Method and device for determining air supply jet velocity, air conditioner and computer readable storage medium

Abstract

The invention relates to the technical field of air conditioners, and discloses a method for determining the air supply jet velocity. The method comprises the steps that an air supply jet velocity correlation formula established through known parameters and unknown coefficients in advance is obtained; wherein the known parameters comprise the air supply speed of the air conditioner and the distance between the air supply surface and the measured point in the vertical direction; determining an initial sample of the unknown coefficient and a logarithmic posterior probability distribution relational expression about the unknown coefficient; performing iterative solution on unknown parameters according to the initial sample and the logarithmic posterior probability distribution relational expression; and under the condition that iteration is finished, calculating a final value of the unknown coefficient according to an iteration solving result. In this way, the air speed on the air supply jet flow trajectory can be predicted, and the air supply mode of the air conditioner can be optimized and controlled. The air conditioner is suitable for dynamically changing air supply jet flow and complex and diversified in air supply outlet form, and the heat comfort of a user can be improved. The invention further discloses a device for determining the air supply jet velocity, the air conditioner and a computer readable storage medium.

Description

Technical Field

[0001] The present application relates to the technical field of air conditioners, for example, to a method and device for determining a supply air jet velocity, an air conditioner, and a computer-readable storage medium. Background Art

[0002] Air conditioners create an indoor environment by supplying or exhausting air into a room or work area. The layout of the space where the air conditioner is located, the layout of the air outlet of the air conditioner itself, the air supply parameters of the air conditioner, etc. will all affect the air speed on the air supply jet line of the air conditioner.

[0003] The related art discloses a control method, which establishes a jet trajectory model of pollutants under different types of air temperature distribution in a limited space. The jet trajectory model is established based on parameters such as jet length, jet deflection angle, nozzle diameter, room height, etc.

[0004] In the process of implementing the embodiments of the present disclosure, it is found that there are at least the following problems in the related technology:

[0005] The related art only provides a jet trajectory model, but is unable to predict the air velocity on the air supply jet trajectory line.

[0006] It should be noted that the information disclosed in the above background technology section is only used to enhance the understanding of the background of the present application, and therefore may include information that does not constitute the prior art known to ordinary technicians in the field. Summary of the invention

[0007] In order to provide a basic understanding of some aspects of the disclosed embodiments, a brief summary is given below. The summary is not an extensive review, nor is it intended to identify key / critical components or delineate the scope of protection of these embodiments, but rather serves as a prelude to the detailed description that follows.

[0008] The embodiments of the present disclosure provide a method and device for determining the velocity of an air supply jet, an air conditioner, and a computer-readable storage medium to predict the air velocity on the trajectory line of the air supply jet.

[0009] In some embodiments, the method for determining the air supply jet velocity includes: obtaining a correlation equation for the air supply jet velocity that is pre-established by known parameters and unknown coefficients; wherein the known parameters include: the air supply velocity of the air conditioner and the distance between the air supply surface and the measured point in the vertical direction; determining the initial sample of the unknown coefficient and the logarithmic posterior probability distribution relationship equation for the unknown coefficient; iteratively solving the unknown parameters based on the initial sample and the logarithmic posterior probability distribution relationship equation; when the iteration is completed, calculating the final value of the unknown coefficient based on the iterative solution result.

[0010] In some embodiments, the device for determining the supply air jet velocity comprises: a processor and a memory storing program instructions, and the processor is configured to execute the aforementioned method for determining the supply air jet velocity when running the program instructions.

[0011] In some embodiments, the air conditioner includes: an air conditioner body; and a device for determining the air supply jet velocity as described above, which is installed on the air conditioner body.

[0012] In some embodiments, the computer-readable storage medium stores program instructions, and when the program instructions are run, the aforementioned method for determining the air supply jet velocity is executed.

[0013] The method and device for determining the air supply jet velocity, the air conditioner, and the computer-readable storage medium provided in the embodiments of the present disclosure can achieve the following technical effects:

[0014] Obtain the pre-established correlation formula of the air supply jet velocity, and then obtain the unknown coefficients that need to be solved in the correlation formula. Determine the initial sample and logarithmic posterior probability distribution relationship of the unknown coefficients, and then iteratively solve the unknown parameters. After the iteration is completed, the final value of the unknown coefficient is calculated based on the iterative solution result. In this way, the Bayesian Markov Chain Monte Carlo method is used to establish a model of the air velocity on the center line of the air supply jet trajectory of the air conditioner based on the air velocity data on the center line of the jet trajectory of the air conditioner, and the unknown coefficients therein are solved, thereby realizing the prediction of the air velocity on the air supply jet trajectory line, which is conducive to optimizing the air supply mode of the air conditioner. It is suitable for air conditioners with dynamically changing air supply jets and complex and diverse air supply outlets, and can improve the thermal comfort and experience of users.

[0015] The above general description and the following description are exemplary and explanatory only and are not intended to limit the present application. BRIEF DESCRIPTION OF THE DRAWINGS

[0016] One or more embodiments are exemplarily described by corresponding drawings, which do not limit the embodiments. Elements with the same reference numerals in the drawings are shown as similar elements, and the drawings do not constitute a scale limitation, and wherein:

[0017] Figure 1 is a schematic diagram of a method for determining air supply jet velocity provided by an embodiment of the present disclosure;

[0018] Figure 2 is a schematic diagram of another method for determining the air supply jet velocity provided by an embodiment of the present disclosure;

[0019] Figure 3is a schematic diagram of another method for determining the air supply jet velocity provided by an embodiment of the present disclosure;

[0020] Figure 4 is a schematic diagram of another method for determining the air supply jet velocity provided by an embodiment of the present disclosure;

[0021] Figure 5 is a schematic diagram of another method for determining the air supply jet velocity provided by an embodiment of the present disclosure;

[0022] Figure 6 is a schematic diagram of the air velocity on the center line of the air supply jet trajectory of the air conditioner provided by the embodiment of the present disclosure;

[0023] Figure 7 It is a schematic diagram comparing the jet velocity model results obtained by the Bayesian Markov chain Monte Carlo method and the simulation method provided by the embodiment of the present disclosure;

[0024] Figure 8 is a schematic diagram of another device for determining the velocity of an air supply jet provided by an embodiment of the present disclosure;

[0025] Fig. 9 is a schematic diagram of another device for determining the velocity of an air supply jet provided by an embodiment of the present disclosure;

[0026] Fig.10 It is a schematic diagram of an air conditioner provided in an embodiment of the present disclosure. DETAILED DESCRIPTION

[0027] In order to be able to understand the features and technical contents of the embodiments of the present disclosure in more detail, the implementation of the embodiments of the present disclosure is described in detail below in conjunction with the accompanying drawings. The attached drawings are for reference only and are not used to limit the embodiments of the present disclosure. In the following technical description, for the convenience of explanation, a full understanding of the disclosed embodiments is provided through multiple details. However, one or more embodiments can still be implemented without these details. In other cases, to simplify the drawings, well-known structures and devices can be simplified for display.

[0028] The terms "first", "second", etc. in the specification and claims of the embodiments of the present disclosure and the above-mentioned drawings are used to distinguish similar objects, and are not necessarily used to describe a specific order or sequence. It should be understood that the terms used in this way can be interchanged where appropriate, so that the embodiments of the embodiments of the present disclosure described herein. In addition, the terms "including" and "having" and any variations thereof are intended to cover non-exclusive inclusions.

[0029] Unless otherwise stated, the term "plurality" means two or more.

[0030] In the embodiment of the present disclosure, the character " / " indicates that the preceding and following objects are in an "or" relationship. For example, A / B indicates: A or B.

[0031] The term "and / or" is a description of the association relationship between objects, indicating that three relationships can exist. For example, A and / or B means: A or B, or, A and B.

[0032] The term "correspondence" may refer to an association relationship or a binding relationship. The correspondence between A and B means that there is an association relationship or a binding relationship between A and B.

[0033] Combination Figure 1 As shown, the embodiment of the present disclosure provides a method for determining the velocity of the air supply jet, comprising:

[0034] S101, the processor obtains a correlation formula of the air supply jet velocity which is pre-established by known parameters and unknown coefficients; wherein the known parameters include: the air supply velocity of the air conditioner and the distance between the air supply surface and the measured point in the vertical direction.

[0035] S102, the processor determines an initial sample of the unknown coefficients and a logarithmic posterior probability distribution relationship about the unknown coefficients.

[0036] S103, the processor iteratively solves the unknown parameters according to the initial sample and the logarithmic posterior probability distribution relationship.

[0037] S104: When the iteration is finished, the processor calculates the final value of the unknown coefficient according to the iterative solution result.

[0038] The processor of the air conditioner obtains a pre-established correlation formula of the air supply jet velocity, which is established by known parameters and unknown coefficients. The known parameters include: the distance between the air supply surface of the air conditioner and the measured point in the vertical plane direction, the air supply velocity v s .

[0039] Optionally, the correlation formula for the air supply jet velocity is:

[0040]

[0041] In the above formula, x is the distance between the air supply surface of the air conditioner and the measured point in the vertical plane direction, and the unit is m. x It is the air velocity on the center line of the air supply jet trajectory of the air conditioner, in m / s. s is the air velocity at the air outlet of the air conditioner, in m / s. k1 and k2 are unknown coefficients adapted to the air conditioner. x 、v s are all known parameters. Optionally, v x Obtained through simulation software.

[0042] Bayesian inference is used to solve the unknown coefficients. Bayesian inference is based on the known to explore the unknown, and the uncertainty of the unknown coefficients is expressed through probability distribution. It comprehensively considers prior knowledge and sample data, and uses the posterior distribution to dynamically update the estimate of the unknown coefficients, thereby improving the accuracy and reliability of model prediction. Specifically, the initial sample corresponding to the unknown coefficients and the logarithmic posterior probability distribution relationship of the unknown coefficients are determined.

[0043] Use the Markov chain Monte Carlo method to solve Bayesian inference: iteratively solve the unknown parameters based on the relationship between the initial sample and the posterior probability distribution. When the Markov chain reaches a stationary distribution, the distribution of the state will not change over time, and the stationary distribution of the chain is exactly the posterior probability distribution of Bayesian inference.

[0044] When the iteration is finished, the final value of the unknown coefficient is calculated according to the iterative solution result. The final value of the unknown coefficient is substituted into the correlation formula of the air supply jet velocity to obtain the correlation formula that can be used to determine the air supply jet velocity of the air conditioner.

[0045] The method for determining the air supply jet velocity provided by the embodiment of the present disclosure is adopted to obtain the pre-established correlation formula of the air supply jet velocity, and then obtain the unknown coefficients that need to be solved in the correlation formula. The initial sample and the logarithmic posterior probability distribution relationship of the unknown coefficients are determined, and then the unknown parameters are iteratively solved. After the iteration is completed, the final value of the unknown coefficient is calculated based on the iterative solution result. In this way, the Bayesian Markov Chain Monte Carlo method is adopted to establish a model of the air velocity on the center line of the air supply jet trajectory of the air conditioner based on the air velocity data on the center line of the jet trajectory of the air conditioner obtained, and the unknown coefficients therein are solved, thereby realizing the prediction of the air velocity on the air supply jet trajectory line, which is conducive to optimizing the air supply mode of the air conditioner. It is suitable for air conditioners with dynamically changing air supply jets and complex and diverse air supply outlets, and can improve the thermal comfort and experience of users.

[0046] Combination Figure 2 As shown, the embodiment of the present disclosure provides another method for determining the air supply jet velocity, comprising:

[0047] S101, the processor obtains a correlation formula of the air supply jet velocity which is pre-established by known parameters and unknown coefficients; wherein the known parameters include: the air supply velocity of the air conditioner and the distance between the air supply surface and the measured point in the vertical direction.

[0048] S112, the processor assumes a priori probability distribution of the unknown coefficients.

[0049] S122: The processor determines the mean of the prior probability distribution as an initial sample of the unknown coefficients.

[0050] S132, the processor determines a logarithmic posterior probability distribution relationship about the unknown coefficients.

[0051] S103, the processor iteratively solves the unknown parameters according to the initial sample and the logarithmic posterior probability distribution relationship.

[0052] S104: When the iteration is finished, the processor calculates the final value of the unknown coefficient according to the iterative solution result.

[0053] When determining the initial sample of the unknown coefficients, first assume that the prior probability distribution of the unknown coefficients is P k (μ, σ). Specifically, when the unknown coefficients include k1 and k2, the prior probability distribution is expressed as Assuming that the prior probability distribution of the unknown coefficients obeys the normal distribution, the polynomial model function is fitted based on the air supply jet velocity data, and the mean is given by the linear least squares method, and the standard deviation is 1. The mean of the prior probability distribution is used as the initial sample of the coefficient to be confirmed. In this way, the initial sample of the unknown coefficient can be determined so that the value of the unknown coefficient can be determined later.

[0054] Combination Figure 3 As shown, the embodiment of the present disclosure provides another method for determining the air supply jet velocity, comprising:

[0055] S101, the processor obtains a correlation formula of the air supply jet velocity which is pre-established by known parameters and unknown coefficients; wherein the known parameters include: the air supply velocity of the air conditioner and the distance between the air supply surface and the measured point in the vertical direction.

[0056] S112, the processor assumes a priori probability distribution of the unknown coefficients.

[0057] S122: The processor determines the mean of the prior probability distribution as an initial sample of the unknown coefficients.

[0058] S1132, the processor determines the likelihood probability distribution of the air velocity data based on the air velocity data on the center line of the air supply jet trajectory.

[0059] S1232, the processor determines a logarithmic posterior probability distribution relationship about the unknown coefficients according to the prior probability distribution and the likelihood probability distribution.

[0060] S103, the processor iteratively solves the unknown parameters according to the initial sample and the logarithmic posterior probability distribution relationship.

[0061] S104: When the iteration is finished, the processor calculates the final value of the unknown coefficient according to the iterative solution result.

[0062] When determining the logarithmic posterior probability distribution relationship for the unknown coefficients, first assume that the prior probability distribution P of the unknown coefficients k (μ, σ). Then, according to the air velocity data on the center line of the air supply jet trajectory (v x ) i , determine the air velocity data (v x ) i The likelihood probability distribution of , which obeys the normal distribution. The air speed data (v x ) i The likelihood probability distribution is expressed as P k ((v x ) i |μ,σ). Then, according to the prior probability distribution and the likelihood probability distribution, the distribution relationship of the logarithmic posterior probability of the unknown coefficient is determined.

[0063] Specifically, when the unknown coefficients include k1 and k2, the prior probability distribution is expressed as The mean of the likelihood probability distribution of the air velocity data on the center line of the air supply jet trajectory is expressed as formula (2), the standard deviation is expressed as formula (3), and the distribution relationship of the posterior probability of the unknown coefficient is expressed as formula (4).

[0064]

[0065] σ=std((v x ) i ) Formula (3)

[0066] In the above formula, μ is the mean of the likelihood probability distribution, and σ is the standard deviation of the likelihood probability distribution.

[0067]

[0068] In the above formula, P((v x ) i ) is the evidence factor distribution, that is, v x , the air velocity on the center line of the split air conditioner air supply jet trajectory, in m / s; P k (μ,σ) is the prior probability distribution, where and are the prior probability distributions of the unknown coefficients k1 and k2 respectively; P k ((v x ) i |μ,σ) is the likelihood probability distribution; P k (μ,σ|(v x ) i ) is the posterior probability distribution.

[0069] In this way, Bayesian inference is as shown in formula (4), and formula (5) is obtained by taking the logarithm of formula (4). Based on the prior probability distribution of the unknown coefficients and the likelihood probability distribution of the air velocity on the center line of the air supply jet trajectory of the air conditioner, the logarithmic posterior probability distribution relationship of the unknown coefficients is obtained. The estimated value of the coefficient to be confirmed is dynamically updated through the posterior probability distribution, and then the unknown coefficient can be solved.

[0070] Combination Figure 4 As shown, the embodiment of the present disclosure provides another method for determining the air supply jet velocity, comprising:

[0071] S101, the processor obtains a correlation formula of the air supply jet velocity which is pre-established by known parameters and unknown coefficients; wherein the known parameters include: the air supply velocity of the air conditioner and the distance between the air supply surface and the measured point in the vertical direction.

[0072] S102, the processor determines an initial sample of the unknown coefficients and a logarithmic posterior probability distribution relationship about the unknown coefficients.

[0073] S113: The processor determines a candidate sample based on the initial sample.

[0074] S123, the processor substitutes the initial sample and the candidate sample into the logarithmic posterior probability distribution relationship to perform iterative calculation.

[0075] S133: The processor determines whether to update the initial sample according to the calculation result.

[0076] S143: The processor determines whether to save the candidate sample and whether to end the iteration according to the number of iterations.

[0077] S104: When the iteration is finished, the processor calculates the final value of the unknown coefficient according to the iterative solution result.

[0078] When iteratively solving the unknown parameters, sufficient samples are required so that the accurate value of the unknown coefficient can be determined based on enough samples. Therefore, the candidate samples are determined based on the initial samples. The initial samples and candidate samples are substituted into formula (5) for iterative calculation. Here, the essence of iterative calculation is to substitute different samples into formula (5) for calculation, and then judge whether to update the initial sample according to the calculation result. If the initial sample is updated, a new candidate sample is determined according to the new initial sample, and the new candidate sample is substituted into formula (5) for calculation, and then judge whether to update the sample according to the calculation result again. Repeat the iterative calculation multiple times. Each iterative calculation is recorded as one time. According to the number of iterations, it is judged whether to save the candidate sample substituted into formula (5) this time, and whether to end the iteration.

[0079] In this way, by substituting the initial sample and candidate sample of the unknown coefficient into the logarithmic posterior probability distribution relationship for iterative calculation, it is determined whether to update the initial sample, whether to save the sample, and whether to end the iteration, thereby obtaining a stable, reliable and accurate calculation result.

[0080] Optionally, at S113, the processor determines a candidate sample according to the initial sample, including:

[0081] The processor adds a random perturbation to the initial sample to generate candidate samples.

[0082] The random perturbation comes from a normal distribution with a set mean and a standard deviation equal to the thinning step size.

[0083] After the initial sample is determined, a random perturbation is added to the initial sample, and the sum of the initial sample and the random perturbation is used as a candidate sample. The random perturbation is randomly generated in the neighborhood around the initial sample. Specifically, the random perturbation comes from a normal distribution with a set mean and a standard deviation of the thinning step size. Optionally, the mean is set to 0. It can be understood that when the initial sample is updated, the candidate sample is generated by adding a random perturbation to the new initial sample. In this way, candidate samples are generated based on the initial sample and the random perturbation to facilitate cyclic sampling.

[0084] Optionally, at S133, the processor determines whether to update the initial sample according to the calculation result, including:

[0085] The processor calculates a difference between the logarithmic posterior probability distribution corresponding to the initial sample and the logarithmic posterior probability distribution corresponding to the candidate sample.

[0086] The processor takes the difference in log-posterior probability distributions as the acceptance ratio.

[0087] The processor determines whether to update the initial sample based on the acceptance ratio.

[0088] Substitute the initial sample into formula (5) to calculate the corresponding logarithmic posterior probability distribution. Substitute the candidate sample into formula (5) to calculate the corresponding logarithmic posterior probability distribution. Calculate the difference between the two logarithmic posterior probability distributions and use the difference as the acceptance ratio. The acceptance ratio is used as the basis for judging whether the candidate sample can be accepted. If the candidate sample is acceptable, the candidate sample is updated to the new initial sample. In this way, by comparing the logarithmic posterior probability distributions corresponding to the initial sample and the candidate sample, it can be determined whether to update the initial sample in order to determine a more reliable sample.

[0089] Optionally, the processor determines whether to update the initial sample according to the acceptance ratio, including:

[0090] The processor determines whether to accept the candidate sample based on the acceptance ratio.

[0091] The processor determines whether to update the initial sample based on the acceptance of the candidate sample.

[0092] When determining whether to update the initial sample, first determine whether to accept the candidate sample based on the acceptance ratio. Optionally, the larger the acceptance ratio, the more the candidate sample deviates from the initial sample, the less accurate the candidate sample is, and the greater the probability that the candidate sample will not be accepted. On this basis, determine whether to update the initial sample based on the acceptance of the candidate sample. In this way, a stable and reliable sample can be obtained, which is conducive to more accurate iterative calculation results.

[0093] Optionally, the processor determines whether to accept the candidate sample according to the acceptance ratio, including:

[0094] The processor determines to accept the candidate sample if the acceptance ratio is less than the ratio threshold.

[0095] The processor determines to discard the candidate sample when the acceptance ratio is greater than or equal to the ratio threshold.

[0096] A ratio threshold is set in advance to determine whether the candidate sample is within the acceptable range. If the acceptance ratio is less than the ratio threshold, it means that the candidate sample is close to the initial sample, and the candidate sample is determined to be accepted. On the contrary, if the acceptance ratio is greater than or equal to the ratio threshold, it means that the candidate sample deviates from the initial sample. At this time, the candidate sample is not reliable, and the candidate sample is determined to be discarded. In this way, the ratio threshold can be used to screen reliable candidate samples.

[0097] Optionally, the ratio threshold is determined by:

[0098] The processor takes the logarithm of a random number that is uniformly distributed within a preselected value interval.

[0099] The processor uses the resulting logarithmic value as a ratio threshold.

[0100] Preselect a numerical interval, and the values ​​in the preselected numerical interval are uniformly distributed. Randomly select a value in the preselected numerical interval, and take the logarithm of the value. The obtained logarithm value is used as the ratio threshold. Optionally, the preselected numerical interval is [0,1). It can be seen that randomly selecting a value in the preselected numerical interval can improve the reliability of the ratio threshold.

[0101] Optionally, if the candidate sample is accepted, the candidate sample is used as a new initial sample, the new initial sample is substituted into formula (5) for calculation and solution, and the candidate sample is determined based on the new initial sample. If the candidate sample is not accepted, the initial sample is kept unchanged, and then the candidate sample is determined based on the initial sample.

[0102] Optionally, at S143, the processor determines whether to save the candidate sample according to the number of iterations, including:

[0103] The processor performs a thinning and burning process on the candidate samples.

[0104] The processor determines to save the candidate sample when the number of iterations meets the preset thinning condition.

[0105] This embodiment uses the Markov Chain Monte Carlo method for iterative solution, and the Markov Chain Monte Carlo method includes thinning and burning the samples. The parameters of the Markov Chain Monte Carlo method include the number of iterations, the burn-in period, and the thinning interval. The burn-in period of thinning and burning refers to a part of iterative calculations before the samples for analysis are formally collected. The sample volume used during this period will be discarded and not used for the final statistical analysis, so that the Markov chain reaches a steady state. When the number of iterations meets the preset thinning condition, the candidate sample is saved. In this way, by judging whether to save the candidate sample based on the number of iterations, a sample set consisting of multiple candidate samples can be obtained, and these samples can be used to determine the final value of the unknown coefficient.

[0106] Optionally, the preset thinning condition includes: the number of iterations is less than or equal to the burn-in period of the thinning combustion process, and the number of iterations is an integer multiple of the thinning interval. The thinning interval is to reduce the autocorrelation between samples. After the burn-in period ends, the processor determines whether the current number of iterations meets this condition, and saves the sample when this condition is met.

[0107] Optionally, the processor compares the accumulated number of iterations with a preset number of times. If the number of iterations reaches the set number of times, the iteration is terminated. Otherwise, the iteration is continued. Optionally, the set number of times is 100,000 times.

[0108] Combination Figure 5 As shown, the embodiment of the present disclosure provides another method for determining the air supply jet velocity, comprising:

[0109] S101, the processor obtains a correlation formula of the air supply jet velocity which is pre-established by known parameters and unknown coefficients; wherein the known parameters include: the air supply velocity of the air conditioner and the distance between the air supply surface and the measured point in the vertical direction.

[0110] S102, the processor determines an initial sample of the unknown coefficients and a logarithmic posterior probability distribution relationship about the unknown coefficients.

[0111] S103, the processor iteratively solves the unknown parameters according to the initial sample and the logarithmic posterior probability distribution relationship.

[0112] S114: The processor calculates the average value of all stored samples.

[0113] S124: The processor uses the average value as the final estimate of the unknown coefficient.

[0114] As mentioned above, when the number of iterations meets the preset thinning conditions, the candidate samples will be saved. In this way, after the iterations are completed, there may be multiple candidate samples saved. In this way, the average value of all the saved samples is calculated, and the average value is used as the final estimate of the unknown coefficient. It can be understood that for multiple unknown coefficients, each unknown coefficient corresponds to the average value of the sample. It can be seen that the saved candidate samples are both samples that have been accepted after acceptance ratio verification and samples that have met the preset thinning conditions and have been saved. Therefore, the accuracy and reliability of these candidate samples are relatively high. Based on this, taking the average value of these saved samples as the final estimate of the unknown coefficient can improve the accuracy and reliability of the final value of the unknown coefficient.

[0115] The following will take a certain type of split air conditioner as an example to further illustrate the application of this embodiment:

[0116] The air velocity data on the air supply jet trajectory obtained by CFD (Computational Fluid Dynamics) simulation software is as follows: Figure 6 Substituting the air supply speed of 2.5 m / s in this experiment into formula (1), the correlation formula of the air speed on the center line of the air supply jet trajectory of this type of split air conditioner is obtained:

[0117]

[0118] This embodiment adopts the Bayesian Markov Chain Monte Carlo method to solve the unknown coefficients k1 and k2 in the above correlation equation, that is, the solution formula of the unknown coefficients k1 and k2 is expressed by the Bayesian formula (5), and the estimated values ​​of the unknown coefficients k1 and k2 are iteratively solved by the Markov Chain Monte Carlo method.

[0119] Step 1: Assume that the prior probability distribution of the unknown coefficients k1 and k2 obeys the normal distribution: the polynomial model function fitted based on the air supply jet velocity data is given by the linear least squares method and the standard deviation is 1.

[0120] The second step is to express the air velocity data on the center line of the air supply jet trajectory (v x ) i The likelihood probability distribution of , which obeys the normal distribution, has a mean of formula (2) and a standard deviation of formula (3).

[0121] Step 3: Define the parameters of the Markov chain Monte Carlo method and iteratively solve the correlation equation. The number of iterations of the Markov chain Monte Carlo method is 100,000, the burn-in period is 10,000, the thinning interval is 50, and the step size is 1. The initial samples of the unknown coefficients k1 and k2 are given as the mean of their prior probability distribution. The new samples generated in each iteration are generated by adding a random perturbation to the current sample. This perturbation comes from a normal distribution with a mean of 0 and a standard deviation of the step size.

[0122] Calculate the difference between the logarithmic posterior probability of the new sample point and the current sample point, and use the difference as the acceptance ratio. By comparing it with the value of the logarithm of a random number uniformly distributed in the interval [0,1) (ratio threshold), decide whether to accept the new candidate sample: if the acceptance ratio is less than the ratio threshold, accept the new candidate sample, update the current sample point and record the sample point. If the acceptance ratio is greater than or equal to the ratio threshold, discard the new candidate sample and keep the original sample point unchanged.

[0123] Finally, determine whether the number of iterations exceeds the burn-in period and whether the thinning condition is met to decide whether to save the current candidate sample: if the number of iterations is less than or equal to the burn-in period of the thinning combustion process, and the number of iterations is an integer multiple of the thinning interval, then save the current candidate sample. Otherwise, do not save the current candidate sample. At the same time, determine whether the number of iterations reaches the set number of times to decide whether to end the iteration process: if the number of iterations reaches the set number of times, then end the iteration. Otherwise, continue to iterate to solve.

[0124] Step 4: After the iteration is completed, the average values ​​of the saved samples of the unknown coefficients k1 and k2 are calculated to be 0.2557 and 0.9004 respectively. The results are as follows Figure 7 Finally, the air supply jet velocity model of this type of split air conditioner is obtained as follows:

[0125]

[0126] Combination Figure 8 As shown, an embodiment of the present disclosure provides a device 80 for determining the air supply jet velocity, comprising: an acquisition module 81, a determination module 82, an iteration module 83 and a calculation module 84. The acquisition module 81 is configured to obtain a correlation formula of the air supply jet velocity established in advance by known parameters and unknown coefficients; wherein the known parameters include: the air supply velocity of the air conditioner and the distance between the air supply surface and the measured point in the vertical direction. The determination module 82 is configured to determine the initial sample of the unknown coefficient and the logarithmic posterior probability distribution relationship formula about the unknown coefficient. The iteration module 83 is configured to iteratively solve the unknown parameters according to the initial sample and the logarithmic posterior probability distribution relationship formula. The calculation module 84 is configured to calculate the final value of the unknown coefficient according to the iterative solution result when the iteration is completed.

[0127] The device 80 for determining the air supply jet velocity provided by the embodiment of the present disclosure is used to obtain the pre-established correlation formula of the air supply jet velocity, and then obtain the unknown coefficients that need to be solved in the correlation formula. The initial sample and the logarithmic posterior probability distribution relationship of the unknown coefficients are determined, and then the unknown parameters are iteratively solved. After the iteration is completed, the final value of the unknown coefficient is calculated based on the iterative solution result. In this way, the Bayesian Markov Chain Monte Carlo method is used to establish a model of the air velocity on the center line of the air supply jet trajectory of the air conditioner based on the air velocity data on the center line of the jet trajectory of the air conditioner, and the unknown coefficients therein are solved, thereby realizing the prediction of the air velocity on the air supply jet trajectory line, which is conducive to optimizing the air supply mode of the air conditioner. It is suitable for air conditioners with dynamically changing air supply jets and complex and diverse air supply outlets, and can improve the thermal comfort and experience of users.

[0128] Combination Fig. 9 As shown, the embodiment of the present disclosure provides a device 90 for determining the velocity of the air supply jet, including a processor 91 and a memory 92. Optionally, the device 90 may also include a communication interface 93 and a bus 94. The processor 91, the communication interface 93, and the memory 92 may communicate with each other through the bus 94. The communication interface 93 may be used for information transmission. The processor 91 may call the logic instructions in the memory 92 to execute the method for determining the velocity of the air supply jet of the above embodiment.

[0129] In addition, the logic instructions in the memory 92 described above can be implemented in the form of software functional units and can be stored in a computer-readable storage medium when sold or used as an independent product.

[0130] The memory 92 is a computer-readable storage medium that can be used to store software programs and computer executable programs, such as program instructions / modules corresponding to the method in the embodiment of the present disclosure. The processor 91 executes the function application and data processing by running the program instructions / modules stored in the memory 92, that is, the method for determining the air supply jet velocity in the above embodiment is implemented.

[0131] The memory 92 may include a program storage area and a data storage area, wherein the program storage area may store an operating system and an application required for at least one function; the data storage area may store data created according to the use of the terminal device, etc. In addition, the memory 92 may include a high-speed random access memory and may also include a non-volatile memory.

[0132] Combination Fig.10As shown, an embodiment of the present disclosure provides an air conditioner 100, comprising: an air conditioner body, and the above-mentioned device 80 (90) for determining the air supply jet velocity. The device 80 (90) for determining the air supply jet velocity is installed on the air conditioner body. The installation relationship described here is not limited to placement inside the air conditioner body, but also includes installation connections with other components of the air conditioner 100, including but not limited to physical connections, electrical connections or signal transmission connections. It can be understood by those skilled in the art that the device 80 (90) for determining the air supply jet velocity can be adapted to a feasible product body, thereby realizing other feasible embodiments.

[0133] An embodiment of the present disclosure provides a computer-readable storage medium storing computer-executable instructions, wherein the computer-executable instructions are configured to execute the above-mentioned method for determining the velocity of the supply air jet.

[0134] The technical solution of the embodiment of the present disclosure can be embodied in the form of a software product, which is stored in a storage medium and includes one or more instructions for causing a computer device (which may be a personal computer, a server, or a network device, etc.) to execute all or part of the steps of the method described in the embodiment of the present disclosure. The aforementioned storage medium may be a non-transient storage medium, such as a USB flash drive, a mobile hard disk, a read-only memory (ROM), a random access memory (RAM), a disk or an optical disk, and other media that can store program codes.

[0135] The above description and the accompanying drawings fully illustrate the embodiments of the present disclosure so that those skilled in the art can practice them. Other embodiments may include structural, logical, electrical, process and other changes. The embodiments represent only possible changes. Unless explicitly required, separate components and functions are optional, and the order of operation may vary. The parts and features of some embodiments may be included in or replace the parts and features of other embodiments. Moreover, the words used in this application are only used to describe the embodiments and are not used to limit the claims. As used in the description of the embodiments and the claims, unless the context clearly indicates, the singular forms of "a", "an" and "the" are intended to include plural forms as well. Similarly, the term "and / or" as used in this application refers to any and all possible combinations of listings containing one or more associated ones. In addition, when used in the present application, the term "comprise" and its variants "comprises" and / or comprising refer to the presence of stated features, wholes, steps, operations, elements, and / or components, but do not exclude the presence or addition of one or more other features, wholes, steps, operations, elements, components and / or groups thereof. In the absence of further restrictions, the elements defined by the sentence "comprising a ..." do not exclude the presence of other identical elements in the process, method or device comprising the elements. In this article, each embodiment may focus on the differences from other embodiments, and the same and similar parts between the various embodiments may refer to each other. For the methods, products, etc. disclosed in the embodiments, if they correspond to the method part disclosed in the embodiments, then the relevant parts can refer to the description of the method part.

[0136] Those skilled in the art will appreciate that the units and algorithm steps of each example described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are performed in hardware or software may depend on the specific application and design constraints of the technical solution. The technicians may use different methods for each specific application to implement the described functions, but such implementations should not be considered to exceed the scope of the embodiments of the present disclosure. The technicians may clearly understand that, for the convenience and simplicity of description, the specific working processes of the systems, devices and units described above may refer to the corresponding processes in the aforementioned method embodiments, and will not be repeated here.

[0137] In the embodiments disclosed herein, the disclosed methods and products (including but not limited to devices, equipment, etc.) can be implemented in other ways. For example, the device embodiments described above are only schematic. For example, the division of the units can be only a logical function division. There may be other division methods in actual implementation, such as multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. In addition, the coupling or direct coupling or communication connection between each other shown or discussed can be through some interfaces, indirect coupling or communication connection of devices or units, which can be electrical, mechanical or other forms. The units described as separate components may or may not be physically separated, and the components displayed as units may or may not be physical units, that is, they may be located in one place, or they may be distributed on multiple network units. Some or all of the units may be selected according to actual needs to implement this embodiment. In addition, each functional unit in the embodiment of the present disclosure may be integrated in a processing unit, or each unit may exist physically alone, or two or more units may be integrated in one unit.

[0138] The flowchart and block diagram in the accompanying drawings show the possible architecture, function and operation of the system, method and computer program product according to the embodiment of the present disclosure. In this regard, each box in the flowchart or block diagram can represent a module, a program segment or a part of the code, and the module, the program segment or a part of the code contains one or more executable instructions for realizing the specified logical function. In some alternative implementations, the functions marked in the box can also occur in a different order from the order marked in the accompanying drawings. For example, two consecutive boxes can actually be executed substantially in parallel, and they can sometimes be executed in the opposite order, which can depend on the functions involved. In the description corresponding to the flowchart and the block diagram in the accompanying drawings, the operations or steps corresponding to different boxes can also occur in a different order from the order disclosed in the description, and sometimes there is no specific order between different operations or steps. For example, two consecutive operations or steps can actually be executed substantially in parallel, and they can sometimes be executed in the opposite order, which can depend on the functions involved. Each block in the block diagrams and / or flowcharts, and combinations of blocks in the block diagrams and / or flowcharts, may be implemented by a dedicated hardware-based system that performs the specified functions or actions, or may be implemented by a combination of dedicated hardware and computer instructions.

Claims

1. A method for determining the velocity of a supply air jet, characterized in that: include: Obtaining a correlation formula of the air supply jet velocity established in advance by known parameters and unknown coefficients; wherein the known parameters include: the air supply velocity of the air conditioner and the distance between the air supply surface and the measured point in the vertical direction; Determine the initial sample of the unknown coefficients and the logarithmic posterior probability distribution relationship about the unknown coefficients; According to the relationship between the initial sample and the logarithmic posterior probability distribution, the unknown parameters are iteratively solved; At the end of the iteration, the final value of the unknown coefficient is calculated based on the iterative solution result.

2. The method according to claim 1, characterized in that: Determine the logarithmic posterior probability distribution relationship about the unknown coefficients, including: Assume a prior probability distribution for the unknown coefficients; Determine the likelihood probability distribution of the air velocity data based on the air velocity data on the center line of the air supply jet trajectory; Based on the prior probability distribution and the likelihood probability distribution, the logarithmic posterior probability distribution relationship about the unknown coefficients is determined.

3. The method according to claim 1, characterized in that According to the relationship between the initial sample and the logarithmic posterior probability distribution, the unknown parameters are iteratively solved, including: Based on the initial sample, determine the candidate sample; Substitute the initial sample and the candidate sample into the logarithmic posterior probability distribution relationship for iterative calculation; According to the calculation results, determine whether to update the initial sample; According to the number of iterations, determine whether to save the candidate sample and whether to end the iteration.

4. The method according to claim 3, characterized in that According to the calculation results, determine whether to update the initial sample, including: Calculate the difference between the logarithmic posterior probability distribution corresponding to the initial sample and the logarithmic posterior probability distribution corresponding to the candidate sample; The difference in log-posterior probability distributions is taken as the acceptance ratio; Based on the acceptance ratio, determine whether to update the initial sample.

5. The method according to claim 3, characterized in that: Determine whether to save candidate samples based on the number of iterations, including: Thinning and burning the candidate samples; When the number of iterations meets the preset thinning condition, it is determined to save the candidate sample.

6. The method according to claim 5, characterized in that The preset thinning conditions include: The number of iterations is less than or equal to the burn-in period of the thinning combustion process, and the number of iterations is an integer multiple of the thinning interval.

7. The method according to any one of claims 1 to 6, characterized in that Calculate the final value of the unknown coefficient based on the iterative solution results, including: Calculate the average of all saved samples; Use the mean as the final estimate for the unknown coefficients.

8. A device for determining the velocity of a supply air jet, comprising a processor and a memory storing program instructions, characterized in that: The processor is configured to execute the method for determining the supply air jet velocity according to any one of claims 1 to 7 when running the program instructions.

9. An air conditioner, characterized in that: include: Air conditioner body; The device for determining the air supply jet velocity as claimed in claim 8 is installed on the air conditioner body.

10. A computer-readable storage medium storing program instructions, characterized in that: When the program instructions are executed, the computer is used to execute the method for determining the air supply jet velocity according to any one of claims 1 to 7.

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