Automatic sensing operation control method and system of fumigation nursing device
By collecting near-device and ambient temperatures, calculating influencing factors, and combining historical data with user preferences, the fumigation temperature is dynamically adjusted, solving the problem of inaccurate temperature control and improving the level of intelligence and nursing effect.
Patent Information
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- Filing Date
- 2026-01-04
- Publication Date
- 2026-04-07
AI Technical Summary
The temperature control system of existing fumigation devices lacks the ability to dynamically respond to changes in ambient temperature, resulting in inaccurate temperature control, which affects the nursing effect. In addition, the level of intelligence is insufficient, requiring users to make frequent manual adjustments.
By synchronously collecting near-device and ambient temperatures, calculating the ambient temperature influencing factor, and combining historical operating data and user preferences, the fumigation temperature is dynamically adjusted to achieve closed-loop control.
It achieves precise response to ambient temperature, reduces temperature misjudgment, improves the level of intelligence, ensures continuity and comfort of care, and reduces the need for manual adjustment by users.
Smart Images
Figure CN121806643A_ABST
Abstract
Description
Technical Field
[0001] This invention relates to the field of fumigation care device control technology, and in particular to an automatic sensing operation control method and system for a fumigation care device. Background Technology
[0002] Fumigation devices are widely used in home and professional care settings. Existing devices generally use preset temperature programs or manual adjustments for fumigation temperature control. Their control logic is based on fixed parameters and lacks dynamic response to changes in ambient temperature. Although some products have built-in temperature sensors, the sensing range is limited to the inside or near the device, failing to capture the actual ambient temperature of the user's activity area, resulting in a disconnect between the temperature control benchmark and the actual usage environment. When the near-device temperature is affected by transient factors such as localized airflow or temporary heat sources, the system is prone to misjudging the environmental state, leading to an output fumigation temperature that deviates from the user's desired temperature, affecting the care effect. Furthermore, current technology does not utilize historical operating data to learn and automatically correct for seasonal changes and user preferences. When the same device is used in different seasons or on different dates, users still need to repeatedly adjust it manually, resulting in insufficient intelligence and poor continuity of care. Summary of the Invention
[0003] Therefore, it is necessary for the present invention to provide an automatic sensing operation control method and system for a fumigation care device to solve at least one of the above-mentioned technical problems.
[0004] To achieve the above objectives, an automatic sensing and operation control method for a fumigation therapy device includes the following steps: Step S1: Before operating the fumigation device, simultaneously collect the near-end temperature of the device and the current ambient temperature; Step S2: Calculate the ambient temperature fluctuation value based on the current ambient monitoring temperature, and calculate the current ambient temperature influencing factor in conjunction with the near-end temperature of the equipment; Step S3: Obtain the historical operation records of the fumigation device, extract the set of successful operation parameters for the historical date segment adjacent to the current date from the historical operation records, and identify the seasonal characteristic parameters and user-preferred temperature parameters in the set of successful operation parameters; Step S4: Dynamically correct the user's preferred temperature parameters based on the current environmental temperature influencing factors to obtain the preliminary target temperature; and make a secondary adjustment to the preliminary target temperature based on seasonal characteristic parameters to generate the final control temperature; Step S5: Control the output power of the heating unit of the fumigation device based on the real-time difference between the final controlled temperature and the near-end temperature of the device.
[0005] Preferably, the present invention also provides an automatic sensing and operation control system for a fumigation device, used to execute the automatic sensing and operation control method for the fumigation device as described above, the automatic sensing and operation control system for the fumigation device comprising: The dual-temperature acquisition module is used to simultaneously acquire the near-end temperature of the device and the current ambient temperature before the fumigation device is operated; The impact factor calculation module is used to calculate the ambient temperature fluctuation value based on the current environmental monitoring temperature, and to calculate the current ambient temperature impact factor in combination with the near-end temperature of the equipment. The historical parameter parsing module is used to obtain the historical operation records of the fumigation care device, extract the set of successful operation parameters for the historical date segment adjacent to the current date from the historical operation records, and identify the seasonal characteristic parameters and user preferred temperature parameters in the set of successful operation parameters. The target temperature generation module is used to dynamically correct the user's preferred temperature parameters based on the current ambient temperature influencing factors to obtain a preliminary target temperature; and to further adjust the preliminary target temperature based on seasonal characteristic parameters to generate the final control temperature. The heating power control module is used to control the output power of the heating unit of the fumigation device based on the real-time difference between the final controlled temperature and the near-end temperature of the device.
[0006] The beneficial effects of this invention are as follows: On the one hand, by synchronously collecting the near-end temperature of the device and the environmental monitoring temperature of the user's activity area, and introducing the environmental temperature fluctuation value and temperature difference coupling calculation influence factor, the local instantaneous interference is essentially eliminated from the temperature control benchmark, so that the target temperature is always anchored to the user's real thermal environment, solving the misjudgment and efficacy reduction caused by the narrow sensing field of fixed parameter system.
[0007] On the other hand, by automatically extracting seasonal characteristic parameters and user preference temperature parameters from historical successful operation data, the preference values are dynamically corrected by influencing factors and fine-tuned again by seasonal characteristics. Essentially, this encodes long-term patterns in the time dimension into instantaneous control decisions, so that the temperature control strategy can be kept in sync with user expectations during seasonal changes without human intervention, eliminating repeated manual adjustments.
[0008] On the other hand, by controlling the temperature and the near-end temperature of the equipment in real time through closed-loop power adjustment, the unique target value after the integration of environment, preference and season is taken as the only source of error. This essentially eliminates the control lag and oscillation caused by the coupling of multiple factors, so that the fumigation temperature converges quickly and remains stable after the first start-up, ensuring the continuity and comfort of care. Attached Figure Description
[0009] Other features, objects, and advantages of the present invention will become more apparent from the following detailed description taken in conjunction with the accompanying drawings: Figure 1 A flowchart illustrating the steps of an automatic sensing operation control method for a fumigation care device according to an embodiment is shown.
[0010] Figure 2A detailed flowchart of step S2 of one embodiment is shown.
[0011] Figure 3 A diagram illustrating the spatial layout of a multi-sensor system and the inverse distance weighted calculation structure of one embodiment is shown. Detailed Implementation
[0012] The technical method of the present invention will now be clearly and completely described with reference to the accompanying drawings. Obviously, the described embodiments are only some, not all, of the embodiments of the present invention. All other embodiments obtained by those skilled in the art based on the embodiments of the present invention without inventive effort are within the scope of protection of the present invention.
[0013] Furthermore, the accompanying drawings are merely illustrative of the invention and are not necessarily drawn to scale. The same reference numerals in the drawings denote the same or similar parts, and therefore repeated descriptions of them will be omitted. Some block diagrams shown in the drawings are functional entities and do not necessarily correspond to physically or logically independent entities. These functional entities can be implemented in software, in one or more hardware modules or integrated circuits, or in different network and / or processor methods and / or microcontroller methods.
[0014] It should be understood that although the terms "first," "second," etc., may be used herein to describe various units, these units should not be limited by these terms. These terms are used merely to distinguish one unit from another. For example, without departing from the scope of the exemplary embodiments, a first unit may be referred to as a second unit, and similarly, a second unit may be referred to as a first unit. The term "and / or" as used herein includes any and all combinations of one or more of the associated listed items.
[0015] To achieve the above objectives, please refer to Figures 1 to 3 This invention provides an automatic sensing and operation control method for a fumigation therapy device, comprising the following steps: Step S1: Before operating the fumigation device, simultaneously collect the near-end temperature of the device and the current ambient temperature; Step S2: Calculate the ambient temperature fluctuation value based on the current ambient monitoring temperature, and calculate the current ambient temperature influencing factor in conjunction with the near-end temperature of the equipment; Step S3: Obtain the historical operation records of the fumigation device, extract the set of successful operation parameters for the historical date segment adjacent to the current date from the historical operation records, and identify the seasonal characteristic parameters and user-preferred temperature parameters in the set of successful operation parameters; Step S4: Dynamically correct the user's preferred temperature parameters based on the current environmental temperature influencing factors to obtain the preliminary target temperature; and make a secondary adjustment to the preliminary target temperature based on seasonal characteristic parameters to generate the final control temperature; Step S5: Control the output power of the heating unit of the fumigation device based on the real-time difference between the final controlled temperature and the near-end temperature of the device.
[0016] Preferably, step S1 includes: During the preheating phase after the fumigation device is turned on, the near-end temperature of the device is collected by the first temperature sensor built into the fumigation device. The near-end temperature of the equipment is the average air temperature within a preset distance from the air outlet.
[0017] In one embodiment, the first temperature sensor is an NTC thermistor; the sampling frequency is set to 1Hz to 4Hz, and 8 to 16 raw temperature points are continuously taken within a single sampling window, which are then averaged to obtain the near-end temperature of the device at that moment.
[0018] In one embodiment, if the fumigation device has a rotating or swinging air outlet, the first temperature sensor is located on the geometric center axis of the air outlet and swings slightly in sync with the swinging mechanism, so that the probe is always in the same relative flow field position.
[0019] A set of environmental monitoring temperatures is collected by one or more second temperature sensors that are pre-deployed in locations commonly used by users; wherein, commonly used locations by users include locations that are more than a preset distance from the fumigation device and are within the user's activity range; In one embodiment, the second temperature sensor is a digital I²C interface MEMS temperature sensing chip.
[0020] In one embodiment, the user's frequently used locations are obtained through statistics during the initial 7-day learning period: the fumigation device records the dwell time of the user's mobile phone Bluetooth signal or infrared motion detection every day, and selects the top 3 points with the highest cumulative dwell time that are geometrically non-collinear as fixed monitoring points; if there are less than 3 valid points counted during the learning period, the device is used as the center, and the points are evenly filled with a radius of 1m and an angle of 120°.
[0021] The current environmental monitoring temperature is obtained by weighted averaging of a set of environmental monitoring temperatures; the weights are allocated based on the distance between each second temperature sensor and the fumigation device. Most importantly, the weights are calculated using the inverse distance weighted (IDW) model as follows: ; Where p is 2.0, Let be the Euclidean distance from the i-th sensor to the care device; in the example above =0.38, =0.45, =0.17, weighted average of current environmental monitoring temperature =25.4℃.
[0022] In one embodiment, if a sensor loses communication packets or experiences a sudden temperature change (the difference from the average of the previous 5 times is >1.5℃), its weight is temporarily reset to 0, and the remaining weights are renormalized; if the failed sensor cannot be recovered for 60 consecutive seconds, an offline sensor notification is pushed through the APP.
[0023] For example, please refer to Figure 3 , Figure 3 This diagram illustrates the spatial layout and inverse distance weighted calculation structure of a multi-sensor system. The fumigation device is positioned at the center of the space, with its built-in first temperature sensor measuring a near-end temperature of 43.2℃. Three second temperature sensors (A, B, and C) are deployed within the user's activity area, at distances of 2.5 meters, 2.2 meters, and 3.5 meters from the fumigation device, respectively, measuring ambient temperatures of 25.1℃, 25.3℃, and 25.9℃. Using the inverse distance weighted (IDW) model, the weights of each sensor are calculated using powers of p=2.0, resulting in w_A=0.38, w_B=0.45, and w_C=0.17. The three temperature data points are weighted and averaged before being fed into a central computing unit to obtain the current ambient temperature. =25.4℃. The dashed circles in the figure represent the effective influence range of each sensor, the arrows indicate the data flow direction, and the calculation box at the bottom shows the complete weighted average algorithm process.
[0024] Collect the current ambient humidity at frequently used locations of the user.
[0025] Optionally, humidity acquisition is synchronously completed by the MEMS humidity unit built into the second temperature sensor; when the absolute humidity is >18g / m³ (corresponding to 30℃, 70%RH), a gain compensation of +5% is introduced into the temperature influence factor.
[0026] For example, the three sensors measured relative humidity {58, 61, 64}%RH within the same 30-second window, and then weighted them with equal weights to obtain the current ambient humidity. =60.4%RH; record this value and compare it with the historical average for the same period. If the deviation exceeds 10%RH, then add an additional standard deviation correction of 0.1℃ when calculating the ambient temperature fluctuation value in step S2.
[0027] Preferably, step S2 includes: Step S21: Calculate the absolute value of the temperature difference between the current ambient monitoring temperature and the near-end temperature of the equipment, and use it as the baseline temperature difference; The baseline temperature difference is used to quantify the degree of thermal imbalance between the area near the air outlet of the fumigation device and the area where people are active. Optionally, the current ambient temperature is output after weighted averaging by a second group of temperature sensors deployed in frequently used locations by the user.
[0028] In one embodiment, the base temperature difference =| – |, For the near-end temperature of the equipment, This refers to the current ambient temperature.
[0029] In one embodiment, the baseline temperature difference is primarily used to determine whether localized heat accumulation exceeds a reasonable range; if If the temperature is too high, it means that the heat from the air conditioner is concentrated near the air outlet and has not been effectively diffused to the user's activity area, which is considered a poor diffusion condition.
[0030] Step S22: Obtain multiple historical environmental monitoring temperatures within a preset time window, and calculate the standard deviation of the historical environmental monitoring temperatures as the environmental temperature fluctuation value; Among them, ambient temperature fluctuation value This is used to quantify the temperature dispersion of a user's activity area over a short period. Optionally, the preset time window defaults to 30 minutes prior to the current time, with a sampling interval of 1 minute, for a total of 30 historical records. Record; if there are fewer than 20 valid samples within 30 minutes, automatically trace back to the most recent 60 minutes until there are ≥20 valid samples; if still insufficient, mark the fluctuation value as having low reliability, and in step S23, give... An additional penalty coefficient of 1.2 times is applied.
[0031] Most importantly, the standard deviation is calculated using an unbiased estimate as follows: ; in, Let n be the standard deviation of the ambient temperature series, and n be the number of valid samples. The mean of the effective samples is given by , and i is the sample number.
[0032] For example, in a bedroom morning fumigation scene, 30 historical records from 00:55 to 01:25 were extracted. Records were taken, of which 2 were removed due to packet loss, leaving n=28 valid entries; mean =24.63℃, calculated as follows =0.39℃. This value is 0.5℃ lower than the steady-state threshold, indicating that the environment is in a low-fluctuation state.
[0033] Step S23: Compare the base temperature difference with the preset base temperature difference threshold. If the base temperature difference is less than or equal to the preset base temperature difference threshold, the ambient temperature fluctuation value is taken as the environmental disturbance intensity. If the base temperature difference is greater than the preset base temperature difference threshold, the weighted sum of the base temperature difference and the ambient temperature fluctuation value is taken as the environmental disturbance intensity. Among them, the comparison results determine the intensity of environmental disturbance. The calculation path is used to distinguish between two scenarios: a generally stable environment with only minor fluctuations and one with significant local thermal imbalances. Optionally, a preset base temperature difference threshold is used. The default temperature is 3.0℃, which allows users to fine-tune it within the app by ±1℃. Each time the device is powered on, it reads the user's last saved value from the Flash memory. If the user has not customized the value, it restores the factory default value.
[0034] In one embodiment, the comparison logic is a single decision, but all input parameters are filtered by a 3-second sliding median filter to prevent frequent path jumps caused by momentary jitter; the decision output is locked for 1 minute, and even if... It fell back, but maintained its original path.
[0035] Example branch ① – Low-bias path: =2.4℃≤3.0℃, select = =0.39℃; This value directly reflects natural environmental fluctuations and requires no additional penalty.
[0036] Example branch ② – High deviation path: If 4.7℃ > 3.0℃, then proceed to the weighted branch: = · + · ; in =0.7, =0.3, and + =1; Substituting into the equation, we get... =0.7×4.7+0.3×0.39≈3.29+0.12=3.41℃.
[0037] In one embodiment, if >1.0℃ and If the temperature exceeds 4.0℃, a double high disturbance is identified. α is temporarily increased to 0.8 and β is decreased to 0.2, while simultaneously the minimum fan speed is forcibly increased by 200 rpm to accelerate heat dissipation; [The text abruptly ends here, likely due to an incomplete sentence or missing information.] Temperatures dropped to below 2.5℃ and After the temperature drops below 0.6℃, the default weight will be restored.
[0038] Step S24: Normalize the intensity of environmental disturbance to obtain the current environmental temperature influence factor.
[0039] Among them, the normalization result ∈[0,1] is used to uniformly measure the magnitude of environmental-equipment thermal coupling disturbances. Optionally, the normalized mapping table is established based on measured data from a calibration chamber at 30m³, 24℃, and 50%RH, covering... The temperature range is 0.1℃ to 5.0℃.
[0040] In one embodiment, the mapping table is embedded in the MCU Flash using a piecewise linear + boundary limiting method, consisting of 11 nodes with a node spacing of 0.5°C; an example of the nodes is shown below: 0.1→ 0.02; 1.0 → 0.20; 2.5→ 0.50; 4.0→ 0.80; 5.0→ 1.00; Intermediate values are obtained using linear interpolation: = +( – ) / ( – )×( – ).
[0041] For example: Previous output =3.41℃, falling between nodes 3.0 and 3.5, according to the table... =0.68, =0.75, interpolation value =0.68+(0.75–0.68) / 0.5×0.41≈0.737.
[0042] Preferably, step S3 includes: Obtain historical operation records within the first preset number of days; wherein, the historical operation records shall include at least the start and end time of each operation, the final stable temperature, the ambient temperature sequence, and the user comfort feedback markers; In one embodiment, the first preset number of days is set to 30 days (calendar days) by default, allowing users to adjust it within the range of 7 to 90 days in the APP.
[0043] Based on preset comfortable operation filtering conditions, historical records that meet the conditions are filtered to form an initial set of successful operation parameters; In one embodiment, if the number of initially successfully run parameter sets is less than 10, the threshold of clause 3 is relaxed to 1.2℃ and the comfort level of clause 4 is ≥60, and a second screening is performed; if it is still less than 10, a message is sent to the APP that there are not enough samples, please continue to use it and provide feedback on the comfort level, and the first preset number of days is automatically extended by 7 days until there are ≥10 available logs.
[0044] Extract historical records within the second preset number of days adjacent to the current date from the initial successful operation parameter set to form a successful operation parameter set; The term "adjacent" refers to moving backward from the current date. Optionally, the second preset number of days is 7 days (calendar days) by default, allowing users to adjust it within the range of 3 to 14 days within the app; it is automatically recalculated at 00:15 every day to ensure successful operation. The parameter set refreshes with the date.
[0045] In one embodiment, the extraction process is as follows: First, read the current date of the RTC (e.g., 2024-12-05), then search the initial successful operation parameter set for all records whose start and end times fall within the interval [2024-11-28 00:00, 2024-12-05 24:00). If the number of records in this interval is ≥5, it directly constitutes the successful operation parameter set. If the number is <5, it is extended forward by 1 day until the number is ≥5 or the total extension days reach 14 days. If the final number is still <5, the seasonal feature reliability is marked as low and the subsequent seasonal fine-tuning function is disabled. Only the user-preferred temperature parameter is used for correction.
[0046] Based on the mean and distribution characteristics of the ambient temperature series in each historical record of the successful operation parameter set, seasonal characteristic parameters that characterize the seasonal warm and cold trends are identified. Among them, seasonal characteristic parameters Used to quantify the warm or cool background of a user's recent environment. Optionally, The extreme values are then removed by taking the weighted average of all ambient temperature sequence points in the successful operation parameter set.
[0047] For example, the aforementioned 9 records: first, summarize a total of 90 records. The sample (excluding invalid 0xFFFF points) had 84 valid points, with a mean of 24.63℃. Points with a standard deviation greater than 1.5℃ were then removed, using a standard deviation of 0.52℃ as the benchmark. The 6 outliers, the remaining 78 points are... =1 / (number of days since record date + 1) is used for recent weighting to obtain a double-weighted average. =24.71℃, classify 24.71℃ into the "Spring / Autumn" segment of 22–26℃ according to the internal range, and output the value 24.71℃ and the enumeration value 3.
[0048] In one embodiment, if the sample span is ≥10 days and If the temperature difference between the measured ambient temperature and the actual ambient temperature on that day is greater than 3°C, it is considered a rapid seasonal change. Marked as transition period.
[0049] In one embodiment, The parameters are updated daily at 00:22 based on the successfully run parameter set; if this continues for 5 consecutive days... If the mean shift is ≥2℃, a seasonal change notification will be sent to the app.
[0050] Use the mode or average of the final stable temperatures from the successfully running parameter set as the user's preferred temperature parameter.
[0051] Among them, user preferred temperature parameter The steady-state fumigation temperature used to characterize the actual preference of target users under recent comfort conditions. Optionally, the mode is preferred to reduce the influence of extreme values; if the mode does not exist (all temperatures are unique) or occurs only once, the arithmetic mean is used instead; if there are ≥10 samples and the standard deviation is >1.5℃, points far from the mean ±2σ are first removed before calculating the second mean.
[0052] The exemplary process is based on a set of 9 successful operation parameters: the final stable temperature field [42.3, 42.3, 41.9, 42.3, 42.0, 42.3, 42.0, 41.8, 42.3]℃ is read, and it is found that 42.3℃ appears 5 times, 42.0℃ appears 2 times, and the rest appear once each. Since the highest frequency 5 > 1, the mode is directly taken. =42.3℃.
[0053] In one embodiment, if the sample still has less than 5 items after two rounds of elimination, the statistics are abandoned and the factory default preference of 41.0℃ is used instead.
[0054] Preferably, dynamically correcting the user's preferred temperature parameter based on the current environmental temperature influencing factors includes: Construct a temperature correction rule base; the temperature correction rule base includes the temperature adjustment direction and adjustment range corresponding to different ranges of environmental temperature influencing factors; In one embodiment, the rule base is stored in the MCU Flash as 8-bit fixed-length records, totaling 20 records, corresponding to... The range is 0.00–1.00 in increments of 0.05; the high 3 bits record the direction (000 for cooling, 001 for heating) and the low 5 bits record the range (0–31, step size 0.1℃, maximum 3.1℃). During power-on initialization, the entire table is loaded into RAM at once, and subsequent corrections are performed by shifting and masking to achieve a one-cycle table lookup.
[0055] For example, when =0.66, falling into the 14th range (0.65–0.70), found to be 0x29, which is interpreted as a temperature increase of 2.5℃; this value serves as the base adjustment amount. Then, combined with the user's preferred temperature parameters The values are proportionally superimposed to form the first correction temperature.
[0056] Based on the temperature correction rule base, the corresponding temperature adjustment amount is determined according to the current environmental temperature influencing factors; In one embodiment, temperature adjustment amount T adopts a two-part structure of base value + proportional value, which reflects both the intensity of environmental disturbance and preserves individual user tolerance differences. The specific formula is as follows: T= + ×( -39℃), among which The base adjustment value output by the rule base. The default scaling factor is 0.2, which can be fine-tuned via cloud OTA. 39℃ is a neutral preference benchmark based on experimental statistics, used to map the user's preferred temperature to ± adjustment amounts. T is restricted to the range of [−3.0℃, +3.0℃].
[0057] For example: Current =0.66, from the table we get Δ =+2.5℃, user preference =42.3℃, proportional term = 0.2 × (42.3 − 39) = +0.66℃, total temperature adjustment T = 2.5 + 0.66 = +3.16℃, which exceeds the upper limit of 3.0℃, so it is truncated to +3.0℃ and the proportional overflow event is recorded.
[0058] In one embodiment, if It is in the low-temperature sensitive range ≤40℃. The value is automatically halved to 0.1 to prevent users with a preference for low temperatures from being excessively exposed to the setting, thus avoiding the risk of burns; conversely, ≥44℃ and When >0.8, Temporarily increased to 0.3.
[0059] The user's preferred temperature parameter is added to the temperature adjustment amount to obtain the first corrected temperature; In one embodiment, the first correction temperature T1 is directly achieved using a fixed-point addition method, as shown in the formula: = + T; among which For user-preferred temperature parameters, T represents the temperature adjustment amount (limited to [-3.0℃, +3.0℃]).
[0060] For example: =42.3℃, T = +3.0℃; → =42.3+3.0=45.3℃.
[0061] In one embodiment, if the addition result overflows (>55°C), the temperature is forcibly set to 55°C and a first correction exceeding the upper limit fault code is recorded; if the temperature is <30°C, the temperature is set to 30°C and a first correction below the lower limit is recorded.
[0062] The first correction temperature is limited to a preset safe human body temperature range to obtain the initial target temperature.
[0063] In one embodiment, the preset safe temperature range for the human body is set to 30℃–55℃ based on the registration certificate of the fumigation device and the IEC60601-1 heat injury risk assessment, and users are allowed to further customize the sub-range of 35℃–50℃ in the APP. If the user does not set it, the factory default temperature range of 30℃–55℃ will be used.
[0064] Preferably, step S4, which involves a secondary adjustment of the initial target temperature based on seasonal characteristic parameters, includes: Determine the season to which the current date belongs, and determine whether the season to which the current date belongs is consistent with the seasonal characteristics represented by the successfully run parameter set; In one embodiment, based on the current RTC date, the weather seasons are divided as follows: March–May (spring), June–August (summer), September–November (autumn), and December–February (winter). The enumerated values of seasonal characteristic parameters (e.g., "Spring / Autumn" = 3) are read. If the two enumerated values are the same, the seasons are determined to be consistent, and the seasonal fine-tuning branch is entered; if they are different or the current enumerated value is 0 (transition period), the cross-seasonal gradual adjustment branch is entered, and the seasonal inconsistency flag is recorded.
[0065] If the seasonal characteristics are consistent, the seasonal characteristic parameters are used as weighting coefficients, and the initial target temperature is fine-tuned according to the weighting coefficients to obtain the seasonal fine-tuning temperature; wherein, the fine-tuning range is smaller than the adjustment range of the first correction temperature. In one embodiment, the seasonal characteristic parameter values are used. Mapped to 0–1 weights : =( -18) / 12, limited to [0.1, 1.0]; fine-tuning amount Δ = ×0.3℃×sign( -24) (24℃ is the neutral reference), ensuring that the amplitude does not exceed ±0.3℃, which is much smaller than the adjustment amount of the first correction temperature of a maximum of 3.0℃.
[0066] For example: =45.3℃, =24.71℃→ =(24.71−18) / 12≈0.56; sign is positive, Δ =+0.56×0.3℃≈+0.17℃; Seasonal temperature adjustment =45.3+0.17=45.47℃, reaching the final safety limit.
[0067] If the seasonal characteristics are inconsistent or the season is in transition, the current measured ambient temperature is obtained. Based on the long-term trend difference between the seasonal characteristic parameters and the current measured ambient temperature, a gradual adjustment amount is calculated and gradually applied to the initial target temperature in this and subsequent runs to obtain the cross-seasonal adaptation temperature. By limiting seasonal temperature adjustments or cross-seasonal temperature adaptations to a preset safe human body temperature range, the final controlled temperature is obtained.
[0068] In one embodiment, the preset safe human body temperature range is 30℃–55℃; regardless of whether the pre-amplifier output is a seasonally adjustable temperature. Or adapting to temperature changes across seasons All are subject to the same hard limit: if the temperature is below 30°C, it is set to 30°C; if the temperature is above 55°C, it is set to 55°C, and the final upper limit or lower limit event is recorded.
[0069] Example limit: The preceding seasonal fine-tuning temperature of 45.47℃ is within the safe range, therefore the final control temperature is... =45.47℃, directly written into the register as the PID setpoint; if the cross-seasonal adaptation temperature calculation result is 56.2℃, then it is forcibly truncated to 55.0℃.
[0070] Preferably, the calculation of the gradual adjustment amount, based on the long-term trend difference between seasonal characteristic parameters and the current measured ambient temperature, includes: Calculate the difference between seasonal characteristic parameters and the current measured ambient temperature as the long-term trend difference; Among them, the long-term trend difference is used to quantify the shift between historical seasonal background and the current real environment, providing a basis for gradual adjustments across seasons. Optionally, the difference formula is Δ. = – , The weighted average of the ambient temperature series taken from the successful operation parameter set (e.g., 24.71℃). The indoor ambient temperature (e.g., 19.5℃) was just collected before the equipment was started. The sign bit determines the direction of subsequent adjustments: a positive value indicates that the historical temperature is warmer and the current temperature is colder, while a negative value indicates the opposite.
[0071] In one embodiment, if |Δ If the temperature exceeds 3℃ and remains above 3℃ for 3 consecutive days, it is considered a rapid seasonal change, and a cross-seasonal activity flag is set, triggering a subsequent progressive mapping table query; if |Δ If the temperature is ≤0.5℃, it is considered seasonal synchronization, skipping the gradual adjustment and directly entering the seasonal fine-tuning branch.
[0072] For example, =24.71℃, =19.5℃, therefore Δ =+5.21℃, with a positive sign and an absolute value greater than 3℃, indicates that the historical spring and autumn background is significantly warmer than the current one. Therefore, a gradual cooling process across seasons is initiated, and the initial target temperature is gradually revised downwards in the subsequent 8 runs.
[0073] Based on the absolute value and sign of the long-term trend difference, query the preset progressive adjustment mapping table to obtain the basic total adjustment amount and the total number of adjustment steps; wherein, the progressive adjustment mapping table contains the basic total adjustment amount and the total number of adjustment steps corresponding to the long-term trend difference in different intervals. In one embodiment, the mapping table is stored in the MCU Flash memory, containing 16 records, arranged according to |Δ The temperature is divided into 0.5℃ increments, with each entry formatted as a 2-byte entry: the high 10 bits store the total base adjustment (0.01℃ increments, range ±5.12℃), the low 6 bits store the total number of adjustment steps (3~20 steps), and the sign bit is represented by Δ. Positive and negative signs directly determine the direction of adjustment; For example, when Δ When the temperature reaches +5.21℃, it falls within the 5.0~5.5℃ range. The value 0x4C05 is found, indicating a base total adjustment of +4.48℃ and a total adjustment step count of 8. Therefore, a gradual cooling process of 8 runs is initiated to avoid a sudden and abrupt temperature drop for the user. If |Δ If | < 0.5℃, the mapping table returns zero adjustment amount and zero steps, then skip the cross-seasonal progressive branch and enter the seasonally consistent fine-tuning path.
[0074] Divide the base total adjustment by the total number of adjustment steps to obtain the single-step adjustment. In one embodiment, the single-step adjustment amount Δ The fixed-point division method is used to complete the adjustment in one step: the total basic adjustment is first multiplied by 100 revolutions to obtain an integer, and then divided by the total number of adjustment steps; for example, the total basic adjustment is found to be +4.48℃ (448 × 0.01℃) from the lookup table, and the total number of adjustment steps is 8, then Δ =448÷8=56, which is 0.56℃.
[0075] The single-step adjustment amount is used as the incremental adjustment amount for this operation, and the remaining total adjustment amount is recorded as the adjustment amount to be allocated, where the remaining total adjustment amount is equal to the basic total adjustment amount minus the single-step adjustment amount. In one embodiment, the system adjusts the single-step adjustment amount Δ within 2 seconds after power-on. =0.56℃ is directly loaded into the progressive correction register. At the same time, the remaining total adjustment amount = 4.48℃ - 0.56℃ = 3.92℃ is calculated, and this remaining value and the remaining number of steps 7 are written into the power-down storage area of the EMMC. If the user accidentally loses power during operation, the MCU will first read 3.92℃ and 7 steps from the EMMC when it starts up again, and continue to decrease by 0.56℃ / step.
[0076] At the start of each subsequent run, check if there is an adjustment to be allocated. If so, extract a single-step adjustment as the incremental adjustment for the current run and deduct it from the adjustment to be allocated until the adjustment to be allocated is zero.
[0077] In one embodiment, after the system powers on, it first reads the EMMC adjustment amount field to be allocated. If the value is greater than 0, it is determined that the cross-seasonal asymptotic link has not ended, and the single-step adjustment amount Δ in RAM is immediately allocated. (0.56℃) Load the PID setpoint offset register, and at the same time subtract 0.56℃ from the remaining adjustment amount stored in the EMMC and write it back. Decrease the step counter by 1. For example, if the remaining value is 3.92℃ in the second run, first output 0.56℃ for correction, then update the remaining value to 3.36℃ and the step count to 6. Repeat this process until the remaining value is 0 after the 8th run. At this time, the MCU clears the adjustment amount flag to be allocated.
[0078] Preferably, step S5 includes: The final control temperature is set as the target value, and the real-time near-end temperature of the equipment is collected as the feedback value. The real-time temperature deviation is calculated based on the target value and the feedback value. The real-time temperature deviation value is used to quantify the difference between the current heat output of the fumigation device and the desired steady-state value. Optionally, the deviation calculation formula is e(t) = - (t), To ultimately control the temperature, (t) represents the near-end temperature of the device transmitted back at a frequency of 1Hz from the built-in first temperature sensor.
[0079] In one embodiment, if |e(t)|>3℃ and lasts for 10s, it is determined to be a significant undertemperature / overtemperature condition, and the heating unit is immediately cut off and a fault code is reported to prevent the user from being scalded or the care from failing; if |e(t)|≤0.2℃ and lasts for 60s, it is marked as entering a steady state, and the fan speed is automatically reduced by 100rpm.
[0080] For example, =45.47℃, at a certain moment (t) = 43.20℃, so e(t) = +2.27℃. The positive sign indicates that heating is still required. The MCU sends this value to the outer loop of the incremental PID controller and calculates the theoretical output power of 68%. It drives the heating unit to continue heating until e(t) converges to the ±0.2℃ range.
[0081] The instantaneous theoretical output power is determined based on the real-time temperature deviation value, its historical cumulative sum, and the instantaneous rate of change. Among them, an incremental PID algorithm is used, which combines the deviation value e(t), the historical cumulative sum ∑e(t), and the instantaneous rate of change. e(t) is used for collaborative calculation to quickly calculate the instantaneous theoretical output power required by the heating unit. Optionally, the formula is: ; , , The preset values are 0.8, 0.05, and 2.0, respectively, and the calculation results are limited to 0–100% in percentage form.
[0082] In one embodiment, ∑e(t) is accumulated once per second, with a maximum absolute value not exceeding 200°C·s, to prevent integral saturation.
[0083] Based on the real-time theoretical output power, actual control commands are generated, and the heating unit of the fumigation device is driven to work according to the actual control commands, so that the near-end temperature of the device approaches the final control temperature and remains stable.
[0084] The MCU converts the percentage power output from the PID into a PWM duty cycle with a period of 20ms. The duty cycle is equal to the theoretical output power multiplied by the cycle count. The maximum count of 1000 corresponds to 100%. The signal is then driven by a bidirectional thyristor after being isolated by an optocoupler to control the heating wire. At the same time, the fan speed increases linearly in sync with the PWM to ensure timely heat dissipation, thus forming a closed-loop temperature control.
[0085] Preferably, the step S1 is followed by: The system monitors the operating status and collected data of each second temperature sensor in real time. If any second temperature sensor fails continuously or its collected data continuously exceeds the preset reasonable temperature range, the corresponding temperature sensor is determined to be abnormal. The MCU polls all the second temperature sensor I²C interfaces at a frequency of 2Hz. If three consecutive reads return NAK or CRC check failures, the communication is marked as failed. If the sampled value is <-10℃ or >50℃ and lasts for 10 seconds, the value is marked as abnormal. If either of these two states lasts for 20 seconds, the sensor is determined to be abnormal and its weight is immediately reset to zero.
[0086] Based on the readings of the remaining effective second temperature sensors, the historical correlation of the near-end temperature of the device, and the current environmental monitoring humidity, the missing environmental monitoring temperature is determined; The MCU calculates the temporary ambient temperature by performing an inverse distance-weighted average of the effective sensor readings. Retrieve the device near-end temperature - ambient monitoring temperature difference stored in the EMMC for the past 7 days. (Resolution 0.01℃) and its linear fitting coefficients a and b with humidity H, calculate the compensation amount Δ =a×(H-50)+b, the final missing temperature is: = (Δ )+Δ .
[0087] Update the current environmental monitoring temperature collected in step S1 using the missing environmental monitoring temperature.
[0088] Among them, immediately after the missing value estimation is completed, the calculated value is used. The data from the original failed sensor channel is replaced, and a new inverse distance-weighted average is performed to obtain the updated current ambient temperature. .
[0089] Preferably, the present invention also provides an automatic sensing and operation control system for a fumigation device, used to execute the automatic sensing and operation control method for the fumigation device as described above, the automatic sensing and operation control system for the fumigation device comprising: The dual-temperature acquisition module is used to simultaneously acquire the near-end temperature of the device and the current ambient temperature before the fumigation device is operated; The impact factor calculation module is used to calculate the ambient temperature fluctuation value based on the current environmental monitoring temperature, and to calculate the current ambient temperature impact factor in combination with the near-end temperature of the equipment. The historical parameter parsing module is used to obtain the historical operation records of the fumigation care device, extract the set of successful operation parameters for the historical date segment adjacent to the current date from the historical operation records, and identify the seasonal characteristic parameters and user preferred temperature parameters in the set of successful operation parameters. The target temperature generation module is used to dynamically correct the user's preferred temperature parameters based on the current ambient temperature influencing factors to obtain a preliminary target temperature; and to further adjust the preliminary target temperature based on seasonal characteristic parameters to generate the final control temperature. The heating power control module is used to control the output power of the heating unit of the fumigation device based on the real-time difference between the final controlled temperature and the near-end temperature of the device.
[0090] Therefore, the embodiments should be considered as exemplary and non-limiting in all respects, and the scope of the invention is defined by the appended claims rather than the foregoing description. Thus, all variations falling within the meaning and scope of the equivalents of the application are intended to be included within the invention.
[0091] The above description is merely a specific embodiment of the present invention, enabling those skilled in the art to understand or implement the invention. Various modifications to these embodiments will be readily apparent to those skilled in the art, and the general principles defined herein may be implemented in other embodiments without departing from the spirit or scope of the invention. Therefore, the present invention is not to be limited to the embodiments shown herein, but is to be accorded the widest scope consistent with the principles and novel features of the invention herein.
Claims
1. An automatic sensing and operation control method for a fumigation therapy device, characterized in that, Applied to a fumigation therapy device, the method includes the following steps: Step S1: Before operating the fumigation device, simultaneously collect the near-end temperature of the device and the current ambient temperature; Step S2: Calculate the ambient temperature fluctuation value based on the current ambient monitoring temperature, and calculate the current ambient temperature influencing factor in conjunction with the near-end temperature of the equipment; Step S3: Obtain the historical operation records of the fumigation device, extract the set of successful operation parameters for the historical date segment adjacent to the current date from the historical operation records, and identify the seasonal characteristic parameters and user-preferred temperature parameters in the set of successful operation parameters; Step S4: Dynamically correct the user's preferred temperature parameters based on the current environmental temperature influencing factors to obtain the preliminary target temperature; and make a secondary adjustment to the preliminary target temperature based on seasonal characteristic parameters to generate the final control temperature; Step S5: Control the output power of the heating unit of the fumigation device based on the real-time difference between the final controlled temperature and the near-end temperature of the device.
2. The automatic sensing and operation control method for the fumigation care device according to claim 1, characterized in that, Step S1 includes: During the preheating phase after the fumigation device is turned on, the near-end temperature of the device is collected by the first temperature sensor built into the fumigation device. A set of environmental monitoring temperatures is collected by one or more second temperature sensors that are pre-deployed in locations commonly used by users; wherein, commonly used locations by users include locations that are more than a preset distance from the fumigation device and are within the user's activity range; The current environmental monitoring temperature is obtained by weighted averaging of a set of environmental monitoring temperatures; the weights are allocated based on the distance between each second temperature sensor and the fumigation device. Collect the current ambient humidity at frequently used locations of the user.
3. The automatic sensing and operation control method for the fumigation care device according to claim 1, characterized in that, Step S2 includes: Step S21: Calculate the absolute value of the temperature difference between the current ambient monitoring temperature and the near-end temperature of the equipment, and use it as the baseline temperature difference; Step S22: Obtain multiple historical environmental monitoring temperatures within a preset time window, and calculate the standard deviation of the historical environmental monitoring temperatures as the environmental temperature fluctuation value; Step S23: Compare the base temperature difference with the preset base temperature difference threshold. If the base temperature difference is less than or equal to the preset base temperature difference threshold, the ambient temperature fluctuation value is taken as the environmental disturbance intensity. If the base temperature difference is greater than the preset base temperature difference threshold, the weighted sum of the base temperature difference and the ambient temperature fluctuation value is taken as the environmental disturbance intensity. Step S24: Normalize the intensity of environmental disturbance to obtain the current environmental temperature influence factor.
4. The automatic sensing and operation control method for the fumigation care device according to claim 1, characterized in that, Step S3 includes: Obtain historical operation records within the first preset number of days; wherein, the historical operation records shall include at least the start and end time of each operation, the final stable temperature, the ambient temperature sequence, and the user comfort feedback markers; Based on preset comfortable operation filtering conditions, historical records that meet the conditions are filtered to form an initial set of successful operation parameters; Extract historical records within the second preset number of days adjacent to the current date from the initial successful operation parameter set to form a successful operation parameter set; Based on the mean and distribution characteristics of the ambient temperature series in each historical record of the successful operation parameter set, seasonal characteristic parameters that characterize the seasonal warm and cold trends are identified. Use the mode or average of the final stable temperatures from the successfully running parameter set as the user's preferred temperature parameter.
5. The automatic sensing and operation control method for the fumigation care device according to claim 1, characterized in that, Dynamically adjusting user-preferred temperature parameters based on current environmental temperature influencing factors includes: Construct a temperature correction rule base; the temperature correction rule base includes the temperature adjustment direction and adjustment range corresponding to different ranges of environmental temperature influencing factors; Based on the temperature correction rule base, the corresponding temperature adjustment amount is determined according to the current environmental temperature influencing factors; The user's preferred temperature parameter is added to the temperature adjustment amount to obtain the first corrected temperature; The first correction temperature is limited to a preset safe human body temperature range to obtain the initial target temperature.
6. The automatic sensing and operation control method for the fumigation care device according to claim 5, characterized in that, Step S4, which involves a secondary adjustment of the initial target temperature based on seasonal characteristic parameters, includes: Determine the season to which the current date belongs, and determine whether the season to which the current date belongs is consistent with the seasonal characteristics represented by the successfully run parameter set; If the seasonal characteristics are consistent, the seasonal characteristic parameters are used as weighting coefficients, and the initial target temperature is fine-tuned according to the weighting coefficients to obtain the seasonal fine-tuning temperature; wherein, the fine-tuning range is smaller than the adjustment range of the first correction temperature. If the seasonal characteristics are inconsistent or the season is in transition, the current measured ambient temperature is obtained. Based on the long-term trend difference between the seasonal characteristic parameters and the current measured ambient temperature, a gradual adjustment amount is calculated and gradually applied to the initial target temperature in this and subsequent runs to obtain the cross-seasonal adaptation temperature. By limiting seasonal temperature adjustments or cross-seasonal temperature adaptations to a preset safe human body temperature range, the final controlled temperature is obtained.
7. The automatic sensing and operation control method for the fumigation care device according to claim 6, characterized in that, Based on the long-term trend difference between seasonal characteristic parameters and the current measured ambient temperature, the gradual adjustment amount is calculated as follows: Calculate the difference between seasonal characteristic parameters and the current measured ambient temperature as the long-term trend difference; Based on the absolute value and sign of the long-term trend difference, query the preset progressive adjustment mapping table to obtain the basic total adjustment amount and the total number of adjustment steps; wherein, the progressive adjustment mapping table contains the basic total adjustment amount and the total number of adjustment steps corresponding to the long-term trend difference in different intervals. Divide the base total adjustment by the total number of adjustment steps to obtain the single-step adjustment. The single-step adjustment amount is used as the incremental adjustment amount for this operation, and the remaining total adjustment amount is recorded as the adjustment amount to be allocated, where the remaining total adjustment amount is equal to the basic total adjustment amount minus the single-step adjustment amount. At the start of each subsequent run, check if there is an adjustment to be allocated. If so, extract a single-step adjustment as the incremental adjustment for the current run and deduct it from the adjustment to be allocated until the adjustment to be allocated is zero.
8. The automatic sensing and operation control method for the fumigation care device according to claim 1, characterized in that, Step S5 includes: The final control temperature is set as the target value, and the real-time near-end temperature of the equipment is collected as the feedback value. The real-time temperature deviation is calculated based on the target value and the feedback value. The instantaneous theoretical output power is determined based on the real-time temperature deviation value, its historical cumulative sum, and the instantaneous rate of change. Based on the real-time theoretical output power, actual control commands are generated, and the heating unit of the fumigation device is driven to work according to the actual control commands, so that the near-end temperature of the device approaches the final control temperature and remains stable.
9. The automatic sensing and operation control method for the fumigation care device according to claim 2, characterized in that, Following step S1: The system monitors the operating status and collected data of each second temperature sensor in real time. If any second temperature sensor fails continuously or its collected data continuously exceeds the preset reasonable temperature range, the corresponding temperature sensor is determined to be abnormal. Based on the readings of the remaining effective second temperature sensors, the historical correlation of the near-end temperature of the device, and the current environmental monitoring humidity, the missing environmental monitoring temperature is determined; Update the current environmental monitoring temperature collected in step S1 using the missing environmental monitoring temperature.
10. An automatic sensing and operation control system for a fumigation care device, characterized in that, For executing the automatic sensing operation control method of the fumigation device as described in claim 1, the automatic sensing operation control system of the fumigation device includes: The dual-temperature acquisition module is used to simultaneously acquire the near-end temperature of the device and the current ambient temperature before the fumigation device is operated; The impact factor calculation module is used to calculate the ambient temperature fluctuation value based on the current environmental monitoring temperature, and to calculate the current ambient temperature impact factor in combination with the near-end temperature of the equipment. The historical parameter parsing module is used to obtain the historical operation records of the fumigation care device, extract the set of successful operation parameters for the historical date segment adjacent to the current date from the historical operation records, and identify the seasonal characteristic parameters and user preferred temperature parameters in the set of successful operation parameters. The target temperature generation module is used to dynamically correct the user's preferred temperature parameters based on the current ambient temperature influencing factors to obtain a preliminary target temperature; and to further adjust the preliminary target temperature based on seasonal characteristic parameters to generate the final control temperature. The heating power control module is used to control the output power of the heating unit of the fumigation device based on the real-time difference between the final controlled temperature and the near-end temperature of the device.