A method and system for detecting connection lines of indoor units of fixed-frequency air conditioners

Abstract

The present invention relates to the field of air conditioning detection technology, and specifically to a method and system for detecting the connection line of an indoor unit of a fixed-frequency air conditioner. The method and system for detecting the connection line of an indoor unit of a fixed-frequency air conditioner calculate the abnormal sound characteristic value of the air conditioner, the wind speed singularity index, and the temperature singularity index during the operation of the fixed-frequency air conditioner, and the three are combined to calculate the air conditioning operation chaos degree. The present invention considers the abnormal sound characteristics of the air conditioner during the operation detection of the fixed-frequency air conditioner, and constructs the air conditioning operation chaos degree in combination with the wind speed singularity characteristics and the temperature singularity characteristics detected during the operation of the fixed-frequency air conditioner. Based on the air conditioning operation chaos degree, the detection of whether the connection line of the indoor unit of the fixed-frequency air conditioner has a connection error is completed, thereby improving the accuracy of the detection of the connection line of the indoor unit of the fixed-frequency air conditioner.

Description

Technical Field

[0001] The present invention relates to the technical field of air conditioner detection, and in particular to a method and system for detecting a connection line of an indoor unit of a fixed-frequency air conditioner. Background Art

[0002] Fixed-frequency air conditioners are a type of air conditioner that uses a fixed-frequency compressor to cool at a constant speed. Due to the advantages of easy control and maintenance, fixed-frequency air conditioners are widely used in rail vehicle regulation systems. However, there are still some problems with fixed-frequency air conditioners during the actual installation process, such as the phenomenon of easy connection line errors.

[0003] The connection wires of the fixed-frequency air conditioner include power wires and signal wires, of which the power wires include live wires and neutral wires, and the signal wires include compressor wires, four-way valve wires, and external fan wires. When the signal wires are connected incorrectly, the air conditioner can still operate, which is abnormal operation. If the fixed-frequency air conditioner operates abnormally for a long time, it will cause unpredictable damage to the fixed-frequency air conditioner. It is often difficult for installers to accurately judge whether there is an error in the connection of the air conditioner indoor unit through experience.

[0004] Most of the existing technologies only judge whether there is an error in the connection of the indoor unit of the fixed-frequency air conditioner through the temperature change characteristics. However, due to the influence of the external environment and the operating power of the air conditioner, the air conditioner itself will generate heat during operation, and the change of the environment will cause the inaccurate temperature change characteristics. Therefore, it is impossible to accurately judge whether there is a connection error in the indoor unit of the fixed-frequency air conditioner based on the temperature change characteristics alone, resulting in poor accuracy in the detection of the connection line of the indoor unit of the fixed-frequency air conditioner. Summary of the invention

[0005] In order to solve the technical problem of poor accuracy in detecting the connection line of the indoor unit of a fixed-frequency air conditioner, the present invention provides a method and system for detecting the connection line of the indoor unit of a fixed-frequency air conditioner. The technical solution adopted is as follows:

[0006] A method for detecting a connection line of an indoor unit of a fixed-frequency air conditioner, comprising:

[0007] Collect the audio amplitude of the indoor unit of the fixed-frequency air conditioner, the wind speed at the vent of the indoor unit, and the indoor temperature during the fixed-frequency air conditioning commissioning operation;

[0008] The total length of the acquisition time is equally divided into a number of detection time periods, and the indoor unit audio amplitudes of each detection time period are arranged in chronological order to form an indoor unit audio amplitude sequence corresponding to each detection time period;

[0009] Calculate the abnormal sound characteristic value of the air conditioner in each detection time period based on each indoor unit audio amplitude sequence;

[0010] Identifying an abnormality detection time period based on the abnormal sound feature value of the air conditioner;

[0011] The vent wind speeds corresponding to each abnormal sound detection time period are respectively arranged in a sequence in chronological order to form a vent wind speed sequence corresponding to each abnormal sound detection time period, and the indoor temperatures corresponding to each abnormal sound detection time period are respectively arranged in a sequence in chronological order to form an indoor temperature sequence corresponding to each abnormal sound detection time period;

[0012] Calculating a wind speed singularity index for each abnormal sound detection time period based on the vent wind speed sequence, and calculating a temperature singularity index for each abnormal sound detection time period based on the indoor temperature sequence;

[0013] Calculating the air conditioner operation chaos degree based on the wind speed singularity index, the temperature singularity index and the air conditioner abnormal sound characteristic value;

[0014] Whether the connection line of the fixed-frequency air conditioner is incorrectly connected is determined according to the air conditioner operation disorder degree.

[0015] The step of calculating the abnormal sound characteristic value of the air conditioner in each detection time period based on each indoor unit audio amplitude sequence specifically includes:

[0016] Calculating the Euclidean distance between each indoor unit audio amplitude and other indoor unit audio amplitudes in the same indoor unit audio amplitude sequence respectively, forming a set of the first n indoor unit audio amplitudes with the closest Euclidean distances, and obtaining an audio transfer feature set corresponding to each indoor unit audio amplitude in the indoor unit audio amplitude sequence, wherein n is a preset number;

[0017] Based on the audio transfer feature set, calculating the air conditioning audio transfer tightness value of each indoor unit audio amplitude;

[0018] Arrange the air conditioner audio transmission density values ​​corresponding to the indoor unit audio amplitudes in the same detection time period in chronological order to form a transmission density feature sequence for the detection time period, and obtain a transmission density feature sequence corresponding to each detection time period;

[0019] The minimum value, maximum value and mean value in each transmission tight feature sequence are counted respectively, and all mutation values ​​in each transmission tight feature sequence are obtained based on the Pettitt mutation point detection algorithm. The sequence composed of the minimum value, maximum value, mean value and all mutation values ​​in the same transmission tight feature sequence is used as the transmission mutation feature sequence of the detection time period to obtain the transmission mutation feature sequence of each detection time period;

[0020] The abnormal sound characteristic value of the air conditioner is calculated according to the transmission tight characteristic sequence and the transmission mutation characteristic sequence.

[0021] The step of calculating the wind speed singularity index of each abnormal sound detection time period based on the vent wind speed sequence specifically includes:

[0022] Calculate the first-order difference sequence of each vent wind speed sequence respectively, where the first-order difference sequence is the wind speed fluctuation sequence of the corresponding abnormal sound detection time period;

[0023] Calculate the average value of all data in the same wind speed fluctuation sequence and the absolute value of the difference between each data in the wind speed fluctuation sequence and the average value, and obtain the wind speed fluctuation distance corresponding to each data;

[0024] Accumulating the wind speed fluctuation distances with corresponding data in the wind speed fluctuation sequence to obtain a first accumulated value, and calculating an average value of the first accumulated values ​​to obtain a wind speed fluctuation factor of the wind speed fluctuation sequence;

[0025] Calculating the mean square error of all data in the wind speed fluctuation sequence to obtain a wind speed singularity value of the wind speed fluctuation sequence;

[0026] The product of the wind speed fluctuation factor and the wind speed singularity value of each wind speed fluctuation sequence is calculated respectively to obtain the wind speed singularity index of each abnormal sound detection time period.

[0027] The step of calculating the temperature singularity index of each abnormal sound detection time period based on the indoor temperature sequence specifically includes:

[0028] Collecting the displayed temperature of the fixed-frequency air conditioner;

[0029] respectively calculating the difference between each indoor temperature in the same indoor temperature sequence and the displayed temperature as the temperature singular distance of the corresponding indoor temperature;

[0030] The temperature singular distances are respectively accumulated with the corresponding indoor temperatures in the indoor temperature sequence to obtain a second accumulated value, and the average of the second accumulated values ​​is calculated to obtain the temperature singularity index of the indoor temperature sequence, thereby obtaining the temperature singularity index of each abnormal sound detection time period.

[0031] The step of calculating the air conditioner operation chaos degree based on the wind speed singularity index, the temperature singularity index and the air conditioner abnormal sound characteristic value specifically includes:

[0032] The chaotic feature weight of the abnormal detection time period is calculated based on the wind speed singularity index and the temperature singularity index corresponding to the same abnormal detection time period, so as to obtain the chaotic feature weight corresponding to each abnormal detection time period;

[0033] The product of the chaos feature weight corresponding to each abnormal sound detection time period and the abnormal sound feature value of the air conditioner is calculated respectively, and the product is added up to obtain the air conditioner operation chaos degree.

[0034] The step of calculating the air conditioning audio transmission tightness value of each indoor unit audio amplitude based on the audio transmission feature set specifically includes:

[0035] For an indoor unit audio amplitude sequence, respectively calculating the absolute value of the difference between the i-th indoor unit audio amplitude of the indoor unit audio amplitude sequence and each indoor unit audio amplitude in the corresponding audio transfer feature set, to obtain the transmission distance between the i-th indoor unit audio amplitude and each indoor unit audio amplitude in the audio transfer feature set, wherein i=(1,2,...,m), and m is the total number of indoor unit audio amplitudes contained in the indoor unit audio amplitude sequence;

[0036] Accumulating the transfer distance and the internal machine audio amplitude in the audio transfer feature set to obtain the transfer metric distance of the i-th internal machine audio amplitude;

[0037] Calculating the transferability variation value of the audio transfer feature set of the i-th internal machine audio amplitude;

[0038] Calculate the product of the transfer metric distance of the i-th indoor unit audio amplitude and the corresponding transferability variation value, and the inverse of the sum of the product and the preset artificial threshold parameter, the inverse of which is the air conditioning audio transfer tightness value of the i-th indoor unit audio amplitude.

[0039] The step of calculating the abnormal sound characteristic value of the air conditioner according to the transmission close characteristic sequence and the transmission mutation characteristic sequence specifically includes:

[0040] For a detection time period, the absolute value of the difference between the j-th data in the transmission tight feature sequence corresponding to the detection time period and each data in the corresponding transmission mutation feature sequence is calculated as the mutation distance of the j-th data, where j=(1,2,...,n), and n is the total number of data contained in the transmission tight feature sequence;

[0041] Calculate the mutation distance and the accumulation of all data in the transfer mutation feature sequence to obtain a third accumulated value, where the third accumulated value is the mutation metric distance of the j-th data;

[0042] The mean value of the mutation metric distance of all data in the transmission close feature sequence is calculated as the abnormal sound feature value of the air conditioner in the detection time period.

[0043] The calculation of the chaos feature weight of the abnormal detection time period based on the wind speed singularity index and the temperature singularity index corresponding to the same abnormal detection time period to obtain the chaos feature weight corresponding to each abnormal detection time period specifically includes:

[0044] The product of the wind speed singularity index and the corresponding temperature singularity index in each abnormal sound detection time period is calculated respectively, and the product is normalized to obtain the chaos feature weight of each abnormal sound detection time period.

[0045] Wherein, judging whether the connection line of the fixed-frequency air conditioner is incorrectly connected according to the air conditioner operation disorder degree specifically includes:

[0046] comparing the air conditioner operation chaos degree with a preset chaos degree threshold of the air conditioner system operation when the fixed-frequency air conditioner is correctly wired;

[0047] If the air conditioner operation disorder degree is greater than the preset disorder degree threshold, it is determined that the connection line of the fixed-frequency air conditioner indoor unit is incorrectly connected;

[0048] If the air conditioner operation chaos degree is less than or equal to the preset chaos degree threshold, it is determined that the connection line of the fixed-frequency air conditioner indoor unit is correctly connected.

[0049] A fixed-frequency air conditioner indoor unit connection line detection system, comprising:

[0050] A collection module is used to collect the audio amplitude of the indoor unit of the fixed-frequency air conditioner, the air velocity at the vent of the indoor unit, and the indoor temperature during the fixed-frequency air conditioning commissioning operation;

[0051] The first data processing module is used to divide the total length of the acquisition time into a number of detection time periods, and arrange the indoor unit audio amplitudes of each detection time period in chronological order to form an indoor unit audio amplitude sequence corresponding to each detection time period;

[0052] A first calculation module is used to calculate the abnormal sound characteristic value of the air conditioner in each detection time period based on each indoor unit audio amplitude sequence;

[0053] An identification module, used for identifying an abnormality detection time period based on the abnormal sound characteristic value of the air conditioner;

[0054] The second data processing module is used to respectively sequence the vent wind speeds corresponding to each abnormal sound detection time period in chronological order to form a vent wind speed sequence corresponding to each abnormal sound detection time period, and respectively sequence the indoor temperatures corresponding to each abnormal sound detection time period in chronological order to form an indoor temperature sequence corresponding to each abnormal sound detection time period;

[0055] A second calculation module is used to calculate the wind speed singularity index of each abnormal sound detection time period based on the ventilation outlet wind speed sequence;

[0056] A third calculation module, configured to calculate a temperature singularity index for each abnormal sound detection time period based on the indoor temperature sequence;

[0057] a fourth calculation module, configured to calculate the degree of chaos in air-conditioning operation based on the wind speed singularity index, the temperature singularity index and the air-conditioning abnormal sound characteristic value;

[0058] The judging module is used to judge whether the connecting line of the fixed-frequency air conditioner is connected incorrectly according to the chaotic degree of the air conditioner operation.

[0059] The present invention has the following beneficial effects: the present invention realizes the detection of whether a connection error occurs in the connecting line of the indoor unit of the fixed-frequency air conditioner. Compared with the prior art which judges whether a connection error occurs in the indoor unit of the fixed-frequency air conditioner only by the temperature change characteristics, the present invention takes into account the abnormal sound characteristics of the air conditioner during the operation detection of the fixed-frequency air conditioner, and combines the wind speed singularity characteristics and the temperature singularity characteristics detected when the fixed-frequency air conditioner is running to construct the air-conditioning operation chaos degree, and completes the detection of whether a connection error occurs in the connecting line of the indoor unit of the fixed-frequency air conditioner based on the air-conditioning operation chaos degree, which solves the problem that the prior art is only based on the temperature change characteristics, resulting in the inability to accurately judge whether a connection error occurs in the indoor unit of the fixed-frequency air conditioner, and improves the accuracy of the detection of the connecting line of the indoor unit of the fixed-frequency air conditioner. BRIEF DESCRIPTION OF THE DRAWINGS

[0060] In order to more clearly illustrate the technical solutions and advantages in the embodiments of the present invention or the prior art, the drawings required for use in the embodiments or the prior art descriptions are briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0061] Figure 1 A flow chart of a method for detecting a connection line of an indoor unit of a fixed-frequency air conditioner provided by an embodiment of the present invention;

[0062] Figure 2 A schematic diagram of an audio amplitude sequence of an indoor unit of a fixed-frequency air conditioner provided by an embodiment of the present invention;

[0063] Figure 3 A schematic diagram of a first sub-process of a method for detecting a connection line of an indoor unit of a fixed-frequency air conditioner provided by an embodiment of the present invention;

[0064] Figure 4 A schematic diagram of a second sub-process of a method for detecting a connection line of an indoor unit of a fixed-frequency air conditioner provided by an embodiment of the present invention;

[0065] Figure 5 A schematic diagram of a transmission close characteristic sequence of a fixed-frequency air-conditioning audio provided by an embodiment of the present invention;

[0066] Figure 6 A schematic diagram of a transmission mutation characteristic sequence of a fixed-frequency air-conditioning audio provided by an embodiment of the present invention;

[0067] Figure 7 A schematic diagram of a third sub-process of a method for detecting a connection line of an indoor unit of a fixed-frequency air conditioner provided by an embodiment of the present invention;

[0068] Figure 8 A first schematic diagram of a fixed-frequency air conditioner indoor unit connection line detection system provided by an embodiment of the present invention;

[0069] Fig. 9 A sub-schematic diagram of a fixed-frequency air conditioner indoor unit connection line detection system provided by an embodiment of the present invention;

[0070] Fig.10 A second schematic diagram of a fixed-frequency air conditioner indoor unit connection line detection system provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0071] In order to further explain the technical means and effects adopted by the present invention to achieve the predetermined invention purpose, the following is a detailed description of a method and system for detecting the connection line of the indoor unit of a fixed-frequency air conditioner proposed by the present invention, its specific implementation method, structure, features and effects, in combination with the accompanying drawings and preferred embodiments. In the following description, different "one embodiment" or "another embodiment" does not necessarily refer to the same embodiment. In addition, specific features, structures or characteristics in one or more embodiments may be combined in any suitable form.

[0072] Unless defined otherwise, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this invention belongs.

[0073] The following specifically describes a method and system for detecting indoor unit connection lines of a fixed-frequency air conditioner provided by the present invention in conjunction with the accompanying drawings.

[0074] See also Figure 1 , which shows a method flow chart of a method for detecting a connection line of an indoor unit of a fixed-frequency air conditioner provided by an embodiment of the present invention. The method for detecting a connection line of an indoor unit of a fixed-frequency air conditioner comprises steps S101-S108:

[0075] S101, collecting the audio amplitude of the indoor unit of the fixed-frequency air conditioner, the air velocity at the vent of the indoor unit, and the indoor temperature during the fixed-frequency air conditioning commissioning operation.

[0076] When conducting a connection line test on the fixed-frequency air conditioner to be tested, after the fixed-frequency air conditioner is powered on and runs to a stable state, the temperature sensor in the fixed-frequency air conditioner is used to collect the indoor temperature, the sound sensor is used to collect the audio amplitude of the indoor unit in the indoor unit of the fixed-frequency air conditioner, and the wind speed sensor is used to collect the vent wind speed out of the vent of the indoor unit.

[0077] S102: Divide the total acquisition time into a number of detection time periods, and obtain the indoor unit audio amplitude value sequences corresponding to the number of detection time periods.

[0078] In one embodiment of the invention, the collection time interval is 0.5s, and the total length of the collection time is 30min. The implementer can set the collection time interval and the total length of the collection time based on the actual situation. In order to facilitate the subsequent analysis of the operating status of the fixed-frequency air conditioner, each minute of the collection process is used as the detection time period, and the total length of the collection time is divided into 30 detection time periods. At this point, the indoor temperature, the indoor unit audio amplitude, the vent wind speed and 30 detection time periods are obtained. After obtaining these detection time periods, all the indoor unit audio amplitudes in each detection time period are arranged into a sequence in chronological order, and the indoor unit audio amplitude sequence for each detection time period is obtained. Figure 2 As shown, Figure 2 Schematic diagram of an internal machine audio amplitude sequence in one embodiment.

[0079] S103, calculating the abnormal sound characteristic value of the air conditioner in each detection time period based on each indoor unit audio amplitude sequence.

[0080] The abnormal sound characteristic value of the air conditioner reflects the abnormal sound occurring in the indoor unit of the fixed-frequency air conditioner. The larger the abnormal sound characteristic value of the air conditioner is, the more likely it is that the abnormal sound characteristic will occur in the corresponding detection time period.

[0081] In one embodiment, see Figure 3 , the step S103 specifically includes steps S1031-S1035.

[0082] S1031. Calculate the Euclidean distances between each indoor unit audio amplitude and other indoor unit audio amplitudes in the same indoor unit audio amplitude sequence respectively, and form a set of first n indoor unit audio amplitudes with the closest Euclidean distances to obtain an audio transmission feature set corresponding to each indoor unit audio amplitude in the indoor unit audio amplitude sequence, wherein n is a preset number.

[0083] Taking the total acquisition time of 30 minutes, the acquisition time interval of 0.5 seconds and 1 minute as a detection time period as an example, the total acquisition time is evenly divided into 30 detection time periods to obtain 30 indoor unit audio amplitude sequences. For each indoor unit audio amplitude sequence, the audio transmission feature set of each indoor unit audio amplitude is calculated.

[0084] In one embodiment, n=20, that is, for each indoor unit audio amplitude in the indoor unit audio amplitude sequence, a set consisting of the first 20 indoor unit audio amplitudes with the closest Euclidean distance is taken as the audio transfer feature set of the indoor unit audio amplitude.

[0085] S1032. Calculate the air conditioning audio transmission tightness value of each indoor unit audio amplitude based on the audio transmission feature set.

[0086] At this point, the air conditioning audio transmission tightness value of each indoor unit audio amplitude in all detection time periods is obtained.

[0087] In one embodiment, if Figure 4 As shown, the process of S1032: calculating the air-conditioning audio transmission tightness value of each indoor unit audio amplitude based on the audio transmission feature set specifically includes steps S201-S204:

[0088] S201. For an indoor unit audio amplitude sequence, respectively calculate the absolute value of the difference between the i-th indoor unit audio amplitude of the indoor unit audio amplitude sequence and each indoor unit audio amplitude in the corresponding audio transmission feature set to obtain a transmission distance between the i-th indoor unit audio amplitude and each indoor unit audio amplitude in the audio transmission feature set, wherein i=(1,2,...,m), and m is the total number of indoor unit audio amplitudes contained in the indoor unit audio amplitude sequence.

[0089] Taking the total acquisition time of 30 minutes, the acquisition time interval of 0.5 seconds and 1 minute as a detection time period as an example, the total acquisition time is evenly divided into 30 detection time periods to obtain 30 indoor unit audio amplitude sequences. Each indoor unit audio amplitude sequence contains 120 indoor unit audio amplitudes, that is, m=120.

[0090] S202: Accumulate the transfer distance and the internal unit audio amplitude in the audio transfer feature set to obtain the transfer metric distance of the i-th internal unit audio amplitude.

[0091] S203: Calculate the transferability variation value of the audio transfer feature set of the i-th internal machine audio amplitude.

[0092] The coefficient of variation of the audio transfer feature set of the i-th internal machine audio amplitude is used as the transferability variation value, wherein the calculation of the coefficient of variation is a well-known technology, and the specific process is not repeated here.

[0093] S204, calculating the product of the transfer metric distance of the i-th indoor unit audio amplitude and the corresponding transferability variation value, and the inverse of the sum of the product and the preset artificial threshold parameter, wherein the inverse is the air conditioning audio transfer tightness value of the i-th indoor unit audio amplitude.

[0094] In one embodiment, the reciprocal of the sum of the product of the transmission metric distance and the transmission variation value and the preset artificial threshold parameter is used as the air conditioning audio transmission tightness value of the i-th indoor unit audio amplitude, wherein the preset artificial threshold parameter is 0.01, and its function is to avoid the denominator being 0.

[0095] S1033, arranging the air-conditioning audio transmission density values ​​corresponding to the indoor unit audio amplitudes in the same detection time period in chronological order to form a transmission density feature sequence for the detection time period, and obtaining a transmission density feature sequence corresponding to each detection time period.

[0096] In one embodiment, for each indoor unit audio amplitude sequence in each detection time period, after steps S201-204, the air conditioner audio transmission density value is obtained, and the air conditioner audio transmission density values ​​in the same detection time period are arranged in chronological order to form a transmission density feature sequence of the detection time period. In this way, a transmission density feature sequence corresponding to each detection time period can be obtained, such as Figure 5 As shown, Figure 5 Schematic diagram of a transmission tight feature sequence of fixed-frequency air-conditioning audio in one embodiment.

[0097] S1034. Count the minimum value, maximum value and mean value in each transmission tight feature sequence respectively, and obtain all mutation values ​​in each transmission tight feature sequence based on the Pettitt mutation point detection algorithm respectively, and use the sequence composed of the minimum value, maximum value, mean value and all mutation values ​​in the same transmission tight feature sequence as the transmission mutation feature sequence of the detection time period to obtain the transmission mutation feature sequence of each detection time period.

[0098] The transmission tight feature sequence of each detection time period is respectively used as the input of the Pettitt mutation point detection algorithm, the output of the Pettitt mutation point detection algorithm is used as all mutation values ​​in the transmission tight feature sequence, and the sequence composed of the minimum value, the maximum value, the mean and all mutation values ​​is used as the transmission mutation feature sequence of each detection time period, wherein the Pettitt mutation point detection algorithm is a well-known technology and the specific process is not repeated here. Figure 6 Schematic diagram of a transfer mutation feature sequence in an embodiment, wherein the first three values ​​are the minimum value, maximum value and mean value, respectively.

[0099] S1035. Calculate the abnormal sound feature value of the air conditioner according to the transmission tight feature sequence and the transmission mutation feature sequence.

[0100] In one embodiment, if Figure 7 As shown, step S1035 specifically includes steps S301-S303:

[0101] S301. For a detection time period, the absolute value of the difference between the j-th data in the transmission tight feature sequence corresponding to the detection time period and each data in the corresponding transmission mutation feature sequence is calculated as the mutation distance of the j-th data, where j=(1,2,...,n), and n is the total number of data contained in the transmission tight feature sequence.

[0102] Similarly, taking the total acquisition time of 30 minutes, the acquisition time interval of 0.5 seconds, and 1 minute as a detection time period as an example, the total acquisition time is evenly divided into 30 detection time periods, and 30 internal machine audio amplitude sequences are obtained. Each internal machine audio amplitude sequence contains 120 internal machine audio amplitudes, and each transmission tight feature sequence contains a total of 120 data, that is, n=120.

[0103] S302: Calculate the accumulation of the mutation distance and all data in the transfer mutation feature sequence to obtain a third accumulated value, where the third accumulated value is the mutation metric distance of the j-th data.

[0104] S303: Calculate the mean of the mutation metric distances of all data in the transmission close feature sequence as the abnormal sound feature value of the air conditioner in the detection time period.

[0105] The abnormal sound characteristic value of the air conditioner reflects the abnormal sound occurring in the indoor unit of the fixed-frequency air conditioner. The larger the abnormal sound characteristic value of the air conditioner is, the more likely it is that the abnormal sound characteristic will occur in the corresponding detection time period.

[0106] S104: Identify an abnormality detection time period based on the abnormal sound feature value of the air conditioner.

[0107] In one embodiment, the air conditioner abnormal sound feature values ​​of all detection time periods are used as the input of the maximum inter-class variance algorithm, the detection time period corresponding to the air conditioner abnormal sound feature values ​​higher than the segmentation threshold is used as the abnormal sound detection time period, and the output of the maximum inter-class variance algorithm is used as all abnormal sound detection time periods, wherein the maximum inter-class variance algorithm is a well-known technology, and the specific process is not repeated here.

[0108] S105. Arrange the vent wind speeds corresponding to each abnormal sound detection time period into a sequence in chronological order to form a vent wind speed sequence corresponding to each abnormal sound detection time period, and arrange the indoor temperatures corresponding to each abnormal sound detection time period into a sequence in chronological order to form an indoor temperature sequence corresponding to each abnormal sound detection time period.

[0109] S106. Calculate the wind speed singularity index of each abnormal sound detection time period based on the vent wind speed sequence, and calculate the temperature singularity index of each abnormal sound detection time period based on the indoor temperature sequence.

[0110] In one embodiment, threshold segmentation technology is used to identify the abnormal detection time period based on the characteristic value of the air conditioner abnormal sound, and then the temperature characteristics and wind speed characteristics of the abnormal detection time period are analyzed based on the vent wind speed sequence and indoor temperature sequence corresponding to the detection time period.

[0111] In one embodiment, the step S106 of calculating the wind speed singularity index of each abnormal sound detection time period based on the vent wind speed sequence specifically includes the following steps:

[0112] The first-order difference sequence of the wind speed sequence of each vent is calculated respectively, and the first-order difference sequence is the wind speed fluctuation sequence of the corresponding abnormal sound detection time period.

[0113] The average value of all data in the same wind speed fluctuation sequence and the absolute value of the difference between each data in the wind speed fluctuation sequence and the average value are calculated to obtain the wind speed fluctuation distance corresponding to each data.

[0114] The wind speed fluctuation distances are respectively accumulated with corresponding data in the wind speed fluctuation sequence to obtain a first accumulated value, and an average value of the first accumulated value is calculated to obtain a wind speed fluctuation factor of the wind speed fluctuation sequence.

[0115] The mean square error of all data in the wind speed fluctuation sequence is calculated to obtain the wind speed singularity value of the wind speed fluctuation sequence.

[0116] The product of the wind speed fluctuation factor and the wind speed singularity value of each wind speed fluctuation sequence is calculated respectively to obtain the wind speed singularity index of each abnormal sound detection time period.

[0117] During the abnormal noise detection period, there is a high possibility of pressure imbalance in the air conditioning system. At this time, the air velocity at the vents will fluctuate abnormally. In order to extract the confusion in the operation of the air conditioning system when the signal line is connected incorrectly, it is necessary to analyze the abnormal fluctuations in the air velocity at the vents. In order to extract the abnormal characteristics of the wind speed fluctuation at the vents, it is necessary to use the first-order difference sequence of the vent wind speed sequence in each abnormal sound detection time period as the wind speed fluctuation sequence in each abnormal sound detection time period, and then calculate the wind speed fluctuation distance through the wind speed fluctuation sequence. The wind speed fluctuation distance is related to the wind speed fluctuation factor. The larger the wind speed fluctuation distance, the larger the wind speed fluctuation factor. The size of the wind speed fluctuation factor can preliminarily reflect the abnormal wind speed to a certain extent. In addition, the wind speed fluctuation sequence can reflect the wind speed singularity value, and the wind speed singularity value can also reflect the abnormal fluctuation changes in the vent wind speed to a certain extent. The wind speed singularity index obtained by combining the wind speed singularity value and the wind speed fluctuation factor reflects the singularity characteristics of the wind speed when the fixed-frequency air-conditioning indoor unit is running. The larger the wind speed singularity index, the greater the singularity characteristics of the wind speed when the fixed-frequency air-conditioning indoor unit is running, and the more prominent the chaotic information of the air-conditioning system operation is.

[0118] In one embodiment, the step S106 of calculating the temperature singularity index of each abnormal sound detection time period based on the indoor temperature sequence specifically includes the following:

[0119] The displayed temperature of the fixed-frequency air conditioner is collected.

[0120] The difference between each indoor temperature in the same indoor temperature sequence and the displayed temperature is calculated respectively as the temperature singular distance of the corresponding indoor temperature.

[0121] The temperature singular distances are respectively accumulated with the corresponding indoor temperatures in the indoor temperature sequence to obtain a second accumulated value, and the average of the second accumulated values ​​is calculated to obtain the temperature singularity index of the indoor temperature sequence, thereby obtaining the temperature singularity index of each abnormal sound detection time period.

[0122] Normally, when the signal lines of the fixed-frequency air conditioner are connected incorrectly, it can operate abnormally, but it will cause pressure imbalance in the air conditioning system, which will cause confusion in the air conditioning system operation and abnormal fluctuations in the indoor temperature. In order to extract the confusion in the operation of the air conditioning system when the signal lines are connected incorrectly, it is necessary to analyze the abnormal fluctuations in the indoor temperature. Therefore, it is necessary to calculate the temperature singularity index during the abnormal sound detection period.

[0123] Furthermore, under normal circumstances, the temperature difference between the indoor temperature and the displayed temperature of the fixed-frequency air-conditioning system is small, but when the fixed-frequency air-conditioning system operates chaotically, there is a large difference between the indoor temperature and the displayed temperature, and the greater the temperature difference, the greater the temperature singularity index.

[0124] S107. Calculate the air-conditioning operation chaos degree based on the wind speed singularity index, the temperature singularity index and the air-conditioning abnormal sound characteristic value.

[0125] In one embodiment, step S107 specifically includes:

[0126] The chaotic feature weight of the abnormal detection time period is calculated based on the wind speed singularity index and the temperature singularity index corresponding to the same abnormal detection time period, and the chaotic feature weight corresponding to each abnormal detection time period is obtained.

[0127] The product of the chaos feature weight corresponding to each abnormal sound detection time period and the abnormal sound feature value of the air conditioner is calculated respectively, and the product is added up to obtain the air conditioner operation chaos degree.

[0128] Among them, the calculation of the chaos feature weight of the abnormal detection time period based on the wind speed singularity index and the temperature singularity index corresponding to the same abnormal detection time period to obtain the chaos feature weight corresponding to each abnormal detection time period specifically includes: respectively calculating the product of the wind speed singularity index and the corresponding temperature singularity index of each abnormal sound detection time period, normalizing the product, and obtaining the chaos feature weight of each abnormal sound detection time period.

[0129] Both the wind speed singularity index and the temperature singularity index can characterize the chaotic characteristics of the fixed-frequency air-conditioning operation. Combining the two can more accurately characterize the degree of chaos in the operation of the fixed-frequency air-conditioning. The chaos feature weight obtained after normalization is used to characterize the degree of chaos in the operation of the fixed-frequency air-conditioning. The closer the chaos feature weight is to 1, the more it can reflect the abnormal operation of the fixed-frequency air-conditioning.

[0130] The wind speed singularity index, temperature singularity index and air-conditioning abnormal sound characteristic value can all reflect the degree of chaos in the operation of the air-conditioning. Combining the three together can improve the accuracy of the detection of the connection line of the indoor unit of the fixed-frequency air-conditioning.

[0131] S108: Determine whether a connection line of the fixed-frequency air conditioner is incorrectly connected according to the degree of chaos in the operation of the air conditioner.

[0132] The air-conditioning operation chaos degree reflects the chaotic characteristics of the air-conditioning system operation when the connection lines of the indoor units of the fixed-frequency air-conditioning are connected incorrectly. The greater the air-conditioning operation chaos degree, the more it indicates that there is a connection error in the signal line of the indoor unit of the fixed-frequency air-conditioning.

[0133] In one embodiment, step S108: determining whether the connection line of the fixed-frequency air conditioner is incorrectly connected according to the air conditioner operation disorder degree specifically includes:

[0134] The air conditioner operation chaos degree is compared with a preset chaos degree threshold of the air conditioner system operation when the fixed-frequency air conditioner is correctly connected.

[0135] If the air conditioner operation chaos degree is greater than the preset chaos degree threshold, it is determined that the connection line of the fixed-frequency air conditioner indoor unit is incorrectly connected.

[0136] If the air conditioner operation chaos degree is less than or equal to the preset chaos degree threshold, it is determined that the connection line of the fixed-frequency air conditioner indoor unit is correctly connected.

[0137] Furthermore, the preset chaos threshold is 20.

[0138] Further, the judgment result is displayed through the display module of the fixed-frequency air conditioner. For example, if the connection line of the fixed-frequency air conditioner indoor unit is correctly connected, the qualified display light is on; if the connection line of the fixed-frequency air conditioner indoor unit is incorrectly connected, the unqualified display light is on.

[0139] The present application provides a method for detecting the connection line of an indoor unit of a fixed-frequency air conditioner. The method takes into account the abnormal sound characteristics of the fixed-frequency air conditioner during the operation detection of the fixed-frequency air conditioner, and combines the wind speed singularity characteristics and temperature singularity characteristics detected when the fixed-frequency air conditioner is running. The air-conditioning operation chaos degree is constructed by the wind speed singularity index, the temperature singularity index and the air-conditioning abnormal sound characteristic value. Based on the air-conditioning operation chaos degree, the detection of whether the connection line of the indoor unit of the fixed-frequency air conditioner has a connection error is completed. The problem that the prior art is only based on the temperature change characteristics, resulting in the inability to accurately judge whether the indoor unit of the fixed-frequency air conditioner has a connection error, improves the accuracy of the detection of the connection line of the indoor unit of the fixed-frequency air conditioner.

[0140] See also Figure 8 , which shows a schematic diagram of a fixed-frequency air conditioner indoor unit connection line detection system provided by an embodiment of the present application. The fixed-frequency air conditioner indoor unit connection line detection system in the embodiment of the present application corresponds to the above-mentioned fixed-frequency air conditioner indoor unit connection line detection method. For the details of the fixed-frequency air conditioner indoor unit connection line detection system in the embodiment of the present application, please refer to the description in the above-mentioned fixed-frequency air conditioner indoor unit connection line detection method embodiment, and this embodiment will not be repeated.

[0141] The fixed-frequency air conditioner indoor unit connection line detection system comprises:

[0142] The collection module 101 is used to collect the audio amplitude of the indoor unit of the fixed-frequency air conditioner, the air velocity at the vent of the indoor unit, and the indoor temperature when the fixed-frequency air conditioner is running.

[0143] The first data processing module 102 is used to divide the total length of the acquisition time into a number of detection time periods, and arrange the indoor unit audio amplitude of each detection time period in chronological order to form an indoor unit audio amplitude sequence corresponding to each detection time period.

[0144] The first calculation module 103 is used to calculate the abnormal sound characteristic value of the air conditioner in each detection time period based on each indoor unit audio amplitude sequence.

[0145] The identification module 104 is used to identify the abnormality detection time period based on the abnormal sound feature value of the air conditioner.

[0146] The second data processing module 105 is used to respectively arrange the vent wind speeds corresponding to each abnormal sound detection time period into a sequence in chronological order to form a vent wind speed sequence corresponding to each abnormal sound detection time period, and respectively arrange the indoor temperatures corresponding to each abnormal sound detection time period into a sequence in chronological order to form an indoor temperature sequence corresponding to each abnormal sound detection time period.

[0147] The second calculation module 106 is used to calculate the wind speed singularity index of each abnormal sound detection time period based on the ventilation outlet wind speed sequence.

[0148] The third calculation module 107 is used to calculate the temperature singularity index of each abnormal sound detection time period based on the indoor temperature sequence.

[0149] The fourth calculation module 108 is used to calculate the air-conditioning operation chaos degree based on the wind speed singularity index, the temperature singularity index and the air-conditioning abnormal sound characteristic value.

[0150] The judgment module 109 is used to judge whether the connection line of the fixed-frequency air conditioner is connected incorrectly according to the chaotic degree of the air conditioner operation.

[0151] In one embodiment, if Fig. 9 As shown, the first calculation module 103 specifically includes:

[0152] The first calculation unit 1031 is used to respectively calculate the Euclidean distance between each indoor unit audio amplitude and other indoor unit audio amplitudes in the same indoor unit audio amplitude sequence, and form a set of first n indoor unit audio amplitudes with the closest Euclidean distances to obtain an audio transfer feature set corresponding to each indoor unit audio amplitude in the indoor unit audio amplitude sequence, wherein n is a preset number.

[0153] The second calculation unit 1032 is used to calculate the air-conditioning audio transmission tightness value of each indoor unit audio amplitude based on the audio transmission feature set.

[0154] The first arrangement unit 1033 is used to arrange the air-conditioning audio transmission density values ​​corresponding to the indoor unit audio amplitudes in the same detection time period in chronological order to form a transmission density feature sequence of the detection time period, and obtain a transmission density feature sequence corresponding to each detection time period.

[0155] The first data processing unit 1034 is used to count the minimum value, maximum value and mean value in each transmission tight feature sequence respectively, and obtain all mutation values ​​in each transmission tight feature sequence based on the Pettitt mutation point detection algorithm respectively, and use the sequence composed of the minimum value, maximum value, mean value and all mutation values ​​in the same transmission tight feature sequence as the transmission mutation feature sequence of the detection time period to obtain the transmission mutation feature sequence of each detection time period.

[0156] The third calculation unit 1035 is used to calculate the abnormal sound feature value of the air conditioner according to the transmission tight feature sequence and the transmission mutation feature sequence.

[0157] In one embodiment, the second calculation unit 1032 is specifically configured to:

[0158] For an indoor unit audio amplitude sequence, the absolute values ​​of the differences between the i-th indoor unit audio amplitude of the indoor unit audio amplitude sequence and each indoor unit audio amplitude in the corresponding audio transmission feature set are calculated respectively to obtain the transmission distance between the i-th indoor unit audio amplitude and each indoor unit audio amplitude in the audio transmission feature set, wherein i=(1,2,...,m), and m is the total number of indoor unit audio amplitudes contained in the indoor unit audio amplitude sequence.

[0159] The transfer distance is accumulated with the internal machine audio amplitude in the audio transfer feature set to obtain the transfer metric distance of the i-th internal machine audio amplitude.

[0160] Calculate the transferability variation value of the audio transfer feature set of the i-th internal machine audio amplitude.

[0161] Calculate the product of the transfer metric distance of the i-th indoor unit audio amplitude and the corresponding transferability variation value, and the inverse of the sum of the product and the preset artificial threshold parameter, the inverse of which is the air conditioning audio transfer tightness value of the i-th indoor unit audio amplitude.

[0162] In one embodiment, the third calculation unit 1035 is specifically configured to:

[0163] For a detection time period, the absolute value of the difference between the j-th data in the transmission tight feature sequence corresponding to the detection time period and each data in the corresponding transmission mutation feature sequence is calculated as the mutation distance of the j-th data, where j=(1,2,…,n), and n is the total number of data contained in the transmission tight feature sequence.

[0164] The mutation distance and the accumulation of all data in the transfer mutation feature sequence are calculated to obtain a third accumulated value, where the third accumulated value is the mutation metric distance of the j-th data.

[0165] The mean value of the mutation metric distance of all data in the transmission close feature sequence is calculated as the abnormal sound feature value of the air conditioner in the detection time period.

[0166] In one embodiment, the second calculation module 106 is specifically configured to:

[0167] The first-order difference sequence of the wind speed sequence of each vent is calculated respectively, and the first-order difference sequence is the wind speed fluctuation sequence of the corresponding abnormal sound detection time period.

[0168] The average value of all data in the same wind speed fluctuation sequence and the absolute value of the difference between each data in the wind speed fluctuation sequence and the average value are calculated to obtain the wind speed fluctuation distance corresponding to each data.

[0169] The wind speed fluctuation distances are respectively accumulated with corresponding data in the wind speed fluctuation sequence to obtain a first accumulated value, and an average value of the first accumulated value is calculated to obtain a wind speed fluctuation factor of the wind speed fluctuation sequence.

[0170] Calculating the mean square error of all data in the wind speed fluctuation sequence to obtain a wind speed singularity value of the wind speed fluctuation sequence;

[0171] The product of the wind speed fluctuation factor and the wind speed singularity value of each wind speed fluctuation sequence is calculated respectively to obtain the wind speed singularity index of each abnormal sound detection time period.

[0172] In one embodiment, the third calculation module 107 is specifically used to:

[0173] Collecting the displayed temperature of the fixed-frequency air conditioner;

[0174] The difference between each indoor temperature in the same indoor temperature sequence and the displayed temperature is calculated respectively as the temperature singular distance of the corresponding indoor temperature.

[0175] The temperature singular distances are respectively accumulated with the corresponding indoor temperatures in the indoor temperature sequence to obtain a second accumulated value, and the average of the second accumulated values ​​is calculated to obtain the temperature singularity index of the indoor temperature sequence, thereby obtaining the temperature singularity index of each abnormal sound detection time period.

[0176] In one embodiment, the fourth calculation module 108 is specifically configured to:

[0177] The chaotic feature weight of the abnormal detection time period is calculated based on the wind speed singularity index and the temperature singularity index corresponding to the same abnormal detection time period, and the chaotic feature weight corresponding to each abnormal detection time period is obtained.

[0178] The product of the chaos feature weight corresponding to each abnormal sound detection time period and the abnormal sound feature value of the air conditioner is calculated respectively, and the product is added up to obtain the air conditioner operation chaos degree.

[0179] Furthermore, the chaos feature weight of the abnormal detection time period is calculated based on the wind speed singularity index and the temperature singularity index corresponding to the same abnormal detection time period to obtain the chaos feature weight corresponding to each abnormal detection time period, specifically including: respectively calculating the product of the wind speed singularity index and the corresponding temperature singularity index of each abnormal sound detection time period, normalizing the product, and obtaining the chaos feature weight of each abnormal sound detection time period.

[0180] In one embodiment, the determination module 109 is specifically configured to:

[0181] The air conditioner operation chaos degree is compared with a preset chaos degree threshold of the air conditioner system operation when the fixed-frequency air conditioner is correctly connected.

[0182] If the air conditioner operation chaos degree is greater than the preset chaos degree threshold, it is determined that the connection line of the fixed-frequency air conditioner indoor unit is incorrectly connected.

[0183] If the air conditioner operation chaos degree is less than or equal to the preset chaos degree threshold, it is determined that the connection line of the fixed-frequency air conditioner indoor unit is correctly connected.

[0184] Further, in one embodiment, if Fig.10 As shown, the fixed-frequency air conditioner indoor unit connection line detection also includes a display module 110, and the display module 110 is specifically used for: when the fixed-frequency air conditioner indoor unit connection line is connected correctly, the qualified display light is on, and when the fixed-frequency air conditioner indoor unit connection line is connected incorrectly, the unqualified display light is on.

[0185] The present application provides a fixed-frequency air-conditioner indoor unit connection line detection system, which realizes the detection of whether the fixed-frequency air-conditioner indoor unit connection line has a connection error. Compared with the prior art that only judges whether the fixed-frequency air-conditioner indoor unit connection has an error based on the temperature change characteristics, the present invention takes into account the abnormal sound characteristics of the air conditioner during the fixed-frequency air-conditioner operation detection, and combines the wind speed singularity characteristics and temperature singularity characteristics detected when the fixed-frequency air-conditioner is running to construct the air-conditioner operation chaos degree, and completes the detection of whether the fixed-frequency air-conditioner indoor unit connection line has a connection error based on the air-conditioner operation chaos degree, which solves the problem that the prior art is only based on the temperature change characteristics, resulting in the inability to accurately judge whether the fixed-frequency air-conditioner indoor unit has a connection error, and improves the accuracy of the fixed-frequency air-conditioner indoor unit connection line detection.

[0186] It should be noted that the sequence of the above embodiments of the present invention is only for description and does not represent the advantages and disadvantages of the embodiments. The processes depicted in the accompanying drawings do not necessarily require the specific order or continuous order shown to achieve the desired results. In some embodiments, multitasking and parallel processing are also possible or may be advantageous.

[0187] The various embodiments in this specification are described in a progressive manner, and the same or similar parts between the various embodiments can be referenced to each other, and each embodiment focuses on the differences from other embodiments.

Claims

1. A method for detecting the connection line of the indoor unit of a fixed-frequency air conditioner, characterized in that: The method comprises: Collect the audio amplitude of the indoor unit of the fixed-frequency air conditioner, the vent wind speed at the vent of the indoor unit, and the indoor temperature during the fixed-frequency air conditioning commissioning operation; The total length of the acquisition time is equally divided into a number of detection time periods, and the indoor unit audio amplitudes of each detection time period are arranged in chronological order to form an indoor unit audio amplitude sequence corresponding to each detection time period; Calculate the abnormal sound characteristic value of the air conditioner in each detection time period based on each indoor unit audio amplitude sequence; Identifying an abnormality detection time period based on the abnormal sound feature value of the air conditioner; The vent wind speeds corresponding to each abnormal sound detection time period are respectively arranged in a sequence in chronological order to form a vent wind speed sequence corresponding to each abnormal sound detection time period, and the indoor temperatures corresponding to each abnormal sound detection time period are respectively arranged in a sequence in chronological order to form an indoor temperature sequence corresponding to each abnormal sound detection time period; Calculating a wind speed singularity index for each abnormal sound detection time period based on the vent wind speed sequence, and calculating a temperature singularity index for each abnormal sound detection time period based on the indoor temperature sequence; Calculating the air conditioner operation chaos degree based on the wind speed singularity index, the temperature singularity index and the air conditioner abnormal sound characteristic value; determining whether a connection line of the fixed-frequency air conditioner is incorrectly connected according to the degree of chaos in the operation of the air conditioner; The calculating of the air conditioner operation chaos degree based on the wind speed singularity index, the temperature singularity index and the air conditioner abnormal sound characteristic value specifically includes: The chaotic feature weight of the abnormal detection time period is calculated based on the wind speed singularity index and the temperature singularity index corresponding to the same abnormal detection time period, so as to obtain the chaotic feature weight corresponding to each abnormal detection time period; Calculate the product of the chaos feature weight corresponding to each abnormal sound detection time period and the abnormal sound feature value of the air conditioner respectively, and add up the products to obtain the chaos degree of the air conditioner operation; The calculating of the chaos feature weight of the abnormal detection time period based on the wind speed singularity index and the temperature singularity index corresponding to the same abnormal detection time period to obtain the chaos feature weight corresponding to each abnormal detection time period specifically includes: The product of the wind speed singularity index and the corresponding temperature singularity index in each abnormal sound detection time period is calculated respectively, and the product is normalized to obtain the chaos feature weight of each abnormal sound detection time period.

2. The method for detecting the connection line of the indoor unit of a fixed-frequency air conditioner according to claim 1, characterized in that: The calculating of the abnormal sound characteristic value of the air conditioner in each detection time period based on each indoor unit audio amplitude sequence specifically includes: Calculating the Euclidean distance between each indoor unit audio amplitude and other indoor unit audio amplitudes in the same indoor unit audio amplitude sequence respectively, forming a set of the first n indoor unit audio amplitudes with the closest Euclidean distances, and obtaining an audio transfer feature set corresponding to each indoor unit audio amplitude in the indoor unit audio amplitude sequence, wherein n is a preset number; Based on the audio transfer feature set, calculating the air conditioning audio transfer tightness value of each indoor unit audio amplitude; Arrange the air conditioner audio transmission density values ​​corresponding to the indoor unit audio amplitudes in the same detection time period in chronological order to form a transmission density feature sequence for the detection time period, and obtain a transmission density feature sequence corresponding to each detection time period; The minimum value, maximum value and mean value in each transmission tight feature sequence are counted respectively, and all mutation values ​​in each transmission tight feature sequence are obtained based on the Pettitt mutation point detection algorithm. The sequence composed of the minimum value, maximum value, mean value and all mutation values ​​in the same transmission tight feature sequence is used as the transmission mutation feature sequence of the detection time period to obtain the transmission mutation feature sequence of each detection time period; The abnormal sound characteristic value of the air conditioner is calculated according to the transmission tight characteristic sequence and the transmission mutation characteristic sequence.

3. The method for detecting the connection line of the indoor unit of a fixed-frequency air conditioner according to claim 1, characterized in that: The calculating of the wind speed singularity index of each abnormal sound detection time period based on the vent wind speed sequence specifically includes: Calculate the first-order difference sequence of each vent wind speed sequence respectively, where the first-order difference sequence is the wind speed fluctuation sequence of the corresponding abnormal sound detection time period; Calculate the average value of all data in the same wind speed fluctuation sequence and the absolute value of the difference between each data in the wind speed fluctuation sequence and the average value, and obtain the wind speed fluctuation distance corresponding to each data; Accumulating the wind speed fluctuation distances with corresponding data in the wind speed fluctuation sequence to obtain a first accumulated value, and calculating an average value of the first accumulated values ​​to obtain a wind speed fluctuation factor of the wind speed fluctuation sequence; Calculating the mean square error of all data in the wind speed fluctuation sequence to obtain a wind speed singularity value of the wind speed fluctuation sequence; The product of the wind speed fluctuation factor and the wind speed singularity value of each wind speed fluctuation sequence is calculated respectively to obtain the wind speed singularity index of each abnormal sound detection time period.

4. The method for detecting the connection line of the indoor unit of a fixed-frequency air conditioner according to claim 1, characterized in that: The calculating the temperature singularity index of each abnormal sound detection time period based on the indoor temperature sequence specifically includes: Collecting the displayed temperature of the fixed-frequency air conditioner; respectively calculating the difference between each indoor temperature in the same indoor temperature sequence and the displayed temperature as the temperature singular distance of the corresponding indoor temperature; The temperature singular distances are respectively accumulated with the corresponding indoor temperatures in the indoor temperature sequence to obtain a second accumulated value, and the average of the second accumulated values ​​is calculated to obtain the temperature singularity index of the indoor temperature sequence, thereby obtaining the temperature singularity index of each abnormal sound detection time period.

5. The method for detecting the connection line of the indoor unit of a fixed-frequency air conditioner according to claim 2, characterized in that: The step of calculating the air conditioning audio transmission tightness value of each indoor unit audio amplitude based on the audio transmission feature set specifically includes: For an indoor unit audio amplitude sequence, respectively calculating the absolute value of the difference between the i-th indoor unit audio amplitude of the indoor unit audio amplitude sequence and each indoor unit audio amplitude in the corresponding audio transfer feature set, to obtain the transmission distance between the i-th indoor unit audio amplitude and each indoor unit audio amplitude in the audio transfer feature set, wherein i=(1,2,...,m), and m is the total number of indoor unit audio amplitudes contained in the indoor unit audio amplitude sequence; Accumulating the transfer distance and the internal machine audio amplitude in the audio transfer feature set to obtain the transfer metric distance of the i-th internal machine audio amplitude; Calculating the transferability variation value of the audio transfer feature set of the i-th internal machine audio amplitude; Calculate the product of the transfer metric distance of the i-th indoor unit audio amplitude and the corresponding transferability variation value, and the inverse of the sum of the product and the preset artificial threshold parameter, the inverse of which is the air conditioning audio transfer tightness value of the i-th indoor unit audio amplitude.

6. The method for detecting the connection line of the fixed-frequency air conditioner indoor unit according to claim 2, characterized in that: The calculating the abnormal sound characteristic value of the air conditioner according to the transmission close characteristic sequence and the transmission mutation characteristic sequence specifically includes: For a detection time period, the absolute value of the difference between the j-th data in the transmission tight feature sequence corresponding to the detection time period and each data in the corresponding transmission mutation feature sequence is calculated as the mutation distance of the j-th data, where j=(1,2,...,n), and n is the total number of data contained in the transmission tight feature sequence; Calculate the mutation distance and the accumulation of all data in the transfer mutation feature sequence to obtain a third accumulated value, where the third accumulated value is the mutation metric distance of the j-th data; The mean value of the mutation metric distance of all data in the transmission close feature sequence is calculated as the abnormal sound feature value of the air conditioner in the detection time period.

7. The method for detecting the connection line of the indoor unit of a fixed-frequency air conditioner according to claim 1, characterized in that: The determining whether the connection line of the fixed-frequency air conditioner is incorrectly connected according to the air conditioner operation disorder degree specifically includes: comparing the air conditioner operation chaos degree with a preset chaos degree threshold of the air conditioner system operation when the fixed-frequency air conditioner is correctly wired; If the air conditioner operation disorder degree is greater than the preset disorder degree threshold, it is determined that the connection line of the fixed-frequency air conditioner indoor unit is incorrectly connected; If the air conditioner operation chaos degree is less than or equal to the preset chaos degree threshold, it is determined that the connection line of the fixed-frequency air conditioner indoor unit is correctly connected.

8. A fixed-frequency air conditioner indoor unit connection line detection system, characterized in that: The system comprises: A collection module, used to collect the audio amplitude of the indoor unit of the fixed-frequency air conditioner, the vent wind speed at the vent of the indoor unit, and the indoor temperature during the fixed-frequency air conditioning commissioning operation; The first data processing module is used to divide the total length of the acquisition time into a number of detection time periods, and arrange the indoor unit audio amplitudes of each detection time period in chronological order to form an indoor unit audio amplitude sequence corresponding to each detection time period; A first calculation module is used to calculate the abnormal sound characteristic value of the air conditioner in each detection time period based on each indoor unit audio amplitude sequence; An identification module, used for identifying an abnormality detection time period based on the abnormal sound characteristic value of the air conditioner; The second data processing module is used to respectively sequence the vent wind speeds corresponding to each abnormal sound detection time period in chronological order to form a vent wind speed sequence corresponding to each abnormal sound detection time period, and respectively sequence the indoor temperatures corresponding to each abnormal sound detection time period in chronological order to form an indoor temperature sequence corresponding to each abnormal sound detection time period; A second calculation module is used to calculate the wind speed singularity index of each abnormal sound detection time period based on the ventilation outlet wind speed sequence; A third calculation module, configured to calculate a temperature singularity index for each abnormal sound detection time period based on the indoor temperature sequence; a fourth calculation module, configured to calculate the degree of chaos in air-conditioning operation based on the wind speed singularity index, the temperature singularity index and the air-conditioning abnormal sound characteristic value; A judgment module, used for judging whether the connection line of the fixed-frequency air conditioner is connected incorrectly according to the chaotic degree of operation of the air conditioner; The calculating of the air conditioner operation chaos degree based on the wind speed singularity index, the temperature singularity index and the air conditioner abnormal sound characteristic value specifically includes: The chaotic feature weight of the abnormal detection time period is calculated based on the wind speed singularity index and the temperature singularity index corresponding to the same abnormal detection time period, so as to obtain the chaotic feature weight corresponding to each abnormal detection time period; Calculate the product of the chaos feature weight corresponding to each abnormal sound detection time period and the abnormal sound feature value of the air conditioner respectively, and add up the products to obtain the chaos degree of the air conditioner operation; The calculating of the chaos feature weight of the abnormal detection time period based on the wind speed singularity index and the temperature singularity index corresponding to the same abnormal detection time period to obtain the chaos feature weight corresponding to each abnormal detection time period specifically includes: The product of the wind speed singularity index and the corresponding temperature singularity index in each abnormal sound detection time period is calculated respectively, and the product is normalized to obtain the chaos feature weight of each abnormal sound detection time period.

Images