Air conditioner running state monitoring method and system

Abstract

The invention relates to the technical field of air conditioner operation state monitoring, in particular to an air conditioner operation state monitoring method and system. The method comprises the steps that a pressure time sequence of an air conditioner compressor is evenly divided into a plurality of pressure data segments, and the pressure data segments containing end points are marked as end point data segments; the dispersion degree of the data points is obtained through the difference between the data points in the endpoint data segments and the whole endpoint data segments; acquiring a temperature data segment corresponding to the endpoint data segment in the temperature sequential sequence; calculating continuation demand degrees of data points in the endpoint data segments; determining the demand length of each data point in the endpoint data segment for the continuation data segment, wherein the demand length is in positive correlation with the continuation demand degree; and after the end point data segment is prolonged according to the required length of the continuation data segment, the Z-Score algorithm is used for monitoring the abnormity, so that the air conditioner operation state monitoring result is obtained, and the accuracy of air conditioner operation state monitoring is effectively improved.

Description

Technical Field

[0001] The present invention relates to the technical field of air conditioner operation status monitoring, and in particular to an air conditioner operation status monitoring method and system. Background Art

[0002] The compressor is the core component of the air conditioning system. The compressor continuously compresses the refrigerant from a low-pressure state to a high-pressure state, thereby increasing its temperature and pressure, and releases it into the condenser to complete the refrigeration cycle. If the compressor fails during the operation of the air conditioner, it will not only affect the normal operation of the compressor itself, but may also cause the pressure in the air conditioning system to increase or decrease abnormally, thereby causing failures in other system components and ultimately causing the air conditioner to fail, posing a serious safety hazard.

[0003] The prior art provides a variety of methods for monitoring the operating status of air-conditioning compressors. For example, a patent application document with publication number CN115077031A discloses a compressor bus voltage control method, device and air conditioner. The application calculates the target bus voltage of the compressor by detecting the real-time bus voltage, phase current and motor speed of the air-conditioning compressor; when the monitoring and calculation shows that the target bus voltage is too high or too low during the operation of the compressor, the abnormal situation of the target bus voltage is confirmed based on the real-time bus voltage, thereby realizing the monitoring of the air-conditioning compressor.

[0004] However, the real-time bus voltage, phase current and motor speed used in the above-mentioned prior art can only reflect the state of the compressor drive system, rather than the direct working state of the compressor itself or the entire air-conditioning system, resulting in the monitoring results being unable to fully reflect the air-conditioning operating state.

[0005] Based on this, how to accurately obtain the air conditioner operation status monitoring results is a problem that needs to be solved urgently by technical personnel in this field. Summary of the invention

[0006] In order to solve the technical problem of how to accurately obtain air conditioner operation status monitoring results, the present invention provides an air conditioner operation status monitoring method and system.

[0007] In a first aspect, the present invention provides a method for monitoring the operating status of an air conditioner, which adopts the following technical solution: A method for monitoring the operating status of an air conditioner comprises the following steps: The pressure time series of the air-conditioning compressor is divided into multiple pressure data segments, and the pressure data segment containing the endpoint is recorded as the endpoint data segment; the discrete degree of the data point is obtained by the difference between the data point in the endpoint data segment and the endpoint data segment as a whole; the temperature data segment corresponding to the endpoint data segment is obtained in the temperature time series; the discrete degree of the data point is corrected based on the number of abnormal data points in other pressure data segments and the correlation between pressure data and temperature data, and the extension demand degree of the data point is obtained: ; The endpoint data segment The extension requirement of data points, For endpoint data segments except The total number of data points outside the data point, The endpoint data segment The degree of dispersion of the data points, is the number of pressure data segments, , They are endpoint data segment, The number of abnormal data points in a pressure data segment, , are the information entropy values ​​of the endpoint data segment and the temperature data segment corresponding to the endpoint data segment, is an exponential function with base e, is a linear normalized function; the required length of each data point in the endpoint data segment for the extended data segment is determined, and the required length is positively correlated with the extended demand degree; after extending the endpoint data segment according to the required length of the extended data segment, the Z-Score algorithm is used for abnormal monitoring to obtain the air conditioning operation status monitoring result.

[0008] The present invention combines the pressure data segment and the temperature data segment of the compressor to jointly determine the required length of the extended data segment of the target data point in the endpoint data segment, thereby effectively improving the accuracy of the required length of the extended data segment of the target data point; then the endpoint data segment is extended based on the required length of the extended data segment with higher accuracy, effectively reducing the error caused by insufficient data points at the endpoint data segment in the pressure data segment, and determining the operating status of the air-conditioning compressor based on the extended pressure data segment, which can effectively improve the accuracy of analyzing the operating status of the air-conditioning.

[0009] According to a method for monitoring the operating status of an air conditioner provided by the present invention, the discrete degree of each data point in the endpoint data segment satisfies the relationship: ; The endpoint data segment The degree of dispersion of the data points, For endpoint data segments except The total number of data points outside the data point, For the The pressure value of the data point, For each endpoint data segment except Data points outside the The pressure value of the data point, is the pressure value variance of all data points in the endpoint data segment, For each endpoint data segment except The variance of the pressure values ​​outside the data points, For the The sampling time interval between the data point and the endpoint in the endpoint data segment, is a hyperparameter, is a linear normalization function.

[0010] The present invention provides an accurate method for calculating the degree of discreteness of data points. By analyzing the difference between the data points in the endpoint data segment and the endpoint data segment as a whole, the degree of discreteness of each data point in the endpoint data segment can be accurately obtained.

[0011] According to a method for monitoring the operating status of an air conditioner provided by the present invention, the number of abnormal data points in a pressure data segment is obtained by a box plot method.

[0012] According to a method for monitoring the operating status of an air conditioner provided by the present invention, the step of determining the required length of the extended data segment for each data point in the endpoint data segment includes: ; The endpoint data segment The required length of the extended data segment by the data point, The endpoint data segment The extension requirement of data points, To preset the length of the extended data segment, The floor symbol.

[0013] According to a method for monitoring the operating status of an air conditioner provided by the present invention, an endpoint data segment is extended according to the required length of the extended data segment, including: taking the maximum value of the required length corresponding to each data point in the endpoint data segment as the extended length of the endpoint data segment, and extending the endpoint data segment in accordance with the collected data points.

[0014] The present invention obtains the extended length of the endpoint data segment by calculating the required length of the extended data segment for each data point in the endpoint data segment, which can make the calculation of the required length of the extended data segment more flexible and the obtained pressure data more complete, thus preparing for the subsequent determination of the operating status of the air conditioner.

[0015] According to a method for monitoring the operating status of an air conditioner provided by the present invention, the endpoint data segment is extended according to the required length of the extended data segment, and then the Z-Score algorithm is used to perform abnormal monitoring to obtain the air conditioner operating status monitoring result, including: using the Z-Score algorithm to calculate the score of each data point in the extended pressure time series sequence; if the score of the data point is greater than a preset score threshold, the air conditioner operating status monitoring result is abnormal; otherwise, the air conditioner operating status monitoring result is normal.

[0016] The present invention determines the operating status of a data point by presetting a score threshold and a score comparison result of the data point, and can adjust the score threshold according to actual needs to achieve the best classification or detection effect and reduce false positives and false negatives.

[0017] According to an air conditioner operation status monitoring method provided by the present invention, the air conditioner operation status monitoring result is obtained, and then the method further comprises: in response to the air conditioner operation status monitoring result being abnormal, issuing an air conditioner abnormality warning.

[0018] The present invention acquires and analyzes data in real time during the operation of the compressor, thereby timely discovering potential problems such as air-conditioning abnormalities and issuing warnings. Through preventive maintenance strategies, the occurrence of compressor failures can be reduced and the reliability and service life of the air conditioner can be improved.

[0019] In a second aspect, the present invention provides an air conditioner operation status monitoring system, which adopts the following technical solution: An air conditioner operation status monitoring system comprises: a processor and a memory, wherein the memory stores computer program instructions, and when the computer program instructions are executed by the processor, the above-mentioned air conditioner operation status monitoring method is implemented.

[0020] By adopting the above technical solution, the above-mentioned air conditioner operation status monitoring method is generated into a computer program and stored in a memory so as to be loaded and executed by a processor, thereby making a terminal device based on the memory and the processor for easy use.

[0021] The present invention has the following technical effects: Based on the above technical scheme, the present invention provides an air-conditioning operating status monitoring method and system, which have the following beneficial effects: when analyzing the operating status of the compressor, an accurate method for calculating the required length of the extended data segment is provided, which comprehensively considers the data characteristics in the pressure data segment and the temperature data, and effectively improves the accuracy of the calculation of the required length of the extended data segment; then the endpoint data segment is extended based on the more accurate required length of the extended data segment, which effectively reduces the monitoring error caused by insufficient number of data points at the endpoint data segment in the pressure data segment, and determines the operating status of the compressor based on the extended pressure data, which can effectively improve the accuracy of the obtained air-conditioning operating status monitoring results. BRIEF DESCRIPTION OF THE DRAWINGS

[0022] By reading the detailed description below with reference to the accompanying drawings, the above and other purposes, features and advantages of the exemplary embodiments of the present invention will become readily understood. In the accompanying drawings, several embodiments of the present invention are shown in an exemplary and non-restrictive manner, and the same or corresponding reference numerals represent the same or corresponding parts.

[0023] Figure 1 A flow chart of a method for monitoring the operating status of an air conditioner provided in an embodiment of the present invention. DETAILED DESCRIPTION

[0024] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative work are within the scope of protection of the present invention.

[0025] It should be understood that when the terms "first", "second", etc. are used in the claims, descriptions, and drawings of the present invention, they are only used to distinguish different objects, rather than to describe a specific order. The terms "include" and "comprise" used in the description and claims of the present invention indicate the presence of the described features, wholes, steps, operations, elements, and / or components, but do not exclude the presence or addition of one or more other features, wholes, steps, operations, elements, components, and / or their collections.

[0026] The compressor is the core component of the air conditioning system. The compressor continuously compresses the refrigerant from a low-pressure state to a high-pressure state, thereby increasing its temperature and pressure, and releases it into the condenser to complete the refrigeration cycle. If a compressor fails during operation, it will not only affect the normal operation of the compressor itself, but may also cause the pressure in the air conditioning system to increase or decrease abnormally, thereby causing failures in other system components and ultimately causing the air conditioner to fail, posing a serious safety hazard.

[0027] The pressure data generated during the operation of the air-conditioning compressor can directly reflect the internal working status of the air-conditioning compressor system. By monitoring the pressure data, the fault points in the compressor system can be intuitively identified, such as blockage, leakage, abnormal pressure, etc.

[0028] The Z-Score algorithm can effectively identify outliers in a data set. It can quickly locate data points that deviate from normal levels by calculating the degree of deviation of each data point from the average value of the data set.

[0029] Based on this, the embodiment of the present invention discloses a method for monitoring the operating status of an air conditioner. By processing the pressure data time series of the compressor through the Z-Score algorithm, abnormal data in the compressor can be identified, thereby accurately obtaining the operating monitoring result of the air conditioner. For details, please refer to Figure 1 , Figure 1 A flow chart of a method for monitoring the operating status of an air conditioner provided in an embodiment of the present invention, the method specifically includes the following steps.

[0030] S1: Divide the pressure time series of the air-conditioning compressor into multiple pressure data segments, and record the pressure data segment containing the endpoint as the endpoint data segment.

[0031] The pressure may be an intake pressure, an exhaust pressure, etc.; the pressure may be set according to actual needs, and the embodiment of the present invention does not impose too many limitations on this.

[0032] Among them, the pressure data segment includes endpoint data segments and other pressure data segments, and the number of data points in each data segment is the same; the number of data points in the pressure data segment can be set to 60, which can be set specifically according to actual needs, and the embodiment of the present invention does not impose too many restrictions on this.

[0033] For example, the pressure sensor data may be collected by a pressure sensor, and the pressure sensor data may be converted into a digital form by an analog-to-digital conversion device to obtain a pressure timing sequence of the compressor.

[0034] When the pressure sensor data of the compressor is collected by the pressure sensor, the collection time may be 30 minutes and the collection frequency may be once per second. The collection time and collection frequency may be set according to actual needs, and the embodiment of the present invention does not impose too many restrictions on this.

[0035] Specifically, when dividing the pressure time series sequence, the pressure time series sequence may be divided into a plurality of pressure data segments based on a preset length of the pressure data segment.

[0036] It can be understood that the pressure timing sequence has two endpoints, namely the beginning and the end, and therefore there are also two endpoint data segments containing the endpoints.

[0037] For example, in the embodiment of the present invention, the endpoint data segment may be a start data segment and / or an end data segment, wherein the start data segment is a start data point in the pressure data segment and several data points around the start data point, and the end data segment is an end data point in the pressure data segment and several data points around the end data point.

[0038] For ease of understanding, the embodiment of the present invention refers to the beginning data segment or the ending data segment as an endpoint data segment for illustration, but this does not mean that the embodiment of the present invention is limited thereto.

[0039] Based on the above method, pressure data segments can be obtained. However, in the pressure time series generated by the operation of the compressor, the endpoint data segment often contains important information, which may reflect the turning point of the compressor state change. However, when analyzing the endpoint data based on the data points near the endpoint in the pressure time series, usually only one-side data can be used for the endpoint analysis, resulting in low accuracy of the endpoint operation status monitoring results. Therefore, it is necessary to extend the endpoint, that is, perform the following steps.

[0040] S2: The degree of discreteness of the data point is obtained by the difference between the data point in the endpoint data segment and the endpoint data segment as a whole.

[0041] It should be noted that the required length of the extended data segment of a data point is related to the degree of discreteness of the data point. The higher the degree of discreteness of the data point in the endpoint data segment, the greater the numerical difference between the data point and other data points in the endpoint data segment, and the higher the degree of discreteness. In order to reduce the boundary effect, the required length of the extended data segment is also higher.

[0042] Based on this, in order to accurately obtain the required length of the extended data segment of the data point in the endpoint data segment, the embodiment of the present invention can determine the discrete degree of the data point in the endpoint data segment by analyzing the degree of data change.

[0043] It can be understood that, in the endpoint data segment of the pressure data segment, the degree of discreteness between the current data point and other data points can reflect the deviation between the pressure value of the current data point and the pressure values ​​of other data points.

[0044] For example, in the embodiment of the present invention, the discrete degree of each data point in the endpoint data segment is determined, and the specific details can be referred to the following formula: ; The endpoint data segment The degree of dispersion of the data points, For endpoint data segments except The total number of data points outside the data point, For the The pressure value of the data point, For each endpoint data segment except Data points outside the The pressure value of the data point, is the pressure value variance of all data points in the endpoint data segment, For each endpoint data segment except The variance of the pressure values ​​outside the data points, For the The sampling time interval between the data point and the endpoint in the endpoint data segment, is a hyperparameter, is a linear normalization function.

[0045] Among them, The sampling time interval between the data point and the endpoint in the endpoint data segment can be The absolute value of the difference in sampling time between the data point and the endpoint in the endpoint data segment is obtained.

[0046] In the above formula, the hyperparameter can prevent the current data point from being the endpoint. The denominator is 0. The value of the hyperparameter can be set to 0.01. The value of the hyperparameter can be set according to actual needs, and the embodiment of the present invention does not impose too many restrictions here.

[0047] No. The smaller the sampling time interval between the data point and the endpoint, the The closer the data point is to the endpoint, the The stronger the boundary effect at a data point, the higher the need for extension.

[0048] Indicates The pressure value of the data point is the same as that of the endpoint data segment except The sum of the pressure differences of all data points other than the data point. The larger the value, the The greater the difference between the pressure value of the data point and other data points in the endpoint data segment, the greater the difference between the pressure value of the data point and other data points in the endpoint data segment. The greater the difference between a data point and the endpoint data segment as a whole, the greater the corresponding degree of isolation and dispersion.

[0049] The variance of the pressure values ​​of all data points in the endpoint data segment is the same as that of the pressure values ​​of all data points in the endpoint data segment except the The difference in the variance of the pressure values ​​of all data points other than the data point. The larger the value, the The data point will make the data in the entire endpoint data segment more discrete, that is, The greater the numerical difference between a data point and other data points in the endpoint data segment, the greater the corresponding degree of isolation and dispersion will be.

[0050] Indicates The trend of the data point in the endpoint data segment. The larger the value, the The more dispersed the data points are in the endpoint data segment.

[0051] Based on the above method, the discrete degree of each data point in the endpoint data segment can be obtained. However, when calculating the discrete degree of the data points in the endpoint data segment, the basis is the pressure data change characteristics of the endpoint data segment. If there are many abnormal data in the endpoint data segment, it may be impossible to accurately determine the discrete degree of the data point, thereby affecting the accuracy of abnormal monitoring. Therefore, it is also necessary to obtain the relationship between the pressure data and other parameters to verify the discrete degree of the pressure data, that is, perform the following steps.

[0052] S3: Obtain the temperature data segment corresponding to the endpoint data segment in the temperature time series, and calculate the extension requirement of each data point in the endpoint data segment.

[0053] The temperature data segment and the endpoint data segment have the same length, and the data values ​​in the temperature data segment and the endpoint data segment correspond one to one.

[0054] It should be noted that the suction pressure in the compressor corresponds to the evaporation temperature, and the exhaust pressure corresponds to the condensation temperature. As the suction pressure decreases, the saturated vapor pressure of the refrigerant also decreases accordingly, resulting in a decrease in the evaporation temperature. As the condensation temperature increases, the pressure of the refrigerant in the condenser also increases accordingly, and the corresponding exhaust pressure will increase accordingly. Therefore, the pressure data in the air-conditioning compressor and the corresponding temperature data show a good positive correlation.

[0055] Based on this, in order to improve the accuracy of the required length of the extended data segment for calculating the data point, the embodiment of the present invention can also obtain other pressure data segments in the pressure time series except the endpoint data segment for comparative analysis with the endpoint data segment, and correct the degree of discreteness of the compressor temperature data that is correlated with the pressure of the compressor, and combine the other pressure data segments with the temperature data segments to jointly determine the required length of the extended data segment, so that the extended data segment is more in line with the actual situation, thereby improving the accuracy of abnormality analysis.

[0056] For example, the temperature data of the compressor can be collected by a temperature sensor, and then the temperature sensor data can be converted into a temperature time series of the compressor based on an analog-to-digital conversion device. The data values ​​in the obtained temperature time series and pressure time series correspond to each other one by one.

[0057] Among them, the collection time and frequency of the compressor temperature data collected by the temperature sensor can refer to the collection time and frequency of the pressure data mentioned above, and the embodiment of the present invention will not be described in detail here.

[0058] For example, in the same time period, if the endpoint data segment in the obtained pressure data segment of the compressor contains 60 data points, the temperature data segment can be obtained within the time period of the pressure data segment, and the obtained temperature data segment also contains 60 data points.

[0059] For example, when determining the extension requirement of a data point, other pressure data segments except the endpoint data segment can be obtained in the pressure data segment to determine the number of abnormal data points in other pressure data segments; based on the number of abnormal data points in other pressure data segments and the correlation between pressure data and temperature data, the discrete degree of the data point is corrected to obtain the extension requirement of the data point.

[0060] For example, in an embodiment of the present invention, the number of abnormal data points in the pressure data segment can be obtained by the box plot method; the specific steps of obtaining the number of abnormal data points by the box plot method can be implemented by the existing technology, and the embodiment of the present invention will not be described in detail here.

[0061] It can be understood that the number of pressure data segments is determined by the length of the pressure time series, and the number of other pressure data segments except the endpoint data segment is the total number of pressure data segments minus 1.

[0062] For example, in the embodiment of the present invention, the extension requirement of the data point is determined, and the specific relationship can be as follows: ; The endpoint data segment The extension requirement of data points, For endpoint data segments except The total number of data points outside the data point, The endpoint data segment The degree of dispersion of the data points, is the number of pressure data segments, For the The number of abnormal data points in a pressure data segment, is the number of abnormal data points in the endpoint data segment, is the information entropy value of the endpoint data segment, is the information entropy value of the temperature data segment corresponding to the endpoint data segment, is an exponential function with base e, is the linear normalization function, is the absolute value symbol.

[0063] In the above formula, It represents the sum of the discrete degrees of all data points in the endpoint data segment. The larger the value, the more discrete the data points in the endpoint data segment. The data point has a lower demand for extended data segments.

[0064] It represents the sum of the difference between the number of abnormal data points in the endpoint data segment and the number of abnormal data points in each pressure data segment. The larger the value, the more discrete the data points in the endpoint data segment are and the greater the degree of abnormality.

[0065] It represents the absolute value of the difference between the information entropy of the data point in the endpoint data segment and the information entropy of the data point in the temperature data segment. The larger the value is, the smaller the correlation between the data point in the endpoint data segment and the corresponding temperature data segment in the same time period is, and the lower the demand of the current data point for the extended data segment is.

[0066] After determining the extension requirement of each data point in the endpoint data segment based on the above formula, the required length of the extended data segment can be determined based on the extension requirement and the endpoint data segment can be extended, that is, the following steps are performed.

[0067] S4: Determine the required length of the extended data segment for each data point in the endpoint data segment, where the required length is positively correlated with the extension requirement; extend the endpoint data segment according to the required length of the extended data segment.

[0068] It should be noted that the greater the extension requirement of the data point in the endpoint data segment, the longer the required length of the corresponding extension data segment.

[0069] For example, in the embodiment of the present invention, the required length of the extended data segment of the data point is determined, and the specific details can be referred to the following formula: ; The endpoint data segment The required length of the extended data segment by the data point, The endpoint data segment The extension requirement of data points, To preset the length of the extended data segment, The floor symbol.

[0070] The preset extended data segment length may be set to 100, and the extended data segment length may be specifically set according to actual needs, and the embodiment of the present invention does not impose too many restrictions on this.

[0071] The value 0.5 in the above formula is the initial value of the calculated extension data segment length. Rounding is performed to better obtain the required length of the extended data segment of the current data point.

[0072] When determining the required length of the extended data segment of the data point based on the above steps, since the endpoint data segment contains multiple data points, the required length of the extended data segment of each of the multiple data points in the endpoint data segment can be obtained based on the above steps. In order to better meet the required length of the extended data segment of each of the data points, the maximum value of the required length of the extended data segment of each of the multiple data points can be determined as the required length of the extended data segment of the endpoint data segment, and the endpoint data segment can be extended based on the required length of the extended data segment. In this way, the final required length of the extended data segment obtained can achieve the extension of the endpoint data segment without causing data redundancy, and is ready for the subsequent determination of the operating status of the compressor based on this.

[0073] Based on this, an embodiment of the present invention can obtain the maximum value of the extended data segment required length of the data point in the endpoint data segment, extend the endpoint data segment based on the extended data segment required length of the data point, and then obtain a more accurate compressor status analysis result based on the extended endpoint data segment.

[0074] By way of example, in an embodiment of the present invention, the endpoint data segment is extended according to the required length of the extended data segment, including: taking the maximum required length corresponding to each data point in the endpoint data segment as the extended length of the endpoint data segment, and extending the endpoint data segment in accordance with the collected data points.

[0075] It can be understood that, based on the above steps, the extended length of the start data segment and / or the end data segment can be obtained respectively. When the endpoint data segment of the pressure data segment of the compressor is the start data segment and the end data segment, the start data segment and the end data segment can be extended respectively based on the required length of the extended data segment; when the data segment of the pressure data segment of the compressor is the start data segment or the end data segment, the start data segment or the end data segment can be extended based on the required length of the extended data segment. When extending the start data segment, data points can continue to be collected on the left side of the start; when extending the end data segment, data points can continue to be collected on the right side of the end.

[0076] After extending the endpoint data segment of the pressure timing sequence of the air-conditioning compressor, continue to perform the following steps.

[0077] S5: Use the Z-Score algorithm to monitor the abnormality of the pressure time series after extending the endpoint data segment to obtain the air conditioning operation status monitoring result.

[0078] It can be understood that the extended pressure timing sequence obtained based on the above steps includes an extended start data segment, an extended end data segment and an initial pressure timing sequence.

[0079] By way of example, in an embodiment of the present invention, the Z-Score algorithm is used to perform abnormality monitoring after the endpoint data segment is extended according to the required length of the extended data segment to obtain the air conditioning operation status monitoring result, including: using the Z-Score algorithm to calculate the score of each data point in the extended pressure time series sequence; if the score of the data point is greater than a preset score threshold, the air conditioning operation status monitoring result is abnormal; otherwise, the air conditioning operation status monitoring result is normal.

[0080] The preset score threshold may be set to 2.5, which may be set according to actual needs, and the embodiment of the present invention does not impose too many restrictions on this.

[0081] Specifically, when using the Z-Score algorithm to calculate the score of each data point in the extended pressure time series sequence, the data point window length of the extended pressure time series sequence can be preset, and the mean and standard deviation in the window can be determined; the difference between the data point and the window mean is recorded as the abnormality index of the data point, and the score of the data point is obtained by the ratio of the abnormality index of the data point to the window standard deviation.

[0082] The window length can be set to 31, which can be set according to actual needs. When obtaining a window of a data point, the current data point can be taken as the center, and other data points can be obtained equally on both sides of the data point to construct a window of the current data point.

[0083] It should be noted that when the compressor operating status of any data point is determined based on the extended pressure time series, the abnormal operating status of the compressor can be warned and the operator can be notified in time to handle it, thereby effectively avoiding accidents and reducing the wear of the compressor.

[0084] For example, the air conditioner operation status monitoring result is obtained, and then the method further includes: in response to the air conditioner operation status monitoring result being abnormal, issuing an air conditioner abnormality warning.

[0085] Among them, the air conditioning abnormality warning can be set as a voice alarm or a signal light alarm, which can be set according to actual needs, and the embodiment of the present invention does not impose too many restrictions on this.

[0086] For example, in an embodiment of the present invention, after an air conditioning abnormality warning is issued, the number of air conditioning abnormality warnings can also be counted. If the number of abnormal warnings exceeds the air conditioning warning threshold, the compressor can be stopped directly, and the information of the corresponding data point when the compressor warning is generated can be recorded and reported.

[0087] For example, the information of the data point may include the pressure value, temperature value, time and number of times the warning is generated, etc. of the compressor, which can be set according to actual needs.

[0088] The air conditioning warning threshold may be set according to actual needs, and the embodiment of the present invention does not impose too many limitations thereon.

[0089] It can be seen that in the embodiment of the present invention, when analyzing the operating state of the air-conditioning compressor, the pressure time series of the air-conditioning compressor can be evenly divided into multiple pressure data segments, and the pressure data segment containing the endpoint is recorded as the endpoint data segment; the discrete degree of the data point is obtained by the difference between the data point in the endpoint data segment and the endpoint data segment as a whole; the temperature data segment corresponding to the endpoint data segment is obtained in the temperature time series; the discrete degree of the data point is corrected based on the number of abnormal data points in other pressure data segments and the correlation between the pressure data and the temperature data, and the extension demand degree of the data point is obtained: ; The endpoint data segment The extension requirement of data points, For endpoint data segments except The total number of data points outside the data point, For the The degree of dispersion of data points in the endpoint data segment, is the number of pressure data segments, , They are endpoint data segment, The number of abnormal data points in a pressure data segment, , are the information entropy values ​​of the endpoint data segment and the temperature data segment corresponding to the endpoint data segment, is an exponential function with base e, is a linear normalized function; the required length of each data point in the endpoint data segment for the extended data segment is determined, and the required length is positively correlated with the extended demand degree; after extending the endpoint data segment according to the required length of the extended data segment, the Z-Score algorithm is used for abnormal monitoring to obtain the air conditioning operation status monitoring result.

[0090] In this way, an accurate method for calculating the required length of the extended data segment provided by an embodiment of the present invention comprehensively considers the data characteristics in the pressure data segment and the temperature data, effectively improving the accuracy of the required length of the extended data segment; then, based on the required length of the extended data segment with higher accuracy, the endpoint data segment is extended, effectively reducing the error caused by insufficient data points at the endpoint data segment in the pressure data segment, and determining the operating status of the compressor based on the extended pressure data segment, effectively improving the accuracy of the air conditioning operating status monitoring results.

[0091] An embodiment of the present invention further discloses an air conditioner operation status monitoring system, including a processor and a memory, wherein the memory stores computer program instructions, and when the computer program instructions are executed by the processor, an air conditioner operation status monitoring method according to the present invention is implemented.

[0092] In the present invention, the aforementioned memory may be any tangible medium containing or storing a program that can be used by or in combination with an instruction execution system, apparatus, or device. For example, a computer-readable storage medium may be any suitable magnetic storage medium or magneto-optical storage medium, such as a resistive random access memory RRAM, a dynamic random access memory DRAM, a static random access memory SRAM, an enhanced dynamic random access memory EDRAM, a high bandwidth memory HBM, a hybrid memory cube HMC, etc., or any other medium that can be used to store the required information and can be accessed by an application, a module, or both. Any such computer storage medium may be part of a device or accessible or connectable to a device.

[0093] Although this specification has shown and described a number of embodiments of the present invention, it will be apparent to those skilled in the art that such embodiments are provided by way of example only. Those skilled in the art will conceive of many modifications, changes and alternatives without departing from the ideas and spirit of the present invention. It should be understood that in the practice of the present invention, various alternatives to the embodiments of the present invention described herein may be employed.

[0094] The above are all preferred embodiments of the present invention, and are not intended to limit the protection scope of the present invention. Therefore, any equivalent changes made based on the structure, shape, and principle of the present invention should be included in the protection scope of the present invention.

Claims

1. A method for monitoring the operating status of an air conditioner, characterized in that: include: The pressure time series of the air-conditioning compressor is divided into a plurality of pressure data segments, and the pressure data segment containing the endpoint is recorded as the endpoint data segment; The discrete degree of the data point is obtained by the difference between the data point in the endpoint data segment and the endpoint data segment as a whole; the temperature data segment corresponding to the endpoint data segment is obtained in the temperature time series; the discrete degree of the data point is corrected based on the number of abnormal data points in other pressure data segments and the correlation between pressure data and temperature data, and the extension requirement of the data point is obtained: ; The endpoint data segment The extension requirement of data points, For endpoint data segments except The total number of data points outside the data point, The endpoint data segment The degree of dispersion of the data points, is the number of pressure data segments, , They are endpoint data segment, The number of abnormal data points in a pressure data segment, , are the information entropy values ​​of the endpoint data segment and the temperature data segment corresponding to the endpoint data segment, is an exponential function with base e, is a linear normalized function; the required length of each data point in the endpoint data segment for the extended data segment is determined, and the required length is positively correlated with the extended demand degree; after extending the endpoint data segment according to the required length of the extended data segment, the Z-Score algorithm is used for abnormal monitoring to obtain the air conditioning operation status monitoring result.

2. The method for monitoring the operating status of an air conditioner according to claim 1, characterized in that: The discrete degree of each data point in the endpoint data segment satisfies the relationship: ； The endpoint data segment The degree of dispersion of the data points, For endpoint data segments except The total number of data points outside the data point, For the The pressure value of the data point, For each endpoint data segment except Data points outside the The pressure value of the data point, is the pressure value variance of all data points in the endpoint data segment, For each endpoint data segment except The variance of the pressure values ​​outside the data points, For the The sampling time interval between the data point and the endpoint in the endpoint data segment, is a hyperparameter, is a linear normalization function.

3. The method for monitoring the operating status of an air conditioner according to claim 1, characterized in that: The number of abnormal data points in the pressure data segment is obtained by the box plot method.

4. The method for monitoring the operating status of an air conditioner according to claim 1, characterized in that: The step of determining the required length of the extended data segment for each data point in the endpoint data segment includes: ； The endpoint data segment The required length of the extended data segment by the data point, The endpoint data segment The extension requirement of data points, To preset the length of the extended data segment, The floor symbol.

5. The method for monitoring the operating status of an air conditioner according to claim 1, characterized in that: Extend the endpoint data segment according to the required length of the extended data segment, including: The maximum value of the required length corresponding to each data point in the endpoint data segment is used as the extended length of the endpoint data segment, and the endpoint data segment is extended in accordance with the collected data points.

6. The method for monitoring the operating status of an air conditioner according to claim 5, characterized in that: The method of using the Z-Score algorithm to perform abnormal monitoring after extending the endpoint data segment according to the required length of the extended data segment to obtain the air conditioner operation status monitoring result includes: The Z-Score algorithm is used to calculate the score of each data point in the extended pressure time series; if the score of the data point is greater than the preset score threshold, the air conditioner operation status monitoring result is abnormal; otherwise, the air conditioner operation status monitoring result is normal.

7. The method for monitoring the operating status of an air conditioner according to claim 6, characterized in that: The air conditioner operation status monitoring result is obtained, and then the following steps are further included: In response to the air-conditioning operation status monitoring result being abnormal, an air-conditioning abnormality warning is issued.

8. An air conditioning operation status monitoring system, characterized in that: include: A processor and a memory, wherein the memory stores computer program instructions, and when the computer program instructions are executed by the processor, an air conditioning operation status monitoring method according to any one of claims 1-7 is implemented.

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