Temperature control method and device and electronic equipment

Abstract

The invention provides a temperature control method and device and electronic equipment, and relates to the technical field of smart home. Comprising the steps that air conditioner temperature information set by a user at multiple moments in a specified time period is collected; determining prior state temperature estimation information corresponding to each moment according to the air conditioner temperature information set at each moment and the posterior state temperature estimation information corresponding to each moment; and according to the priori state temperature estimation information corresponding to all moments, the air conditioner target set temperature corresponding to the specified time period is determined, and temperature control over the air conditioner is conducted according to the target set temperature. Air conditioner temperature information set by a user for multiple times in a specified time period is collected, and based on a Kalman filtering algorithm, the air conditioner set temperature matched with the user is determined by continuously iteratively learning the habit of setting the air conditioner temperature of the user, so that the air conditioner is controlled to operate according to the determined set temperature, and the user experience is improved. The optimal sensible temperature is provided for the user, and the comfort requirement of the user is met.

Description

Technical Field

[0001] This application relates to the field of smart home technology, and more specifically, to a temperature control method, device, and electronic device. Background Technology

[0002] As people's demands for quality of life increase, smart home systems are gradually entering thousands of households. Users no longer need to actively control switches; instead, the smart home system automatically provides users with comfortable indoor temperature and lighting, etc.

[0003] Currently, smart home systems set the air conditioner's cooling temperature to a fixed value to provide most users with an indoor temperature that meets their needs.

[0004] However, due to individual differences among users, different users have different physical sensations of a fixed cooling temperature. Cooling according to a fixed cooling temperature cannot meet the personalized needs of users. Summary of the Invention

[0005] The purpose of this application is to address the shortcomings of the prior art by providing a temperature control method, device, and electronic device to enable personalized air conditioning temperature settings that match the user's perceived temperature, thereby improving the user's indoor living comfort.

[0006] To achieve the above objectives, the technical solutions adopted in the embodiments of this application are as follows:

[0007] In a first aspect, embodiments of this application provide a temperature control method, including:

[0008] Collect air conditioning temperature information set by the user at multiple times within a specified time period;

[0009] Based on the air conditioning temperature information set at each time point and the posterior state temperature estimation information corresponding to each time point, determine the prior state temperature estimation information corresponding to each time point.

[0010] Based on the prior state temperature estimation information corresponding to each time moment, the target set temperature of the air conditioner corresponding to the specified time period is determined, and the temperature control of the air conditioner is performed according to the target set temperature.

[0011] Optionally, determining the prior state temperature estimation information corresponding to each time point based on the air conditioning temperature information set at each time point and the corresponding posterior state temperature estimation information at each time point includes:

[0012] Based on the indoor temperature measurement value of the previous time, the prior state temperature estimation information of the previous time, and the Kalman gain information of the previous time, the posterior state temperature estimation information of the previous time is determined. The current time is any time other than the first time among the plurality of times.

[0013] Based on the posterior state temperature estimate information corresponding to the previous moment and the air conditioning temperature information set at the previous moment, determine the prior state temperature estimate information corresponding to the current moment.

[0014] Optionally, it also includes:

[0015] Based on the initial posterior state temperature estimation information corresponding to the initial time and the reference air conditioning temperature information at the initial time, the prior state temperature estimation information corresponding to the initial time is determined.

[0016] Optionally, the calculation process of the Kalman gain information includes:

[0017] Based on the posterior state temperature estimation covariance matrix of the previous time step, the system state transition matrix, and the covariance matrix of the noisy system, determine the prior state temperature estimation covariance matrix of the current time step.

[0018] Based on the covariance matrix of the prior state temperature, the system output matrix, and the covariance matrix of the measurement noise at the current moment, determine the Kalman gain information at the current moment;

[0019] Update the posterior state temperature estimation covariance matrix based on the Kalman gain information at the current time and the prior state temperature estimation covariance matrix at the current time.

[0020] Optionally, determining the prior state temperature estimate information for the current moment based on the posterior state temperature estimate information corresponding to the previous moment and the air conditioning temperature information set at the previous moment includes:

[0021] Based on the air conditioner temperature information set at the previous moment, determine the air conditioner operating power at the previous moment, wherein the air conditioner operating power includes: cooling power and dehumidification power;

[0022] Based on the posterior state temperature estimation information corresponding to the previous moment and the air conditioning operating power at the previous moment, the prior state temperature estimation information corresponding to the current moment is determined.

[0023] Optionally, determining the target set temperature for the air conditioner corresponding to the specified time period based on the prior state temperature estimation information corresponding to each time moment includes:

[0024] If the difference between the prior state temperature estimates corresponding to at least two consecutive adjacent time points is less than a preset threshold, then the prior state temperature estimate corresponding to the last time point in the consecutive adjacent time points is determined as the target set temperature of the air conditioner for the specified time period.

[0025] Optionally, the temperature control of the air conditioner based on the target set temperature includes:

[0026] Control the air conditioner to operate at the fan speed corresponding to the target set temperature.

[0027] Optionally, after controlling the air conditioner to operate at the fan speed corresponding to the target set temperature, the method further includes:

[0028] Acquire the current temperature and humidity values ​​collected by the temperature and humidity sensors, and acquire the wind speed value experienced by the user collected by the wind speed sensor.

[0029] Based on the current temperature value, the current humidity value, and the wind speed value experienced by the user, determine the estimated value of the user's perceived temperature.

[0030] Secondly, embodiments of this application also provide a temperature control device, including: a data acquisition module, a determination module, and a control module;

[0031] The data acquisition module is used to collect air conditioning temperature information set by the user at multiple times within a specified time period;

[0032] The determining module is used to determine the prior state temperature estimation information corresponding to each time moment based on the air conditioning temperature information set at each time moment and the posterior state temperature estimation information corresponding to each time moment.

[0033] The control module is used to determine the target set temperature of the air conditioner for the specified time period based on the prior state temperature estimation information corresponding to each time moment, and to perform temperature control of the air conditioner based on the target set temperature.

[0034] Optionally, the determining module is specifically used to determine the posterior state temperature estimation information corresponding to the previous moment based on the indoor temperature measurement value of the previous moment, the prior state temperature estimation information corresponding to the previous moment, and the Kalman gain information of the previous moment, wherein the current moment is any moment other than the first moment among the plurality of moments.

[0035] Based on the posterior state temperature estimate information corresponding to the previous moment and the air conditioning temperature information set at the previous moment, determine the prior state temperature estimate information corresponding to the current moment.

[0036] Optionally, the determining module is further configured to determine the prior state temperature estimation information corresponding to the initial time based on the initial posterior state temperature estimation information corresponding to the initial time and the reference air conditioning temperature information at the initial time.

[0037] Optionally, the determining module is specifically used to determine the prior state temperature estimation covariance matrix at the current moment based on the posterior state temperature estimation covariance matrix, the system state transition matrix, and the covariance matrix of the noisy system at the previous moment.

[0038] Based on the covariance matrix of the prior state temperature, the system output matrix, and the covariance matrix of the measurement noise at the current moment, determine the Kalman gain information at the current moment;

[0039] Update the posterior state temperature estimation covariance matrix based on the Kalman gain information at the current time and the prior state temperature estimation covariance matrix at the current time.

[0040] Optionally, the determining module is specifically used to determine the air conditioner operating power at the previous moment based on the air conditioner temperature information set at the previous moment, wherein the air conditioner operating power includes: cooling power and dehumidification power;

[0041] Based on the posterior state temperature estimation information corresponding to the previous moment and the air conditioning operating power at the previous moment, the prior state temperature estimation information corresponding to the current moment is determined.

[0042] Optionally, the determining module is specifically used to determine the prior state temperature estimation information corresponding to the last moment of the consecutive adjacent moments as the target setting temperature of the air conditioner corresponding to the specified time period if the difference between the prior state temperature estimation information corresponding to at least two consecutive adjacent moments is less than a preset threshold.

[0043] Optionally, the control module is specifically used to control the air conditioner to operate at the fan speed corresponding to the target set temperature.

[0044] Optionally, the determining module is further configured to acquire the current temperature value and the current humidity value collected by the temperature and humidity sensor, and to acquire the wind speed value experienced by the user collected by the wind speed sensor.

[0045] Based on the current temperature value, the current humidity value, and the wind speed value experienced by the user, determine the estimated value of the user's perceived temperature.

[0046] Thirdly, embodiments of this application provide an electronic device, including: a processor, a storage medium, and a bus. The storage medium stores machine-readable instructions executable by the processor. When the electronic device is running, the processor communicates with the storage medium via the bus, and the processor executes the machine-readable instructions to implement the temperature control method provided in the first aspect.

[0047] Fourthly, embodiments of this application provide a computer-readable storage medium storing a computer program that, when executed by a processor, performs the temperature control method as provided in the first aspect.

[0048] The beneficial effects of this application are:

[0049] This application provides a temperature control method, apparatus, and electronic device, comprising: collecting air conditioner temperature information set by a user at multiple times within a specified time period; determining prior state temperature estimation information corresponding to each time period based on the air conditioner temperature information set at each time period and the corresponding posterior state temperature estimation information; determining the target air conditioner setting temperature for the specified time period based on the prior state temperature estimation information; and controlling the air conditioner temperature according to the target setting temperature. This method collects air conditioner temperature information set by the user multiple times within a specified time period, and based on a Kalman filter algorithm, iteratively learns the user's air conditioner temperature setting habits to predict the target air conditioner temperature required for the specified time period. This achieves the goal of determining the air conditioner setting temperature that matches the user's preferences through iterative learning, thereby controlling the air conditioner to operate at the determined setting temperature, providing the user with the optimal perceived temperature, and meeting the user's comfort needs. Attached Figure Description

[0050] To more clearly illustrate the technical solutions of the embodiments of this application, the accompanying drawings used in the embodiments will be briefly introduced below. It should be understood that the following drawings only show some embodiments of this application and should not be regarded as a limitation of the scope. For those skilled in the art, other related drawings can be obtained based on these drawings without creative effort.

[0051] Figure 1 A schematic flowchart of a temperature control method provided in an embodiment of this application;

[0052] Figure 2 A schematic flowchart illustrating another temperature control method provided in an embodiment of this application;

[0053] Figure 3 A schematic flowchart illustrating another temperature control method provided in an embodiment of this application;

[0054] Figure 4 A schematic flowchart illustrating another temperature control method provided in an embodiment of this application;

[0055] Figure 5 A schematic flowchart illustrating another temperature control method provided in an embodiment of this application;

[0056] Figure 6 A schematic diagram of a temperature control device provided in an embodiment of this application;

[0057] Figure 7 This is a schematic diagram of the structure of an electronic device provided in an embodiment of this application. Detailed Implementation

[0058] To make the objectives, technical solutions, and advantages of the embodiments of this application clearer, the technical solutions of the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings. It should be understood that the accompanying drawings in this application are for illustrative and descriptive purposes only and are not intended to limit the scope of protection of this application. Furthermore, it should be understood that the schematic drawings are not drawn to scale. The flowcharts used in this application illustrate operations implemented according to some embodiments of this application. It should be understood that the operations in the flowcharts may not be implemented in sequence, and steps without logical contextual relationships may be reversed or implemented simultaneously. In addition, those skilled in the art, guided by the content of this application, may add one or more other operations to the flowcharts, or remove one or more operations from the flowcharts.

[0059] Furthermore, the described embodiments are merely some, not all, of the embodiments of this application. The components of the embodiments of this application described and illustrated herein can typically be arranged and designed in various different configurations. Therefore, the following detailed description of the embodiments of this application provided in the accompanying drawings is not intended to limit the scope of the claimed application, but merely to illustrate selected embodiments of the application. All other embodiments obtained by those skilled in the art based on the embodiments of this application without inventive effort are within the scope of protection of this application.

[0060] With the continuous advancement of technology, more and more artificial intelligence technologies have been quietly applied to our lives. Today, people have increasingly higher demands for quality of life, and smart home systems are gradually entering thousands of households. For example, once users arrive home, they don't need to search for switch buttons or remote controls for air conditioners or fresh air systems; the smart home system can provide them with suitable lighting and a comfortable indoor air environment.

[0061] Indoor air comfort is closely related to the user's perceived temperature, which is a human body's feeling of hot or cold. It is neither the human skin temperature, nor the air temperature in the weather forecast, nor the set air conditioner temperature. Rather, it is a quantitative analysis of comfort based on people's feelings when they come into contact with the external environment through their skin and other senses.

[0062] The most comfortable perceived temperature for the human body is generally between 20°C and 25°C. This is a general range that takes into account the comfort of most people. Within this temperature range, the human body feels comfortable, neither too cold nor too hot. In indoor environments, it is generally recommended to keep the perceived temperature within this range to ensure that most people feel comfortable. For environments used for sleep, slightly lowering the perceived temperature (approximately 18°C ​​to 22°C) may be more conducive to good sleep quality.

[0063] Home air conditioners typically require a target temperature to operate. For example, smart home systems usually set the air conditioner's cooling mode to 24°C, which can meet the indoor environmental needs of most people.

[0064] However, due to individual differences and the influence of some external factors on perceived temperature, the target temperature and humidity settings of smart home systems often cannot meet the personalized needs of different groups of people. Different users often adjust the temperature and humidity of the air conditioner according to their own feelings of being hot or cold and the concept of low carbon and energy saving, in order to achieve a comfortable and energy-saving state. This leads to a game between the standardization of indoor perceived temperature and humidity and personalized needs.

[0065] Based on this, this solution provides a temperature control method that collects air conditioner temperature information set by the user multiple times within a specified time period. Based on the Kalman filter algorithm, it continuously learns the user's air conditioner temperature setting habits, thereby estimating the target air conditioner temperature to be set within the specified time period. This achieves the goal of determining the air conditioner temperature setting that matches the user by continuously learning the user's air conditioner temperature setting habits, thereby controlling the air conditioner to operate at the determined setting temperature, providing the user with the best perceived temperature, and meeting the user's comfort needs.

[0066] Figure 1 This is a flowchart illustrating a temperature control method provided in an embodiment of this application; the executing entity of this method can be a computer device such as a terminal or server. Figure 1 As shown, the method may include:

[0067] S101. Collect air conditioning temperature information set by the user at multiple times within a specified time period.

[0068] Optionally, the specified time period here can refer to a future time period or the current time period. When collecting air conditioner temperature information set by the user at multiple times within the specified time period, it changes continuously over time. Every time the user resets the air conditioner temperature, the air conditioner temperature information set by the user at the corresponding time is collected in real time.

[0069] S102. Based on the air conditioning temperature information set at each time point and the posterior state temperature estimation information corresponding to each time point, determine the prior state temperature estimation information corresponding to each time point.

[0070] In this embodiment, iterative prediction processing can be performed based on the Kalman filter algorithm. For any given time, the prior state temperature estimate information corresponding to the current time can be calculated based on the air conditioning temperature information at the current time, the air conditioning temperature information at the previous time corresponding to the current time, the posterior state temperature estimate information corresponding to the current time, and the posterior state temperature estimate information corresponding to the previous time.

[0071] For example, suppose a user sets the air conditioner temperature information once at time a and once at time b, and time b occurs after time a. Then, based on the air conditioner temperature information set at time a, the air conditioner temperature information set at time b, the posterior state temperature estimation information corresponding to time a, and the posterior state temperature estimation information corresponding to time b, the prior state temperature estimation information corresponding to time b can be calculated.

[0072] As long as the user resets the air conditioner temperature at the current moment, generating new air conditioner temperature information, the corresponding prior state temperature estimate information at the current moment can be obtained based on the generated new air conditioner temperature information and the user's previous air conditioner temperature setting information.

[0073] It is worth noting that the posterior state temperature estimate refers to the temperature estimate obtained after acquiring the current temperature measurement, calculated by combining the prior state temperature estimate and the measured value (and considering their noise characteristics). It is the result of the algorithm update phase and also the starting point for the prior state temperature estimate in the next iteration.

[0074] Prior state temperature estimation information refers to the temperature estimate for the current moment calculated based on the posterior state temperature estimation information from the previous moment (or the previous time step) and the system dynamic model (such as the state transition matrix) before obtaining the current measurement value. It is the result of the algorithm's prediction phase and the starting point of the update phase.

[0075] S103. Based on the prior state temperature estimation information corresponding to each time moment, determine the target set temperature of the air conditioner for the specified time period, and perform temperature control of the air conditioner according to the target set temperature.

[0076] Optionally, based on the estimated prior state temperature information obtained at each time point, if the iteration termination condition is met, the iteration is stopped, and the estimated prior state temperature information at the current time point is determined as the target set temperature for the air conditioner for the specified time period. In other words, by controlling the operation of the air conditioner according to the target set temperature, the perceived temperature felt by the user is more in line with the user's needs, and the user's comfort is optimized.

[0077] In summary, the temperature control method provided in this embodiment includes: collecting air conditioning temperature information set by the user at multiple times within a specified time period; determining prior state temperature estimation information corresponding to each time period based on the air conditioning temperature information set at each time period and the corresponding posterior state temperature estimation information; determining the target air conditioning temperature setting for the specified time period based on the prior state temperature estimation information corresponding to each time period; and controlling the air conditioning temperature according to the target setting temperature. This method collects air conditioning temperature information set by the user multiple times within a specified time period, and based on the Kalman filter algorithm, iteratively learns the user's habits of setting the air conditioning temperature, thereby estimating the target air conditioning temperature required for the specified time period. This achieves the goal of determining the air conditioning temperature setting that matches the user's habits through iterative learning, thereby controlling the air conditioning to operate at the determined setting temperature, providing the user with the optimal perceived temperature, and meeting the user's comfort needs.

[0078] Figure 2 This is a flowchart illustrating another temperature control method provided in an embodiment of this application; optionally, in step S102, determining the prior state temperature estimation information corresponding to each time point based on the air conditioning temperature information set at each time point and the posterior state temperature estimation information corresponding to each time point may include:

[0079] S201. Based on the indoor temperature measurement value of the previous time, the prior state temperature estimation information of the previous time, and the Kalman gain information of the previous time, determine the posterior state temperature estimation information of the previous time. The current time is any time other than the first time among multiple times.

[0080] Optionally, the indoor temperature measurement value can be the temperature value output by the temperature and humidity sensor. Typically, the posterior state temperature estimate information at any time k can be calculated using the following formula (1):

[0081]

[0082] in, This represents the posterior state temperature estimate at time k. This represents the prior state temperature estimate at time k, where k is the given information. k This represents the Kalman gain information at time k. Let K represent the indoor temperature measurement at time k, and H represent the system output matrix.

[0083] So, assuming the previous time is k-1, the prior state temperature estimate information at k-1, the Kalman gain information at k-1, and the indoor temperature measurement value at k-1 can be substituted into formula (1) to calculate the posterior state temperature estimate information corresponding to the previous time.

[0084] S202. Based on the posterior state temperature estimation information corresponding to the previous moment and the air conditioning temperature information set at the previous moment, determine the prior state temperature estimation information corresponding to the current moment.

[0085] The set air conditioner temperature information refers to the air conditioner temperature information set by the user, that is, the air conditioner temperature set by the user by pressing the air conditioner remote control.

[0086] Optionally, assuming the current time is time k and the previous time is time k-1, the prior state temperature estimate corresponding to the current time can be calculated using the following formula (2):

[0087]

[0088] in, This represents the prior state temperature estimate information at the current moment. Let u(k-1) represent the posterior state temperature estimate information of the previous time step, u(k-1) represent the cooling and dehumidification power related to the air conditioning temperature setting information of the previous time step, A represent the system state transition matrix, and B represent the system input matrix.

[0089] Given that the posterior state temperature estimate information and the air conditioning temperature setting information of the previous time are known, they can be substituted into formula (2) to calculate the prior state temperature estimate information corresponding to the current time.

[0090] It is worth noting that, through system identification methods, we can obtain the undetermined system state transition matrix A, input matrix B, output matrix H, and direct transfer matrix D. Suppose that in a certain apartment type equipped with air conditioning, through system identification, we obtain the following discrete-time matrix:

[0091]

[0092] Optionally, the method further includes: determining the prior state temperature estimation information corresponding to the initial time based on the initial posterior state temperature estimation information corresponding to the initial time and the reference air conditioning temperature information at the initial time.

[0093] In some embodiments, for the initial moment within a specified time period, since there is no relevant information about the previous moment, the calculation of the prior state temperature estimation information corresponding to the initial moment can be obtained based on the initially set posterior state temperature estimation information and the reference air conditioning temperature information.

[0094] Optionally, the initial posterior state temperature estimation information set at the initial moment is 0 by default, while the reference air conditioning temperature information at the initial moment can be the expected air conditioning temperature set by the system for the user. For each room or each user, the reference air conditioning temperature information set by the system at the initial moment is the same, for example, 26 degrees. The system believes that the user's perceived temperature is best when the air conditioning temperature is 26 degrees.

[0095] Therefore, based on the initial posterior state temperature estimation information corresponding to the initial time and the reference air conditioning temperature information at the initial time, the prior state temperature estimation information corresponding to the initial time can be calculated using formula (2).

[0096] Figure 3 This is a flowchart illustrating another temperature control method provided in an embodiment of this application; optionally, the calculation process of Kalman gain information in step S201 above may include:

[0097] S301. Based on the posterior state temperature estimation covariance matrix, the system state transition matrix, and the covariance matrix of the noisy system at the previous moment, determine the prior state temperature estimation covariance matrix at the current moment.

[0098] In some embodiments, the prior state temperature estimation covariance matrix at the current time can be calculated first. This matrix consists of the covariance introduced by the posterior state temperature estimation information at the previous time and the uncertainty value generated by the estimation at the current time.

[0099] Alternatively, the prior state temperature estimation covariance matrix at the current moment can be calculated using the following formula (3):

[0100]

[0101] in, Let P represent the prior state temperature estimation covariance matrix at the current moment. K-1 Let A be the covariance matrix of the posterior state temperature estimate from the previous time step. T Let represent the transpose of the system state transition matrix, and let Q represent the covariance matrix of the system noise w(k), where w(k) represents the system noise and follows a normal distribution.

[0102] It should be noted that AP K-1 A T The temperature estimate is derived from the posterior state of the previous time step. This data contains noise and is inherently uncertain. Therefore, the prediction is based on the posterior state temperature estimate from the previous time step, and it inevitably carries the uncertainty from the previous time step. In addition, the prediction itself introduces new noise.

[0103] S302. Based on the prior state temperature estimation covariance matrix, system output matrix, and measurement noise covariance matrix at the current moment, determine the Kalman gain information at the current moment.

[0104] The purpose of calculating the Kalman gain is to minimize the variance of all gain values ​​that incorporates prior estimates and measurements. This determines whether to assign a higher weight to the user-defined air conditioning temperature or to the prior state temperature estimate obtained from this scheme.

[0105] Alternatively, the Kalman gain information at any given time can be calculated using the following formula (4):

[0106]

[0107] Where, k k This represents the Kalman gain information at time k. H represents the prior state temperature estimation covariance matrix at time k. T Let R be the transpose of the system output matrix, and let R be the covariance of the measurement noise v(k), where v(k) is the measurement noise, which also follows a normal distribution.

[0108] Then, by substituting the parameters such as the prior state temperature estimation covariance matrix, the system output matrix, and the measurement noise covariance matrix into formula (4), the Kalman gain information at the current moment can be calculated.

[0109] S303. Based on the Kalman gain information at the current time and the prior state temperature estimation covariance matrix at the current time, update the posterior state temperature estimation covariance matrix at the current time.

[0110] Optionally, based on the prior state temperature estimation covariance matrix at the current time and the Kalman gain information at the current time, the posterior state temperature estimation covariance matrix at the current time can be calculated, thereby updating the posterior state temperature estimation covariance matrix at the current time.

[0111] In some embodiments, the posterior state temperature estimation covariance matrix at the current time can be updated using the following formula (5):

[0112]

[0113] Among them, P K Let k represent the covariance matrix of the posterior state temperature estimate at time k. k This represents the Kalman gain information at time k. Let represent the prior state temperature estimation covariance matrix at time k.

[0114] The Kalman gain information at the current time and the covariance matrix of the prior state temperature estimation at the current time can be substituted into formula (5) to update the covariance matrix of the posterior state temperature estimation at the current time.

[0115] The above calculations using formulas (1) to (5) complete one iterative calculation process. In practical applications, when the user resets the air conditioning temperature at a new time, the new time becomes the new current time, and the current time at which the calculation ends becomes the previous time of the new current time. Thus, the prior state temperature estimation information for the new current time can continue to be calculated according to the above steps.

[0116] For example, assuming time k is the current time and time k-1 is the previous time, based on the above series of calculations, the Kalman gain at time k, the prior state temperature estimate at time k, the posterior state temperature estimate at time k, the prior state temperature estimate covariance matrix at time k, and the posterior state temperature estimate covariance matrix at time k can be calculated. If the user resets the air conditioning temperature at time k+1, then time k+1 becomes the new current time, and the corresponding time k becomes the previous time of time k+1, that is, time k becomes the previous time of the new current time. Based on the relevant data calculated at time k, the air conditioning temperature set by the user at time k+1, the measured value of the air conditioning temperature at time k+1, and other relevant system data, the prior state temperature estimate at time k+1 can be calculated by following the above steps. By repeating this process, the prior state temperature estimate for each current time can be calculated.

[0117] Figure 4 This is a flowchart illustrating another temperature control method provided in an embodiment of this application; optionally, in step S301, determining the prior state temperature estimation information corresponding to the current moment based on the posterior state temperature estimation information corresponding to the previous moment and the air conditioning temperature information set at the previous moment may include:

[0118] S401. Based on the air conditioning temperature information set at the previous moment, determine the air conditioning operating power at the previous moment. The air conditioning operating power includes: cooling power and dehumidification power.

[0119] In one feasible approach, u in formula (2) does not directly refer to the air conditioning temperature set by the user at a certain moment, but rather to data related to the air conditioning temperature set by the user.

[0120] Optionally, u can include the cooling power and dehumidification power corresponding to the set air conditioning temperature. Therefore, if the user's previously set air conditioning temperature is known, the air conditioning operating power at the previous moment can be obtained, that is, the cooling power and dehumidification power at the previous moment.

[0121] S402. Based on the posterior state temperature estimation information corresponding to the previous moment and the air conditioning operating power at the previous moment, determine the prior state temperature estimation information corresponding to the current moment.

[0122] Then, by substituting the posterior state temperature estimate information corresponding to the previous moment and the air conditioning operating power of the previous moment into formula (2), the prior state temperature estimate information corresponding to the current moment can be calculated.

[0123] Optionally, in step S103, determining the target setting temperature of the air conditioner for the specified time period based on the prior state temperature estimation information corresponding to each time moment may include: if the difference between the prior state temperature estimation information corresponding to at least two consecutive adjacent time moments is less than a preset threshold, then the prior state temperature estimation information corresponding to the last time moment among the consecutive adjacent time moments is determined as the target setting temperature of the air conditioner for the specified time period.

[0124] In some embodiments, the above iterative process is repeated. When the difference between the prior state temperature estimation information obtained from at least two rounds of calculation is less than a preset threshold, the prior state temperature estimation information obtained in the current round of calculation can be determined as the target set temperature of the air conditioner for the specified time period.

[0125] In practical applications, in order to improve the accuracy of the calculation results, when the difference between the prior state temperature estimation information obtained after 3-5 consecutive iterations is less than the preset threshold, the prior state temperature estimation information calculated in the current round is determined to be the target setting temperature of the air conditioner for the specified time period.

[0126] For example, assuming the difference between the prior state temperature estimate at time k-1 and the prior state temperature estimate at time k is less than a preset threshold, and the difference between the prior state temperature estimate at time k and the prior state temperature estimate at time k+1 is also less than a preset threshold, then the current prediction result can be considered close to the expected value. Therefore, the prior state temperature estimate at time k+1 can be used as the target air conditioning setting temperature for the specified time period. The preset threshold can be 0.3℃, which can be flexibly adjusted in practical applications.

[0127] Optionally, in step S103, controlling the air conditioner's temperature according to the target set temperature may include controlling the air conditioner to operate at the fan speed corresponding to the target set temperature.

[0128] Based on the determined target temperature setting of the air conditioner, the air conditioner can be controlled to operate at the fan speed corresponding to the target temperature setting. That is, assuming the target temperature setting is 24 degrees, the smart home system can automatically set the air conditioner temperature to 24 degrees. Since this target temperature setting is learned and estimated based on the user's air conditioner setting habits, the user can obtain the best body temperature at this air conditioner temperature, and the user's comfort is better.

[0129] Figure 5 A flowchart illustrating another temperature control method provided in this application embodiment; optionally, after controlling the air conditioner to operate at the fan speed corresponding to the target set temperature in the above steps, the method may further include:

[0130] S501. Obtain the current temperature and humidity values ​​collected by the temperature and humidity sensor, and obtain the wind speed value experienced by the user collected by the wind speed sensor.

[0131] In some embodiments, after the air conditioner operates at the fan speed corresponding to the target set temperature, the current indoor temperature and humidity values ​​can be collected by a temperature and humidity sensor. In addition, the fan speed value experienced by the user's skin can be collected by a fan speed sensor. Based on the collected temperature, humidity, and fan speed values ​​experienced by the user's skin, the perceived temperature felt by the user when the air conditioner is running at the target set temperature can be estimated.

[0132] The wind speed value experienced by the user can also be obtained through wind speed field simulation studies of indoor air.

[0133] S502. Determine the estimated perceived temperature of the user based on the current temperature, humidity, and wind speed.

[0134] Optionally, based on the data collected above, the user's perceived temperature can be estimated using the following formula (6):

[0135] AT=0.81T+0.01RH×(0.99T-14.3)-0.65WS (6)

[0136] Where AT represents the user's estimated perceived temperature, T represents the collected temperature value, RH represents the collected humidity value, and WS represents the wind speed value experienced by the user's skin.

[0137] In summary, the temperature control method provided in this embodiment includes: collecting air conditioning temperature information set by the user at multiple times within a specified time period; determining prior state temperature estimation information corresponding to each time period based on the air conditioning temperature information set at each time period and the corresponding posterior state temperature estimation information; determining the target air conditioning temperature setting for the specified time period based on the prior state temperature estimation information corresponding to each time period; and controlling the air conditioning temperature according to the target setting temperature. This method collects air conditioning temperature information set by the user multiple times within a specified time period, and based on the Kalman filter algorithm, iteratively learns the user's habits of setting the air conditioning temperature, thereby estimating the target air conditioning temperature required for the specified time period. This achieves the goal of determining the air conditioning temperature setting that matches the user's habits through iterative learning, thereby controlling the air conditioning to operate at the determined setting temperature, providing the user with the optimal perceived temperature, and meeting the user's comfort needs.

[0138] The following describes the apparatus, equipment, and storage medium used to implement the temperature control method provided in this application. The specific implementation process and technical effects are described above and will not be repeated below.

[0139] Figure 6 This is a schematic diagram of a temperature control device provided in an embodiment of this application. The function of this temperature control device corresponds to the steps performed by the method described above. This device can be understood as the aforementioned server, or the server's processor, or as a component that implements the functions of this application under the control of the server, independent of the aforementioned server or processor. Figure 6 As shown, the device may include: a data acquisition module 610, a determination module 620, and a control module 630;

[0140] The data acquisition module 610 is used to collect air conditioning temperature information set by the user at multiple times within a specified time period;

[0141] The determination module 620 is used to determine the prior state temperature estimation information corresponding to each time based on the air conditioning temperature information set at each time and the posterior state temperature estimation information corresponding to each time.

[0142] The control module 630 is used to determine the target set temperature of the air conditioner for a specified time period based on the prior state temperature estimation information corresponding to each time moment, and to perform temperature control of the air conditioner based on the target set temperature.

[0143] Optionally, the determining module 620 is specifically used to determine the posterior state temperature estimation information corresponding to the previous moment based on the indoor temperature measurement value of the previous moment, the prior state temperature estimation information corresponding to the previous moment, and the Kalman gain information of the previous moment. The current moment is any moment other than the first moment among multiple moments.

[0144] Based on the posterior state temperature estimate information corresponding to the previous moment and the air conditioning temperature information set at the previous moment, determine the prior state temperature estimate information corresponding to the current moment.

[0145] Optionally, the determining module 620 is further configured to determine the prior state temperature estimation information corresponding to the initial time based on the initial posterior state temperature estimation information corresponding to the initial time and the reference air conditioning temperature information at the initial time.

[0146] Optionally, the determining module 620 is specifically used to determine the prior state temperature estimation covariance matrix at the current moment based on the posterior state temperature estimation covariance matrix of the previous moment, the system state transition matrix, and the covariance matrix of the noisy system.

[0147] Based on the covariance matrix of the prior state temperature, the system output matrix, and the covariance matrix of the measurement noise at the current moment, determine the Kalman gain information at the current moment;

[0148] Update the posterior state temperature estimation covariance matrix based on the Kalman gain information at the current time and the prior state temperature estimation covariance matrix at the current time.

[0149] Optionally, the determining module 620 is specifically used to determine the air conditioner operating power at the previous moment based on the air conditioner temperature information set at the previous moment. The air conditioner operating power includes: cooling power and dehumidification power.

[0150] Based on the posterior state temperature estimate information corresponding to the previous moment and the air conditioning operating power at the previous moment, determine the prior state temperature estimate information corresponding to the current moment.

[0151] Optionally, the determining module 620 is specifically used to determine the prior state temperature estimation information corresponding to the last time of the consecutive adjacent time as the target setting temperature of the air conditioner for the specified time period if the difference between the prior state temperature estimation information corresponding to at least two consecutive adjacent time periods is less than a preset threshold.

[0152] Optionally, the control module 630 is specifically used to control the air conditioner to operate at the fan speed corresponding to the target set temperature.

[0153] Optionally, the determining module 620 is also used to acquire the current temperature value and the current humidity value collected by the temperature and humidity sensor, and to acquire the wind speed value experienced by the user collected by the wind speed sensor.

[0154] Based on the current temperature, humidity, and wind speed, determine the estimated perceived temperature for the user.

[0155] The above-described device is used to execute the method provided in the foregoing embodiments, and its implementation principle and technical effect are similar, so they will not be described again here.

[0156] These modules can be one or more integrated circuits configured to implement the above methods, such as one or more Application Specific Integrated Circuits (ASICs), one or more digital signal processors (DSPs), or one or more Field Programmable Gate Arrays (FPGAs). Alternatively, when a module is implemented using processing element scheduler code, the processing element can be a general-purpose processor, such as a Central Processing Unit (CPU) or other processor capable of calling program code. Furthermore, these modules can be integrated together as a system-on-a-chip (SoC).

[0157] The modules described above can be connected or communicate with each other via wired or wireless connections. Wired connections can include metal cables, optical fibers, hybrid cables, or any combination thereof. Wireless connections can include connections via LAN, WAN, Bluetooth, ZigBee, or NFC, or any combination thereof. Two or more modules can be combined into a single module, and any module can be divided into two or more units. Those skilled in the art will understand that, for the sake of convenience and brevity, the specific working processes of the systems and devices described above can be referred to the corresponding processes in the method embodiments, and will not be repeated here.

[0158] Figure 7 This is a schematic diagram of the structure of an electronic device provided in an embodiment of this application. The device may be a computing device with data processing capabilities.

[0159] The device may include: a processor 801 and a storage medium 802.

[0160] Storage medium 802 is used to store programs, and processor 801 calls the programs stored in storage medium 802 to execute the above method embodiments. The specific implementation and technical effects are similar, and will not be described in detail here.

[0161] The storage medium 802 stores program code, which, when executed by the processor 801, causes the processor 801 to perform various steps in the temperature control method according to various exemplary embodiments of this application as described in the "Exemplary Methods" section above.

[0162] The processor 801 can be a general-purpose processor, such as a central processing unit (CPU), digital signal processor (DSP), application-specific integrated circuit (ASIC), field-programmable gate array (FPGA), or other programmable logic device, discrete gate or transistor logic device, or discrete hardware component, capable of implementing or executing the methods, steps, and logic block diagrams disclosed in the embodiments of this application. The general-purpose processor can be a microprocessor or any conventional processor. The steps of the methods disclosed in the embodiments of this application can be directly manifested as being executed by a hardware processor, or executed by a combination of hardware and software modules within the processor.

[0163] Storage medium 802, as a non-volatile computer-readable storage medium, can be used to store non-volatile software programs, non-volatile computer-executable programs, and modules. The storage medium can include at least one type of storage medium, such as flash memory, hard disk, multimedia card, card-type storage medium, random access memory (RAM), static random access memory (SRAM), programmable read-only memory (PROM), read-only memory (ROM), electrically erasable programmable read-only memory (EEPROM), magnetic storage medium, magnetic disk, optical disk, etc. The storage medium is any other medium capable of carrying or storing desired program code in the form of instructions or data structures that can be accessed by a computer, but is not limited thereto. In the embodiments of this application, storage medium 802 can also be a circuit or any other device capable of implementing storage functions for storing program instructions and / or data.

[0164] Optionally, this application also provides a program product, such as a computer-readable storage medium, including a program that, when executed by a processor, performs the above-described method embodiments.

[0165] In the several embodiments provided in this application, it should be understood that the disclosed apparatus and methods can be implemented in other ways. For example, the apparatus embodiments described above are merely illustrative; for instance, the division of units is only a logical functional division, and in actual implementation, there may be other division methods. For example, multiple units or components may be combined or integrated into another system, or some features may be ignored or not executed. Furthermore, the coupling or direct coupling or communication connection shown or discussed may be through some interfaces; the indirect coupling or communication connection between apparatuses or units may be electrical, mechanical, or other forms.

[0166] The units described as separate components may or may not be physically separate. The components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the units can be selected to achieve the purpose of this embodiment according to actual needs.

[0167] Furthermore, the functional units in the various embodiments of this application can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit. The integrated unit can be implemented in hardware or in a combination of hardware and software functional units.

[0168] The integrated units implemented as software functional units described above can be stored in a computer-readable storage medium. These software functional units, stored in a storage medium, include several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) or processor to execute some steps 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), random access memory (RAM), magnetic disks, or optical disks.

Claims

1. A temperature control method, characterized in that, include: Collect air conditioning temperature information set by the user at multiple times within a specified time period; Based on the air conditioning temperature information set at each time point and the posterior state temperature estimation information corresponding to each time point, determine the prior state temperature estimation information corresponding to each time point. Based on the prior state temperature estimation information corresponding to each time moment, the target set temperature of the air conditioner corresponding to the specified time period is determined, and the temperature control of the air conditioner is performed according to the target set temperature.

2. The method according to claim 1, characterized in that, The step of determining the prior state temperature estimation information corresponding to each time moment based on the air conditioning temperature information set at each time moment and the corresponding posterior state temperature estimation information at each time moment includes: Based on the indoor temperature measurement value of the previous time, the prior state temperature estimation information of the previous time, and the Kalman gain information of the previous time, the posterior state temperature estimation information of the previous time is determined. The current time is any time other than the first time among the plurality of times. Based on the posterior state temperature estimate information corresponding to the previous moment and the air conditioning temperature information set at the previous moment, determine the prior state temperature estimate information corresponding to the current moment.

3. The method according to claim 2, characterized in that, Also includes: Based on the initial posterior state temperature estimation information corresponding to the initial time and the reference air conditioning temperature information at the initial time, the prior state temperature estimation information corresponding to the initial time is determined.

4. The method according to claim 2, characterized in that, The calculation process for the Kalman gain information includes: Based on the posterior state temperature estimation covariance matrix of the previous time step, the system state transition matrix, and the covariance matrix of the noisy system, determine the prior state temperature estimation covariance matrix of the current time step. Based on the covariance matrix of the prior state temperature, the system output matrix, and the covariance matrix of the measurement noise at the current moment, determine the Kalman gain information at the current moment; Update the posterior state temperature estimation covariance matrix based on the Kalman gain information at the current time and the prior state temperature estimation covariance matrix at the current time.

5. The method according to claim 2, characterized in that, The step of determining the prior state temperature estimate information corresponding to the current moment based on the posterior state temperature estimate information corresponding to the previous moment and the air conditioning temperature information set at the previous moment includes: Based on the air conditioner temperature information set at the previous moment, determine the air conditioner operating power at the previous moment, wherein the air conditioner operating power includes: cooling power and dehumidification power; Based on the posterior state temperature estimation information corresponding to the previous moment and the air conditioning operating power at the previous moment, the prior state temperature estimation information corresponding to the current moment is determined.

6. The method according to claim 1, characterized in that, The step of determining the target set temperature for the air conditioner for the specified time period based on the prior state temperature estimation information corresponding to each time moment includes: If the difference between the prior state temperature estimates corresponding to at least two consecutive adjacent time points is less than a preset threshold, then the prior state temperature estimate corresponding to the last time point in the consecutive adjacent time points is determined as the target set temperature of the air conditioner for the specified time period.

7. The method according to claim 1, characterized in that, The temperature control of the air conditioner according to the target set temperature includes: Control the air conditioner to operate at the fan speed corresponding to the target set temperature.

8. The method according to claim 7, characterized in that, After the air conditioner is controlled to operate at the fan speed corresponding to the target set temperature, the system further includes: Acquire the current temperature and humidity values ​​collected by the temperature and humidity sensors, and acquire the wind speed value experienced by the user collected by the wind speed sensor. Based on the current temperature value, the current humidity value, and the wind speed value experienced by the user, determine the estimated value of the user's perceived temperature.

9. A temperature control device, characterized in that, include: The module consists of an acquisition module, a determination module, and a control module. The data acquisition module is used to collect air conditioning temperature information set by the user at multiple times within a specified time period; The determining module is used to determine the prior state temperature estimation information corresponding to each time based on the air conditioning temperature information set at each time and the posterior state temperature estimation information corresponding to each time. The control module is used to determine the target set temperature of the air conditioner for the specified time period based on the prior state temperature estimation information corresponding to each time moment, and to perform temperature control of the air conditioner based on the target set temperature.

10. An electronic device, characterized in that, include: The device includes a processor, a storage medium, and a bus, wherein the storage medium stores program instructions executable by the processor, and when the electronic device is running, the processor communicates with the storage medium via the bus, and the processor executes the program instructions to implement the temperature control method as described in any one of claims 1 to 8.

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