Appliance control method, apparatus, device, storage medium, and program product

Abstract

The present application relates to the technical field of household appliances, and discloses an appliance control method, device, equipment, storage medium and program product. The present application collects environmental temperature data in real time, identifies temperature change events in a sliding window, quantifies the influence of repeated temperature change stimuli on the temperature difference fatigue of a user according to characteristic parameters of the temperature change events, and obtains a temperature difference fatigue cumulative value. Furthermore, the present application predicts the risk level of the user's body discomfort in a future period according to the current temperature difference fatigue cumulative value and its change trend. Once the discomfort risk level exceeds a risk level threshold, the present application controls the appliance in advance to slow down the accumulation speed of temperature difference fatigue, effectively avoids human discomfort caused by frequent or sharp temperature fluctuations, significantly improves environmental comfort, and thus improves the user experience.

Description

Technical Field

[0001] This invention relates to the field of household appliance technology, specifically to electrical equipment control methods, devices, equipment, storage media, and program products. Background Technology

[0002] Currently, the intelligent control of air conditioning equipment mainly involves real-time detection of indoor temperature, comparison with the set temperature, and automatic adjustment of compressor power or fan speed when the deviation exceeds a certain range, so that the room temperature approaches the set value. Some high-end air conditioners have intelligent sensing capabilities, which can identify the location, activity level, or sleep state of a person, and thus implement different control strategies based on the person's condition.

[0003] The above technologies all control the temperature in real time based on the user's or the environment's status. However, in reality, even if the temperature fluctuation is not large, frequent temperature fluctuations can disrupt the body's autonomic nervous system. Users may experience temperature difference fatigue reactions such as feeling colder or hotter, fatigue, or restlessness, which is detrimental to the user experience. Summary of the Invention

[0004] This invention provides an electrical equipment control method, apparatus, device, storage medium, and program product to solve the problem of temperature fatigue that users are prone to when frequently adjusting the temperature in the prior art.

[0005] In a first aspect, the present invention provides a method for controlling electrical equipment, the method comprising: Collect ambient temperature data and construct the ambient temperature sequence for the current sliding window; Based on the ambient temperature sequence, multiple temperature change events within the current sliding window are identified, and feature parameters of each temperature change event are extracted. Based on the characteristic parameters of each temperature change event, the cumulative value of temperature difference fatigue is obtained; among which, the cumulative value of temperature difference fatigue is used to characterize the degree of fatigue impact on users caused by repeated occurrences of temperature change events; Based on the cumulative value of temperature difference fatigue and its changing trend, the user's discomfort risk level is predicted in the future. When the detected discomfort risk level reaches the risk level threshold, electrical equipment is controlled to suppress the increase of the cumulative value of temperature difference fatigue.

[0006] This invention collects ambient temperature data in real time, identifies temperature change events within a sliding window, and quantifies the impact of repeated temperature changes on user fatigue based on the characteristic parameters of these events, obtaining a cumulative temperature fatigue value. Then, based on the current cumulative temperature fatigue value and its trend, it predicts the risk level of user discomfort in the future. Once the discomfort risk level exceeds a threshold, electrical equipment is controlled in advance to slow down the accumulation of temperature fatigue, effectively avoiding discomfort caused by frequent or drastic temperature fluctuations, significantly improving environmental comfort, and thus enhancing the user experience.

[0007] In some optional implementations, the characteristic parameters include event type, event occurrence time, temperature change amplitude, temperature change rate, change duration, and temperature change direction. Event types include short-term fluctuation events and continuous change events. Based on the characteristic parameters of each temperature change event, the cumulative value of temperature difference fatigue is obtained, including: The first event score for each temperature change event is obtained based on the temperature change amplitude, temperature change rate, and change duration. Based on the event type, determine the number of short-term fluctuation events, and based on the temperature change direction of each temperature change event, determine the number of times the temperature change direction is reversed within the current sliding window. The window score is obtained based on the number of events and the number of times the temperature change direction is reversed; The cumulative value of temperature difference fatigue is obtained based on the window score, the event occurrence time of each temperature change event, and the score of the first event.

[0008] This invention determines the intensity of each temperature difference stimulus by analyzing the characteristic parameters of various temperature change events. The greater the amplitude, the faster the rate of change, and the longer the duration of the change, the easier it is for temperature difference fatigue to accumulate, thus obtaining a first event score. Furthermore, by statistically analyzing the number of events and the number of direction reversals within a sliding window, additional penalties are applied to high-frequency, reciprocating, and other fatigue-inducing fluctuation patterns, resulting in a window score. Then, based on the window score and the first event score, the cumulative value of temperature difference fatigue within the sliding window is calculated, more comprehensively and accurately reflecting the intensity of temperature difference stimulation in individual events and the degree of impact of overall fluctuations, thereby providing a reliable quantitative basis for predicting the actual risk of discomfort in the human body.

[0009] In some optional implementations, the cumulative value of temperature difference fatigue is obtained based on the window score, the event occurrence time of each temperature change event, and the score of the first event, including: Based on the time difference between the current time and the occurrence time of each temperature change event, the time decay coefficient of each temperature change event is obtained; The second event score for each temperature change event is calculated by multiplying the time decay coefficient and the first event score. The cumulative value of temperature difference fatigue is calculated by accumulating the window score and the second event score of each temperature change event.

[0010] This invention determines the time decay coefficient by using the time difference between the current moment and the moment when the temperature change event occurs, thereby correcting the score of the first event. This makes the temperature change event that occurred recently contribute more to the cumulative value, while the influence of the event that occurred in the long term gradually weakens. The cumulative value of temperature difference fatigue is formed by combining the cumulative corrected score with the window score, which is more in line with the actual impact of the environment on human fatigue.

[0011] In some alternative implementations, multiple temperature change events within the current sliding window are identified based on the ambient temperature sequence, including: Based on the preset sampling time, the ambient temperature sequence is divided into multiple ambient temperature subsequences; For each ambient temperature subsequence, if the detected temperature change amplitude of the ambient temperature subsequence reaches the first change amplitude threshold, the ambient temperature subsequence is identified as a short-term fluctuation event. If multiple consecutive ambient temperature subsequences are detected to have the same direction of temperature change, and the overall temperature change amplitude of the multiple consecutive ambient temperature subsequences reaches a second change amplitude threshold within a preset duration, then the multiple consecutive ambient temperature subsequences are identified as a continuous change event.

[0012] This invention accurately identifies short-term fluctuation events and long-term unidirectional continuous change events by dividing temperature change events within a time window. This avoids misjudging continuous slow changes as multiple fluctuation events or merging high-frequency fluctuations into a single event, thus improving the accuracy of calculating the cumulative value of temperature difference fatigue.

[0013] In some optional implementations, the user's discomfort risk level in the future is predicted based on the cumulative value of temperature difference fatigue and the trend of its change, including: Based on the changing trend of the cumulative value of temperature difference fatigue, determine the growth time required for the cumulative value of temperature difference fatigue to reach the temperature difference fatigue threshold. Based on the relationship between the growth time and the growth time threshold, the user's discomfort risk level in the future time period is determined.

[0014] This invention predicts the time required for the cumulative temperature fatigue value to reach the temperature fatigue threshold based on the changing trend of the cumulative temperature fatigue value. It also quantifies the urgency of the discomfort risk based on the magnitude of the growth time, achieving a more refined discomfort risk classification. This allows for early intervention in the behavior of electrical equipment based on different discomfort risk levels, thereby improving environmental comfort and user experience.

[0015] In some alternative implementations, the method further includes: Obtain user behavior feedback data; The temperature difference fatigue threshold is adjusted based on behavioral feedback data.

[0016] This invention acquires and learns actual user behavior feedback data to make personalized and dynamic adjustments to the temperature difference fatigue threshold, so that the setting of the temperature difference fatigue threshold can better reflect the actual physical feeling and comfort preference of a specific user, thereby significantly improving the control accuracy and user experience in long-term use.

[0017] In some alternative implementations, the electrical equipment includes a compressor and a fan; controlling the electrical equipment includes: Reduce the power regulation rate of the compressor, and / or reduce the fan speed regulation rate, and / or reduce the temperature regulation frequency of the electrical equipment.

[0018] When the present invention anticipates a high risk of user discomfort in the future, it will not wait for the user discomfort to occur, but will immediately take early intervention strategies, such as reducing the power adjustment rate of the compressor, reducing the fan speed adjustment rate, and limiting the temperature adjustment frequency, so that the temperature slowly approaches the set temperature, avoiding the cumulative effect of temperature difference fatigue caused by rapid temperature changes, thereby improving the user's comfort experience.

[0019] In a second aspect, the present invention provides an electrical equipment control device, the device comprising: The first processing module is used to collect ambient temperature data and construct the ambient temperature sequence of the current sliding window. The second processing module is used to identify multiple temperature change events within the current sliding window based on the ambient temperature sequence, and to extract the feature parameters of each temperature change event. The third processing module is used to obtain the cumulative value of temperature difference fatigue based on the characteristic parameters of each temperature change event; the cumulative value of temperature difference fatigue is used to characterize the degree of fatigue impact on the user caused by the repeated occurrence of temperature change events. The fourth processing module is used to predict the user's discomfort risk level in the future based on the cumulative value of temperature difference fatigue and the changing trend of the cumulative value of temperature difference fatigue. The fifth processing module is used to control electrical equipment when the detected discomfort risk level reaches the risk level threshold, so as to suppress the growth of the cumulative value of temperature difference fatigue.

[0020] Thirdly, the present invention provides an electrical device, comprising: a memory and a processor, wherein the memory and the processor are communicatively connected to each other, the memory stores computer instructions, and the processor executes the computer instructions to perform the electrical device control method of the first aspect or any corresponding embodiment described above.

[0021] In some alternative implementations, the electrical appliance is an air conditioner.

[0022] Fourthly, the present invention provides a computer-readable storage medium storing computer instructions for causing a computer to perform the electrical equipment control method of the first aspect or any corresponding embodiment described above.

[0023] Fifthly, the present invention provides a computer program product, including computer instructions for causing a computer to execute the electrical equipment control method of the first aspect or any corresponding embodiment described above. Attached Figure Description

[0024] To more clearly illustrate the specific embodiments of the present invention or the technical solutions in the prior art, the drawings used in the description of the specific embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are some embodiments of the present invention. For those skilled in the art, other drawings can be obtained from these drawings without creative effort.

[0025] Figure 1 This is a schematic diagram of an application scenario according to an embodiment of the present invention; Figure 2 This is a schematic flowchart of a first embodiment of an electrical equipment control method according to the present invention; Figure 3 This is a second flowchart illustrating an electrical equipment control method according to an embodiment of the present invention; Figure 4 This is a schematic diagram of a third process for controlling electrical equipment according to an embodiment of the present invention; Figure 5 This is a structural block diagram of an electrical equipment control device according to an embodiment of the present invention; Figure 6 This is a schematic diagram of the hardware structure of the electrical device according to an embodiment of the present invention. Detailed Implementation

[0026] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, the technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0027] It is understood that before using the technical solutions disclosed in the various embodiments of the present invention, users should be informed of the types, scope of use, and usage scenarios of the personal information involved in the present invention and their authorization should be obtained in accordance with relevant laws and regulations through appropriate means.

[0028] The terms "first" and "second" are used for descriptive purposes only and should not be construed as indicating or implying relative importance or implicitly specifying the number of technical features indicated. Thus, a feature defined as "first" or "second" may explicitly or implicitly include one or more of that feature. In the description of this invention, "a plurality of" means two or more, unless otherwise explicitly specified.

[0029] As an optional application scenario of this invention, such as Figure 1 As shown, the electrical equipment control system may include at least one terminal device and at least one server. Figure 1 The system is illustrated in the example, which includes a computer 101, a mobile terminal 102, and a server 103, and the terminal devices such as the computer 101 and the mobile terminal 102 are connected to the server 103 through a network 110.

[0030] The terminal device can specifically be a smartphone, tablet, laptop, PDA, home appliance, desktop computer, game console, smart TV, smart wearable device, in-vehicle terminal, VR (Virtual Reality) device, AR (Augmented Reality) device, etc. Server 103 can be a standalone physical server, a server cluster, a distributed system, or a cloud server providing cloud services. Network 110 can be a wired or wireless network, examples of which include, but are not limited to, the Internet, corporate intranet, local area network, wide area network, mobile communication network, and combinations thereof.

[0031] Currently, intelligent control solutions for air conditioning equipment typically revolve around the real-time environmental conditions, mainly including: (1) Temperature-based feedback control scheme: By comparing the indoor temperature with the set temperature in real time, when the deviation exceeds a certain range, the compressor power or fan speed of the air conditioner is automatically adjusted so that the room temperature approaches the set value. It only focuses on the single temperature deviation and does not consider the delayed discomfort caused to the human body by the accumulation of temperature changes over time.

[0032] (2) Body-sensing optimization scheme based on human presence and activity status: Through the sensing capabilities of infrared sensing, human position recognition, millimeter-wave radar, etc., the human position, activity level and whether the user is asleep are identified, and then different strategies are used to control the system in direct blowing mode, avoidance mode and sleep mode.

[0033] (3) Sleep mode control scheme based on preset curves: In order to improve nighttime comfort, some air conditioners provide preset heating or cooling curves, such as reducing the fan speed in the first hour of sleep and increasing the set temperature in the second hour. However, such curves cannot be dynamically adjusted according to the fluctuations of the actual environment.

[0034] (4) Intelligent adjustment scheme based on the perceived temperature model of predicted mean vote (PMV) or predicted percentage of dissatisfaction (PPD): The perceived temperature of the user is estimated by parameters such as temperature, humidity and activity level, and the air conditioning parameters are automatically fine-tuned according to the perceived temperature. It can judge whether the user will feel comfortable in the future based on the short-term trend of perceived temperature. If the user feels cold, the cooling is reduced; if the user feels hot, the cooling is increased. This type of method relies on the real-time calculation of the perceived temperature model and does not adequately consider the cumulative effect of temperature changes.

[0035] The relevant technologies are based on static or real-time sensory assessments. For example, if the current temperature is not significantly different from the set temperature, the system will consider the user comfortable and will not adjust the device. However, in real-world scenarios, even if the temperature fluctuations are small, frequent fluctuations over a past period can still cause users to experience sensations of cold, heat, fatigue, or restlessness. The above solutions do not consider the cumulative effect of temperature changes on user discomfort.

[0036] Related technologies typically only take action after the user has already felt cold or hot; for example, raising the set temperature when the user feels cold and lowering it when the user feels hot. Essentially, this is a reactive adjustment solution, lacking proactive intervention and negatively impacting user experience. Furthermore, motion-sensing prediction solutions rely on complex monitoring devices such as cameras, infrared human detection, and millimeter-wave radar, resulting in high hardware costs, complex installation, privacy concerns, and limited applicability.

[0037] Furthermore, the aforementioned methods rapidly adjust to the set temperature, potentially leading to overcooling, rapid temperature rebound, and subsequent cooling. This makes it difficult to avoid high-frequency, high-amplitude fluctuations during temperature control, and this repeated hot-and-cold control method is more likely to cause thermal fatigue. Thermal fatigue refers to the phenomenon where a user's tolerance to temperature differences gradually decreases after experiencing multiple hot and cold stimuli over a continuous period. Thermal fatigue is a cumulative effect over time, manifesting as users being more prone to feeling hot and cold, experiencing sleep disruption or irritability, more frequently manually adjusting the temperature, and feeling uncomfortable even when the temperature is not high / low.

[0038] This invention provides a method for controlling electrical equipment. By using a sliding window to statistically analyze temperature change events and quantifying the cumulative value of temperature difference fatigue based on the characteristic parameters of these events, the method can identify the level of user discomfort risk caused by multiple temperature difference accumulations in the future. This allows for early intervention in the operation of electrical equipment when the level of discomfort risk is high, thereby improving user comfort.

[0039] According to an embodiment of the present invention, an embodiment of an electrical equipment control method is provided. It should be noted that the steps shown in the flowchart in the accompanying drawings can be executed in a computer system such as a set of computer-executable instructions. Furthermore, although a logical order is shown in the flowchart, in some cases, the steps shown or described may be executed in a different order than that shown here.

[0040] This embodiment provides an electrical equipment control method, which can be used for the aforementioned terminal devices, such as air conditioners and other electrical equipment. Figure 2 This is a flowchart of an electrical equipment control method according to an embodiment of the present invention, such as... Figure 2 As shown, the process includes the following steps: Step S201: Collect ambient temperature data and construct the ambient temperature sequence of the current sliding window.

[0041] Specifically, a temperature data acquisition module, such as a temperature sensor, is used to collect ambient temperature data at a fixed sampling period to form a real-time ambient temperature data stream. The sampling period can be 1 second, 2 seconds, or 5 seconds, and can be dynamically adjusted according to the scenario. For example, when the system is first turned on, in the initial stage of temperature control, sampling needs to be more frequent, and the sampling period can be shorter; when the system is running stably or in night mode, the sampling period can be appropriately increased to save system energy.

[0042] Furthermore, since the collected raw ambient temperature data may be affected by instantaneous noise, data preprocessing is performed on the ambient temperature data, including outlier removal (removing obviously unreasonable jump values), short-window smoothing, and detection of the basic trend of the ambient temperature data to preliminarily determine whether there is a significant warming or cooling trend. The preprocessed ambient temperature data is stored in a fixed-length temperature buffer (e.g., the most recent 30 minutes or 1 hour) in chronological order as the basic input for subsequent temperature difference analysis.

[0043] In step S201, based on the current sliding window (e.g., 10 minutes, which can be adjusted according to the actual scenario), data is read from the temperature buffer to obtain the ambient temperature sequence within the current sliding window.

[0044] Step S202: Based on the ambient temperature sequence, identify multiple temperature change events within the current sliding window and extract the feature parameters of each temperature change event.

[0045] Specifically, the ambient temperature sequence is sampled and analyzed to identify temperature changes within each preset sampling time (e.g., 30 seconds or 1 minute), resulting in multiple temperature change events occurring within the current sliding window. The characteristic parameters of each temperature change event are then analyzed. These characteristic parameters may include: event type, event occurrence time, temperature change amplitude (characterizing the intensity of heating or cooling), temperature change rate (fast, medium, or slow), duration of change (e.g., one or two sampling times), direction of temperature change (e.g., heating or cooling), and fluctuation frequency.

[0046] It should be noted that event types include short-term fluctuation events and continuous change events. For example, short-term rapid cooling or heating, and high-frequency small-amplitude temperature fluctuations (typically leading to temperature difference fatigue) can be classified as short-term fluctuation events, while long-term rapid cooling or heating, and low-frequency slow and stable temperature changes can be classified as continuous change events.

[0047] In some embodiments, each of the temperature change events and its characteristic parameters can be combined in chronological order to form a cumulative temperature difference trajectory, which is used to characterize the overall situation of recent temperature difference changes. That is, around the ambient temperature sequence within the current sliding window, multidimensional characteristic parameters are continuously extracted for each sampling time according to a uniform preset sampling time, forming a feature parameter sequence that evolves over time, which is used to calculate the cumulative value of temperature difference fatigue.

[0048] Traditional solutions control temperature deviation based on a single moment. This embodiment constructs a cumulative temperature difference trajectory and incorporates multi-dimensional feature parameters such as the magnitude and rate of temperature change into the somatosensory prediction to identify potential discomfort caused by long-term fluctuations.

[0049] Step S203: Based on the characteristic parameters of each temperature change event, obtain the cumulative value of temperature difference fatigue; wherein, the cumulative value of temperature difference fatigue is used to characterize the degree of fatigue impact on the user caused by the repeated occurrence of temperature change events.

[0050] Specifically, based on characteristic parameters such as the amplitude and rate of temperature change, the impact of each temperature change event on user fatigue is quantified and accumulated to obtain the cumulative temperature difference fatigue value corresponding to the current sliding window. This cumulative temperature difference fatigue value is accumulated over time and does not disappear instantly due to short-term temperature stabilization. A higher cumulative temperature difference fatigue value indicates a greater impact on user fatigue and more recent repeated hot and cold stimuli experienced by the user.

[0051] Step S204: Based on the cumulative value of temperature difference fatigue and the changing trend of the cumulative value of temperature difference fatigue, predict the user's discomfort risk level in the future time period.

[0052] Specifically, the time granularity of the sliding window is determined (e.g., 10 seconds, 30 seconds, or 1 minute), and features are updated according to this time granularity. For example, the latest cumulative temperature fatigue value is calculated every 10 seconds. By statistically analyzing the cumulative temperature fatigue values ​​corresponding to multiple consecutive sliding windows, the changing trend of the cumulative temperature fatigue value is analyzed.

[0053] In step S204, the cumulative thermal fatigue value corresponding to the current sliding window is compared with the user's thermal fatigue threshold. Based on the trend of the cumulative thermal fatigue value, the discomfort risk level for the future time period (e.g., the next 5 minutes, 10 minutes, or 30 minutes, which can be adjusted according to actual needs) is determined. The thermal fatigue threshold represents the upper limit of the cumulative thermal fatigue value that the user can tolerate within the current sliding window period. The discomfort risk level can be divided into low, medium, and high levels, which can be specifically set according to the actual scenario.

[0054] It should be noted that the temperature fatigue threshold can dynamically change with time and equipment usage. The temperature fatigue threshold can be associated with a specific user; for example, users who are more sensitive to temperature changes will have a lower temperature fatigue threshold, while those who are less sensitive will have a higher threshold. Furthermore, the system can adjust the temperature fatigue threshold based on long-term user behavior. If the cumulative temperature fatigue value is approaching the temperature fatigue threshold, the system can predict the user's potential discomfort risk level in the future and intervene in advance.

[0055] Step S205: When the detected discomfort risk level reaches the risk level threshold, control the electrical equipment to suppress the growth of the cumulative value of temperature difference fatigue.

[0056] Specifically, if the user's discomfort risk level reaches the set risk level threshold in the future, the operation of the electrical equipment will be intervened in advance, and the operation of the compressor and fan of the electrical equipment will be adjusted to make the temperature control process smoother and avoid the continuous accumulation of temperature difference fatigue due to frequent temperature change events.

[0057] Related technologies rely on devices such as cameras, infrared human body detection, millimeter-wave radar, and sleep monitoring hardware to achieve temperature regulation, resulting in high hardware costs. This embodiment monitors ambient temperature data to detect temperature difference changes and calculate the cumulative value of temperature difference fatigue, achieving high-precision body sensation prediction without the need for human body detection equipment, thus reducing costs.

[0058] The electrical equipment control method provided in this embodiment collects ambient temperature data in real time, identifies temperature change events within a sliding window, and quantifies the impact of repeated temperature changes on user fatigue based on the characteristic parameters of these events, obtaining a cumulative temperature fatigue value. Then, based on the current cumulative temperature fatigue value and its trend, it predicts the risk level of user discomfort in the future. Once the discomfort risk level exceeds a threshold, the electrical equipment is controlled in advance to slow down the accumulation of temperature fatigue, effectively avoiding discomfort caused by frequent or drastic temperature fluctuations, significantly improving environmental comfort, and thus enhancing the user experience.

[0059] This embodiment provides an electrical equipment control method, which can be used for the aforementioned terminal devices, such as air conditioners and other electrical equipment. Figure 3 This is a flowchart of an electrical equipment control method according to an embodiment of the present invention, such as... Figure 3 As shown, the process includes the following steps: Step S301: Collect ambient temperature data and construct the ambient temperature sequence for the current sliding window. For details, please refer to [link to relevant documentation]. Figure 2 Step S201 of the illustrated embodiment will not be described again here.

[0060] Step S302: Based on the ambient temperature sequence, identify multiple temperature change events within the current sliding window and extract the feature parameters of each temperature change event.

[0061] Specifically, step S302 includes: Step S3021: Based on the preset sampling time, the ambient temperature sequence is divided into multiple ambient temperature subsequences.

[0062] Specifically, within the current sliding window, two types of events are identified from the ambient temperature sequence: short-term fluctuation events and continuous change events. First, the ambient temperature sequence of the current sliding window is divided based on a preset sampling time (e.g., 30 seconds or 1 minute) to obtain multiple ambient temperature subsequences, where the duration of change for each ambient temperature subsequence is one sampling time.

[0063] Step S3022: For each ambient temperature subsequence, if the detected temperature change amplitude of the ambient temperature subsequence reaches the first change amplitude threshold, the ambient temperature subsequence is identified as a short-term fluctuation event.

[0064] For example, if the temperature change of the corresponding ambient temperature subsequence reaches the first change threshold (e.g., 0.3 ℃) within a preset sampling time, it is recorded as a short-term fluctuation event.

[0065] Step S3023: If multiple consecutive ambient temperature subsequences are detected to have the same temperature change direction, and the overall temperature change amplitude of the multiple consecutive ambient temperature subsequences reaches the second change amplitude threshold within a preset duration, then the multiple consecutive ambient temperature subsequences are identified as a continuous change event.

[0066] For example, if the temperatures of multiple consecutive ambient temperature subsequences change in the same direction (e.g., both heating up or cooling down), and the cumulative overall temperature change amplitude within a preset duration (e.g., 2 minutes) reaches a set second change amplitude threshold (e.g., 1.0 ℃), the multiple consecutive ambient temperature subsequences are merged into a single continuous change event.

[0067] This embodiment divides temperature change events within a time window to accurately identify short-term fluctuation events and long-term unidirectional continuous change events, avoiding misjudging continuous slow changes as multiple fluctuation events or merging high-frequency fluctuations into a single event, thus improving the accuracy of calculating the cumulative value of temperature difference fatigue.

[0068] Step S303: Based on the characteristic parameters of each temperature change event, obtain the cumulative value of temperature difference fatigue; wherein, the cumulative value of temperature difference fatigue is used to characterize the degree of fatigue impact on the user caused by the repeated occurrence of temperature change events.

[0069] Specifically, step S303 includes: Step S3031: Based on the temperature change amplitude, temperature change rate, and change duration of each temperature change event, obtain the first event score for each temperature change event.

[0070] Specifically, the temperature change range is categorized into different levels, each with a different score, and a base intensity score is calculated for each temperature change event. For example, a small change of 0.3–0.5 ℃ is scored as 1; a moderate change of 0.5–1.0 ℃ is scored as 2; a large change of 1.0–2.0 ℃ is scored as 4; and a temperature change exceeding 2.0 ℃ is considered a very large change and is scored as 6.

[0071] Specifically, scores are assigned based on the rate of temperature change, resulting in a speed score for each temperature change event. For example, a slow rate of temperature change (mild change) receives a score of 0; a medium rate of temperature change (relatively fast change) receives a score of 1; and a fast rate of temperature change (very fast change) receives a score of 2.

[0072] Specifically, since temperature change events also include continuous change events, the impact of temperature difference fatigue varies depending on the duration of the change. Therefore, a score is assigned based on the duration of the change to obtain a score for each temperature change event. For example, if the preset sampling time is 1 minute, a change duration of 1-2 minutes is considered short and receives a score of 0; a change duration of 3-5 minutes is considered medium and receives a score of 1; and a change duration of 3-5 minutes is considered long and receives a score of 3.

[0073] Furthermore, for each temperature change event, its corresponding base intensity score, velocity score, and duration score are summed to obtain the first event score for that temperature change event.

[0074] Step S3032: Based on the event type, determine the number of short-term fluctuation events, and based on the temperature change direction of each temperature change event, determine the number of times the temperature change direction is reversed within the current sliding window.

[0075] Specifically, the number of short-term fluctuation events within the current sliding window is counted, and the number of times the temperature change direction reverses within the current sliding window is counted based on the temperature change direction of each temperature change.

[0076] Step S3033: Obtain the window score based on the number of events and the number of times the temperature change direction is reversed.

[0077] Specifically, the more short-term fluctuation events and the more times the temperature change direction reverses, the higher the frequency of temperature fluctuations and the more times the temperature changes cycle between hot and cold, making it more prone to temperature fatigue. Based on the number of short-term fluctuation events and the number of times the temperature change direction reverses, the current sliding window is categorized and scored to obtain a window score.

[0078] For example, if the number of events is ≤5, 0 points are awarded; if the number of events is 6-10, 3 points are awarded; if the number of events is >10, 6 points are awarded. If the number of times the temperature change direction reverses is ≤2, 0 points are awarded; if the number of times the temperature change direction reverses is 3-5, 3 points are awarded; if the number of times the temperature change direction reverses is >5, 6 points are awarded.

[0079] Step S3034: Based on the window score, the event occurrence time of each temperature change event, and the score of the first event, obtain the cumulative value of temperature difference fatigue.

[0080] In some optional implementations, step S3034 above includes: Step a1: Based on the time difference between the current time and the occurrence time of each temperature change event, obtain the time decay coefficient of each temperature change event.

[0081] Specifically, for each temperature change event, the time difference between the current moment and the moment the temperature change event occurred is determined. A matching time decay coefficient is then determined based on this time difference; the larger the time difference, the smaller the time decay coefficient. For example, the time decay coefficient is 1.0 for a time difference of 0–5 minutes; 0.7 for a time difference of 5–10 minutes; 0.4 for a time difference of 10–20 minutes; and 0.2 for a time difference of 20–40 minutes.

[0082] Step a2: Calculate the second event score for each temperature change event based on the product of the time decay coefficient and the first event score.

[0083] Specifically, for each temperature change event, the product of the time decay coefficient of the temperature change event and the score of the first event is calculated to obtain the score of the second event for that temperature change event. This increases the contribution of recent events to the cumulative value of temperature difference fatigue and reduces the contribution of long-term events to the cumulative value of temperature difference fatigue. That is, the more recent the temperature change, the greater its impact, and the more distant the change, the less its impact.

[0084] Step a3: Accumulate the window score and the second event score for each temperature change event to calculate the cumulative value of temperature difference fatigue.

[0085] Specifically, the second event score of each temperature change event and the window score of the current sliding window are summed to gradually accumulate multiple temperature difference change stimuli over a period of time to form a cumulative value of temperature difference fatigue, while maintaining time decay, to obtain the cumulative value of temperature difference fatigue corresponding to the current sliding window.

[0086] This embodiment determines the time decay coefficient by using the time difference between the current time and the time when the temperature change event occurs, thereby correcting the score of the first event. This makes the temperature change event that occurred recently contribute more to the cumulative value, while the influence of the event that occurred in the long term gradually weakens. The cumulative value of temperature difference fatigue is formed by the cumulative corrected score and the window score, which is more in line with the actual impact of the environment on human fatigue.

[0087] In this embodiment, the intensity of each temperature change event is determined by its characteristic parameters. The greater the amplitude, the faster the rate of change, and the longer the duration of the change, the easier it is for temperature fatigue to accumulate, thus obtaining a first event score. Furthermore, by statistically analyzing the number of events and the number of direction reversals within a sliding window, additional penalties are applied to high-frequency, reciprocating, and other fatigue-inducing fluctuation patterns, resulting in a window score. Finally, based on the window score and the first event score, the cumulative temperature fatigue value of the sliding window is calculated, more comprehensively and accurately reflecting the intensity of temperature stimulation in individual events and the overall impact of fluctuations, thereby providing a reliable quantitative basis for predicting the actual risk of discomfort in the human body.

[0088] Step S304: Based on the cumulative value of temperature difference fatigue and the changing trend of the cumulative value of temperature difference fatigue, predict the user's discomfort risk level in the future time period.

[0089] Specifically, step S304 includes: Step S3041: Based on the changing trend of the cumulative value of temperature difference fatigue, determine the growth time required for the cumulative value of temperature difference fatigue to reach the temperature difference fatigue threshold.

[0090] Specifically, based on the cumulative temperature difference fatigue value statistically obtained from the historical sliding window, the instantaneous rate of change of the cumulative temperature difference fatigue value is obtained. Based on this instantaneous rate of change, the current cumulative temperature difference fatigue value, and the temperature difference fatigue threshold, the growth time for the cumulative temperature difference fatigue value to reach the temperature difference fatigue threshold is calculated.

[0091] It should be noted that each user has a set of dynamic temperature difference fatigue thresholds, including: basic tolerance threshold, night mode threshold, seasonal correction threshold, and individualized threshold after long-term learning. The appropriate temperature difference fatigue threshold can be dynamically selected in combination with the working mode of the electrical equipment.

[0092] Step S3042: Based on the relationship between the growth time and the growth time threshold, determine the user's discomfort risk level in the future time period.

[0093] Specifically, the higher the cumulative value of temperature difference fatigue, the higher the risk of user discomfort. For example, a cumulative value of 0-15 indicates stable user sensation and usually requires no intervention; a cumulative value of 15-35 indicates low cumulative stimulation and requires mild smoothing control; a cumulative value of 35-55 is close to the temperature difference fatigue threshold and requires early intervention; a cumulative value of 55-100 is very likely to cause user discomfort and requires immediate entry into the temperature difference fatigue suppression mode.

[0094] Specifically, if the cumulative value of temperature difference fatigue is already in the warning range (e.g., 35-55), and its growth time is relatively short, if the detected growth time is less than the first growth time threshold, the discomfort risk level is determined to be high; if the detected growth time is not less than the first growth time threshold but less than the second growth time threshold, the discomfort risk level is determined to be medium; if the detected growth time is not less than the second growth time threshold, the discomfort risk level is determined to be low.

[0095] This embodiment predicts the growth time required for the cumulative temperature fatigue value to reach the temperature fatigue threshold based on the changing trend of the cumulative temperature fatigue value. It also quantifies the urgency of the discomfort risk based on the magnitude of the growth time, achieving a more refined discomfort risk classification. This allows for early intervention in the behavior of electrical equipment based on different discomfort risk levels, thereby improving environmental comfort and user experience.

[0096] Step S305: When the detected discomfort risk level reaches the risk level threshold, control the electrical equipment to suppress the growth of the cumulative value of temperature difference fatigue.

[0097] Specifically, the electrical equipment includes compressors and fans. By reducing the power regulation rate of the compressor, and / or reducing the fan speed regulation rate, and / or reducing the temperature regulation frequency of the electrical equipment, the growth of the cumulative value of thermal fatigue can be suppressed.

[0098] In some embodiments, proactive adjustment measures are taken before potential user discomfort is anticipated, including: reducing compressor output power to limit the rate of temperature increase / decrease; reducing fan speed to make temperature changes more gentle; adjusting airflow direction to avoid direct airflow on the user causing localized temperature difference stimulation; and entering a Temperature Variation Suppression Mode to smooth temperature changes. This eliminates potential sources of discomfort before the user experiences them.

[0099] It should be noted that the temperature difference suppression mode refers to a mode that actively restricts temperature changes when the system predicts that the discomfort risk level has reached the risk level threshold, thereby slowing down the rate of temperature change, avoiding rapid changes or temperature spikes, achieving smooth temperature regulation, and reducing the rate of temperature difference fatigue accumulation.

[0100] In this embodiment, when a high risk of discomfort is predicted for the user in the future, the system will not wait for the user's discomfort to occur, but will immediately take early intervention strategies, such as reducing the power adjustment rate of the compressor, reducing the fan speed adjustment rate, and limiting the temperature adjustment frequency, so that the temperature slowly approaches the set temperature, avoiding the cumulative effect of temperature difference fatigue caused by rapid temperature changes, thereby improving the user's comfort experience.

[0101] In some alternative implementations, user behavior feedback data is acquired, and the temperature difference fatigue threshold is adjusted based on the behavior feedback data.

[0102] Specifically, the human body's tolerance to temperature fluctuations varies. If a person experiences multiple slight fluctuations within a short period, their tolerance will decrease. Furthermore, a person is more sensitive to temperature fluctuations when at rest or asleep. Therefore, it is necessary to continuously learn from and dynamically adjust the temperature fatigue threshold based on long-term user behavior.

[0103] In some embodiments, the system continuously records basic logs, which include ambient temperature sequences, cumulative temperature difference change trajectories (including characteristic parameters of each temperature change event), system output action logs (such as compressor output power level, fan speed, airflow mode, whether to enter temperature difference suppression mode, intervention level, etc.), and user behavior logs (such as manually raising / lowering the set temperature, manually adjusting the fan speed / airflow direction, switching modes, turning the air conditioner on / off, exiting automatic mode, timed shutdown / start, etc.).

[0104] Next, a reaction window is set up to determine whether the user is satisfied with the system's output action based on the basic logs, and the user behavior is mapped into feedback signals that can be used for learning.

[0105] For example, after the system performs an electrical equipment control action, observe the user behavior within the reaction window (e.g., 3-15 minutes after the action is performed). For instance, within the reaction window, if the user manually adjusts the set temperature in the opposite direction (raising the temperature after the system cools or lowering the temperature after the system heats), adjusts the temperature / fan multiple times in a short period, turns off the air conditioner or exits the automatic / comfort mode, or frequently starts / stops or changes the mode at night, these behaviors are mapped as a discomfort signal (negative feedback). If, within the reaction window, the user does not manually intervene after the system action, the user does not exit automatic control and the temperature difference accumulates, or the system runs stably for a long time at night without frequent changes, these behaviors are mapped as a comfort signal (positive feedback).

[0106] Then, to facilitate embedded implementation, the characteristic parameters of each temperature change event are discretized into a combination of several dimensions, and the system's adjustable strategy is discretized into a finite set of actions.

[0107] For example, the cumulative temperature difference fatigue value can be classified as low, medium, high, and extremely high; the number of events can be classified as few, medium, and many; the number of times the temperature change direction reverses can be classified as few, medium, and many; the current scene label can be classified as nighttime sleep, daytime activity, lunch break, energy saving away from home, etc. (which can be inferred from time period, operating mode, and historical habits); the current equipment action level can be classified as output intensity level, wind speed level, wind direction mode, whether temperature difference suppression is enabled, etc., which can be set according to actual needs.

[0108] For example, intervention action levels include no intervention, mild intervention, moderate intervention, and strong intervention; wind direction actions include maintaining, blowing upward, and dispersing; wind speed actions include maintaining, reducing by one level, and reducing by two levels; early intervention timing includes short-term early intervention (e.g., 5 minutes in advance), medium-term early intervention (e.g., 15 minutes in advance), and long-term early intervention (e.g., 30 minutes in advance); temperature difference fatigue threshold fine-tuning levels include: increasing, decreasing, and remaining unchanged, which can be set according to actual needs.

[0109] Furthermore, a reward result is generated for each policy execution. Whenever the system takes an action in a certain state and observes the reward result, the expected effect record of that state-action is updated once.

[0110] For example, if a comfort signal is detected within the reaction window, a comfort reward is given; if an discomfort signal is detected, a discomfort penalty is given; a stability reward can also be given by combining the stability of the cumulative trajectory of temperature difference changes (low number of events, few reversals); if excessive system intervention leads to a significant increase in energy consumption, a slight penalty is given, thus combining various rewards and penalties to obtain a reward result. If the reward result is positive, the priority of choosing that action in the same / similar state in the future is increased; if the reward result is negative, the priority of that action is decreased, and the priority of other actions is increased. When entering a similar state again, the system prioritizes actions with better historical performance. To avoid getting trapped in local optima, the system can retain a small amount of exploration (e.g., occasionally trying adjacent gear actions) and conduct it under safety constraints.

[0111] In some embodiments, if the cumulative temperature fatigue level, event quantity level, and temperature change direction reversal number of times level are detected to be at or above the medium level, and the system does not trigger intervention or the intervention action level is at a mild level, it indicates that the temperature fatigue threshold is too high; and / or, if it is detected that the user frequently and significantly adjusts the temperature / airflow manually or reverses the temperature within the reaction window, and this phenomenon occurs repeatedly in the same scenario (such as at night) several times (e.g., two consecutive nights or more), it indicates that the temperature fatigue threshold is too high. When the temperature fatigue threshold is too high, the temperature fatigue threshold in the above scenarios is lowered, or the early intervention timing is advanced, or the risk weights corresponding to the event quantity level and temperature change direction reversal number of times level are increased.

[0112] In some embodiments, if the system frequently intervenes or enters temperature difference suppression mode, but the user frequently performs the opposite operation (e.g., the user immediately lowers the temperature or increases the fan speed after the system cools down), it indicates that the temperature difference fatigue threshold is too low. If the cumulative temperature difference fatigue value is not high, the number of events and the number of times the temperature change direction reverses are stable, but intervention is still triggered and the user's reverse adjustment is obvious, it indicates that the temperature difference fatigue threshold is too low. When the temperature difference fatigue threshold is too low, the temperature difference fatigue threshold can be increased, the intervention level can be reduced, the timing of early intervention can be delayed, or aggressive switching of wind speed / direction can be reduced.

[0113] In some embodiments, when an discomfort signal is detected, the system traces back the trajectory features before the reaction window and labels the discomfort event with discomfort cause tags, such as large amplitude tags (recent large temperature difference events), fast speed tags (recent rapid change speed), multiple frequency tags (dense effective fluctuation events), multiple reversal tags (obvious hot and cold back and forth), and direct blowing related tags (direct wind direction and high wind speed), etc.

[0114] Then, the sensitivity of users to various labels is statistically analyzed to construct a sensitivity profile for each stimulus type. For example, if negative feedback events are concentrated in scenarios with frequent reversals, the user is determined to be more sensitive to back-and-forth fluctuations; if negative feedback is concentrated in scenarios with high speed and large amplitude, the user is determined to be more sensitive to rapid temperature rise; if negative feedback is concentrated in scenarios related to direct airflow, the user is determined to be more sensitive to airflow stimulation. For scenarios corresponding to sensitive labels, the temperature difference fatigue threshold is lowered, and gentler actions are adopted.

[0115] In this embodiment, user behavior feedback data is recorded, such as whether the user manually changes the set temperature, whether the air conditioner is turned off at night, and whether the user is particularly sensitive to small fluctuations. Based on the behavior feedback data, it is determined whether the current temperature difference fatigue threshold is too high or too low, whether a certain type of temperature change is more stimulating to the user, and whether certain scenarios (sleep, rest) require enhanced protection. This allows for dynamic adjustment of the temperature difference fatigue threshold (including temperature difference fatigue thresholds under different seasons or time periods) to accurately adjust the timing of early intervention by electrical appliances, making the system increasingly suitable for the user's physical condition and improving comfort at night and throughout the day.

[0116] This embodiment acquires and learns user feedback data on actual behavior to make personalized and dynamic adjustments to the temperature difference fatigue threshold. This makes the setting of the temperature difference fatigue threshold more reflective of the actual physical sensation and comfort preference of a specific user, thereby significantly improving the accuracy of control and user experience in long-term use.

[0117] The following uses an air conditioner as an example to illustrate the electrical equipment control scheme of the present invention in detail with a specific application example, such as... Figure 4 As shown, this application example includes: Step 1: Collect ambient temperature data.

[0118] The air conditioner uses a built-in temperature sensor to collect real-time indoor ambient temperature data at a fixed sampling period and stores it in a temperature buffer. Basic preprocessing of the ambient temperature data is performed, including outlier removal and smoothing, to form an ambient temperature sequence within the current sliding window.

[0119] Step 2: Temperature difference change analysis and construction of cumulative temperature difference change trajectory.

[0120] The system extracts characteristic parameters of each temperature change event from the environmental temperature sequence, including the magnitude, rate, and duration of temperature change, and constructs a cumulative temperature difference trajectory in chronological order. This trajectory reflects the overall temperature difference change over a period of time and is a key input for subsequent fatigue assessment.

[0121] Step 3: Generate the cumulative value of temperature difference fatigue.

[0122] Based on the characteristic parameters of different temperature change events in the temperature difference accumulation trajectory, such as the temperature change amplitude, temperature change rate and change duration, the time accumulation of multiple recent temperature difference changes is performed to generate a temperature difference fatigue accumulation value. The larger the temperature difference fatigue accumulation value, the stronger the cold and heat stimulation experienced by the user recently.

[0123] Step 4: Match with the user's temperature difference fatigue threshold.

[0124] The system compares the current accumulated temperature fatigue value with the user's temperature fatigue threshold to determine whether the current accumulated temperature fatigue value is close to the user's tolerance limit. The temperature fatigue threshold can be dynamically adjusted according to season, time period, and user behavior.

[0125] Step 5: Predict the level of future discomfort risk.

[0126] By combining the changing trend of the cumulative value of temperature difference fatigue and the comparison results of the cumulative value of temperature difference fatigue and the temperature difference fatigue threshold, the possible changes in the user's physical sensation in the future time period (e.g., 5-30 minutes) can be predicted, and it can be determined whether the user may experience discomfort, thus obtaining the discomfort risk level.

[0127] Step 6: Intervene and adjust the air conditioning in advance.

[0128] When the forecast results indicate a high risk of future discomfort, the system automatically makes adjustments in advance, including reducing the cooling / heating intensity, controlling the rate of temperature change, lowering the fan speed, or adjusting the airflow direction, so as to make the temperature control process smoother and avoid the continued accumulation of temperature difference fatigue.

[0129] Step 7: User-individualized learning and model update.

[0130] Based on the user's manual adjustment behavior and actual feedback, the user's temperature difference fatigue threshold is automatically updated, making the next fatigue judgment and prediction more accurate, and achieving long-term individualized adaptation and optimization.

[0131] This embodiment also provides an electrical equipment control device for implementing the above embodiments and preferred embodiments; details already described will not be repeated. As used below, the term "module" can refer to a combination of software and / or hardware that performs a predetermined function. Although the device described in the following embodiments is preferably implemented in software, hardware implementation, or a combination of software and hardware, is also possible and contemplated.

[0132] This embodiment provides an electrical equipment control device, such as... Figure 5 As shown, it includes: The first processing module 501 is used to collect ambient temperature data and construct the ambient temperature sequence of the current sliding window. The second processing module 502 is used to identify multiple temperature change events within the current sliding window based on the ambient temperature sequence, and extract the feature parameters of each temperature change event. The third processing module 503 is used to obtain the cumulative value of temperature difference fatigue based on the characteristic parameters of each temperature change event; wherein, the cumulative value of temperature difference fatigue is used to characterize the degree of fatigue impact on the user caused by the repeated occurrence of temperature change events; The fourth processing module 504 is used to predict the user's discomfort risk level in the future time period based on the cumulative value of temperature difference fatigue and the changing trend of the cumulative value of temperature difference fatigue. The fifth processing module 505 is used to control electrical equipment when the detected discomfort risk level reaches the risk level threshold in order to suppress the growth of the cumulative value of temperature difference fatigue.

[0133] In some optional implementations, the second processing module 502 is further configured to: Based on the preset sampling time, the ambient temperature sequence is divided into multiple ambient temperature subsequences; For each ambient temperature subsequence, if the detected temperature change amplitude of the ambient temperature subsequence reaches the first change amplitude threshold, the ambient temperature subsequence is identified as a short-term fluctuation event. If multiple consecutive ambient temperature subsequences are detected to have the same direction of temperature change, and the overall temperature change amplitude of the multiple consecutive ambient temperature subsequences reaches a second change amplitude threshold within a preset duration, then the multiple consecutive ambient temperature subsequences are identified as a continuous change event.

[0134] In some optional implementations, the characteristic parameters include event type, event occurrence time, temperature change amplitude, temperature change rate, change duration, and temperature change direction; the event type includes short-term fluctuation events and continuous change events; the third processing module 503 is also used for: The first event score for each temperature change event is obtained based on the temperature change amplitude, temperature change rate, and change duration. Based on the event type, determine the number of short-term fluctuation events, and based on the temperature change direction of each temperature change event, determine the number of times the temperature change direction is reversed within the current sliding window. The window score is obtained based on the number of events and the number of times the temperature change direction is reversed; The cumulative value of temperature difference fatigue is obtained based on the window score, the event occurrence time of each temperature change event, and the score of the first event.

[0135] In some optional implementations, the third processing module 503 is further configured to: Based on the time difference between the current time and the occurrence time of each temperature change event, the time decay coefficient of each temperature change event is obtained; The second event score for each temperature change event is calculated by multiplying the time decay coefficient and the first event score. The cumulative value of temperature difference fatigue is calculated by accumulating the window score and the second event score of each temperature change event.

[0136] In some optional implementations, the fourth processing module 504 is further configured to: Based on the changing trend of the cumulative value of temperature difference fatigue, determine the growth time required for the cumulative value of temperature difference fatigue to reach the temperature difference fatigue threshold. Based on the relationship between the growth time and the growth time threshold, the user's discomfort risk level in the future time period is determined.

[0137] In some optional implementations, the fourth processing module 504 is further configured to: Obtain user behavior feedback data; The temperature difference fatigue threshold is adjusted based on behavioral feedback data.

[0138] In some alternative implementations, the electrical equipment includes a compressor and a fan; the fifth processing module 505 is also used for: Reduce the power regulation rate of the compressor, and / or reduce the fan speed regulation rate, and / or reduce the temperature regulation frequency of the electrical equipment.

[0139] The electrical equipment control device provided in this embodiment of the invention can execute the electrical equipment control method provided in any embodiment of the invention, and has the corresponding functional modules and beneficial effects for executing the method. Further functional descriptions of the various modules and units described above are the same as in the corresponding embodiments described above, and will not be repeated here.

[0140] Figure 6 This is a schematic diagram of the structure of an electrical device provided in an embodiment of the present invention. For example, the electrical device may be an air conditioner.

[0141] The following is a detailed reference. Figure 6 The diagram illustrates a structural schematic suitable for implementing an electrical device according to an embodiment of the present invention. The electrical device may include a processor (e.g., a central processing unit, graphics processor, etc.) 601, which can perform various appropriate actions and processes according to a program stored in a read-only memory (ROM) 602 or a program loaded from memory 608 into random access memory (RAM) 603. The RAM 603 also stores various programs and data required for the operation of the electrical device. The processor 601, ROM 602, and RAM 603 are interconnected via a bus 604. An input / output (I / O) interface 605 is also connected to the bus 604.

[0142] Typically, the following devices can be connected to I / O interface 605: input devices 606 including, for example, touchscreens, touchpads, keyboards, mice, cameras, microphones, accelerometers, gyroscopes, etc.; output devices 607 including, for example, liquid crystal displays (LCDs), speakers, vibrators, etc.; memory devices 608 including, for example, magnetic tapes, hard disks, etc.; and communication devices 609. Communication device 609 allows electrical equipment to communicate wirelessly or wiredly with other devices to exchange data. Although Figure 6 Electrical equipment with various devices is shown, but it should be understood that it is not required to implement or have all of the devices shown, and more or fewer devices may be implemented or have instead.

[0143] In particular, according to embodiments of the present invention, the processes described above with reference to the flowcharts can be implemented as computer software programs. For example, embodiments of the present invention include a computer program product comprising a computer program carried on a non-transitory computer-readable medium, the computer program containing program code for performing the methods shown in the flowcharts. In such embodiments, the computer program can be downloaded and installed from a network via a communication device 609, or installed from a memory 608, or installed from a ROM 602. When the computer program is executed by the processor 601, it performs the functions defined in the electrical equipment control method of the embodiments of the present invention.

[0144] Figure 6 The electrical equipment shown is merely an example and should not be construed as limiting the functionality and scope of the embodiments of the present invention.

[0145] This invention also provides a computer-readable storage medium. The methods described above according to embodiments of the invention can be implemented in hardware or firmware, or implemented as computer code that can be recorded on a storage medium, or implemented as computer code downloaded via a network and originally stored on a remote storage medium or a non-transitory machine-readable storage medium and then stored on a local storage medium. Thus, the methods described herein can be processed by software stored on a storage medium using a general-purpose computer, a dedicated processor, or programmable or dedicated hardware. The storage medium can be a magnetic disk, optical disk, read-only memory, random access memory, flash memory, hard disk, or solid-state drive, etc.; further, the storage medium can also include combinations of the above types of memory. It is understood that computers, processors, microprocessor controllers, or programmable hardware include storage components capable of storing or receiving software or computer code. When the software or computer code is accessed and executed by the computer, processor, or hardware, the electrical equipment control method shown in the above embodiments is implemented.

[0146] A portion of this invention can be applied as a computer program product, such as computer program instructions, which, when executed by a computer, can invoke or provide the methods and / or technical solutions according to the invention through the operation of the computer. Those skilled in the art will understand that the forms in which computer program instructions exist in a computer-readable medium include, but are not limited to, source files, executable files, installation package files, etc. Correspondingly, the ways in which computer program instructions are executed by a computer include, but are not limited to: the computer directly executing the instructions, or the computer compiling the instructions and then executing the corresponding compiled program, or the computer reading and executing the instructions, or the computer reading and installing the instructions and then executing the corresponding installed program. Here, the computer-readable medium can be any available computer-readable storage medium or communication medium accessible to a computer.

[0147] Although embodiments of the invention have been described in conjunction with the accompanying drawings, those skilled in the art can make various modifications and variations without departing from the spirit and scope of the invention, and such modifications and variations all fall within the scope defined by the appended claims.

Claims

1. A method for controlling electrical equipment, characterized in that, The method includes: Collect ambient temperature data and construct the ambient temperature sequence for the current sliding window; Based on the ambient temperature sequence, multiple temperature change events within the current sliding window are identified, and feature parameters of each temperature change event are extracted. Based on the characteristic parameters of each temperature change event, the cumulative value of temperature difference fatigue is obtained; wherein, the cumulative value of temperature difference fatigue is used to characterize the degree of fatigue impact on the user caused by the repeated occurrence of temperature change events; Based on the cumulative value of temperature difference fatigue and the changing trend of the cumulative value of temperature difference fatigue, the user's discomfort risk level in the future time period is predicted; When the detected discomfort risk level reaches the risk level threshold, the electrical equipment is controlled to suppress the increase of the cumulative value of temperature difference fatigue.

2. The electrical equipment control method according to claim 1, characterized in that, The characteristic parameters include event type, event occurrence time, temperature change amplitude, temperature change rate, change duration, and temperature change direction. The event type includes short-term fluctuation events and continuous change events. The process of obtaining the cumulative value of temperature difference fatigue based on the characteristic parameters of each temperature change event includes: The first event score for each temperature change event is obtained based on the temperature change amplitude, temperature change rate, and change duration. Based on the event type, determine the number of short-term fluctuation events, and based on the temperature change direction of each temperature change event, determine the number of times the temperature change direction is reversed within the current sliding window. The window score is obtained based on the number of events and the number of times the temperature change direction is reversed. Based on the window score, the event occurrence time of each temperature change event, and the score of the first event, the cumulative value of temperature difference fatigue is obtained.

3. The electrical equipment control method according to claim 2, characterized in that, The cumulative value of temperature difference fatigue is obtained based on the window score, the event occurrence time of each temperature change event, and the score of the first event, including: Based on the time difference between the current time and the occurrence time of each temperature change event, the time decay coefficient of each temperature change event is obtained; The second event score for each temperature change event is calculated by multiplying the time decay coefficient and the first event score. The cumulative value of temperature difference fatigue is calculated by accumulating the window score and the second event score of each temperature change event.

4. The electrical equipment control method according to claim 2, characterized in that, The step of identifying multiple temperature change events within the current sliding window based on the ambient temperature sequence includes: Based on a preset sampling time, the ambient temperature sequence is divided into multiple ambient temperature subsequences; For each ambient temperature subsequence, if the detected temperature change amplitude of the ambient temperature subsequence reaches a first change amplitude threshold, the ambient temperature subsequence is identified as a short-term fluctuation event. If multiple consecutive ambient temperature subsequences are detected to have the same direction of temperature change, and the overall temperature change amplitude of the multiple consecutive ambient temperature subsequences reaches a second change amplitude threshold within a preset duration, then the multiple consecutive ambient temperature subsequences are identified as a continuous change event.

5. The electrical equipment control method according to any one of claims 1-4, characterized in that, The method of predicting the user's discomfort risk level in the future time period based on the cumulative value of temperature difference fatigue and the changing trend of the cumulative value of temperature difference fatigue includes: Based on the changing trend of the cumulative temperature difference fatigue value, determine the growth time required for the cumulative temperature difference fatigue value to reach the temperature difference fatigue threshold. Based on the relationship between the growth time and the growth time threshold, the user's discomfort risk level in the future time period is determined.

6. The electrical equipment control method according to claim 5, characterized in that, The method further includes: Obtain user behavior feedback data; Based on the behavioral feedback data, the temperature difference fatigue threshold is adjusted.

7. The electrical equipment control method according to any one of claims 1-4, characterized in that, The electrical equipment includes a compressor and a fan; controlling the electrical equipment includes: Reduce the power regulation rate of the compressor, and / or reduce the wind speed regulation rate of the fan, and / or reduce the temperature regulation frequency of the electrical equipment.

8. An electrical equipment control device, characterized in that, The device includes: The first processing module is used to collect ambient temperature data and construct the ambient temperature sequence of the current sliding window. The second processing module is used to identify multiple temperature change events within the current sliding window based on the ambient temperature sequence, and extract feature parameters of each temperature change event. The third processing module is used to obtain the cumulative value of temperature difference fatigue based on the characteristic parameters of each temperature change event; wherein, the cumulative value of temperature difference fatigue is used to characterize the degree of fatigue impact on the user caused by the repeated occurrence of temperature change events; The fourth processing module is used to predict the user's discomfort risk level in the future time period based on the cumulative value of temperature difference fatigue and the changing trend of the cumulative value of temperature difference fatigue. The fifth processing module is used to control the electrical equipment when the detected discomfort risk level reaches the risk level threshold, so as to suppress the growth of the cumulative value of temperature difference fatigue.

9. An electrical appliance, characterized in that, include: A memory and a processor are communicatively connected, the memory stores computer instructions, and the processor executes the computer instructions to perform the electrical equipment control method according to any one of claims 1 to 7.

10. The electrical equipment according to claim 9, characterized in that, The electrical appliance is an air conditioner.

11. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores computer instructions for causing the computer to perform the electrical equipment control method according to any one of claims 1 to 7.

12. A computer program product, characterized in that, Includes computer instructions for causing a computer to perform the electrical equipment control method according to any one of claims 1 to 7.

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