasable Programmable ROM), a hard disk drive (HDD), and a removable medium. Examples of the removable medium include disk recording media such as a USB (Universal Serial Bus) memory, a CD (Compact Disc), a DVD (Digital Versatile Disc), and a BD (Blu-ray (registered trademark) Disc).

[0038] The storage unit 20 can store an operating system (OS), various programs, various tables, various databases, etc. Further, the storage unit 20 may store, for example, the estimation results in the situation estimation unit 12 and the emotion estimation unit 13. Further, the storage unit 20 may store a machine learning trained model (trained model) used in the air conditioning control unit 11, the situation estimation unit 12, and the emotion estimation unit 13.

[0039] The sensor group 30 acquires data for estimating the situation of the occupant himself / herself, the situation inside the vehicle cabin, and the situation around the occupant. This sensor group 30 is installed, for example, inside the vehicle cabin. Examples of the sensor group 30 include a camera for photographing images of the occupant and inside the vehicle cabin, a temperature sensor and an infrared sensor for detecting the situation of the occupant and inside the vehicle cabin, a biometric sensor for acquiring biometric data of the occupant, and an in-vehicle sensor for detecting the situation around the vehicle. Examples of the biometric sensor include a temperature sensor, a heart rate sensor, a pulse sensor, a blood pressure sensor, an electroencephalogram sensor, etc. Examples of the in-vehicle sensor include a temperature sensor, a millimeter wave sensor, an infrared sensor, a laser sensor, a 3D-LiDAR, etc.

[0040] Note that in the sensor group 30, the sensor used for emotion estimation and the sensor for estimating the situation inside the vehicle cabin to determine the on / off of spot air conditioning may be the same, partially overlapping, or completely separate.

[0041] (Air Conditioning Control Method) An example of the processing procedure of the air conditioning control method executed by the air conditioning control device according to the embodiment will be described with reference to FIG. 3.

[0042] First, the in-vehicle sensors among the sensor group 30 are used to detect the situation in the passenger compartment (step S1). Subsequently, the air conditioning control unit 11 determines whether to perform spot air conditioning based on the sensor data of the in-vehicle sensors and predetermined spot air conditioning conditions (step S2).

[0043] In step S2, if it is determined to perform spot air conditioning (Yes in step S2), the air conditioning control unit 11 performs spot air conditioning on the corresponding occupant (step S3). Subsequently, the emotion estimation unit 13 estimates the emotion of the occupant based on the detection data (image data, biological data) by the sensor group 30 (step S4).

[0044] Subsequently, the air conditioning control unit 11 controls the spot air conditioning based on the estimation result by the emotion estimation unit 13 (step S5), and this process is completed. Note that in step S2, if it is determined not to perform spot air conditioning (No in step S2), the air conditioning control unit 11 completes this process.

[0045] According to the air conditioning control device and the air conditioning control program according to the embodiment described above, it is possible to estimate whether the spot air conditioning is comfortable for the occupant and control the spot air conditioning based on the estimation result, thereby performing air conditioning control that reflects the way the occupant feels.

[0046] Also, in the air conditioning control device and the air conditioning control program according to the embodiment, since it is possible to perform air conditioning control that reflects the way the occupant feels, the comfort of the occupant is improved. In addition, since the setting operation of the air conditioning is reduced, the occupant can concentrate on driving, and the driving safety is improved.

[0047] Further effects and modifications can be easily derived by those skilled in the art. Therefore, the broader aspects of the present invention are not limited to the specific details and representative embodiments shown and described above. Accordingly, various changes can be made without departing from the spirit or scope of the general inventive concept defined by the appended claims and their equivalents.

Description of Reference Numerals

[0048] 1 Air conditioning control device 10 Control unit 11 Air conditioning control unit 12 Situation estimation unit 13 Emotion estimation unit 20 Memory unit 30 Sensor group

Claims

1. Comprising a processor, The processor Estimates the situation in the passenger compartment and the situation around the passenger, Estimates the emotion of the passenger when spot air conditioning is performed on the passenger in the passenger compartment, Controls the spot air conditioning based on the estimation result of the situation and the estimation result of the emotion, An air conditioning control device.

2. Estimating the emotion of the passenger Includes obtaining sensor data from a sensor that observes the state of the passenger, and Using a trained machine learning model to estimate the emotion of the passenger from the obtained sensor data, and is constituted by, The air conditioning control device according to claim 1.

3. The sensor includes a camera installed in the passenger compartment, The sensor data includes image data obtained by the camera, The air conditioning control device according to claim 2.

4. Estimating the emotion of the passenger is repeatedly executed, Controlling the spot air conditioning based on the estimation result includes adjusting the parameters of the spot air conditioning so that the repeatedly obtained estimation result of the passenger's emotion varies from discomfort to comfort, The air conditioning control device according to any one of claims 1 to 3.

5. An air conditioning control program that causes a processor to Estimate the situation in the passenger compartment and the situation around the passenger, Estimate the emotion of the passenger when spot air conditioning is performed on the passenger in the passenger compartment, Control the spot air conditioning based on the estimation result of the situation and the estimation result of the emotion, To execute.

Images