Ventilation system, learning device, and inference device

Abstract

This ventilation system is provided with: a ventilation device installed in a building; and a ventilation control device (2) for controlling the ventilation device on the basis of weather prediction data. The ventilation control device (2) has: a rainwater entry possibility determination unit (21) for determining whether or not there is a possibility that rainwater may enter the ventilation device on the basis of the weather prediction data; and an air supply amount management unit (22) for managing an air supply amount of the ventilation device by stopping an air supply with the ventilation device or reducing the air supply amount with the ventilation device when there is a possibility that rainwater may enter the ventilation device.

Description

Ventilation system, learning device and inference device

[0001] The present disclosure relates to a ventilation system including a ventilation device installed in a building and a ventilation control device that controls the ventilation device, as well as a learning device and an inference device used therein.

[0002] BACKGROUND ART In a ventilation system including a ventilation device installed in a building and a ventilation control device that controls the ventilation device, it is required to prevent rainwater and the like from entering the room.

[0003] Patent Document 1 discloses a ventilation device that stops ventilation when the measured outdoor humidity is higher than the indoor humidity. The ventilation device disclosed in Patent Document 1 stops ventilation when the outdoor humidity becomes higher than the indoor humidity due to rainfall, thereby suppressing the infiltration of rainwater during rainfall.

[0004] Japanese Patent Application Publication No. 5-280774

[0005] The ventilation device disclosed in Patent Document 1 has a problem in that when rain begins to fall suddenly due to a sudden downpour, the humidity sensor does not keep up with the sudden increase in outside humidity, resulting in a delay in stopping the air supply fan and allowing rainwater to seep in.

[0006] The present disclosure has been made in view of the above, and aims to provide a ventilation system that prevents rainwater from entering a ventilation device even when it suddenly starts to rain.

[0007] In order to solve the above-mentioned problems and achieve the object, the present disclosure provides a ventilation system including a ventilation device installed in a building and a ventilation control device that controls the ventilation device based on weather forecast data. The ventilation control device includes a rainwater intrusion possibility determination unit that determines whether or not rainwater is likely to infiltrate the ventilation device based on the weather forecast data, and an air supply volume management unit that manages the volume of air supplied by the ventilation device by stopping air supply by the ventilation device or reducing the volume of air supplied by the ventilation device when there is a possibility of rainwater intrusion into the ventilation device.

[0008] The ventilation system according to the present disclosure has the effect of making it difficult for rainwater to infiltrate the ventilation device even when it suddenly starts to rain.

[0009] FIG. 1 shows the configuration of a ventilation system according to embodiment 1. FIG. 1 shows the configuration of a ventilation device of a ventilation system according to embodiment 1. FIG. 1 shows the configuration of a ventilation control device of a ventilation system according to embodiment 1. Flowchart showing the flow of operation of a ventilation system according to embodiment 1. Flowchart showing the flow of operation of a ventilation system according to embodiment 2. FIG. 1 shows an example of a rainwater infiltration determination table held by a ventilation control device according to embodiment 2. FIG. 1 shows an installation example of a ventilation device of a ventilation system according to embodiment 3. FIG. 1 shows the configuration of a ventilation system according to embodiment 5. FIG. 1 shows the configuration of a ventilation system according to embodiment 6. FIG. 1 shows the configuration of a ventilation system according to embodiment 9. FIG. 1 shows the configuration of a learning device for a ventilation system according to embodiment 9. Flowchart related to learning processing of the learning device for a ventilation system according to embodiment 9. Flowchart related to inference processing of the inference device for a ventilation system according to embodiment 9. FIG. 1 shows the configuration of a ventilation system according to embodiment 10. FIG. 1 shows the hardware configuration of a learning device according to embodiments 9 and 10.

[0010] A ventilation system, a learning device, and an inference device according to an embodiment will be described in detail below with reference to the drawings.

[0011] Embodiment 1. Fig. 1 is a diagram showing the configuration of a ventilation system according to embodiment 1. The ventilation system 100 includes a ventilation device 1 installed in a building and a ventilation control device 2 that controls the ventilation device 1 based on weather forecast data.

[0012] FIG. 2 is a diagram showing the configuration of a ventilation device of the ventilation system according to the first embodiment. The ventilation device 1 includes a ventilation fan 11, a speed regulator 12 that adjusts the airflow rate of the ventilation fan 11, and a damper 13 that opens and closes a ventilation port. The ventilation device 1 may also include a ventilation hood and a filter installed in the ventilation duct. The speed regulator 12 stops and adjusts the speed of the ventilation fan 11 in response to instructions from the ventilation control device 2. The damper 13 adjusts the opening degree of the ventilation port in response to instructions from the ventilation control device 2. The ventilation device 1 is, for example, a propeller fan-type positive pressure ventilation fan installed on the wall of a building. The positive pressure ventilation fan may allow rainwater to enter through the wind tunnel of the propeller fan that connects the inside and outside of the building.

[0013] FIG. 3 is a diagram showing the configuration of the ventilation control device of the ventilation system according to the first embodiment. The ventilation control device 2 includes a rainwater intrusion possibility determination unit 21 and an air supply volume management unit 22. The rainwater intrusion possibility determination unit 21 determines whether or not there is a possibility of rainwater intrusion into the ventilation device 1 based on weather forecast data and pre-set ventilation control conditions. The air supply volume management unit 22 outputs a control instruction to the ventilation device 1 to stop the ventilation device 1 or reduce the air supply volume when there is a possibility of rainwater intrusion into the ventilation device 1. Furthermore, when there is no longer a possibility of rainwater intrusion into the ventilation device 1, the air supply volume management unit 22 outputs a control instruction to the ventilation device 1 to resume operation of the ventilation device 1 or to return the air supply volume to the original volume. In this way, the air supply volume management unit 22 manages the air supply volume by instructing the ventilation device 1 to operate, stop, or increase or decrease the air supply volume.

[0014] The weather forecast data may be forecast result data based on current and past weather measurement data obtained by various weather sensors such as temperature sensors, humidity sensors, and anemometers installed at the installation site of the ventilation device 1, or weather forecast data based on regional information for the installation site of the ventilation device 1 released by meteorological agencies or weather information providers may be obtained from outside.

[0015] The weather forecast data includes at least precipitation amount. In addition to precipitation amount, the weather forecast data may also include one or more of temperature, humidity, atmospheric pressure, solar radiation, wind direction, wind speed, etc. The forecast value of the weather forecast data is associated with the forecast time.

[0016] The ventilation control device 2 may be configured integrally with the ventilation device 1 or may be installed at a location separate from the ventilation device 1.

[0017] Next, the operation of the ventilation system 100 will be described. FIG. 4 is a flowchart showing the operation of the ventilation system according to the first embodiment. In step S1, the rainwater intrusion possibility determination unit 21 acquires weather forecast data. In step S2, the rainwater intrusion possibility determination unit 21 determines whether the predicted precipitation amount X minutes from now is greater than a preset value. Here, the value of X can be set arbitrarily in advance, but if the value of X is too large, rain may start falling before the ventilation device 1 is controlled. For this reason, it is preferable to set X to a time of about 5 minutes. If the predicted precipitation amount X minutes from now is equal to or less than the preset value, step S2 returns to No, and the process returns to step S1. If the predicted precipitation amount X minutes from now is greater than the preset value, step S2 returns to Yes, and in step S3, the supply air volume management unit 22 controls the ventilation device 1 so that the supply air volume X minutes from now is equal to or less than the preset air volume.

[0018] In step S4, the rainwater intrusion possibility determination unit 21 acquires weather forecast data. In step S5, the rainwater intrusion possibility determination unit 21 determines whether the expected precipitation Y minutes from now is less than a preset value. Here, the value of Y can be set arbitrarily in advance, but if the value of Y is too large, the ventilation device 1 will be stopped for a long time despite the low possibility of rainwater intrusion. For this reason, it is preferable to set Y to about 5 minutes. If the expected precipitation Y minutes from now is equal to or greater than the preset value, step S5 returns to No, and the process returns to step S4. If the expected precipitation Y minutes from now is less than the preset value, step S5 returns to Yes, and in step S6, the supply air volume management unit 22 controls the ventilation device 1 so that the supply air volume Y minutes from now returns to the original air volume. Note that the "original air volume" refers to the air volume before the change in step S3.

[0019] The rainwater infiltration possibility determination unit 21 may acquire precipitation forecast data in step S1, and determine whether the expected precipitation amount X minutes from now is greater than a preset value based on the precipitation forecast data in step S2. Alternatively, the rainwater infiltration possibility determination unit 21 may acquire one or more weather forecast data of temperature, humidity, atmospheric pressure, solar radiation, wind direction, wind speed, etc. in step S1, and determine whether the expected precipitation amount X minutes from now is greater than a preset value based on the acquired weather forecast data in step S2.

[0020] Alternatively, the rainwater infiltration possibility determination unit 21 may acquire precipitation forecast data in step S4, and determine whether the forecast precipitation amount Y minutes from now is smaller than a preset value based on the precipitation forecast data in step S5. Alternatively, the rainwater infiltration possibility determination unit 21 may acquire weather forecast data of one or more of temperature, humidity, atmospheric pressure, solar radiation, wind direction, wind speed, etc. in step S4, and determine whether the forecast precipitation amount Y minutes from now is smaller than a preset value based on the acquired weather forecast data in step S5.

[0021] The control of the ventilation device 1 in steps S3 and S6 is performed by adjusting the speed of the ventilation fan 11 and the opening of the damper 13. If multiple pieces of weather forecast data of the same type are acquired in step S1, the determination in step S3 of whether the expected precipitation amount X minutes from now is greater than a preset value is made based on the most recent weather forecast data. If multiple pieces of weather forecast data of the same type are acquired in step S4, the determination in step S5 of whether the expected precipitation amount Y minutes from now is less than a preset value is made based on the most recent weather forecast data.

[0022] In the ventilation system 100 of embodiment 1, when the expected precipitation amount in X minutes is predicted based on weather forecast data to be greater than a preset value, the ventilation device 1 is controlled so that the air supply volume in X minutes will be equal to or less than the preset air volume.Therefore, compared to ventilation systems that reduce the air supply volume only after actually detecting an increase in precipitation, the amount of rainwater that enters the ventilation device or air supply duct can be reduced.

[0023] Furthermore, when the ventilation system 100 of embodiment 1 determines based on weather forecast data that the expected precipitation amount Y minutes from now is less than a preset value, it controls the ventilation device 1 so that the air supply amount Y minutes from now will be equal to or greater than the preset air volume. Therefore, compared to ventilation systems that increase the air supply amount only after actually detecting a decrease in precipitation, the air supply amount can be restored to its original state more quickly.

[0024] Embodiment 2 The configuration of the ventilation system 100 according to embodiment 2 is the same as that of the ventilation system 100 according to embodiment 1. In the ventilation system 100 according to embodiment 2, the rainwater infiltration possibility determination unit 21 differs from the rainwater infiltration possibility determination unit 21 of the ventilation system 100 according to embodiment 1 in that it determines the possibility of rainwater infiltration based not only on the amount of precipitation but also on a combination of the amount of precipitation and wind speed.

[0025] 5 is a flowchart showing the operation of the ventilation system according to the second embodiment. In step S11, the rainwater intrusion possibility determination unit 21 acquires weather forecast data. In step S12, the rainwater intrusion possibility determination unit 21 determines whether the predicted precipitation and wind speed after X minutes fall within an area where there is a possibility of intrusion in the rainwater intrusion determination table. If the predicted precipitation and wind speed after X minutes do not fall within an area where there is a possibility of intrusion in the rainwater intrusion determination table, the answer in step S12 is No, and the process returns to step S11. If the predicted precipitation and wind speed after X minutes fall within an area where there is a possibility of intrusion in the rainwater intrusion determination table, the answer in step S12 is Yes, and in step S13, the air supply volume management unit 22 controls the ventilation device 1 so that the amount of air supplied after X minutes is equal to or less than a preset air volume.

[0026] In step S14, the rainwater intrusion possibility determination unit 21 acquires weather forecast data. In step S15, the rainwater intrusion possibility determination unit 21 determines whether the predicted precipitation amount and predicted wind speed Y minutes from now will fall outside the area where intrusion is possible in the rainwater intrusion determination table. If the precipitation amount and wind speed Y minutes from now will not fall outside the area where intrusion is possible in the rainwater intrusion determination table, the answer is No in step S15, and the process returns to step S14. If the precipitation amount and wind speed Y minutes from now will fall outside the area where intrusion is possible in the rainwater intrusion determination table, the answer is Yes in step S15, and in step S16, the supply air volume management unit 22 controls the ventilation device so that the supply air volume Y minutes from now returns to the original air volume. Note that the "original air volume" is the air volume before the change in step S13.

[0027] FIG. 6 shows an example of a rainwater intrusion determination table held by the ventilation control device according to the second embodiment. In the rainwater intrusion determination table, a first reference precipitation amount and a second reference precipitation amount are set for precipitation. The first reference precipitation amount is greater than the second reference precipitation amount. The rainwater intrusion determination table can be divided into six regions, from the first region to the sixth region. The first region is a region where precipitation is equal to or less than the second reference precipitation amount and the wind speed is equal to or less than the reference wind speed. The second region is a region where precipitation is equal to or less than the second reference precipitation amount and the wind speed exceeds the reference wind speed. The third region is a region where precipitation exceeds the second reference precipitation amount and is equal to or less than the first reference precipitation amount and the wind speed is equal to or less than the reference wind speed. The fourth region is a region where precipitation exceeds the second reference precipitation amount and is equal to or less than the first reference precipitation amount and the wind speed exceeds the reference wind speed. The fifth region is a region where the precipitation exceeds the first reference precipitation amount and the wind speed is equal to or less than the reference wind speed. The sixth region is a region where the precipitation exceeds the first reference precipitation amount and the wind speed exceeds the reference wind speed.

[0028] If the combination of predicted precipitation and predicted wind speed falls within any of the first, second, and third regions, it is determined that there is no possibility of rainwater infiltration. On the other hand, if the combination of predicted precipitation and predicted wind speed falls within any of the fourth, fifth, and sixth regions, it is determined that there is a possibility of rainwater infiltration. That is, if the predicted precipitation exceeds the first criteria precipitation, it is determined that there is a possibility of rainwater infiltration regardless of the predicted wind speed. If the predicted precipitation is equal to or less than the first criteria precipitation but exceeds the second criteria precipitation, it is determined that there is a possibility of rainwater infiltration if the predicted wind speed at the same time exceeds the criteria wind speed. That is, if the predicted precipitation exceeds the second criteria precipitation, it is determined that there is a possibility of rainwater infiltration if the predicted wind speed is stronger than a certain level.

[0029] In step S11, the ventilation control device 2 acquires weather forecast data for one or more of temperature, humidity, air pressure, solar radiation, and wind direction, as well as wind speed, and based on the acquired weather forecast data, in step S12, determines whether the predicted precipitation and predicted wind speed X minutes from now will fall within an area where infiltration is possible in the rainwater infiltration determination table.

[0030] In step S14, the ventilation control device 2 acquires weather forecast data for one or more of temperature, humidity, air pressure, solar radiation, and wind direction, as well as wind speed, and based on the acquired weather forecast data, in step S15, determines whether the precipitation amount and wind speed Y minutes from now will be outside the area where infiltration is possible in the rainwater infiltration determination table.

[0031] The ventilation system 100 of embodiment 2 reduces the amount of air supplied when the expected wind speed is high, even if the expected precipitation amount is not extremely high, and therefore can better suppress the intrusion of rainwater compared to the ventilation system 100 of embodiment 1.

[0032] Embodiment 3 The ventilation system 100 according to Embodiment 3 has a ventilation device 1 and a ventilation control device 2, similar to the ventilation system 100 according to Embodiment 1. Fig. 7 is a diagram showing an example of installation of ventilation devices in a ventilation system according to Embodiment 3. The ventilation system 100 according to Embodiment 3 is equipped with a plurality of ventilation devices 1. The ventilation devices 1 include a west-side ventilation device 3 installed on the west wall 31 of a building 30 and a north-side ventilation device 4 installed on the north wall 32 of the building 30.

[0033] In the ventilation system 100 according to the third embodiment, the ventilation control device 2 controls the ventilation device 1 based on the predicted wind direction when precipitation that may cause rainwater infiltration is predicted. For example, if a westerly wind is predicted during a time period when precipitation that may cause rainwater infiltration is predicted, the west-side ventilation device 3 installed on the west wall 31 may be subject to rain being blown in by the westerly wind because the wind tunnel of its propeller fan faces west. On the other hand, the north-side ventilation device 4 installed on the north wall 32 is unlikely to be subject to rain being blown in by the westerly wind because the wind tunnel of its propeller fan does not face west. Therefore, the ventilation control device 2 stops the operation of the west-side ventilation device 3 or reduces the amount of air supplied by the west-side ventilation device 3, while maintaining the amount of air supplied by the north-side ventilation device 4 unchanged. In this way, the ventilation system 100 of embodiment 3 can increase the ventilation volume during precipitation by continuing to operate the ventilation devices 1, which are less likely to be blown in by wind, even when precipitation that may cause rainwater to enter the building is predicted, compared to stopping the operation of all ventilation devices 1 or reducing the air supply volume regardless of wind direction.

[0034] Embodiment 4 The configuration of the ventilation system 100 according to embodiment 4 is the same as that of the ventilation system 100 according to embodiment 3. In the ventilation system 100 according to embodiment 4, the ventilation control device 2 separately acquires weather forecast data for the vicinity of the west wall 31 of the building 30 and weather forecast data for the vicinity of the north wall 32 of the building 30, and separately determines whether or not there is a possibility of rainwater infiltration into the west-side ventilation device 3 and whether or not there is a possibility of rainwater infiltration into the north-side ventilation device 4. The ventilation control device 2 then stops operation or reduces the air supply rate of only those ventilation devices 1 into which there is a possibility of rainwater infiltration.

[0035] For example, one method is to predict the local wind speed around the ventilation device 1, determine the possibility of rainwater infiltration, and control the ventilation device 1. By predicting the wind speed that changes locally due to the specific structure of the building 30 and its surroundings, separately from the wind speed based on weather forecasts for the region around the building 30 in which the ventilation device 1 is installed, it is possible to determine the possibility of rainwater infiltration for each installation position of the ventilation device 1 and control the ventilation device 1.

[0036] Embodiment 5. Fig. 8 is a diagram showing the configuration of a ventilation system according to embodiment 5. The ventilation system 100 according to embodiment 5 includes a ventilation control history storage device 5 that stores the ventilation control history of the ventilation control device 2, and a display 6 that displays the ventilation control history, weather forecast data, and actual weather measurement data together with time data.

[0037] The ventilation control history memory device 5 stores ventilation control history that associates the on / off commands for the ventilation blower 11, speed adjustments, or opening / closing instructions for the damper 13 sent from the ventilation control device 2 to the ventilation device 1 with time data, and outputs the ventilation control history to the display 6 in accordance with instructions from the ventilation control device 2.

[0038] The weather forecast data and the measured weather data are input to the display 6 in association with time data and one or more of the weather data such as precipitation, temperature, humidity, atmospheric pressure, solar radiation, wind direction, and wind speed.

[0039] The weather forecast data and actual weather measurement data may be obtained by various weather sensors installed on-site, such as temperature sensors, humidity sensors, and anemometers, or may be obtained from external sources such as meteorological agencies or companies that provide weather information.

[0040] The display 6 shows the weather forecast data, actual weather measurement data, and ventilation control history so that the user can see what conditions were present at the same time on the same date. For example, the display 6 displays a graph with time on the horizontal axis and actual precipitation, predicted precipitation, and ventilation fan speed adjustment amounts on the vertical axis. This allows the user of the ventilation system 100 to determine whether the weather forecast data was correct compared to the actual weather measurement data, and whether the control results of the ventilation device 1 based on the weather forecast data were appropriate.

[0041] The ventilation control device 2, ventilation control history storage device 5, and display device 6 may be integrated together, or may be stored in separate housings. For example, the ventilation control device 2 and ventilation control history storage device 5 may be provided in the same computer system, and a monitor serving as the display device 6 may be connected to a separate computer system.

[0042] 9 is a diagram showing the configuration of a ventilation system according to embodiment 6. The ventilation system 100 according to embodiment 6 differs from the ventilation system 100 according to embodiment 5 in that it includes a ventilation control condition input device 7 for inputting and changing control conditions to the ventilation control device 2.

[0043] The ventilation control condition input device 7 inputs and changes thresholds of weather data set in advance in the ventilation control device 2 and the control content to be applied to the ventilation device 1 when the thresholds are exceeded. For example, if a control condition is set in advance to change the speed adjustment amount of the ventilation fan so as to reduce the amount of air supply at the predicted time when a certain amount of precipitation or wind speed is predicted, the ventilation control condition input device 7 can change the thresholds of precipitation or wind speed and the speed adjustment amount of the ventilation fan 11.

[0044] Therefore, by using the ventilation control condition input device 7 in conjunction with the ventilation control history storage device 5, it is possible to adjust the control conditions to more appropriate ones while checking weather forecast data, actual weather measurement data, and ventilation control history. Because the sensitivity of the sensors used and their ability to follow weather changes, the susceptibility of rainwater to infiltration into the ventilation device 1, and regional weather conditions vary depending on the individual property and the time of year, by making it possible to adjust the precipitation or wind speed threshold and the speed adjustment amount of the ventilation blower 11, it is possible to set ventilation control conditions that appropriately suppress rainwater infiltration regardless of the location and time of year.

[0045] Seventh Embodiment The configuration of the ventilation system 100 according to the seventh embodiment is the same as that of the ventilation system 100 according to any one of the first to sixth embodiments. In the ventilation system 100 according to the seventh embodiment, the supply air volume management unit 22 controls, stores, and displays the volume of supply air based on predicted data on the dispersion of pollen and fine particulate matter. For example, if a large amount of pollen or fine particulate matter is predicted to be dispersed, the ventilation device 1 stops supplying air or reduces the volume of supply air to prevent pollen or fine particulate matter from entering the building 30. In this case, when obtaining weather forecast data, a particle counter for measuring the dispersion of pollen and fine particulate matter may be used, or external pollen dispersion forecast data and fine particulate matter distribution forecast data may be referenced.

[0046] The ventilation system 100 of embodiment 7 can prevent the air inside the building 30 from becoming contaminated with pollen or fine particulate matter by stopping the air supply by the ventilation device 1 or reducing the amount of air supply when it is predicted that the amount of pollen or fine particulate matter will increase.

[0047] Eighth Embodiment. The configuration of the ventilation system 100 according to the eighth embodiment is the same as that of any one of the ventilation systems 100 according to the first to sixth embodiments. In the ventilation system 100 according to the eighth embodiment, the supply air volume management unit 22 controls, stores, and displays the supply air volume based on predicted data of the outdoor carbon monoxide or carbon dioxide concentration. For example, if the outdoor carbon monoxide or carbon dioxide concentration is predicted to be high, the supply air volume by the ventilation device 1 is stopped or the supply air volume is reduced, thereby preventing the carbon monoxide or carbon dioxide concentration in the air inside the building 30 from increasing. In this case, in addition to using a carbon monoxide or carbon dioxide sensor to acquire weather forecast data, road traffic information such as external congestion forecasts may also be used, or traffic volume may be acquired by a camera. This makes it possible to prevent the intake of air indoors when the concentration of automobile exhaust gas in the air becomes high. The trend in automobile exhaust gas concentration can be predicted based on the carbon monoxide or carbon dioxide concentration, as well as based on road traffic information indicating whether traffic volume on nearby roads is high or low.

[0048] The ventilation system 100 of embodiment 8 can prevent the carbon monoxide or carbon dioxide concentration in the air inside the building 30 from increasing by stopping the air supply by the ventilation device 1 or reducing the amount of air supply when it is predicted that the carbon monoxide or carbon dioxide concentration outdoors will increase.

[0049] Ninth Embodiment Fig. 10 is a diagram showing the configuration of a ventilation system according to a ninth embodiment. The ventilation system 100 according to the ninth embodiment includes a ventilation device 1, a ventilation control device 2, a rainwater infiltration amount measuring device 8, a learning device 23, a trained model storage unit 24, and an inference device 25. The learning device 23 learns the relationship between ventilation control conditions, weather forecast data, and the amount of rainwater infiltration. The trained model storage unit 24 stores a trained model 241. The inference device 25 uses the trained model 241 to infer ventilation control conditions.

[0050] 11 is a diagram showing the configuration of a learning device for a ventilation system according to embodiment 9. The learning device 23 includes a learning data acquisition unit 231 and a model generation unit 232. The learning data acquisition unit 231 acquires ventilation control conditions, weather forecast data, and the amount of rainwater infiltration as learning data. The ventilation control conditions are composed of one or more of the speed of the ventilation fan and the opening / closing angle of the damper 13. The weather forecast data is composed of one or more of precipitation, temperature, humidity, atmospheric pressure, solar radiation, wind direction, wind speed, etc.

[0051] The amount of rainwater infiltration is measured by a rainwater infiltration amount measuring device 8. The rainwater infiltration amount measuring device 8 is constructed by installing a commercially available rain sensor or rain gauge on the indoor side of the ventilation system 1. If a rain sensor that cannot detect the amount of rain is used, multiple rain sensors can be installed and the amount of rainwater infiltration can be determined based on the number of sensors that react. Another method is to have an operator or manager directly input the amount of rainwater infiltration into the rainwater infiltration amount measuring device 8 when rainwater infiltration is visually confirmed. In this case, a rain sensor is not required.

[0052] The model generation unit 232 learns the ventilation control conditions based on learning data including the ventilation control conditions, weather forecast data, and the amount of rainwater infiltration. That is, it generates a trained model 241 that infers the ventilation control conditions from the weather forecast data and the amount of rainwater infiltration of the ventilation system 100.

[0053] The learning algorithm used by the model generation unit 232 may be a known algorithm such as supervised learning, unsupervised learning, or reinforcement learning. As an example, a case where reinforcement learning is applied will be described. In reinforcement learning, an agent, which is the subject of action in a certain environment, observes the parameters of the environment, which is the current state, and determines the action to be taken. The environment changes dynamically depending on the agent's actions, and the agent is given a reward according to the environmental changes. The agent repeats this process and learns the action course that will obtain the most reward through a series of actions. Q-learning and TD-learning are known as representative reinforcement learning methods. For example, in the case of Q-learning, a general update formula for the action value function Q(s, a) is expressed as the following formula (1):

[0054]

[0055] In the above formula (1), s ｔ represents the state of the environment at time t, and a ｔ represents the action at time t. ｔ Therefore, the state is s ｔ＋１ It changes to r ｔ＋１ represents the reward obtained by the change in the state, γ represents the discount rate, and α represents the learning coefficient. Note that γ is in the range of 0<γ≦1, and α is in the range of 0<α≦1. When the ventilation control condition is action a ｔ The amount of rainwater infiltration is in state s ｔ and the state s at time t ｔ Best action in a ｔ Learn.

[0056] In addition, since the amount of precipitation and wind speed affect the amount of rainwater infiltration, there is a certain correlation between the weather forecast data and the amount of rainwater infiltration. ｔ Best action in a ｔ The weather forecast data at the same time is related to the best action a ｔ is learned.

[0057] The update formula expressed by equation (1) increases the action value Q if the action value Q of the action a with the highest Q value at time t+1 is greater than the action value Q of the action a executed at time t, and decreases the action value Q in the opposite case. In other words, the action value function Q(s, a) is updated so that the action value Q of the action a at time t approaches the best action value at time t+1. As a result, the best action value in a certain environment is propagated sequentially to the action values ​​in previous environments.

[0058] As described above, the model generation unit 232 that generates the trained model 241 by reinforcement learning includes a reward calculation unit 232a and a function update unit 232b.

[0059] The reward calculation unit 232a calculates a reward based on the ventilation control conditions and the amount of rainwater infiltration. The reward calculation unit 232a calculates a reward r based on an increase or decrease in the amount of rainwater infiltration. For example, if the amount of rainwater infiltration decreases, the reward r is increased, and if the amount of rainwater infiltration increases, the reward r is decreased. For example, if the amount of rainwater infiltration decreases, a reward of "1" is given, and if the amount of rainwater infiltration increases, a reward of "-1" is given.

[0060] The function update unit 232b updates the function for determining the ventilation control condition in accordance with the reward calculated by the reward calculation unit 232a, and outputs the updated function to the learned model storage unit 24. For example, in the case of Q-learning, the action value function Q(s) expressed by the formula (1) is ｔ , a ｔ ) is used as a function to calculate the ventilation control conditions.

[0061] The learning process is repeated as described above. The learned model storage unit 24 stores the action value function Q(s) updated by the function update unit. ｔ , a ｔ ), i.e., the trained model 241 is stored.

[0062] Next, a description will be given of the learning process performed by the learning device 23. Fig. 12 is a flowchart showing the learning process performed by the learning device of the ventilation system according to the ninth embodiment.

[0063] In step S21, the learning data acquisition unit 231 acquires the ventilation control conditions, weather forecast data, and the amount of rainwater infiltration as learning data.

[0064] In step S22, the model generation unit 232 calculates a reward based on the ventilation control conditions and the amount of rainwater infiltration. Specifically, the reward calculation unit 232a acquires the ventilation control conditions and the amount of rainwater infiltration, and determines whether to increase or decrease the reward based on a predetermined increase or decrease in the amount of rainwater infiltration.

[0065] If the amount of rainwater infiltration decreases, the result in step S22 is "rainwater infiltration amount decreased." In this case, the remuneration calculation unit 232a determines that the remuneration should be increased, and therefore increases the remuneration in step S23. On the other hand, if the amount of rainwater infiltration increases, the result in step S22 is "rainwater infiltration amount increased." In this case, the remuneration calculation unit 232a determines that the remuneration should be decreased, and therefore decreases the remuneration in step S24.

[0066] In step S25, the function update unit 232b updates the action value function Q(s) represented by equation (1) stored in the trained model storage unit 24 based on the reward calculated by the reward calculation unit 232a. ｔ , a ｔ ) to update.

[0067] The learning device 23 repeatedly executes the above steps S21 to S25 to generate the action value function Q(s ｔ , a ｔ ) is stored as the trained model 241.

[0068] In addition, in generating the trained model 241, the state s at time t ｔ Best action in a ｔ The weather forecast data at the same time is related to the best action a for the weather forecast data at time t. ｔ is learned.

[0069] The learning device 23 in this embodiment is configured to store the learned model 241 in a learned model memory unit 24 provided outside the learning device 23, but the learned model memory unit 24 may also be provided inside the learning device 23.

[0070] 13 is a diagram showing the configuration of an inference device for a ventilation system according to embodiment 9. The inference device 25 includes an inference data acquisition unit 251 and an inference unit 252.

[0071] The inference data acquisition unit 251 acquires weather forecast data, which may include one or more of precipitation, temperature, humidity, atmospheric pressure, solar radiation, wind direction, and wind speed.

[0072] The inference unit 252 infers ventilation control conditions using the trained model 241. That is, by inputting the weather forecast data acquired by the inference data acquisition unit 251 into the trained model 241, it is possible to infer ventilation control conditions suitable for the weather forecast data.

[0073] At this time, the weather forecast data is associated with the amount of rainwater infiltration when the trained model 241 is generated.

[0074] In the present embodiment, the ventilation control conditions are output using the trained model 241 trained by the model generation unit 232. However, the trained model 241 may be acquired from another ventilation system 100, and the ventilation control conditions may be output based on this trained model 241. Furthermore, the trained model 241 may be generated by a device other than the ventilation system 100. That is, the trained model 241 may be generated by a learning device 23 provided separately from the ventilation system 100.

[0075] Next, a description will be given of the process for obtaining the ventilation control conditions using the inference device 25. Fig. 14 is a flowchart relating to the inference process of the inference device of the ventilation system according to the ninth embodiment.

[0076] In step S31, the inference data acquisition unit 251 acquires weather forecast data, which is inference data.

[0077] In step S32, the inference unit 252 inputs the weather forecast data into the trained model 241 stored in the trained model storage unit 24 to obtain ventilation control conditions.

[0078] In step S33, the inference unit 252 outputs data on ventilation control conditions obtained by the learned model 241 to the supply air volume management unit 22.

[0079] In step S34, the supply air volume management unit 22 uses the output ventilation control conditions to control the ventilation device 1. This makes it possible to further reduce the amount of rainwater seeping in through the ventilation device 1. The ventilation device 1 is controlled by the speed of the ventilation blower and the opening / closing angle of the damper 13.

[0080] In this embodiment, a case where reinforcement learning is applied to the learning algorithm used by the model generation unit 232 has been described, but the present invention is not limited to this. As for the learning algorithm, other than reinforcement learning, supervised learning, unsupervised learning, semi-supervised learning, or the like can also be applied.

[0081] Furthermore, the learning algorithm used in the model generation unit 232 may be deep learning, which learns to extract the features themselves, or machine learning may be performed according to other known methods, such as neural networks, genetic programming, functional logic programming, and support vector machines.

[0082] The learning device 23 and the inference device 25 may be devices separate from the ventilation system 100 and connected to the ventilation system 100 via a network. Furthermore, the learning device 23 and the inference device 25 may exist on a cloud server.

[0083] The model generation unit 232 may also learn ventilation control conditions using learning data acquired from multiple ventilation systems 100. The model generation unit 232 may acquire learning data from multiple ventilation systems 100 used in the same area, or may learn ventilation control conditions using learning data collected from multiple ventilation systems 100 operating independently in different areas. It is also possible to add or remove ventilation systems 100 from which learning data is collected during the process. Furthermore, the learning device 23 that has learned ventilation control conditions for a certain ventilation system 100 may be applied to another ventilation system 100, and the ventilation control conditions for the other ventilation system 100 may be relearned and updated.

[0084] The ventilation system 100 of embodiment 9 learns ventilation control conditions based on learning data including ventilation control conditions, weather forecast data, and rainwater infiltration amounts to generate a learned model 241, and infers ventilation control conditions using the learned model 241.Therefore, even in weather conditions where it is difficult for humans to determine whether or not rainwater is likely to infiltrate, the possibility of rainwater infiltration can be reduced.

[0085] Tenth embodiment. Figure 15 is a diagram showing the configuration of a ventilation system according to a tenth embodiment. The ventilation system 100 according to the tenth embodiment is a system in which a trained model 241 is shared among multiple buildings. In the ventilation system 100 according to the tenth embodiment, multiple buildings 30 are targeted for ventilation, and each ventilation control device 2 communicates with a server 50 via a network 40. Data to be communicated includes ventilation control conditions, weather forecast data, and the amount of rainwater infiltration, as well as information on the size of each building 30 and the installation location of the ventilation devices 1, etc.

[0086] The learning device 23, the learned model storage unit 24, and the inference device 25 are provided in a server 50.

[0087] In the ventilation system 100 according to the tenth embodiment, the learning device 23 generates a trained model 241 for each building 30. The trained model 241 varies depending on the location of the building 30, the size of the building 30, and the installation position of the ventilation blower 11. Therefore, the trained model 241 created by the ventilation control device 2 of one building 30 cannot be applied as is to the ventilation control device 2 of another building 30. Therefore, the learning device 23 learns the trained models 241 of multiple buildings 30 using learning data to which building information has been added. The inference device 25 infers ventilation control conditions based on the weather forecast data and the building information.

[0088] The building information includes the location of the building 30, the size of the building 30, the location of the ventilation device 1, the location of the rainwater infiltration amount measuring device 8, and the ventilation control conditions of each ventilation device 1.

[0089] By having the learning device 23 learn using learning data that includes building information, it becomes possible to utilize the learned model 241 to perform ventilation control for different buildings.

[0090] Furthermore, by installing rainwater infiltration volume measuring devices 8 in only some of the multiple buildings 30 and generating a trained model 241, the ventilation device 1 can be controlled using inference data including the trained model 241 and building information, even if rainwater infiltration volume measuring devices 8 are not installed in the other buildings 30.

[0091] Furthermore, when this system is introduced into a certain building 30, the loaned rainwater infiltration amount measuring device 8 is installed for a certain period of time after the introduction to generate a trained model 241, and after a certain period of time has passed, the rainwater infiltration amount measuring device 8 is used sequentially in other buildings 30 to generate a trained model 241, thereby minimizing the burden on equipment and generating a trained model 241 for each building 30.

[0092] By managing the trained model 241 on the server 50, it is also possible to update the trained model 241 at any time.

[0093] The following describes the hardware configuration of the learning device 23 and the inference device 25 according to embodiments 1 to 10. Fig. 16 is a diagram showing the hardware configuration of the learning device according to embodiments 9 and 10. The learning device 23 is realized by a computer system including a processor 91 that executes various processes, a memory 92 that is a main memory, and a storage device 93 that stores information.

[0094] The processor 91 may be a computing device such as a microprocessor, microcomputer, CPU (Central Processing Unit), or DSP (Digital Signal Processor). The memory 92 may be a non-volatile or volatile semiconductor memory such as RAM (Random Access Memory), ROM (Read Only Memory), flash memory, EPROM (Erasable Programmable Read Only Memory), or EEPROM (Electrically Erasable Programmable Read Only Memory). The storage device 93 stores a program for learning ventilation control conditions.

[0095] In the computer system described above, the processor 91 reads into the memory 92 a program stored in the storage device 93 and corresponding to the processing of each component, and executes the program, thereby realizing the functions of the model generation unit 232. The memory 92 is also used as a temporary memory for each process executed by the processor 91. The program executed by the processor 91 may be provided in a state stored in a storage medium, or may be provided via a network.

[0096] Like the learning device 23, the inference device 25 is realized by a computer system including a processor 91 that executes various processes, a memory 92 that serves as a main memory, and a storage device 93 that stores information. In the computer system that realizes the inference device 25, the storage device 93 stores a program for performing a process of inferring ventilation control conditions using a trained model.

[0097] The configurations shown in the above embodiments are merely examples of the content, and may be combined with other known technologies, or parts of the configurations may be omitted or modified without departing from the spirit of the invention.

[0098] 1 Ventilation device, 2 Ventilation control device, 3 West side ventilation device, 4 North side ventilation device, 5 Ventilation control history storage device, 6 Display, 7 Ventilation control condition input device, 8 Rainwater infiltration amount measurement device, 11 Ventilation blower, 12 Speed ​​regulator, 13 Damper, 21 Rainwater infiltration possibility determination unit, 22 Air supply amount management unit, 23 Learning device, 24 Learned model storage unit, 25 Inference device, 30 Building, 31 West wall, 32 North wall, 40 Network, 50 Server, 91 Processor, 92 Memory, 93 Storage device, 100 Ventilation system, 231 Learning data acquisition unit, 232 Model generation unit, 232a Reward calculation unit, 232b Function update unit, 241 Learned model, 251 Inference data acquisition unit, 252 Inference unit.

Claims

1. A ventilation system comprising: a ventilation device installed in a building; and a ventilation control device that controls the ventilation device based on weather forecast data, wherein the ventilation control device has a rainwater intrusion possibility determination unit that determines whether or not there is a possibility of rainwater intrusion into the ventilation device based on the weather forecast data; and an air supply volume management unit that, when there is a possibility of rainwater intrusion into the ventilation device, manages the volume of air supply by the ventilation device by stopping air supply by the ventilation device or reducing the volume of air supply by the ventilation device.

2. The ventilation system described in claim 1, further comprising a plurality of ventilation devices, the plurality of ventilation devices being installed in different locations of the building, the rainwater intrusion possibility determination unit determining whether or not there is a possibility of rainwater intrusion for each of the plurality of ventilation devices, and the air supply volume management unit stopping air supply only to the ventilation device into which there is a possibility of rainwater intrusion or reducing the volume of air supply by the ventilation device.

3. The ventilation system described in claim 2, characterized in that the rainwater infiltration possibility determination unit acquires the weather forecast data individually for the installation locations of each of the multiple ventilation devices and determines whether or not there is a possibility of rainwater infiltrating into the multiple ventilation devices.

4. A ventilation system as described in any one of claims 1 to 3, characterized in that it is equipped with a ventilation control history storage device that stores ventilation control history, which is the control history of the ventilation device by the ventilation control device, and a display that displays weather forecast data, meteorological observation data, which is actual weather data, and the ventilation control history together with time data.

5. A ventilation system as described in any one of claims 1 to 4, characterized in that the ventilation control device is provided with a ventilation control condition input device for inputting ventilation control conditions which are control conditions for the ventilation device.

6. A ventilation system as described in any one of claims 1 to 5, characterized in that the air supply volume management unit stops air supply by the ventilation device or reduces the amount of air supply by the ventilation device based on the amount of pollen or fine particulate matter dispersed or predicted amount of dispersion.

7. A ventilation system as described in any one of claims 1 to 5, characterized in that the air supply volume management unit stops the air supply by the ventilation device or reduces the amount of air supply by the ventilation device based on predicted concentration data of carbon monoxide or carbon dioxide in the outside air.

8. A ventilation system as described in any one of claims 1 to 7, characterized in that it comprises a learning device having a rainwater infiltration amount measuring device that measures the amount of rainwater infiltration into the ventilation device, a learning data acquisition unit that acquires learning data including the rainwater infiltration amount, the weather forecast data, and ventilation control conditions that are control conditions for the ventilation device, and a model generation unit that uses the learning data to generate a trained model for inferring the ventilation control conditions from the weather forecast data.

9. A ventilation system as described in any one of claims 1 to 8, characterized in that it is provided with an inference device having an inference data acquisition unit that acquires the weather forecast data, and an inference unit that outputs the ventilation control conditions, which are the control conditions of the ventilation device, from the weather forecast data acquired by the inference data acquisition unit using a learned model for inferring the ventilation control conditions, which are the control conditions of the ventilation device, from the weather forecast data.

10. A learning device comprising: a learning data acquisition unit that acquires learning data including the amount of rainwater infiltrating into a ventilation device installed in a building, weather forecast data for the location where the ventilation device is installed, and ventilation control conditions for the ventilation device; and a model generation unit that uses the learning data to generate a trained model for inferring the ventilation control conditions from the weather forecast data.

11. The learning device described in claim 10, characterized in that the model generation unit has: a reward calculation unit that calculates a reward based on a reward standard for the amount of rainwater infiltration into the ventilation device; and a function update unit that updates a function for determining the ventilation control conditions in accordance with the reward calculated by the reward calculation unit.

12. An inference device comprising: an inference data acquisition unit that acquires weather forecast data for a location where a ventilation device is installed in a building; and an inference unit that infers the ventilation control conditions, which are the control conditions for the ventilation device, from the weather forecast data acquired by the inference data acquisition unit using a learned model for inferring the ventilation control conditions, which are the control conditions for the ventilation device, from the weather forecast data.

13. A learning device comprising: a learning data acquisition unit that acquires learning data including the amount of rainwater infiltration into each of the ventilation devices installed in each of a plurality of buildings, weather forecast data for the locations where the ventilation devices of each of the plurality of buildings are installed, ventilation control conditions of the ventilation devices installed in each of the plurality of buildings, and location information for each of the plurality of buildings; and a model generation unit that uses the learning data to generate a trained model for inferring the ventilation control conditions from the weather forecast data.

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