Clothes drying method and device, clothes drying equipment and storage medium
By balancing and correcting the predicted and actual values in the drying equipment, and adjusting the measurement residuals using covariance prediction parameters and gain parameters, the problem of clothing drying errors caused by sensor differences is solved, ensuring accurate judgment of clothing dryness, avoiding over-drying, and improving the user experience.
Patent Information
- Application Number
- CN202511397408.1
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- Filing Date
- 2025-09-28
- Publication Date
- 2025-11-14
AI Technical Summary
Differences between different sensors or devices can lead to significant errors in clothes drying results, potentially causing over-drying, damaging clothes, and reducing the user experience.
By balancing and correcting the predicted and actual values, and adjusting the measurement residuals using covariance prediction parameters, noise operators, and gain parameters, accurate judgment of clothing dryness can be achieved. This includes predicting current state change parameters, determining measurement residuals, calculating gain parameters and state weights, and ensuring that drying stops when the clothing dryness meets the requirements.
It enables accurate and timely judgment of the drying results of clothes, protects clothes from damage due to over-drying, and improves the user experience.
Smart Images
Figure CN120945650A_ABST
Abstract
Description
Technical Field
[0001] The present invention relates to the field of household appliance technology, and in particular to a method, apparatus, drying equipment and storage medium for drying clothes. Background Technology
[0002] Clothes dryers, commonly used to dry clothes, are equipped with multiple sensors or devices to collect data on the drying process, such as temperature and humidity sensors. Based on the data collected by these sensors and devices, it is possible to determine in real time whether the clothes in the dryer are dry.
[0003] However, due to differences between different sensors or devices, there will be a large error in determining the final drying result of clothes based on the data collected by different sensors or devices, which may lead to over-drying of clothes. Summary of the Invention
[0004] This invention provides a method, apparatus, drying equipment, and storage medium for drying clothes. It achieves a balance and correction between predicted values and actual values obtained from equipment detection, ensuring accurate and timely judgment of the drying results, thereby protecting clothes from damage due to over-drying and enhancing the user experience.
[0005] In a first aspect, embodiments of the present invention provide a method for drying clothes, the method being applied to a clothes drying device, the method comprising:
[0006] The predicted change parameters for the current state are predicted based on the predicted change parameters of the previous state within the drying equipment, and the measurement residual is determined based on the actual change parameters of the current state and the predicted change parameters of the current state.
[0007] The current covariance prediction parameter is determined based on the previous covariance prediction parameter and the process noise, and the gain parameter is determined based on the current covariance prediction parameter and the sensor's noise operator.
[0008] Based on the current state, the predicted change parameters, the gain parameters, and the measurement residuals, the predicted estimation parameters of the state change parameters are determined. Based on the predicted estimation parameters and the state weights corresponding to the predicted estimation parameters, the dryness of the clothes is predicted, and the drying of the clothes is stopped when the dryness of the clothes meets the dryness requirements.
[0009] The clothing drying method provided in this invention predicts the current state change parameters based on the predicted change parameters of the previous state. This enables the prediction of the current state change parameters during the current state prediction cycle based on the dynamic characteristics between the drying equipment and the clothing to be processed. By determining the measurement residual, the difference between the predicted state change and the actual state change detected by sensors or detection devices can be clearly identified. Updating the covariance prediction parameters ensures the accuracy of the parameters measuring the uncertainty of state prediction in each state prediction cycle. Furthermore, by determining the gain parameter based on the current covariance prediction parameters and the noise operator, the error of the actual state parameters detected by the device is incorporated into the calculation process. This solves the problem that current methods do not consider the differences between different sensors or devices, leading to errors in the collected data and thus affecting the final drying result with significant errors. This allows for the determination of the confidence level between the predicted state change and the actual detected state change. By using the gain parameter to correct the measurement residual, a correction value is obtained that adjusts the ratio between the actual change parameter and the predicted change parameter of the current state. Then, based on the correction value, the predicted change parameter of the current state is corrected, realizing the balance and correction between the predicted value and the actual value obtained from the device detection. This ensures the accurate and timely judgment of the clothes drying results, thereby protecting the clothes from damage due to over-drying and improving the user experience.
[0010] Secondly, embodiments of the present invention also provide a clothes drying device, which is applied to clothes drying equipment, and the device includes:
[0011] The prediction module is used to predict the change parameters of the current state based on the change parameters of the previous state in the drying equipment, and to determine the measurement residual based on the actual change parameters of the current state and the predicted change parameters of the current state.
[0012] The calculation module is used to determine the current covariance prediction parameters based on the previous covariance prediction parameters and process noise, and to determine the gain parameters based on the current covariance prediction parameters and the sensor's noise operator.
[0013] The control module is used to determine the prediction and estimation parameters of the state change parameters based on the current state prediction change parameters, gain parameters, and measurement residuals. It predicts the dryness of the clothes based on the prediction and estimation parameters and the state weights corresponding to the prediction and estimation parameters, and stops drying the clothes when the dryness of the clothes meets the dryness requirements.
[0014] Thirdly, embodiments of the present invention also provide a clothes drying device, the clothes drying device comprising:
[0015] An electronic device includes at least one processor and a memory communicatively connected to the at least one processor; wherein the memory stores a computer program executable by the at least one processor, the computer program being executed by the at least one processor to enable the at least one processor to perform the clothes drying method of any embodiment of the present invention.
[0016] Fourthly, embodiments of the present invention also provide a computer-readable storage medium storing computer instructions that, when executed by a processor, implement the clothes drying method of any embodiment of the present invention.
[0017] Fifthly, embodiments of the present invention also provide a computer program product, including a computer program that, when executed by a processor, implements the clothes drying method of any embodiment of the present invention.
[0018] It should be noted that the aforementioned computer instructions may be stored, in whole or in part, on a computer-readable storage medium. This computer-readable storage medium may be packaged together with the processor of the clothes drying device, or it may be packaged separately from the processor of the clothes drying device; this application does not impose any limitations on this.
[0019] The descriptions of the second, third, fourth, and fifth aspects in this application can be referred to the detailed description of the first aspect; and the beneficial effects of the descriptions of the second, third, fourth, and fifth aspects can be referred to the analysis of the beneficial effects of the first aspect, which will not be repeated here.
[0020] In this application, the name of the aforementioned clothes drying device does not limit the device or functional module itself. In actual implementation, these devices or functional modules may appear under other names. As long as the function of each device or functional module is similar to that of this application, it falls within the scope of the claims of this application and its equivalents.
[0021] These or other aspects of this application will become more readily apparent in the following description. Attached Figure Description
[0022] To more clearly illustrate the technical solutions in the embodiments of the present invention, the accompanying drawings used in the description of the embodiments will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.
[0023] Figure 1 A schematic flowchart of a clothes drying method provided in an embodiment of the present invention;
[0024] Figure 2A schematic flowchart of another clothes drying method provided in an embodiment of the present invention;
[0025] Figure 3 This is a schematic diagram of the structure of a clothes drying device provided in an embodiment of the present invention;
[0026] Figure 4 This is a schematic diagram of the structure of an electronic device in a clothes drying device provided in an embodiment of the present invention. Detailed Implementation
[0027] The present invention will now be described in further detail with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative of the invention and not intended to limit it. Furthermore, it should be noted that, for ease of description, the accompanying drawings show only the parts relevant to the present invention, and not all of the structures.
[0028] In this article, the term "and / or" is merely a description of the relationship between related objects, indicating that there can be three relationships. For example, A and / or B can represent three situations: A exists alone, A and B exist simultaneously, and B exists alone.
[0029] The terms “previous” and “current” in the specification and drawings of this application are used to distinguish different objects or to distinguish different processing of the same object, rather than to describe a specific order of objects.
[0030] Furthermore, the terms "comprising" and "having," and any variations thereof, used in the description of this application are intended to cover non-exclusive inclusion. For example, a process, method, system, product, or apparatus that includes a series of steps or units is not limited to the steps or units listed, but may optionally include other steps or units not listed, or may optionally include other steps or units inherent to such process, method, product, or apparatus.
[0031] Before discussing the exemplary embodiments in more detail, it should be noted that some exemplary embodiments are described as processes or methods depicted as flowcharts. Although the flowcharts describe the operations (or steps) as sequential processes, many of these operations can be performed in parallel, concurrently, or simultaneously. Furthermore, the order of the operations can be rearranged. The process can be terminated when its operation is completed, but may also have additional steps not included in the figures. The process can correspond to a method, function, procedure, subroutine, subroutine, etc. Moreover, embodiments and features in the embodiments of the present invention can be combined with each other without conflict.
[0032] It should be noted that in the embodiments of this application, the words "exemplary" or "for example" are used to indicate examples, illustrations, or explanations. Any embodiment or design scheme described as "exemplary" or "for example" in the embodiments of this application should not be construed as being more preferred or advantageous than other embodiments or design schemes. Specifically, the use of the words "exemplary" or "for example" is intended to present the relevant concepts in a specific manner.
[0033] In the description of this application, unless otherwise stated, "a plurality of" means two or more.
[0034] Figure 1 This is a schematic flowchart illustrating a clothes drying method provided in an embodiment of the present invention. This embodiment is applicable to the drying of clothes using a clothes drying device. The method can be executed by a clothes drying device, which can be implemented in hardware and / or software and can be configured within the clothes drying device. In this embodiment, the clothes drying device is equipped with electronic equipment such as a computer, or the clothes drying device is presented in the form of an electronic device. Figure 1 As shown, the method specifically includes the following steps:
[0035] S101. Predict the current state change parameters based on the previous state change parameters in the drying equipment, and determine the measurement residual based on the actual change parameters of the current state and the current state change parameters.
[0036] In this embodiment, the drying equipment can refer to a dedicated drying device such as a dryer, or any clothing processing device with drying / tumble drying functions, such as a washer-dryer combo or a heat pump dryer. The state prediction change parameter is used to characterize the change state of each collected data point in the drying equipment across different prediction periods. In this embodiment, the previous state prediction change parameter is the "current state prediction change parameter" calculated in the previous prediction period. Furthermore, in the first state prediction period, the "previous state prediction change parameter" can be the actual state change parameter. The current actual state change parameter is the difference between the actual collected data from the sensor or device in the current state prediction period and the actual collected data from the previous state prediction period.
[0037] Specifically, since the drying effect of the clothes drying equipment changes continuously over time during the drying process, an initial state change parameter can be determined based on the state of the clothes drying equipment initially. This parameter serves as the initial "predicted change parameter of the previous state." Based on this "predicted change parameter of the previous state" and the dynamic characteristics between the clothes drying equipment and the clothes to be processed, the current state of the clothes drying equipment is predicted, yielding the predicted change parameter of the current state. After obtaining the predicted change parameter of the current state, the actual change parameter of the current state can be determined based on the acquired current state parameter and the previous state parameter. Finally, the measurement residual is determined based on the actual change parameter of the current state and the predicted change parameter of the current state.
[0038] For example, assuming a prediction cycle of 3 minutes, the clothes dryer starts drying clothes at 10:00:00. Therefore, at 10:00:00, the state parameters 1 of the clothes dryer can be acquired, and at 10:03:00, the state parameters 2 can be acquired. Based on the changes in state parameters 1 and 2, the "previous state prediction change parameter" corresponding to the first state prediction cycle is determined. Furthermore, the current state prediction change parameter is predicted using this "previous state prediction change parameter," and the measurement residual is determined based on the current state prediction change parameter and the actual change parameter of the current state determined based on state parameters 1 and 2. Similarly, during the subsequent processing of clothes by the clothes dryer, the "current state prediction change parameter" finally predicted in the previous state prediction cycle can be directly used as the "previous state prediction change parameter" for the current state prediction cycle to achieve the calculation of each prediction cycle.
[0039] In this embodiment, the predicted change parameters for the current state are predicted based on the predicted change parameters of the previous state. This enables the prediction of the predicted change parameters for the current state during the prediction period based on the dynamic characteristics between the drying equipment and the clothes to be processed. Subsequently, by determining the measurement residual, the difference between the predicted state change and the actual state change detected by sensors or detection devices can be clearly identified, providing a basis for accurately predicting the changes in clothes drying after the current moment.
[0040] S102. Determine the current covariance prediction parameter based on the previous covariance prediction parameter and the process noise, and determine the gain parameter based on the current covariance prediction parameter and the sensor's noise operator.
[0041] The covariance prediction parameter is used to measure the uncertainty of state prediction. The previous covariance prediction parameter is the "current covariance prediction parameter" obtained in the previous state prediction period. In this embodiment, the "previous covariance prediction parameter" for the first state prediction period can be preset to indicate a low confidence level in the initial actual state parameters and a high confidence level in the data collected by the sensor or detection device. Process noise is determined in experiments or tests based on observing changes in different state parameters in the drying equipment under uncontrolled conditions. In this embodiment, the dryer heater and fan can be turned off in the experiment or test, and the changes in each state parameter over time can be recorded in a natural environment to determine the process noise. The noise operator is used to reflect the unreliability of the sensor or detection device itself. In this embodiment, the noise operator can be determined based on the detection accuracy of the sensor or detection device and its resistance to environmental interference or the impact of environmental interference.
[0042] Specifically, since the covariance prediction parameter can measure the uncertainty of state prediction, it can be corrected for each state prediction cycle based on the previous covariance prediction parameter and the process noise of the state parameters within the drying equipment under natural conditions determined during the experiment, thus determining the current covariance prediction parameter. After obtaining the current covariance prediction parameter, a gain parameter can be determined based on the current covariance prediction parameter and the sensor's noise operator to characterize whether the state parameters detected by the sensor or the predicted state parameters are more accurate in the current state prediction cycle.
[0043] In this embodiment, updating the covariance prediction parameters ensures the accuracy of the parameters measuring the uncertainty of state prediction in each state prediction cycle. Furthermore, determining the gain parameters based on the current covariance prediction parameters and the noise operator incorporates the error of the actual state parameters detected by the device into the calculation process. This solves the problem that current methods do not consider the differences between different sensors or devices, leading to errors in the collected data and consequently significant errors in the final judgment result. It also enables confidence assessment of the predicted state changes versus the actual detected state changes, providing a foundation for accurately determining the proportion of the actual change parameters of the current state and the predicted change parameters of the current state.
[0044] S103. Determine the prediction estimation parameters of the state change parameters based on the current state prediction change parameters, gain parameters, and measurement residuals. Predict the dryness of the clothes based on the prediction estimation parameters and the state weights corresponding to the prediction estimation parameters. Stop drying the clothes when the dryness of the clothes meets the dryness requirements.
[0045] The prediction and estimation parameters are the corrected parameters based on the current state parameters collected by the sensor. State weights characterize the correlation between different state parameters and clothing dryness; in this embodiment, different state parameters correspond to different state weights. Clothing dryness characterizes the predicted degree of drying of the clothing at the current state prediction period, rather than the degree detected by the instrument. Dryness requirement is a pre-set or experimentally determined optimal dryness index that does not damage the clothing material.
[0046] Specifically, by correcting the measurement residual using the gain parameter, a correction value can be obtained that adjusts the ratio between the actual change parameter and the predicted change parameter of the current state. Furthermore, by correcting the predicted change parameter of the current state based on this correction value, the optimal estimate of the state parameter change for the current state prediction period can be obtained, i.e., the predicted estimate parameter. Then, based on all predicted estimate parameters and the state weights corresponding to the state parameters of each predicted estimate parameter, the dryness of the clothes is comprehensively predicted. The dryness of the clothes is then compared with the clothing requirements to determine whether the dryness of the clothes meets the requirements for the current state prediction period. If the dryness requirements are met, the drying process is stopped promptly.
[0047] In this embodiment, the measurement residual is corrected using the gain parameter to obtain a correction value that adjusts the ratio between the actual change parameter of the current state and the predicted change parameter of the current state. Then, the predicted change parameter of the current state is corrected based on the correction value, realizing the balance and correction between the predicted value and the actual value obtained by the device detection. This ensures the accurate and timely judgment of the clothes drying results, thereby protecting the clothes from damage due to over-drying and increasing the user experience.
[0048] The clothing drying method provided in this invention predicts the current state change parameters based on the predicted change parameters of the previous state. This enables the prediction of the current state change parameters during the current state prediction cycle based on the dynamic characteristics between the drying equipment and the clothing to be processed. By determining the measurement residual, the difference between the predicted state change and the actual state change detected by sensors or detection devices can be clearly identified. Updating the covariance prediction parameters ensures the accuracy of the parameters measuring the uncertainty of state prediction in each state prediction cycle. Furthermore, by determining the gain parameter based on the current covariance prediction parameters and the noise operator, the error of the actual state parameters detected by the device is incorporated into the calculation process. This solves the problem that current methods do not consider the differences between different sensors or devices, leading to errors in the collected data and thus affecting the final drying result with significant errors. This allows for the determination of the confidence level between the predicted state change and the actual detected state change. By using the gain parameter to correct the measurement residual, a correction value is obtained that adjusts the ratio between the actual change parameter and the predicted change parameter of the current state. Then, based on the correction value, the predicted change parameter of the current state is corrected, realizing the balance and correction between the predicted value and the actual value obtained from the device detection. This ensures the accurate and timely judgment of the clothes drying results, thereby protecting the clothes from damage due to over-drying and improving the user experience.
[0049] Figure 2 This is a schematic flowchart of another clothes drying method provided by an embodiment of the present invention. This embodiment specifies the steps of predicting the change parameters of the current state, determining the measurement residual, determining the gain parameter, and predicting and estimating the parameters based on the above embodiment. In this embodiment, the method may include:
[0050] S201. Predict the current state's predicted change parameters based on the state transition parameters and the predicted change parameters of the previous state.
[0051] Specifically, the state transition parameters can be determined based on the degree of change of state parameters within a short period of time during an experiment or test. For example, if the degree of change of state parameters is low within a short period of time, it corresponds to a low-dynamic scenario, and therefore the state transition parameters can be determined as those corresponding to the low-dynamic scenario. Furthermore, the product of the state transition parameters and the predicted change parameters of the previous state can be calculated and used as the predicted change parameters of the current state.
[0052] For example, the predicted change parameters of the current state can be calculated using the following formula:
[0053]
[0054] in, The predicted change parameters for the current state are the prior estimates of the state change parameters; F is the state transition parameter, which is the state transition matrix. In this embodiment, F is the identity matrix. The parameters for predicting changes in the previous state are the posterior estimates of the state change parameters.
[0055] S202. Determine the measurement residual based on the conversion relationship between the predicted value and the measured value, the actual change parameters of the current state, and the predicted change parameters of the current state.
[0056] The conversion relationship between the predicted value and the measured value can be predetermined. In this embodiment, the conversion relationship between the predicted value and the measured value can be determined during an experiment or test. Optionally, in this embodiment, the conversion relationship between the predicted value and the measured value is 1:1.
[0057] Specifically, the actual parameters of the current state are obtained from various sensors or detection devices of the drying equipment, and the actual change parameters of the current state are determined based on the actual parameters of the previous state and the actual parameters of the current state. Since there is a conversion relationship between the predicted value and the measured value in actual practice, the predicted change parameters of the current state can be multiplied by the conversion relationship to convert them into values of the same dimension as the measured values. Then, the difference between the actual change parameters and the predicted change parameters of the current state can be calculated to obtain the error between the actual value and the predicted value, that is, to determine the true gap between the actual value and the predicted value, and to obtain the measurement residual.
[0058] For example, the measurement residual can be calculated based on the following formula:
[0059]
[0060] Among them, y k To measure the residual; z k H represents the actual change parameter of the current state; H is the conversion relationship between the predicted value and the measured value. In this embodiment, H = 1.
[0061] S203. Determine the initial covariance prediction parameters based on the state transition parameters and the previous covariance prediction parameters.
[0062] Specifically, based on the state transition parameters and the previous covariance prediction parameters, the covariance prediction parameters for the current state prediction period can be predicted based on the previous covariance prediction parameters and the state transition parameters that can characterize the degree of change of state parameters in a short time, thus obtaining the initial covariance prediction parameters.
[0063] S204. Determine the current covariance prediction parameters based on the initial covariance parameters and process noise.
[0064] Specifically, since the initial covariance parameter is only a parameter that is theoretically updated based on the degree of change of the state parameter in a short period of time, but in reality, new errors, i.e. process noise, will be generated due to unconventional influences on the drying equipment during the drying process. Therefore, the process noise can be added to the initial covariance parameter to determine the current covariance prediction parameter.
[0065] For example, the current covariance prediction parameter can be calculated using the following formula:
[0066] P k|k-1 =F*P k-1|k-1 *F T +Q;
[0067] Among them, P k|k-1 P represents the current covariance prediction parameter. k-1|k-1 F is the previous covariance prediction parameter; T That is, F is the transpose of F; F*P k-1|k-1 *F T That is, the initial covariance prediction parameter; Q is the process noise.
[0068] Optionally, in this embodiment, the method for determining process noise is as follows:
[0069] (a) Turn off the heater and fan in the dryer and obtain the status test parameters corresponding to multiple consecutive time points.
[0070] The heater and fan are key sub-equipment in the clothes drying equipment used for drying clothes. The time point refers to the test time point in the experiment or test. In this embodiment, multiple consecutive time points refer to acquiring the state test parameters of the clothes drying equipment according to a certain test cycle.
[0071] Specifically, all key sub-equipment used for drying clothes in the drying equipment is turned off, such as the heater and fan, and the status test parameters of the drying equipment at each time point of the preset test cycle are recorded.
[0072] For example, with a 1-second cycle, starting from 12 o'clock, the clothes to be dried are placed in the drying equipment at 12 o'clock and left to stand, and the status test parameters of the drying equipment at 12 o'clock are recorded; the status test parameters of the drying equipment at 12:00:01 are recorded at 12:00:01; the status test parameters of the drying equipment at 12:00:02 are recorded at 12:00:02; the status test parameters of the drying equipment at 12:00:03 are recorded at 12:00:03, and so on, until at least a preset number of time points are obtained, for example, the status test parameters corresponding to 1000 time points are obtained; the status test parameters corresponding to these 1000 time points are the status test parameters corresponding to multiple consecutive time points.
[0073] (ii) For any given time point, determine the error noise corresponding to that time point based on the state test parameters corresponding to that time point, the state test parameters corresponding to the previous time point, and the state transition parameters.
[0074] Specifically, except for the state test parameter corresponding to the first time point which has no corresponding error noise, or the error noise is the state test parameter corresponding to the first time point, the error noise corresponding to other time points can be calculated according to the following formula:
[0075] w = x curr -F*x prev ;
[0076] Where w is the error noise; x curr That is, the state test parameters corresponding to that time point; F is the state transition parameter; x prev This refers to the state test parameters corresponding to the previous time point.
[0077] (iii) Determine the noise mean based on the error noise corresponding to all time points, and determine the process noise based on the noise mean.
[0078] Specifically, after obtaining the error noise corresponding to each time point, the error noise corresponding to all time points can be averaged to obtain the noise mean, and the variance can be calculated based on the noise mean to obtain the process noise.
[0079] In this embodiment, the numerical values of the state test parameters recorded in the natural environment over time are used to approximate the process noise, thereby realizing the direct simulation of the real-time scene. This ensures that the process noise is closer to the actual situation and provides a basis for incorporating the error caused by the process noise into the judgment process during actual use.
[0080] S205. Determine the gain coefficient based on the current covariance prediction parameters, transformation relationship, and noise operator.
[0081] Specifically, to incorporate the unreliability of various sensors and detection devices in the drying equipment into the calculation process, the errors can first be determined by consulting the equipment manual or conducting calibration tests. This includes determining the detection accuracy and the degree of environmental interference. The errors corresponding to all devices are then used to form a noise operator, which can be presented as a noise matrix. Furthermore, the gain coefficient can be determined based on the current covariance prediction parameters, transformation relationships, and the noise operator. This gain coefficient can be used to quantify the uncertainty between observed data (based on real-time data collected by sensors or devices, such as actual changes in parameters at the current state) and predicted data (predicted data, such as predicted changes in parameters at the current state).
[0082] For example, the gain coefficient can be calculated according to the following formula:
[0083] k=(H*P k|k-1 *H T +R) -1 ;
[0084] Where k is the gain coefficient; H is the conversion relationship; H T R is the transpose of the transformation relation; R is the noise operator.
[0085] In this embodiment, the detection error of the sensor or detection device, i.e., the noise operator, is introduced into the calculation. This can clarify the error introduced by the detection device during the interference calculation process, and provide a basis for accurate prediction and estimation of parameters later.
[0086] S206. Determine the gain parameter based on the current covariance prediction parameter, transformation relationship, and gain coefficient.
[0087] Specifically, the gain parameter can be obtained by calculating the product of the current covariance prediction parameter, the transpose of the transformation relation, and the gain coefficient, as shown in the following formula:
[0088] K k =P k|k-1 *H T *k;
[0089] Among them, K k This is the gain parameter.
[0090] S207. Determine the residual gain based on the measurement residual and gain parameters.
[0091] Specifically, after determining the gain parameter and the measurement residual, the product of the measurement residual and the gain parameter can be calculated to obtain the residual gain. The residual gain is the correction amount obtained by combining the "detected deviation" with the "confidence weight".
[0092] S208. Add the residual gain to the current state prediction change parameters to obtain the prediction estimate parameters.
[0093] Specifically, the predicted parameters can be obtained by summing the predicted change parameters of the current state with the residual gain.
[0094] For example, the calculation method for the predicted estimation parameters is as follows:
[0095]
[0096] in, These are the parameters for prediction.
[0097] In this embodiment, after obtaining the current state prediction change parameters based on the previous state prediction change parameters and the current state actual change parameters collected by the detection device, the process noise and noise operator are used to continuously determine the optimal balance value between the predicted value and the collected actual value, so as to extract the best estimated parameters of the dryer's current real state from the noisy data, thereby realizing the accurate prediction of the dryer's operating state based on the dryer system itself.
[0098] S209. Determine the difference in predicted humidity based on the humidity prediction estimation parameters and humidity weights.
[0099] In this embodiment, the prediction estimation parameters include temperature prediction estimation parameters, humidity prediction estimation parameters, and volatile matter prediction estimation parameters. That is, the "state change parameters" in this embodiment include temperature change parameters, humidity change parameters, and volatile matter change parameters. Any of the "state prediction change parameters," "actual state change parameters," and "covariance prediction parameters" in the above and following steps can be temperature-related, humidity-related, or volatile matter-related parameters. Each relevant parameter can be substituted into the above and following steps for calculation to obtain the prediction estimation parameter corresponding to each relevant parameter; this will not be elaborated further here. The humidity prediction estimation parameters are the estimated humidity of the clothes in the current clothes dryer and the humidity of the air inside the dryer drum. In this embodiment, the humidity can be detected by a humidity sensor installed on the inner wall of the dryer drum.
[0100] Specifically, the difference in predicted humidity can be obtained by subtracting the humidity prediction estimation parameter from the humidity weight.
[0101] S210. Determine the predicted temperature product value based on the temperature prediction estimation parameters and temperature weights.
[0102] The temperature prediction parameters are the estimated current air temperature inside the dryer drum and the temperature of the condenser of the dryer's heat pump system.
[0103] Specifically, the predicted temperature product can be obtained by multiplying the temperature weight by the temperature prediction estimation parameter.
[0104] S211. Determine the predicted volatile product value based on the volatile prediction estimation parameters and volatile weights.
[0105] Volatile substances refer to substances that easily evaporate or sublimate from solids or liquids into a gaseous state. In this embodiment, the volatile substance prediction and estimation parameters are the estimated content of each volatile substance in the current drying equipment, such as organic compounds or odor molecules (e.g., sweat odor, smoke odor), to determine whether further sterilization or deodorization treatment based on the drying temperature is required.
[0106] Specifically, the volatile matter prediction estimation parameters can be multiplied by the volatile matter weights to obtain the predicted volatile matter product.
[0107] It is worth noting that S209, S210 and S211 are parallel steps, which can be executed simultaneously or in any order. No restrictions are placed here.
[0108] S212. Determine the dryness of clothing based on the predicted humidity difference, the predicted temperature product, and the predicted volatile product.
[0109] Specifically, the dryness of clothes can be calculated using the following formula:
[0110] Dry = W d -W+T d *T-VOC d *VOC;
[0111] Where Dry refers to the dryness of the clothing; W d The humidity weight is 100 in this embodiment; W is the humidity prediction estimation parameter; W d -W represents the predicted humidity difference; T d For temperature weighting, the value can optionally be 0.5 in this embodiment; T is the temperature prediction estimation parameter; T d -T represents the predicted temperature product. VOC d The value of VOC is 0.01, which can be selected as the weight for volatile organic compounds in this embodiment; VOC is the volatile organic compound prediction estimation parameter; VOC d -VOC is the predicted volatile matter accumulation value.
[0112] S213. When the dryness of the clothes is greater than or equal to the dryness requirement and the volatile matter prediction estimate parameter is less than the volatile matter threshold, stop drying the clothes.
[0113] The dryness requirement and the volatile matter threshold are both predetermined.
[0114] Specifically, if the calculated dryness of the clothing is greater than or equal to the required dryness, and the estimated volatile matter prediction parameter is less than the volatile matter threshold, it means that the clothing has been dried and there is no need to further reduce the proportion of volatile matter in the drying equipment by increasing the drying temperature. Therefore, the drying process can be stopped to prevent over-drying and overheating of the clothing.
[0115] Optionally, in this embodiment, the dryness of clothing is expressed as a percentage, the dryness requirement is 95%, and the volatile matter threshold is 50 parts per billion (ppd).
[0116] In this embodiment, in addition to using the temperature and humidity in the drying equipment to determine the drying status of the drying equipment, volatile matter prediction and estimation parameters are also introduced to determine the current sterilization and deodorization status of the clothes, further ensuring the care of the clothes during the drying process, improving the user experience, and enhancing the accuracy of the drying judgment.
[0117] S214. When the dryness of the clothing is less than the dryness requirement or the volatile matter prediction estimation parameter is greater than or equal to the volatile matter threshold, update the current covariance prediction parameter according to the current covariance prediction parameter, gain parameter and the conversion relationship between the predicted value and the measured value, and use the updated current covariance prediction parameter as the previous covariance prediction parameter and the current state prediction change parameter as the previous state prediction change parameter, and return to execute S201.
[0118] Specifically, if the dryness of the clothing is less than the required dryness, or if the estimated volatile matter prediction parameter is greater than or equal to the volatile matter threshold, then the drying process is not yet complete. The former indicates the clothing still contains a significant amount of moisture and requires further drying; the latter indicates a high concentration of volatile matter emitted by the clothing, such as a strong sweat or smoke odor, requiring further sterilization and deodorization based on the drying temperature. Therefore, the current covariance prediction parameter can be updated based on the current covariance prediction parameter, gain parameter, and transformation relationship. The updated current covariance prediction parameter is then used as the previous covariance prediction parameter, and the current state prediction change parameter is used as the previous state prediction change parameter. The process then returns to the first step in this embodiment to further calculate the parameters for the next state prediction cycle.
[0119] For example, the formula for updating the current covariance prediction parameter is as follows:
[0120] P k|k = (1-K) k *H)*P k|k-1 ;
[0121] Among them, P k|k The updated current covariance prediction parameters; P k|k-1 These are the current covariance prediction parameters before the update.
[0122] It is worth noting that in this embodiment, S213 and S214 are parallel steps, and only one of them will be executed.
[0123] In this embodiment, if it is determined that the dryness of the current clothing does not meet all the requirements, the covariance prediction parameters can be updated using the gain parameters and transformation relationships, providing a basis for predicting the state parameters corresponding to the next prediction state.
[0124] Figure 3This is a schematic diagram of a clothes drying device provided in an embodiment of the present invention. This device is applied to clothes drying equipment. Figure 3 As shown, the device includes:
[0125] The prediction module 301 is used to predict the current state change parameters based on the previous state change parameters in the drying equipment, and to determine the measurement residual based on the actual change parameters of the current state and the current state change parameters.
[0126] The calculation module 302 is used to determine the current covariance prediction parameter based on the previous covariance prediction parameter and the process noise, and to determine the gain parameter based on the current covariance prediction parameter and the sensor's noise operator.
[0127] The control module 303 is used to determine the prediction estimation parameters of the state change parameters based on the current state prediction change parameters, gain parameters and measurement residuals, predict the dryness of the clothes based on the prediction estimation parameters and the state weights corresponding to the prediction estimation parameters, and stop drying the clothes when the dryness of the clothes meets the dryness requirements.
[0128] Based on the above embodiments, the prediction module 301 is specifically used for:
[0129] Predict the current state's predicted change parameters based on the state transition parameters and the predicted change parameters of the previous state; determine the measurement residual based on the conversion relationship between the predicted and measured values, the actual change parameters of the current state, and the predicted change parameters of the current state.
[0130] Based on the above embodiments, the device further includes a determination module; the determination module is specifically used for: determining process noise.
[0131] The heater and fan inside the dryer are turned off, and the state test parameters corresponding to multiple consecutive time points are obtained. For any given time point, the error noise corresponding to that time point is determined based on the state test parameters corresponding to that time point, the state test parameters corresponding to the previous time point, and the state transition parameters. The noise mean is determined based on the error noise corresponding to all time points, and the process noise is determined based on the noise mean.
[0132] Based on the above embodiments, the calculation module 302 is specifically used for:
[0133] The initial covariance prediction parameters are determined based on the state transition parameters and the previous covariance prediction parameters; the current covariance prediction parameters are determined based on the initial covariance parameters and the process noise; the gain coefficient is determined based on the current covariance prediction parameters, the transition relationship, and the noise operator; and the gain parameters are determined based on the current covariance prediction parameters, the transition relationship, and the gain coefficient.
[0134] Based on the above embodiments, the control module 303 is specifically used to determine the predicted estimation parameters of the state change parameters according to the current state prediction change parameters, gain parameters, and measurement residuals.
[0135] The residual gain is determined based on the measurement residual and the gain parameter; the residual gain is added to the current state prediction change parameter to obtain the prediction estimate parameter.
[0136] Based on the above embodiments, the prediction estimation parameters include temperature prediction estimation parameters, humidity prediction estimation parameters, and volatile matter prediction estimation parameters; the control module 303 predicts the dryness of clothing based on the prediction estimation parameters and the corresponding state weights, and is specifically used for:
[0137] The predicted humidity difference is determined based on the humidity prediction estimation parameters and humidity weights; the predicted temperature product is determined based on the temperature prediction estimation parameters and temperature weights; the predicted volatile product is determined based on the volatile product prediction estimation parameters and volatile product weights; and the dryness of the clothing is determined based on the predicted humidity difference, the predicted temperature product, and the predicted volatile product.
[0138] Based on the above embodiments, the drying of clothes is stopped when the dryness of the clothes meets the dryness requirements. The control module 303 is specifically used for:
[0139] Stop drying the clothes when the dryness of the clothes is greater than or equal to the dryness requirement and the volatile matter prediction estimate parameter is less than the volatile matter threshold;
[0140] After predicting the dryness of the clothes based on the predicted parameters and the corresponding state weights, the control module 303 is further used for:
[0141] When the dryness of the clothes is less than the dryness requirement or the volatile matter prediction estimate parameter is greater than or equal to the volatile matter threshold, the current covariance prediction parameter is updated according to the current covariance prediction parameter, gain parameter, and the conversion relationship between the predicted value and the measured value. The updated current covariance prediction parameter is used as the previous covariance prediction parameter, and the current state prediction change parameter is used as the previous state prediction change parameter. Then, the process returns to the step of predicting the current state prediction change parameter based on the previous state prediction change parameter in the drying equipment.
[0142] The clothes drying device provided in the embodiments of the present invention can perform the clothes drying method provided in any embodiment of the present invention, and has the corresponding functional modules and beneficial effects of performing the method.
[0143] It is worth noting that in the embodiments of the above-mentioned clothes drying device, the various units and modules included are only divided according to functional logic, but are not limited to the above division, as long as the corresponding functions can be achieved; in addition, the specific names of each functional unit are only for easy differentiation and are not used to limit the scope of protection of the present invention.
[0144] Figure 4 This is a schematic diagram of the structure of an electronic device in a clothes drying device provided in an embodiment of the present invention. Figure 4 A block diagram is shown of an exemplary electronic device 11 suitable for implementing embodiments of the present invention. Figure 4 The electronic device 11 shown is merely an example and should not impose any limitations on the functionality and scope of use of the embodiments of the present invention.
[0145] like Figure 4 As shown, the electronic device 11 is represented in the form of a general-purpose computing electronic device. The components of the electronic device 11 may include, but are not limited to: one or more processors or processing units 16, system memory 28, and bus 18 connecting different system components (including system memory 28 and processing unit 16).
[0146] Bus 18 represents one or more of several bus architectures, including a memory bus or memory controller, a peripheral bus, a graphics acceleration port, a processor, or a local bus using any of the various bus architectures. For example, these architectures include, but are not limited to, the Industry Standard Architecture (ISA) bus, the Micro Channel Architecture (MAC) bus, the Enhanced ISA bus, the Video Electronics Standards Association (VESA) local bus, and the Peripheral Component Interconnect (PCI) bus.
[0147] Electronic device 11 typically includes a variety of computer system readable media. These media can be any available media that can be accessed by electronic device 11, including volatile and non-volatile media, removable and non-removable media.
[0148] System memory 28 may include computer system readable media in the form of volatile memory, such as random access memory (RAM) 30 and / or cache memory 32. Electronic device 11 may further include other removable / non-removable, volatile / non-volatile computer system storage media. By way of example only, storage system 34 may be used to read and write non-removable, non-volatile magnetic media (… Figure 4 Not shown; usually referred to as a "hard drive"). Although Figure 4Not shown, a disk drive for reading and writing to a removable non-volatile disk (e.g., a "floppy disk") and an optical disk drive for reading and writing to a removable non-volatile optical disk (e.g., a CD-ROM, DVD-ROM, or other optical media) may be provided. In these cases, each drive may be connected to bus 18 via one or more data media interfaces. System memory 28 may include at least one program product having a set (e.g., at least one) of program modules configured to perform the functions of the embodiments of the present invention.
[0149] A program / utility 40 having a set (at least one) of program modules 42 may be stored, for example, in system memory 28. Such program modules 42 include, but are not limited to, an operating system, one or more application programs, other program modules, and program data. Each or some combination of these examples may include an implementation of a network environment. Program modules 42 typically perform the functions and / or methods described in the embodiments of the present invention.
[0150] Electronic device 11 can also communicate with one or more external devices 14 (e.g., keyboard, pointing device, display 24, etc.), and with one or more devices that enable a user to interact with electronic device 11, and / or with any device that enables electronic device 11 to communicate with one or more other computing devices (e.g., network card, modem, etc.). This communication can be performed via input / output (I / O) interface 22. Furthermore, electronic device 11 can also communicate with one or more networks (e.g., local area network (LAN), wide area network (WAN), and / or public networks, such as the Internet) via network adapter 20. Figure 4 As shown, network adapter 20 communicates with other modules of electronic device 11 via bus 18. It should be understood that, although... Figure 4 As not shown, other hardware and / or software modules may be used in conjunction with electronic device 11, including but not limited to: microcode, device drivers, redundant processing units, external disk drive arrays, RAID systems, tape drives, and data backup storage systems.
[0151] The processing unit 16 executes various functional applications and page displays by running programs stored in the system memory 28, such as implementing the clothes drying method provided in this embodiment. Of course, those skilled in the art will understand that the processor can also implement the technical solutions of the clothes drying method provided in any embodiment of the present invention.
[0152] This invention provides a computer-readable storage medium storing a computer program that, when executed by a processor, implements, for example, the clothes drying method provided in this invention. The computer storage medium of this invention can be any combination of one or more computer-readable media. The computer-readable medium can be a computer-readable signal medium or a computer-readable storage medium. For example, a computer-readable storage medium can be, but is not limited to, an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any combination thereof. More specific examples (a non-exhaustive list) of a computer-readable storage medium include: an electrical connection having one or more wires, a portable computer disk, a hard disk, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), optical fiber, portable compact disk read-only memory (CD-ROM), optical storage device, magnetic storage device, or any suitable combination thereof. In this document, a computer-readable storage medium can be any tangible medium containing or storing a program that can be used by or in conjunction with an instruction execution system, apparatus, or device.
[0153] Computer-readable signal media may include data signals propagated in baseband or as part of a carrier wave, carrying computer-readable program code. Such propagated data signals may take various forms, including but not limited to electromagnetic signals, optical signals, or any suitable combination thereof. Computer-readable signal media may also be any computer-readable medium other than computer-readable storage media, capable of sending, propagating, or transmitting programs for use by or in connection with an instruction execution system, apparatus, or device.
[0154] Program code contained on a computer-readable medium may be transmitted using any suitable medium, including but not limited to: wireless, wire, optical fiber, RF, etc., or any suitable combination thereof.
[0155] This invention also provides a computer program product, including a computer program that, when executed by a processor, implements the clothes drying method provided in any embodiment of this invention.
[0156] Computer program code for performing the operations of this invention can be written in one or more programming languages or a combination thereof. Programming languages include object-oriented programming languages such as Java, Smalltalk, and C++, as well as conventional procedural programming languages—such as the "C" language or similar programming languages. The program code can be executed entirely on the user's computer, partially on the user's computer, as a standalone software package, partially on the user's computer and partially on a remote computer, or entirely on a remote computer or server. In cases involving remote computers, the remote computer can be connected to the user's computer via any type of network, including a local area network (LAN) or a wide area network (WAN), or it can be connected to an external computer (e.g., via the Internet using an Internet service provider).
[0157] Those skilled in the art will understand that the modules or steps of the present invention described above can be implemented using general-purpose computing devices. They can be centralized on a single computing device or distributed across a network of multiple computing devices. Optionally, they can be implemented using computer-executable program code, thereby allowing them to be stored in a storage device for execution by a computing device, or they can be fabricated as separate integrated circuit modules, or multiple modules or steps can be fabricated as a single integrated circuit module. Thus, the present invention is not limited to any particular combination of hardware and software.
[0158] Furthermore, the acquisition, storage, use, and processing of data in the technical solution of this invention all comply with the relevant provisions of national laws and regulations.
[0159] Note that the above description is merely a preferred embodiment of the present invention and the technical principles employed. Those skilled in the art will understand that the present invention is not limited to the specific embodiments described herein, and various obvious changes, readjustments, and substitutions can be made without departing from the scope of protection of the present invention. Therefore, although the present invention has been described in detail through the above embodiments, the present invention is not limited to the above embodiments, and may include many other equivalent embodiments without departing from the concept of the present invention, the scope of which is determined by the scope of the appended claims.
Claims
1. A method for drying clothes, characterized in that, Applied to clothes drying equipment, the method includes: The current state prediction change parameter is predicted based on the previous state prediction change parameter in the drying equipment, and the measurement residual is determined based on the actual change parameter of the current state and the current state prediction change parameter. The current covariance prediction parameter is determined based on the previous covariance prediction parameter and the process noise, and the gain parameter is determined based on the current covariance prediction parameter and the sensor's noise operator. Based on the current state prediction change parameters, the gain parameters, and the measurement residual, the prediction estimation parameters of the state change parameters are determined. Based on the prediction estimation parameters and the state weights corresponding to the prediction estimation parameters, the dryness of the clothes is predicted, and the drying of the clothes is stopped when the dryness of the clothes meets the dryness requirements.
2. The method according to claim 1, characterized in that, The step of predicting the current state change parameters based on the previous state change parameters within the drying equipment, and determining the measurement residual based on the actual current state change parameters and the current state prediction change parameters, includes: Predict the current state prediction change parameters based on the state transition parameters and the previous state prediction change parameters; The measurement residual is determined based on the conversion relationship between the predicted and measured values, the actual change parameters of the current state, and the predicted change parameters of the current state.
3. The method according to claim 1, characterized in that, The method for determining process noise includes: The heater and fan inside the clothes drying equipment are turned off, and the status test parameters corresponding to multiple consecutive time points are obtained; For any given time point, the error noise corresponding to that time point is determined based on the state test parameters corresponding to that time point, the state test parameters corresponding to the previous time point, and the state transition parameters. The noise mean is determined based on the error noise corresponding to all time points, and the process noise is determined based on the noise mean.
4. The method according to claim 2, characterized in that, The step of determining the current covariance prediction parameter based on the previous covariance prediction parameter and process noise, and determining the gain parameter based on the current covariance prediction parameter and the sensor's noise operator, includes: The initial covariance prediction parameters are determined based on the state transition parameters and the previous covariance prediction parameters. The current covariance prediction parameter is determined based on the initial covariance parameter and the process noise. The gain coefficient is determined based on the current covariance prediction parameters, the transformation relationship, and the noise operator. The gain parameter is determined based on the current covariance prediction parameter, the transformation relationship, and the gain coefficient.
5. The method according to claim 1, characterized in that, The prediction estimation parameters for determining the state change parameters based on the current state, the gain parameter, and the measurement residual include: The residual gain is determined based on the measurement residual and the gain parameter. The prediction estimation parameters are obtained by adding the residual gain to the current state prediction change parameters.
6. The method according to claim 1, characterized in that, The prediction estimation parameters include temperature prediction estimation parameters, humidity prediction estimation parameters, and volatile matter prediction estimation parameters; the step of predicting clothing dryness based on the prediction estimation parameters and the state weights corresponding to the prediction estimation parameters includes: The predicted humidity difference is determined based on the humidity prediction estimation parameters and humidity weights. The predicted temperature product is determined based on the temperature prediction estimation parameters and temperature weights. The predicted volatile product value is determined based on the volatile prediction estimation parameters and volatile weights; The dryness of the clothing is determined based on the predicted humidity difference, the predicted temperature product, and the predicted volatile matter product.
7. The method according to claim 6, characterized in that, The step of stopping the drying of the clothes when the dryness of the clothes meets the dryness requirement includes: The drying of the clothes shall be stopped when the dryness of the clothes is greater than or equal to the dryness requirement and the volatile matter prediction estimation parameter is less than the volatile matter threshold. After predicting the dryness of clothing based on the predicted estimation parameters and the state weights corresponding to the predicted estimation parameters, the method further includes: When the dryness of the clothes is less than the dryness requirement or the volatile matter prediction estimation parameter is greater than or equal to the volatile matter threshold, the current covariance prediction parameter is updated according to the current covariance prediction parameter, the gain parameter, and the conversion relationship between the predicted value and the measured value. The updated current covariance prediction parameter is used as the previous covariance prediction parameter, and the current state prediction change parameter is used as the previous state prediction change parameter. Then, the process returns to the step of predicting the current state prediction change parameter based on the previous state prediction change parameter in the drying equipment.
8. A clothes drying device, characterized in that, Applied to clothes drying equipment, the device includes: The prediction module is used to predict the current state change parameters based on the previous state change parameters in the drying equipment, and to determine the measurement residual based on the actual change parameters of the current state and the current state change parameters. The calculation module is used to determine the current covariance prediction parameter based on the previous covariance prediction parameter and the process noise, and to determine the gain parameter based on the current covariance prediction parameter and the sensor's noise operator. The control module is used to determine the prediction estimation parameters of the state change parameters based on the current state prediction change parameters, the gain parameters, and the measurement residuals; predict the dryness of the clothes based on the prediction estimation parameters and the state weights corresponding to the prediction estimation parameters; and stop drying the clothes when the dryness of the clothes meets the dryness requirements.
9. A clothes drying device, characterized in that, include: Electronic devices; The electronic device includes one or more processors; A memory for storing one or more programs that, when executed by one or more processors, cause the one or more processors to implement the clothes drying method as described in any one of claims 1 to 7.
10. A readable storage medium having a computer program stored thereon, characterized in that, When the program is executed by the processor, it implements the clothes drying method as described in any one of claims 1 to 7.