Parameter processing method of air device and electronic device

Abstract

This invention provides a parameter processing method and electronic device for air equipment. The method includes: determining target parameters for the air equipment; determining a first sample and a second sample corresponding to the target parameters; mixing the data from the first sample and the second sample, sorting the mixed sample in ascending order of data size, and determining the rank of the sorted sample data; determining the reliability of the target parameters based on the data quantity of the first sample, the data quantity of the second sample, and the rank of the second sample data; and using the target parameters as the final parameters of the air equipment. In this method, a rank-sum test is performed using a non-parametric method, resulting in parameters that are comfortable for the user, improving the user experience, and providing the user with more stable, accurate, and data-driven interval predictions.

Description

Technical Field

[0001] This invention relates to the technical field of smart home devices, and in particular to a parameter processing method and electronic device for an air device. Background Technology

[0002] Each user has different perceptions and requirements for the environment. In order to meet the needs of users, it is generally necessary to determine the parameters of air equipment based on the user's usage habits. For example, the historical comfort zone prediction method usually takes the quantile of the user's usage sequence. For example, for the fresh air function, the statistical value of the 80th quantile of the ambient CO2 concentration after each time the user turns on the fresh air function is used as the user's expected value.

[0003] However, this approach is often highly volatile, and the choice of quantile thresholds is arbitrary. The quantiles of the sequence used by the user may not be the user's actual expected value, resulting in a poor user experience. Summary of the Invention

[0004] In view of this, the purpose of the present invention is to provide a parameter processing method and electronic device for air equipment to improve the user experience.

[0005] In a first aspect, embodiments of the present invention provide a parameter processing method for an air device, applied to the controller of the air device; the method includes: determining a target parameter of the air device; determining a first sample and a second sample corresponding to the target parameter; mixing the data of the first sample and the data of the second sample, sorting the mixed sample in ascending order of data, and determining the rank of the sorted sample data; determining the reliability of the target parameter based on the data quantity of the first sample, the data quantity of the second sample, and the rank of the second sample data; and using the target parameter as the final parameter of the air device.

[0006] In a preferred embodiment of this application, the first sample represents a sample whose target parameters are unreliable; the second sample represents a sample whose target parameters are reliable.

[0007] In a preferred embodiment of this application, the air device is a fresh air ventilator, the target parameter is the upper limit of carbon dioxide concentration, the first sample is a sample with a carbon dioxide concentration less than a preset concentration threshold, and the second sample is a sample with a carbon dioxide concentration greater than or equal to the concentration threshold.

[0008] In a preferred embodiment of this application, the steps of determining the first sample and the second sample corresponding to the target parameter include: acquiring historical operating data of the air equipment; acquiring a sample from the historical operating data that characterizes the unreliability of the target parameter as the first sample; and acquiring a sample from the historical operating data that characterizes the reliability of the target parameter as the second sample.

[0009] In a preferred embodiment of this application, the median of the first sample is equal to the median of the second sample.

[0010] In a preferred embodiment of this application, the step of determining the rank of the sorted sample data includes: using the position of the sorted sample data as the rank of the data.

[0011] In a preferred embodiment of this application, the step of determining whether the target parameter is reliable based on the number of data in the first sample, the number of data in the second sample, and the rank of the data in the second sample includes: determining a statistic based on the number of data in the first sample, the number of data in the second sample, and the rank of the data in the second sample; if the statistic meets a preset threshold range, the target parameter is determined reliably; if the statistic does not meet the threshold range, the target parameter is determined unreliably.

[0012] In a preferred embodiment of this application, the statistic is determined based on the number of data points in the first sample, the number of data points in the second sample, and the rank of the second sample using the following formula: Where Z is the statistic, n1 is the number of data points in the first sample, n2 is the number of data points in the second sample, and T is the sum of the ranks of all data points in the second sample.

[0013] In a preferred embodiment of this application, after the above-described step of determining whether the target parameter is reliable, the method further includes: if it is unreliable, continuing to perform the step of determining the target parameter of the air device.

[0014] In a preferred embodiment of this application, the step of continuing to perform the determination of the target parameters of the air device includes: increasing or decreasing the target parameters by a preset step value.

[0015] Secondly, embodiments of the present invention also provide an electronic device, including a processor and a memory, the memory storing computer-executable instructions that can be executed by the processor, the processor executing the computer-executable instructions to implement the parameter processing method of the air device described above.

[0016] Thirdly, embodiments of the present invention also provide a computer-readable storage medium storing computer-executable instructions, which, when invoked and executed by a processor, cause the processor to implement the parameter processing method of the air device described above.

[0017] The embodiments of the present invention bring the following beneficial effects:

[0018] This invention provides a parameter processing method and electronic device for air equipment, which can determine the reliability of target parameters based on the number of data points in a first sample, the number of data points in a second sample, and the rank of the second sample. In this method, a rank-sum test is performed using a non-parametric method, and the calculated parameters are user-friendly, improving the user experience and providing users with more stable, accurate, and data-driven interval predictions.

[0019] Other features and advantages of this disclosure will be set forth in the following description, or some features and advantages may be inferred from the description or determined without doubt, or may be learned by practicing the techniques described above.

[0020] To make the above-mentioned objects, features and advantages of this disclosure more apparent and understandable, preferred embodiments are described below in detail with reference to the accompanying drawings. Attached Figure Description

[0021] To more clearly illustrate the specific embodiments of the present invention or the technical solutions in the prior art, the drawings used in the description of the specific embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are some embodiments of the present invention. For those skilled in the art, other drawings can be obtained from these drawings without creative effort.

[0022] Figure 1 A flowchart illustrating a parameter processing method for an air device provided in an embodiment of the present invention;

[0023] Figure 2 A flowchart of another parameter processing method for an air device provided in an embodiment of the present invention;

[0024] Figure 3 A schematic diagram illustrating the determination of the upper limit threshold of fresh air turbidity provided in an embodiment of the present invention;

[0025] Figure 4 This is a schematic diagram of the structure of a parameter processing device for an air equipment provided in an embodiment of the present invention;

[0026] Figure 5 This is a schematic diagram of the structure of another parameter processing device for an air equipment provided in an embodiment of the present invention;

[0027] Figure 6 This is a schematic diagram of the structure of an electronic device provided in an embodiment of the present invention. Detailed Implementation

[0028] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, the technical solutions of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0029] Each user has different perceptions and requirements for the environment. In order to meet the needs of users, it is generally necessary to determine the parameters of air equipment based on the user's usage habits. For example, the historical comfort zone prediction method usually takes the quantile of the user's usage sequence. For example, for the fresh air function, the statistical value of the 80th quantile of the ambient CO2 concentration after each time the user turns on the fresh air function is used as the user's expected value.

[0030] This approach has the following problems: it only refers to health standards and does not provide personalized settings; it simply uses quantiles, modes, etc., the determination of quantiles is highly arbitrary, and the mode ignores the user's usage time information in different environments; and it relies on conventional hypothesis testing, because conventional hypothesis testing assumes that the latent distribution follows a normal distribution or other assumed distributions, and requires a relatively large sample size to make inferences.

[0031] Therefore, this method is usually highly volatile, and the choice of quantile thresholds is arbitrary. The quantiles of the sequence used by the user may not be the user's actual expected value, resulting in a poor user experience.

[0032] Based on this, the present invention provides a parameter processing method and electronic device for air equipment, specifically involving a rank-sum test to predict thresholds. By learning user habits, the method mines the user's tolerance thresholds for ambient temperature, humidity, and air turbidity, obtains the user's personalized comfort range, and automatically provides corresponding function activation and deactivation, thereby improving the intelligence of the device and the user experience.

[0033] To facilitate understanding of this embodiment, a parameter processing method for an air device disclosed in this embodiment of the invention will first be described in detail.

[0034] Example 1:

[0035] This invention provides a parameter processing method for air equipment, applied to the controller of the air equipment. The air equipment, also known as air conditioning equipment, is used to regulate ambient air. Air equipment can include air conditioners, heaters, fans, humidifiers, dehumidifiers, air purifiers, and fresh air systems, etc. The parameters of the air equipment can be understood as the comfort threshold values ​​for fresh air, humidification, and purification, i.e., the tolerable threshold values ​​for temperature, humidity, and air turbidity around the user.

[0036] Based on the above description, see Figure 1 The flowchart shown illustrates a parameter processing method for an air device, which includes the following steps:

[0037] Step S102: Determine the target parameters of the air equipment.

[0038] Specifically, the parameters of the air conditioning device can be the user's tolerance thresholds for ambient temperature, humidity, and air turbidity. The target parameter can be a value determined from a pre-set parameter range. For example, the parameter range can be 20-100, and the minimum value of 20 or the maximum value of 100 can be used as the initial target parameter. Optionally, the initial target parameter can also be manually selected by the user. For example, when the user selects the "Strong" mode, the maximum value of 100 is used as the initial target parameter, and the target parameter value fluctuates between 70 and 100; when the user selects the "Gentle" mode, the maximum value of 20 is used as the initial target parameter, and the target parameter value fluctuates between 20 and 50; when the user selects the "Normal" mode, the maximum value of 60 is used as the initial target parameter, and the target parameter value fluctuates between 50 and 70.

[0039] Step S104: Determine the first and second samples corresponding to the target parameters.

[0040] After determining the target parameters, two hypotheses can be established: the null hypothesis H_0 and the alternative hypothesis H_1. The null hypothesis H_0 can be understood as the hypothesis that we hope the target parameters will be rejected; that is, if the null hypothesis H_0 is true, the target parameters are unreliable. The alternative hypothesis H_1 can be understood as the hypothesis that we hope the target parameters will be supported; that is, if the alternative hypothesis H_1 is true, the target parameters are reliable.

[0041] The first and second samples are used to verify which of the null hypothesis H_0 and alternative hypothesis H_1 is true, i.e., to verify whether the target parameter is reliable. These can be numerical sequences. For example, suppose we want to infer whether the upper limit of a user's comfortable air turbidity range is n (target parameter is n). The problem can be transformed into: the duration (y) of using fresh air ventilation when CO2 is above n is significantly longer than the duration (x) of using fresh air ventilation when CO2 is below n. Then, the first sample can be the sample when CO2 is below n, and the second sample can be the sample when CO2 is above n.

[0042] To ensure reliability, the number of data points in the first and second samples should be increased as much as possible, with the optimal number of data points in both samples being greater than 10.

[0043] Step S106: Mix the data of the first sample and the data of the second sample, sort the mixed sample in ascending order of data, and determine the rank of the sorted sample data.

[0044] The key to this invention lies in applying the rank-sum test, a non-parametric test method, to the detection of user thresholds.

[0045] The most basic information about a set of data is its order. Sorting the data points in order of magnitude, the order of each specific data point within the entire sequence is called its rank. The number of ranks corresponds to the number of observations. Under certain assumptions, the distribution of these ranks and their statistics can be calculated, and is independent of the original population distribution. Nonparametric means that its methods do not involve parameters describing the population distribution; it is independent of the distribution itself, and its inference methods are unrelated to the population distribution from which the data originates. Nonparametric tests make no assumptions about the population distribution; they directly infer the population distribution from the analysis of the sample.

[0046] Therefore, it is necessary to mix the data from the first sample and the data from the second sample together, sort the mixed sample in ascending order of data, and determine the position of the data in the sorted sample as the rank of the data.

[0047] For example, if the data in the first sample is [3, 8] and the data in the second sample is [2, 6], then the mixed sample is [3, 8, 2, 6], and the sorted sample is [2, 3, 6, 8]. The rank of the data 2 in the second sample is 1, and the rank of the data 6 in the second sample is 3. The rank of the data 3 in the first sample is 2, and the rank of the data 8 in the first sample is 4.

[0048] Step S108: Determine the reliability of the target parameters based on the number of data in the first sample, the number of data in the second sample, and the rank of the data in the second sample.

[0049] The confidence level can be calculated based on the number of data points in the first sample, the number of data points in the second sample, and the rank of the second sample, thereby determining whether the target data is reliable.

[0050] Step S110: The target parameter is used as the final parameter of the air equipment.

[0051] If the target data is reliable, the target parameter can be used as the final parameter of the air equipment, and the air equipment can be controlled to operate according to the final parameter. If the target data is unreliable, the target parameter needs to be reselected, and the parameter processing method of the air equipment described above needs to be continued until reliable target data is determined.

[0052] This invention provides a parameter processing method for air equipment, which can determine the reliability of target parameters based on the number of data points in a first sample, the number of data points in a second sample, and the rank of the second sample. In this method, a rank-sum test is performed using a non-parametric method, and the calculated parameters are user-friendly, improving the user experience and providing users with more stable, accurate, and data-driven interval predictions.

[0053] Example 2:

[0054] This embodiment provides another parameter processing method for air equipment. This method is implemented based on the above embodiment, and focuses on describing the specific implementation steps when the target parameter is unreliable, such as... Figure 2 The flowchart illustrates another parameter processing method for an air device. The parameter processing method for the air device in this embodiment includes the following steps:

[0055] Step S202: Determine the target parameters of the air equipment.

[0056] Specifically, the target parameters of the air equipment can be determined by the following steps: using the maximum or minimum value within the selected range as the target parameter. The air equipment controller pre-stores the parameter selection range; for example, the parameter selection range can be [750, 800, 850, 900, 1000, 1100, 1200, 1300]. Therefore, the maximum value of 1300 or the minimum value of 750 can be used as the target parameter.

[0057] Step S204: Determine the first and second samples corresponding to the target parameters.

[0058] The first sample can represent samples where the target parameter is unreliable; the second sample can represent samples where the target parameter is reliable. For example, if the air equipment is a fresh air system and the target parameter is the upper limit of carbon dioxide concentration, the first sample is a sample where the carbon dioxide concentration is less than the preset concentration threshold, and the second sample is a sample where the carbon dioxide concentration is greater than or equal to the concentration threshold.

[0059] Specifically, the first and second samples corresponding to the target parameters can be determined by the following steps: obtaining historical operating data of the air equipment; obtaining samples from the historical operating data that characterize the unreliability of the target parameters as the first sample; and obtaining samples from the historical operating data that characterize the reliability of the target parameters as the second sample.

[0060] Historical operating data can be the environmental values ​​at each time the air purifier is turned on and off. For example, suppose we want to infer whether the upper limit of a user's comfortable air turbidity range is n. The problem is transformed into whether the duration (y) of using the fresh air system when CO2 is higher than n is significantly longer than the duration (x) of using the fresh air system when CO2 is lower than n. Therefore, the following hypothesis tests are established: Null hypothesis H_0 (the hypothesis we hope to reject): y = x; Alternative hypothesis H_1 (the hypothesis that the data may support): y > x.

[0061] Therefore, the samples (Y1, Y2, ..., Yn) with CO2 higher than n can be used as the second sample, and the samples (X1, X2, ..., Xm) with CO2 lower than n can be used as the first sample. To ensure the accuracy of the verification, the median of the first sample can be equal to the median of the second sample.

[0062] Step S206: Mix the data of the first sample and the data of the second sample, sort the mixed sample in ascending order of data, and determine the rank of the sorted sample data.

[0063] The first and second samples are mixed together, and the N (=m+n) numbers are arranged in ascending order, so that each observation has its own rank in the mixed arrangement. Let Ri be the rank of Yi among these N numbers. Obviously, if Wy=sum(Ri) is large, the value of the Y sample is too large, and the null hypothesis can be questioned.

[0064] Step S208: Determine whether the target parameter is reliable based on the number of data in the first sample, the number of data in the second sample, and the rank of the data in the second sample.

[0065] Specifically, the reliability of the target parameters can be determined through the following steps:

[0066] The statistic is determined based on the number of data points in the first sample, the number of data points in the second sample, and the rank of the second sample. If the statistic meets the preset threshold range, the target parameter is reliably determined; if the statistic does not meet the threshold range, the target parameter is unreliable.

[0067] Specifically, the statistic is determined based on the number of data points in the first sample, the number of data points in the second sample, and the rank of the second sample using the following formula:

[0068]

[0069] Where Z is the statistic, n1 is the number of data points in the first sample, n2 is the number of data points in the second sample, and T is the sum of the ranks of all data points in the second sample. Theoretically, we can calculate the statistic Z and mathematically prove that it follows a normal distribution N(0,1). For example, if the statistic meets a preset threshold range, the target parameter is reliably determined; if the statistic does not meet the threshold range, the target parameter is unreliably determined. If |Z| is less than 1.28 or 1.96, we can retain the null hypothesis H_0 and reject the alternative hypothesis H_1. Therefore, the significance of the target parameter can be determined by calculating the statistic Z.

[0070] Specifically, the detailed process of determining whether sequence Y (the second sample) is significantly greater than sequence X (the first sample) can be as follows: the user inputs sequence X and sequence Y, where sequence Y is a data sequence with a longer running time, for example: sequence X is [3, 15] and sequence Y is [5, 18].

[0071] Next, calculate the rank(s) of sequence X (rankX) and the rank(s) of sequence Y (rankY). Mix sequences X and Y and sort them in ascending order, resulting in [3, 5, 15, 18]. The ranks of sequence X are 1 and 3, and the ranks of sequence Y are 2 and 4. The sum of the ranks of sequence Y is T = 2 + 4 = 6. Note that during the sorting process, if equal values ​​appear, their indices are merged. For example, in the sorted sequence [5, 5, 15, 18], the indices of the two 5s are both 1.

[0072] Determine the number of values ​​n1 in sequence X and the number of values ​​n2 in sequence Y, where n1 = 2 and n2 = 2. Calculate the total number of values ​​for sequences X and Y, N = n1 + n2 = 2 + 2 = 4. Then calculate the mean: mean = n1 × (N + 1) / 2 = 2 × (4 + 1) / 2 = 5. Calculate the variance: std = sart(n1 × n2 × (N + 1) / 12) = sart(2 × 2 × (4 + 1) / 12) = 1.291. Finally, calculate Z = (T - mean) / std = (6 - 5) / 1.291 = 0.775. |Z| = 0.775 is less than 1.28, so we retain the null hypothesis H_0 and reject the alternative hypothesis H_1, meaning the target parameter is unreliable.

[0073] Step S210: If reliable, use the target parameters as the final parameters for the air equipment.

[0074] If the target data is reliable, the target parameters can be used as the final parameters of the air equipment, and the air equipment can be controlled to operate according to the final parameters.

[0075] If step S212 is unreliable, continue with the step of determining the target parameters of the air equipment.

[0076] If the target data is unreliable, the target parameter needs to be reselected, for example, by increasing or decreasing the target parameter by a preset step value. For instance, if the target parameter is 70 and the step value is +5, it can be increased by 5 each time, with 75, 80, and 85 successively used as the target parameter for the air equipment until the target parameter is reliable.

[0077] The following is a detailed explanation of the process of this invention, using the determination of the upper limit threshold for fresh air turbidity as an example: Problem: Given a range of values ​​[750, 800, 850, 900, 1000, 1100, 1200, 1300] (the range can be customized according to requirements, but should follow an ascending order, as we need to select the smallest possible value), we need to determine which value to use as the threshold. See [link to relevant documentation] for details. Figure 3 The diagram shows a method for determining the upper limit threshold of fresh air turbidity.

[0078] like Figure 3 The loop begins from the initial interval. It determines if 750 is the upper limit, i.e., n = 750. The hypotheses are: the null hypothesis is that there is no difference in runtime between CO2 concentrations less than 750 and CO2 concentrations greater than 750; the alternative hypothesis is that runtime is longer for CO2 concentrations greater than 750 than for CO2 concentrations less than 750.

[0079] Obtain the runtime sequence (X1, X2, ..., Xm) when CO2 concentration is less than 750; obtain the sample (Y1, Y2, ..., Yn) when CO2 is higher than n; after mixing the samples, sort each sample to obtain the rank and calculate the statistic Z.

[0080] The null hypothesis is checked against a table to determine if it is a low-probability event. If it is, the null hypothesis is rejected, and the alternative hypothesis is considered valid, meaning the target parameter is n. If the null hypothesis is not rejected, the process moves to the next value.

[0081] For example, taking humidification as an example, the default comfort range is (45, 70). The reliability of the upper limit 70 and the lower limit 45 can be determined by using the method provided in the embodiments of the present invention to obtain a more reliable comfort range.

[0082] The method provided in this embodiment of the invention proposes a way to obtain the user's comfort range through a non-parametric test (rank-sum test), providing users with more stable, accurate, and data-based interval predictions.

[0083] A common approach is to take quantiles or modes based on environmental values ​​during user usage. While quick and convenient, this method is highly arbitrary and prone to data fluctuations. The method provided in this invention determines the critical point of the comfort zone based on the rank-sum test. Although the calculation is more complex, the results are more robust. Furthermore, by incorporating user usage time into the input, it more accurately reflects user habits, resulting in a better user experience.

[0084] This method uses nonparametric hypothesis testing, departing from the general hypothesis testing (which requires a relatively large sample size) that assumes the latent distribution follows a normal distribution. Instead, it uses only the order information of the user's usage time sequence in different environments, based on a reasonable assumption (taking humidifiers as an example): if the user feels dry, their usage time will be ranked in a relatively larger position. With less data, the user's comfort threshold can be inferred.

[0085] Example 3:

[0086] Corresponding to the above method embodiments, this invention provides a parameter processing device for an air equipment, applied to the controller of the air equipment. See [link to relevant documentation]. Figure 4 The diagram shows a structural schematic of a parameter processing device for an air equipment. The parameter processing device includes:

[0087] Target parameter determination module 41 is used to determine the target parameters of the air equipment;

[0088] The first and second sample determination module 42 is used to determine the first and second samples corresponding to the target parameters.

[0089] The sample data processing module 43 is used to mix the data of the first sample and the data of the second sample, sort the mixed sample in ascending order of data, and determine the rank of the sorted sample data.

[0090] The reliability judgment module 44 is used to determine the reliability of the target parameter based on the number of data in the first sample, the number of data in the second sample, and the rank of the data in the second sample.

[0091] The final parameter determination module 45 is used to use the target parameters as the final parameters of the air equipment.

[0092] This invention provides a parameter processing device for an air conditioning system. It can determine the reliability of target parameters based on the number of data points in a first sample, the number of data points in a second sample, and the rank of the second sample. This method uses a non-parametric rank-sum test, and the calculated parameters are user-friendly, improving the user experience and providing more stable, accurate, and data-driven interval predictions.

[0093] The first sample mentioned above represents samples where the target parameters are unreliable; the second sample mentioned above represents samples where the target parameters are reliable.

[0094] The aforementioned air equipment is a fresh air system. The target parameter is the upper limit of carbon dioxide concentration. The first sample is a sample with a carbon dioxide concentration less than the preset concentration threshold, and the second sample is a sample with a carbon dioxide concentration greater than or equal to the concentration threshold.

[0095] The aforementioned first and second sample determination modules are used to acquire historical operating data of the air equipment; acquire samples from the historical operating data that characterize unreliable target parameters as the first sample; and acquire samples from the historical operating data that characterize reliable target parameters as the second sample.

[0096] The median of the first sample is equal to the median of the second sample.

[0097] The aforementioned sample data processing module is used to take the position of the sorted sample data as the rank of the data.

[0098] The aforementioned reliability judgment module is used to determine a statistic based on the number of data in the first sample, the number of data in the second sample, and the rank of the data in the second sample. If the statistic meets the preset threshold range, the target parameter is reliably determined; if the statistic does not meet the threshold range, the target parameter is unreliably determined.

[0099] The aforementioned reliability assessment module is used to determine a statistic based on the number of data points in the first sample, the number of data points in the second sample, and the rank of the second sample using the following formula: Where Z is the statistic, n1 is the number of data points in the first sample, n2 is the number of data points in the second sample, and T is the sum of the ranks of all data points in the second sample.

[0100] See Figure 5 The diagram shows another parameter processing device for an air device. This device further includes a target parameter re-determination module 46, which is connected to the reliability judgment module 44. The target parameter re-determination module 46 is used to continue the step of determining the target parameters of the air device if the reliability is not guaranteed.

[0101] The target parameter redeter module is used to increase or decrease the target parameter by a preset step value.

[0102] Those skilled in the art will clearly understand that, for the sake of convenience and brevity, the specific working process of the parameter processing device for the air equipment described above can be referred to the corresponding process in the embodiments of the parameter processing method for the aforementioned air equipment, and will not be repeated here.

[0103] Example 4:

[0104] This invention also provides an electronic device for processing parameters of the aforementioned air equipment; see [link to related documentation]. Figure 6The diagram shows the structure of an electronic device, which includes a memory 100 and a processor 101. The memory 100 is used to store one or more computer instructions, which are executed by the processor 101 to implement the parameter processing method of the air device described above.

[0105] Furthermore, Figure 6 The electronic device shown also includes a bus 102 and a communication interface 103, with the processor 101, the communication interface 103 and the memory 100 connected via the bus 102.

[0106] The memory 100 may include high-speed random access memory (RAM) or non-volatile memory, such as at least one disk storage device. Communication between this system network element and at least one other network element is achieved through at least one communication interface 103 (which can be wired or wireless), such as the Internet, wide area network, local area network, or metropolitan area network. The bus 102 may be an ISA bus, PCI bus, or EISA bus, etc. The bus can be divided into address bus, data bus, control bus, etc. For ease of representation, Figure 6 The symbol is represented by a single double-headed arrow, but this does not mean that there is only one bus or one type of bus.

[0107] Processor 101 may be an integrated circuit chip with signal processing capabilities. In implementation, each step of the above method can be completed by the integrated logic circuitry in the hardware of processor 101 or by instructions in software form. Processor 101 can be a general-purpose processor, including a Central Processing Unit (CPU), a Network Processor (NP), etc.; it can also be a Digital Signal Processor (DSP), an Application Specific Integrated Circuit (ASIC), a Field-Programmable Gate Array (FPGA), or other programmable logic devices, discrete gate or transistor logic devices, or discrete hardware components. It can implement or execute the methods, steps, and logic block diagrams disclosed in the embodiments of this invention. The general-purpose processor can be a microprocessor or any conventional processor. The steps of the methods disclosed in the embodiments of this invention can be directly manifested as execution by a hardware decoding processor, or execution by a combination of hardware and software modules in the decoding processor. The software module can reside in a readily available storage medium in the art, such as random access memory, flash memory, read-only memory, programmable read-only memory, electrically erasable programmable memory, or registers. This storage medium is located in memory 100, and processor 101 reads information from memory 100 and, in conjunction with its hardware, completes the steps of the method described in the foregoing embodiments.

[0108] This invention also provides a computer-readable storage medium storing computer-executable instructions. When these computer-executable instructions are called and executed by a processor, they cause the processor to implement the parameter processing method for the air device described above. For specific implementation details, please refer to the method embodiments, which will not be repeated here.

[0109] The parameter processing method for air equipment and the computer program product for electronic equipment provided in the embodiments of the present invention include a computer-readable storage medium storing program code. The instructions included in the program code can be used to execute the methods in the preceding method embodiments. For specific implementation, please refer to the method embodiments, which will not be repeated here.

[0110] Those skilled in the art will clearly understand that, for the sake of convenience and brevity, the specific working process of the system and / or device described above can be referred to the corresponding process in the foregoing method embodiments, and will not be repeated here.

[0111] Furthermore, in the description of the embodiments of the present invention, unless otherwise explicitly specified and limited, the terms "installation," "connection," and "linking" should be interpreted broadly. For example, they can refer to a fixed connection, a detachable connection, or an integral connection; they can refer to a mechanical connection or an electrical connection; they can refer to a direct connection or an indirect connection through an intermediate medium; and they can refer to the internal connection of two components. Those skilled in the art can understand the specific meaning of the above terms in the present invention based on the specific circumstances.

[0112] If the aforementioned functions are implemented as software functional units and sold or used as independent products, they can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of this invention, essentially, or the part that contributes to the prior art, or a portion of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods described in the various embodiments of this invention. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.

[0113] In the description of this invention, it should be noted that the terms "center," "upper," "lower," "left," "right," "vertical," "horizontal," "inner," and "outer," etc., indicate the orientation or positional relationship based on the orientation or positional relationship shown in the accompanying drawings. They are used only for the convenience of describing the invention and for simplifying the description, and do not indicate or imply that the device or element referred to must have a specific orientation, or be constructed and operated in a specific orientation. Therefore, they should not be construed as limitations on the invention. Furthermore, the terms "first," "second," and "third" are used for descriptive purposes only and should not be construed as indicating or implying relative importance.

[0114] Finally, it should be noted that the above-described embodiments are merely specific implementations of the present invention, used to illustrate the technical solutions of the present invention, and not to limit it. The scope of protection of the present invention is not limited thereto. Although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that any person skilled in the art can still modify or easily conceive of changes to the technical solutions described in the foregoing embodiments within the technical scope disclosed in the present invention, or make equivalent substitutions for some of the technical features; and these modifications, changes, or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of the present invention, and should all be covered within the scope of protection of the present invention. Therefore, the scope of protection of the present invention should be determined by the scope of the claims.

Claims

1. A parameter processing method of an air device, characterized by, A controller applied to an air equipment; the method includes: Determine the target parameters of the air equipment; Determine a first sample and a second sample corresponding to the target parameter; the first sample is a sample characterizing the unreliability of the target parameter; the second sample is a sample characterizing the reliability of the target parameter. The data from the first sample and the data from the second sample are mixed, and the mixed sample is sorted in ascending order of data to determine the rank of the sorted sample data. The reliability of the target parameter is determined based on the number of data points in the first sample, the number of data points in the second sample, and the rank of the data points in the second sample. The target parameter is used as the final parameter of the air device.

2. The method of claim 1, wherein, The air equipment is a fresh air system, the target parameter is the upper limit of carbon dioxide concentration, the first sample is a sample whose carbon dioxide concentration is less than a preset concentration threshold, and the second sample is a sample whose carbon dioxide concentration is greater than or equal to the concentration threshold.

3. The method of claim 1, wherein, The steps for determining the first and second samples corresponding to the target parameters include: Obtain the historical operating data of the air equipment; The first sample is obtained from the historical operational data, which represents the unreliability of the target parameter. A reliable sample representing the target parameter is obtained from the historical operating data and used as the second sample.

4. The method according to claim 1, characterized in that, The median of the first sample is equal to the median of the second sample.

5. The method of claim 1, wherein, The steps to determine the rank of the sorted sample data include: The position of the data in the sorted sample is used as the rank of the data.

6. The method of claim 1, wherein, The step of determining whether the target parameter is reliable based on the number of data points in the first sample, the number of data points in the second sample, and the rank of the second sample includes: The statistic is determined based on the number of data points in the first sample, the number of data points in the second sample, and the rank of the data points in the second sample; If the statistic meets the preset threshold range, the target parameter is reliably determined; If the statistic does not meet the threshold range, the target parameter determination is unreliable.

7. The method of claim 6, wherein, The statistic is determined using the following formula based on the number of data points in the first sample, the number of data points in the second sample, and the rank of the data points in the second sample: ； Where Z is the statistic, n1 is the number of data points in the first sample, n2 is the number of data points in the second sample, and T is the sum of the ranks of all data points in the second sample.

8. The method according to claim 1, characterized in that, After determining whether the target parameter is reliable, the method further includes: If unreliable, continue with the steps of determining the target parameters of the air equipment.

9. The method of claim 8, wherein, Continuing to perform the step of determining the target parameters of the air equipment includes: Increase or decrease the target parameter by a preset step value.

10. An electronic device, comprising: It includes a processor and a memory, the memory storing computer-executable instructions that can be executed by the processor, the processor executing the computer-executable instructions to implement the parameter processing method of the air device according to any one of claims 1 to 9.

11. A computer readable storage medium, characterized in that, The computer-readable storage medium stores computer-executable instructions, which, when invoked and executed by a processor, cause the processor to implement the parameter processing method of the air device according to any one of claims 1 to 9.

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