Air compressor operating status monitoring data processing method and system

Abstract

The present invention relates to the technical field of air compressor operation status monitoring, and specifically discloses a method and system for processing data for air compressor operation status monitoring, including: based on the acquisition parameters of each sensor of the air compressor at all moments in a preset time period before the current moment, obtaining the acquisition processing parameters of each sensor of the air compressor at each abnormal moment in the preset time period before the current moment; based on each missing moment and all abnormal moments of each sensor of the air compressor in the preset time period before the current moment, obtaining the acquisition filling parameters of each sensor of the air compressor at each missing moment in the preset time period before the current moment, and then obtaining the monitoring coefficient of each sensor of the air compressor at the current moment, and obtaining the air compressor operation status monitoring result at the current moment according to the monitoring coefficients of all sensors of the air compressor at the current moment. The present invention realizes the accurate acquisition of monitoring results when the sensor fails or the accuracy is insufficient.

Description

Technical Field

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

[0002] Air compressors are crucial pneumatic equipment in the industrial sector, and their operating status directly impacts the efficiency and stability of production lines. With technological advancements, modern air compressor systems are becoming increasingly complex, incorporating multiple sensors to monitor key parameters such as temperature, pressure, vibration, and flow. These parameters are crucial for timely troubleshooting and troubleshooting, and optimizing operational efficiency. Therefore, real-time monitoring and assessment of air compressor operating status has become a crucial task. In practical applications, sensor data often exhibits incompleteness and anomalies due to factors such as sensor failure, data transmission errors, and environmental interference. Incomplete data manifests as missing data—meaning that valid data was not collected at certain points in time. Abnormal data manifests as data values ​​that deviate from the normal range, potentially due to sensor failure or abnormal equipment operation. These data issues directly impact the accurate assessment of air compressor operating status. Effectively addressing the incompleteness and anomalies of sensor data, and accurately quantifying sensor performance, are key challenges facing intelligent monitoring technology.

[0003] However, existing air compressor operating status monitoring data processing methods and systems only reduce the complexity of the monitoring method by constructing a comprehensive air compressor correlation index, and at the same time construct a quantitative operating status anomaly coefficient to improve the adaptability of fault monitoring threshold setting, but do not achieve accurate acquisition of monitoring results when the sensor fails or the accuracy is insufficient. For example, the patent with the publication number "CN118332293B" and the patent name "A method and device for monitoring air compressor operating status data processing" includes the following steps: collecting air flow, vibration, and pressure data from a reciprocating air compressor; obtaining a spectrum sequence corresponding to a high-frequency signal sequence and constructing a high-frequency anomaly coefficient; calculating a low-frequency trend coefficient and constructing a rotor vibration high-frequency trend deviation coefficient; calculating an air compressor characteristic comprehensive correlation index based on the rotor vibration high-frequency trend deviation coefficient; obtaining an operating status quantitative anomaly coefficient based on the state quantification sequence variation coefficient and the air compressor characteristic comprehensive correlation index; and completing real-time monitoring of the air compressor operating status. The above patent reduces the complexity of the monitoring method by constructing an air compressor comprehensive correlation index, and at the same time constructs a quantitative operating status anomaly coefficient, thereby improving the adaptability of fault monitoring threshold setting. However, this patent only reduces the complexity of the monitoring method by constructing a comprehensive correlation index of the air compressor, and at the same time constructs a quantitative abnormality coefficient of the operating status to improve the adaptability of the fault monitoring threshold setting, but does not achieve accurate acquisition of monitoring results when the sensor fails or the accuracy is insufficient.

[0004] Therefore, the present invention proposes a method and system for processing air compressor operating status monitoring data. Summary of the Invention

[0005] The present invention provides an air compressor operating status monitoring data processing method and system, which is used to obtain the collection and processing parameters of each sensor of the air compressor at each abnormal moment in the preset time period before the current moment based on the collection parameters of each sensor of the air compressor at all moments in the preset time period before the current moment, accurately predict the parameters that can reflect the operating status of each sensor of the air compressor at each abnormal moment in the preset time period before the current moment, and obtain the filling tree diagram of each missing moment of each sensor of the air compressor in the preset time period before the current moment based on each missing moment and all abnormal moments in the preset time period before the current moment, so as to facilitate the subsequent collection and acquisition of filling parameters, and the filling tree diagram of each missing moment of each sensor of the air compressor in the preset time period before the current moment is obtained. Figure, obtains the collection and filling parameters of each missing moment of each sensor of the air compressor in the preset time period before the current moment, accurately predicts the parameters that can reflect the operating status of each sensor of the air compressor in each missing moment of the preset time period before the current moment, and then according to the collection and processing parameters of all abnormal moments of each sensor of the air compressor in the preset time period before the current moment and the collection and filling parameters of all missing moments, obtains the monitoring coefficient of each sensor of the air compressor at the current moment, realizes quantification of the good or bad degree of the operating condition of each sensor of the air compressor at the current moment, and finally obtains the air compressor operating status monitoring result at the current moment according to the monitoring coefficient of all sensors of the air compressor at the current moment, and realizes accurate acquisition of monitoring results in the case of sensor failure or insufficient accuracy.

[0006] The present invention provides a method for processing air compressor operating status monitoring data, comprising:

[0007] S1: Based on the acquisition parameters of each sensor of the air compressor at each moment in a preset time period before the current moment, obtain the data monitoring and determination result of each sensor of the air compressor at the current moment. If the data monitoring and determination result of a sensor of the air compressor at the current moment indicates that correction and filling is required, obtain all missing moments and all abnormal moments of each sensor of the air compressor in the preset time period before the current moment;

[0008] S2: Based on the collection parameters of each sensor of the air compressor at all moments in the preset time period before the current moment, obtain the collection and processing parameters of each sensor of the air compressor at each abnormal moment in the preset time period before the current moment;

[0009] S3: Based on each missing moment and all abnormal moments of each sensor of the air compressor in the preset time period before the current moment, a filling tree diagram of each missing moment of each sensor of the air compressor in the preset time period before the current moment is obtained, and based on the filling tree diagram of each missing moment of each sensor of the air compressor in the preset time period before the current moment, acquisition filling parameters of each missing moment of each sensor of the air compressor in the preset time period before the current moment are obtained;

[0010] S4: Based on the collection and processing parameters of all abnormal moments of each sensor of the air compressor in the preset time period before the current moment and the collection and filling parameters of all missing moments, the monitoring coefficient of each sensor of the air compressor at the current moment is obtained, and based on the monitoring coefficients of all sensors of the air compressor at the current moment, the air compressor operation status monitoring result at the current moment is obtained.

[0011] Preferably, the air compressor operation status monitoring data processing method, S1: based on the acquisition parameters of each sensor of the air compressor at each moment in a preset time period before the current moment, obtain the data monitoring judgment result of each sensor of the air compressor at the current moment; if there is a sensor of the air compressor whose data monitoring judgment result at the current moment is that correction filling is required, obtain all missing moments and all abnormal moments of each sensor of the air compressor in the preset time period before the current moment, including:

[0012] Obtain the collection parameters of each sensor of the air compressor at each moment in a preset time period before the current moment;

[0013] If each sensor of the air compressor fails to successfully collect the acquisition parameters at the current moment, it will need to be corrected and filled as the data monitoring and judgment result of the corresponding sensor of the air compressor at the current moment;

[0014] If each sensor of the air compressor successfully collects the acquisition parameters at the current moment, and the acquisition parameters of the corresponding sensor of the air compressor at the current moment are greater than the product of the average value of the acquisition parameters of the corresponding sensor of the air compressor at all moments in the preset time period before the current moment and the preset multiple, it will be corrected and filled as the data monitoring judgment result of the corresponding sensor of the air compressor at the current moment;

[0015] If a sensor of the air compressor has a data monitoring result at the current moment that determines that correction and filling are required, all missing moments and all abnormal moments of each sensor of the air compressor in a preset time period before the current moment are obtained.

[0016] Preferably, the method for processing the air compressor operating status monitoring data includes obtaining all missing moments and all abnormal moments of each sensor of the air compressor in a preset time period before the current moment if the data monitoring result of a sensor of the air compressor at the current moment indicates that correction filling is required:

[0017] If the data monitoring result of a sensor of the air compressor at the current moment is that correction filling is required, it is determined whether each sensor of the air compressor successfully collects the acquisition parameters at each moment in the preset time period before the current moment. If so, the corresponding moment is regarded as the time to be analyzed of the corresponding sensor of the air compressor in the preset time period before the current moment; otherwise, the corresponding moment is regarded as the missing moment of the corresponding sensor of the air compressor in the preset time period before the current moment;

[0018] If the difference between the acquisition parameters of each sensor of the air compressor at each time to be analyzed in the preset time period before the current time and the acquisition parameters of all adjacent times corresponding to the time to be analyzed is greater than the preset acquisition parameter threshold of the corresponding sensor, the corresponding time is regarded as an abnormal time of the corresponding sensor of the air compressor in the preset time period before the current time;

[0019] All moments of each sensor of the air compressor within the preset time period before the current moment, except for missing moments and abnormal moments, are regarded as normal moments of the corresponding sensor of the air compressor within the preset time period before the current moment.

[0020] Preferably, the air compressor operation status monitoring data processing method, S2: based on the collection parameters of each sensor of the air compressor at all times within a preset time period before the current moment, obtaining the collection and processing parameters of each sensor of the air compressor at each abnormal moment in the preset time period before the current moment, including:

[0021] The standard deviation of the acquisition parameters of each sensor of the air compressor at all normal times in the preset time period before the current moment is regarded as the acquisition standard deviation of the corresponding sensor of the air compressor in the preset time period before the current moment;

[0022] The average value of the acquisition parameters of each sensor of the air compressor at all normal times in the preset time period before the current moment is used as the acquisition standard value of the corresponding sensor of the air compressor in the preset time period before the current moment;

[0023] All normal moments of each sensor of the air compressor that are sequentially before each abnormal moment in the preset time period before the current moment are regarded as the left adjacent moments of the corresponding abnormal moment, and all normal moments of each sensor of the air compressor that are sequentially after each abnormal moment in the preset time period before the current moment are regarded as the right adjacent moments of the corresponding abnormal moment;

[0024] Based on the acquisition standard deviation, acquisition standard value and all left adjacent moments and all right adjacent moments of each abnormal moment of each sensor of the air compressor in the preset time period before the current moment, the acquisition processing parameters of each abnormal moment of each sensor of the air compressor in the preset time period before the current moment are obtained.

[0025] Preferably, the air compressor operating status monitoring data processing method obtains the acquisition processing parameters of each abnormal moment of each sensor of the air compressor in the preset time period before the current moment based on the acquisition standard deviation, acquisition standard value, and all left adjacent moments and all right adjacent moments of each abnormal moment of each sensor of the air compressor in the preset time period before the current moment, including:

[0026] ;

[0027] in, The collection and processing parameters of the currently calculated abnormal moment of the air compressor sensor in the preset time period before the current moment, The minimum value of the collected parameters of the currently calculated sensor of the air compressor at all left adjacent moments of the currently calculated abnormal moment in the preset time period before the current moment, The maximum value of the collected parameters of the currently calculated sensor of the air compressor at all left adjacent moments of the currently calculated abnormal moment in the preset time period before the current moment, The minimum value of the collected parameters of the currently calculated sensor of the air compressor at all right adjacent moments of the currently calculated abnormal moment in the preset time period before the current moment, is the maximum value of the acquisition parameters of the currently calculated sensor of the air compressor at all right adjacent moments of the currently calculated abnormal moment in the preset time period before the current moment, d is the acquisition standard value of the currently calculated sensor of the air compressor in the preset time period before the current moment, r is the acquisition standard deviation of the currently calculated sensor of the air compressor in the preset time period before the current moment, ln is the natural logarithm, and the value of the natural constant e is 2.718.

[0028] Preferably, the air compressor operating status monitoring data processing method obtains a filling tree diagram of each missing moment of each sensor of the air compressor in a preset time period before the current moment based on each missing moment and all abnormal moments of each sensor of the air compressor in a preset time period before the current moment, including:

[0029] Take each missing moment of each sensor of the air compressor in the preset time period before the current moment as the root node;

[0030] Among all normal moments of each sensor of the air compressor in the preset time period before the current moment, the normal moment with the longest time interval from each missing moment is used as the first construction moment of each missing moment, and the quotient of the time interval between each missing moment of each sensor of the air compressor in the preset time period before the current moment and the first construction moment of the corresponding missing moment and the preset time interval is used as the first construction layer number of the corresponding missing moment;

[0031] Take the normal moment whose quotient between the time interval from each missing moment and the preset time interval among all normal moments of the preset time period before the current moment for each sensor of the air compressor is 1 as the left child node of the first layer, take the normal moment whose quotient between the time interval from each missing moment and the preset time interval among all normal moments of the preset time period before the current moment for each sensor of the air compressor is 2 as the left child node of the second layer, continue to increase the quotient between the time interval from each missing moment and the preset time interval until the quotient between the time interval from each missing moment and the preset time interval is the same as the first construction layer number of the corresponding missing moment, and obtain the left child nodes of all layers;

[0032] Among all abnormal moments of each sensor of the air compressor in the preset time period before the current moment, the abnormal moment with the longest time interval from each missing moment is used as the second construction moment of each missing moment, and the quotient of the time interval between each missing moment of each sensor of the air compressor in the preset time period before the current moment and the second construction moment corresponding to the missing moment and the preset time interval is used as the second construction layer number corresponding to the missing moment;

[0033] Take the abnormal moment whose quotient value between the time interval from each missing moment and the preset time interval among all abnormal moments of the preset time period before the current moment for each sensor of the air compressor, which is 1, as the first-layer right child node; take the abnormal moment whose quotient value between the time interval from each missing moment and the preset time interval among all abnormal moments of the preset time period before the current moment for each sensor of the air compressor, which is 2, as the second-layer right child node; continue to increase the quotient value between the time interval from each missing moment and the preset time interval until the quotient value between the time interval from each missing moment and the preset time interval is the same as the second construction layer number of the corresponding missing moment, and obtain all-layer right child nodes;

[0034] The tree diagram composed of the root node, all layer left child nodes and all layer right child nodes corresponding to each missing moment of each sensor of the air compressor in the preset time period before the current moment is used as the filling tree diagram of the corresponding missing moment of the corresponding sensor of the air compressor in the preset time period before the current moment.

[0035] Preferably, the air compressor operating status monitoring data processing method obtains the collection filling parameters of each missing moment of each sensor of the air compressor in the preset time period before the current moment based on the filling tree diagram of each missing moment of each sensor of the air compressor in the preset time period before the current moment, including:

[0036] ;

[0037] in, The collection filling parameters for the currently calculated missing time of the air compressor's currently calculated sensor in the preset time period before the current moment, The minimum value of the acquisition parameters corresponding to all left child nodes of all layers in the filling tree diagram of the currently calculated missing moment of the air compressor sensor in the preset time period before the current moment, The maximum value of the acquisition parameters corresponding to all left child nodes of all layers in the filling tree diagram of the currently calculated missing moment of the air compressor sensor in the preset time period before the current moment, is the mean value of the acquisition parameters corresponding to all left child nodes of the i-th layer in the filling tree diagram of the currently calculated missing moment of the air compressor sensor in the preset time period before the current moment, The minimum value of the acquisition processing parameters corresponding to all right child nodes of all layers in the filling tree diagram of the currently calculated missing moment of the air compressor sensor in the preset time period before the current moment, The maximum value of the acquisition processing parameters corresponding to all right child nodes of all layers in the filling tree diagram of the currently calculated missing moment of the air compressor sensor in the preset time period before the current moment, is the mean of the acquisition and processing parameters corresponding to all the right child nodes of the i-th layer in the filling tree diagram of the currently calculated missing moment of the air compressor's currently calculated sensor in the preset time period before the current moment, min(p,q) is the minimum value between p and q, p is the total number of layers of left child nodes in the filling tree diagram of the currently calculated missing moment of the air compressor's currently calculated sensor in the preset time period before the current moment, q is the total number of layers of right child nodes in the filling tree diagram of the currently calculated missing moment of the air compressor's currently calculated sensor in the preset time period before the current moment, s is the number and value of all the left child nodes of the i-th layer and all the right child nodes of the i-th layer in the filling tree diagram of the currently calculated missing moment of the air compressor's currently calculated sensor in the preset time period before the current moment, ln is the natural logarithm, and the value of the natural constant e is 2.718.

[0038] Preferably, the air compressor operating status monitoring data processing method obtains the monitoring coefficient of each sensor of the air compressor at the current moment based on the collection and processing parameters of all abnormal moments of each sensor of the air compressor in a preset time period before the current moment and the collection and filling parameters of all missing moments, including:

[0039] The average of the collected parameters of each sensor of the air compressor at all normal moments in the preset time period before the current moment is used as the first reference parameter of the corresponding sensor of the air compressor in the preset time period before the current moment;

[0040] The average value of the collected and processed parameters of each sensor of the air compressor at all abnormal moments in the preset time period before the current moment is used as the second reference parameter of the corresponding sensor of the air compressor in the preset time period before the current moment;

[0041] The average value of the collected filling parameters of each sensor of the air compressor at all missing moments in the preset time period before the current moment is used as the third reference parameter of the corresponding sensor of the air compressor in the preset time period before the current moment;

[0042] The average value of the first reference parameter, the second reference parameter and the third reference parameter of each sensor of the air compressor in a preset time period before the current moment is regarded as the monitoring coefficient of the corresponding sensor of the air compressor at the current moment.

[0043] Preferably, the air compressor operation status monitoring data processing method obtains the air compressor operation status monitoring result at the current moment based on the monitoring coefficients of all sensors of the air compressor at the current moment, including:

[0044] If the monitoring coefficient of a sensor of the air compressor at the current moment is not within the preset coefficient range of the corresponding sensor, the poor operating status will be regarded as the monitoring result of the air compressor operating status at the current moment; otherwise, the good operating status will be regarded as the monitoring result of the air compressor operating status at the current moment.

[0045] The present invention provides an air compressor operating status monitoring data processing system, which is used to execute any one of the air compressor operating status monitoring data processing methods of embodiments 1 to 9, including:

[0046] An analysis module is configured to obtain, based on the acquisition parameters of each sensor of the air compressor at each moment in a preset time period before the current moment, a data monitoring and determination result of each sensor of the air compressor at the current moment; if the data monitoring and determination result of a sensor of the air compressor at the current moment indicates that correction and filling is required, obtain all missing moments and all abnormal moments of each sensor of the air compressor in the preset time period before the current moment;

[0047] a processing module for obtaining, based on the collection parameters of each sensor of the air compressor at all moments in a preset time period before the current moment, collection and processing parameters of each sensor of the air compressor at each abnormal moment in the preset time period before the current moment;

[0048] a filling module, configured to obtain a filling tree diagram for each missing moment in the preset time period before the current moment for each sensor of the air compressor based on each missing moment and all abnormal moments in the preset time period before the current moment, and obtain collection filling parameters for each missing moment in the preset time period before the current moment for each sensor of the air compressor based on the filling tree diagram for each missing moment in the preset time period before the current moment;

[0049] The monitoring module is used to obtain the monitoring coefficient of each sensor of the air compressor at the current moment based on the collection and processing parameters of all abnormal moments of each sensor of the air compressor in a preset time period before the current moment and the collection and filling parameters of all missing moments, and obtain the air compressor operation status monitoring result at the current moment based on the monitoring coefficients of all sensors of the air compressor at the current moment.

[0050] The beneficial effects of the present invention over the prior art are as follows: based on the acquisition parameters of each sensor of the air compressor at all moments in the preset time period before the current moment, the acquisition and processing parameters of each sensor of the air compressor at each abnormal moment in the preset time period before the current moment are obtained, and the parameters that can reflect the operating status of each sensor of the air compressor at each abnormal moment in the preset time period before the current moment are accurately predicted; based on each missing moment and all abnormal moments of each sensor of the air compressor in the preset time period before the current moment, a filling tree diagram of each missing moment of each sensor of the air compressor in the preset time period before the current moment is obtained, which is convenient for subsequent acquisition of the filling parameters; based on the filling tree diagram of each missing moment of each sensor of the air compressor in the preset time period before the current moment, the air compressor is obtained. The collection and filling parameters of each missing moment of each sensor of the air compressor in the preset time period before the current moment are used to accurately predict the parameters that can reflect the operating status of each sensor of the air compressor in each missing moment of the preset time period before the current moment. Then, according to the collection and processing parameters of all abnormal moments and the collection and filling parameters of all missing moments of each sensor of the air compressor in the preset time period before the current moment, the monitoring coefficient of each sensor of the air compressor at the current moment is obtained, which realizes the quantification of the good or bad operation of each sensor of the air compressor at the current moment. Finally, according to the monitoring coefficients of all sensors of the air compressor at the current moment, the operating status monitoring result of the air compressor at the current moment is obtained, and in the case of sensor failure or insufficient accuracy, the monitoring result is accurately obtained.

[0051] Other features and advantages of the present invention will be described in the following description, and in part will become apparent from the description, or will be understood by practicing the present invention. The purpose and other advantages of the present invention can be realized and obtained by the structures particularly pointed out in the written application documents.

[0052] The technical solution of the present invention is further described in detail below through the accompanying drawings and embodiments. BRIEF DESCRIPTION OF THE DRAWINGS

[0053] The accompanying drawings are used to provide a further understanding of the present invention and constitute a part of the specification. Together with the embodiments of the present invention, they are used to explain the present invention and do not constitute a limitation of the present invention. In the accompanying drawings:

[0054] Figure 1 This is a flow chart of a method for processing data of an air compressor operating status monitoring method according to an embodiment of the present invention;

[0055] Figure 2 Schematic diagram of an air compressor operating status monitoring data processing system in an embodiment of the present invention. DETAILED DESCRIPTION

[0056] The preferred embodiments of the present invention are described below with reference to the accompanying drawings. It should be understood that the preferred embodiments described herein are only used to illustrate and explain the present invention, and are not used to limit the present invention.

[0057] Example 1: The present invention provides a method for processing data of monitoring the operating status of an air compressor, referring to Figure 1 ,include:

[0058] S1: Based on the acquisition parameters of each sensor of the air compressor at each moment in a preset time period before the current moment, obtain the data monitoring and determination result of each sensor of the air compressor at the current moment. If the data monitoring and determination result of a sensor of the air compressor at the current moment indicates that correction and filling is required, obtain all missing moments and all abnormal moments of each sensor of the air compressor in the preset time period before the current moment;

[0059] S2: Based on the collection parameters of each sensor of the air compressor at all moments in the preset time period before the current moment, obtain the collection and processing parameters of each sensor of the air compressor at each abnormal moment in the preset time period before the current moment;

[0060] S3: Based on each missing moment and all abnormal moments of each sensor of the air compressor in the preset time period before the current moment, a filling tree diagram of each missing moment of each sensor of the air compressor in the preset time period before the current moment is obtained, and based on the filling tree diagram of each missing moment of each sensor of the air compressor in the preset time period before the current moment, acquisition filling parameters of each missing moment of each sensor of the air compressor in the preset time period before the current moment are obtained;

[0061] S4: Based on the collection and processing parameters of all abnormal moments of each sensor of the air compressor in the preset time period before the current moment and the collection and filling parameters of all missing moments, the monitoring coefficient of each sensor of the air compressor at the current moment is obtained, and based on the monitoring coefficients of all sensors of the air compressor at the current moment, the air compressor operation status monitoring result at the current moment is obtained.

[0062] In this embodiment, the air compressor is a mechanical device used to compress air, and its full name is air compressor.

[0063] In this embodiment, the sensor is a key component installed in the air compressor to monitor the operating status of the air compressor, such as a temperature sensor, a pressure sensor, a flow sensor, etc.

[0064] In this embodiment, the preset time period is a preset time period for obtaining acquisition parameters of each sensor of the air compressor.

[0065] In this embodiment, each moment is a moment selected from a preset time period before the current moment, and the time length between every two adjacent moments is a preset time interval (eg, 1 second).

[0066] In this embodiment, the collected parameters are values ​​collected by each sensor of the air compressor at each moment within a preset time period before the current moment, which can represent a certain operating state of the air compressor, such as temperature values, flow values, etc.

[0067] In this embodiment, the data monitoring and determination result is a monitoring and determination result of the collection parameters of each sensor of the air compressor at the current moment, obtained based on the collection parameters of each sensor of the air compressor at each moment in a preset time period before the current moment.

[0068] In this embodiment, the missing moment is the moment when each sensor of the air compressor fails to successfully collect the acquisition parameters in all moments in a preset time period before the current moment.

[0069] In this embodiment, the abnormal moment is a specific moment in all moments in a preset time period before the current moment when the collected parameters of each sensor of the air compressor may contain errors or inaccuracies due to sensor failure, environmental interference or other reasons.

[0070] In this embodiment, the collected and processed parameters at each abnormal moment are predicted parameters that can reflect the operating status of each sensor of the air compressor at each abnormal moment in a preset time period before the current moment.

[0071] In this embodiment, the filling tree diagram of each missing moment is a tree diagram that can analyze the collection filling parameters of each sensor of the air compressor at each missing moment in a preset time period before the current moment.

[0072] In this embodiment, the acquisition filling parameter at each missing moment is a predicted parameter that can reflect the operating status of each sensor of the air compressor at each missing moment in a preset time period before the current moment.

[0073] In this embodiment, the monitoring coefficient of each sensor of the air compressor at the current moment is a value that can represent the quality of the operation of each sensor of the air compressor at the current moment.

[0074] In this embodiment, the air compressor operating status monitoring result at the current moment is a monitoring result obtained based on the monitoring coefficients of all sensors of the air compressor at the current moment, which can reflect the operating status of the air compressor at the current moment, wherein the air compressor operating status monitoring result includes poor operating status and good operating status.

[0075] The beneficial effects of the above technology are as follows: based on the collection parameters of each sensor of the air compressor at all moments in the preset time period before the current moment, the collection and processing parameters of each sensor of the air compressor at each abnormal moment in the preset time period before the current moment are obtained, and the parameters that can reflect the operating status of each sensor of the air compressor at each abnormal moment in the preset time period before the current moment are accurately predicted; based on each missing moment and all abnormal moments of each sensor of the air compressor in the preset time period before the current moment, a filling tree diagram of each missing moment of each sensor of the air compressor in the preset time period before the current moment is obtained, which is convenient for subsequent collection and filling parameter acquisition; based on the filling tree diagram of each missing moment of each sensor of the air compressor in the preset time period before the current moment, each sensor of the air compressor is obtained. By collecting and filling parameters of each missing moment of each sensor in the preset time period before the current moment, the parameters that can reflect the operating status of each sensor of the air compressor in each missing moment of the preset time period before the current moment are accurately predicted. Then, according to the collection and processing parameters of all abnormal moments of each sensor of the air compressor in the preset time period before the current moment and the collection and filling parameters of all missing moments, the monitoring coefficient of each sensor of the air compressor at the current moment is obtained, and the degree of the operating condition of each sensor of the air compressor at the current moment is quantified. Finally, according to the monitoring coefficients of all sensors of the air compressor at the current moment, the operating status monitoring result of the air compressor at the current moment is obtained, and in the case of sensor failure or insufficient accuracy, the monitoring result is accurately obtained.

[0076] Example 2: Based on Example 1, a method for processing air compressor operating status monitoring data, S1: Based on the acquisition parameters of each sensor of the air compressor at each moment in a preset time period before the current moment, obtain the data monitoring determination result of each sensor of the air compressor at the current moment. If the data monitoring determination result of a sensor of the air compressor at the current moment is that correction and filling is required, obtain all missing moments and all abnormal moments of each sensor of the air compressor in the preset time period before the current moment, including:

[0077] Obtain the collection parameters of each sensor of the air compressor at each moment in a preset time period before the current moment;

[0078] If each sensor of the air compressor fails to successfully collect the acquisition parameters at the current moment, it will need to be corrected and filled as the data monitoring and judgment result of the corresponding sensor of the air compressor at the current moment;

[0079] If each sensor of the air compressor successfully collects the acquisition parameters at the current moment, and the acquisition parameters of the corresponding sensor of the air compressor at the current moment are greater than the product of the average value of the acquisition parameters of the corresponding sensor of the air compressor at all moments in the preset time period before the current moment and the preset multiple, it will be corrected and filled as the data monitoring judgment result of the corresponding sensor of the air compressor at the current moment;

[0080] If a sensor of the air compressor has a data monitoring result at the current moment that determines that correction and filling are required, all missing moments and all abnormal moments of each sensor of the air compressor in a preset time period before the current moment are obtained.

[0081] In this embodiment, the preset multiple is a multiple that is preset to obtain the data monitoring and determination result of the sensor at the current moment, and all sensors correspond to the same preset multiple, for example, 2.5.

[0082] The beneficial effects of the above technology are: based on the collection parameters of each sensor of the air compressor at each moment in the preset time period before the current moment, the data monitoring and judgment results of each sensor of the air compressor at the current moment are obtained, and the data monitoring and judgment results of each sensor of the air compressor at the current moment are accurately obtained, which facilitates the subsequent acquisition of all missing moments and all abnormal moments of each sensor of the air compressor in the preset time period before the current moment.

[0083] Example 3: Based on Example 2, a method for processing air compressor operating status monitoring data, if a sensor of the air compressor has a data monitoring result at the current moment that indicates that correction filling is required, obtain all missing moments and all abnormal moments of each sensor of the air compressor in a preset time period before the current moment, including:

[0084] If the data monitoring result of a sensor of the air compressor at the current moment is that correction filling is required, it is determined whether each sensor of the air compressor successfully collects the acquisition parameters at each moment in the preset time period before the current moment. If so, the corresponding moment is regarded as the time to be analyzed of the corresponding sensor of the air compressor in the preset time period before the current moment; otherwise, the corresponding moment is regarded as the missing moment of the corresponding sensor of the air compressor in the preset time period before the current moment;

[0085] If the difference between the acquisition parameters of each sensor of the air compressor at each time to be analyzed in the preset time period before the current time and the acquisition parameters of all adjacent times corresponding to the time to be analyzed is greater than the preset acquisition parameter threshold of the corresponding sensor, the corresponding time is regarded as an abnormal time of the corresponding sensor of the air compressor in the preset time period before the current time;

[0086] All moments of each sensor of the air compressor within the preset time period before the current moment, except for missing moments and abnormal moments, are regarded as normal moments of the corresponding sensor of the air compressor within the preset time period before the current moment.

[0087] In this embodiment, all adjacent moments to the moment to be analyzed are all moments of each sensor of the air compressor within a preset time period before the current moment, which are adjacent to the moment to be analyzed.

[0088] In this embodiment, the preset acquisition parameter threshold of the sensor is a preset acquisition parameter threshold used to obtain the abnormal moment of the corresponding sensor of the air compressor in a preset time period before the current moment, and each sensor of the air compressor corresponds to a preset acquisition parameter threshold separately.

[0089] In this embodiment, the normal time is a time when each sensor of the air compressor is in a normal operating state among all times in a preset time period before the current time.

[0090] The beneficial effect of the above technology is: a specific method for obtaining all missing moments and all abnormal moments of each sensor of the air compressor in a preset time period before the current moment is given in detail, which is convenient for subsequent data processing.

[0091] Example 4: Based on Example 3, an air compressor operating status monitoring data processing method, S2: Based on the acquisition parameters of each sensor of the air compressor at all times within a preset time period before the current moment, obtaining the acquisition processing parameters of each sensor of the air compressor at each abnormal moment in the preset time period before the current moment, including:

[0092] The standard deviation of the acquisition parameters of each sensor of the air compressor at all normal times in the preset time period before the current moment is regarded as the acquisition standard deviation of the corresponding sensor of the air compressor in the preset time period before the current moment;

[0093] The average value of the acquisition parameters of each sensor of the air compressor at all normal times in the preset time period before the current moment is used as the acquisition standard value of the corresponding sensor of the air compressor in the preset time period before the current moment;

[0094] All normal moments of each sensor of the air compressor that are sequentially before each abnormal moment in the preset time period before the current moment are regarded as the left adjacent moments of the corresponding abnormal moment, and all normal moments of each sensor of the air compressor that are sequentially after each abnormal moment in the preset time period before the current moment are regarded as the right adjacent moments of the corresponding abnormal moment;

[0095] Based on the acquisition standard deviation, acquisition standard value and all left adjacent moments and all right adjacent moments of each abnormal moment of each sensor of the air compressor in the preset time period before the current moment, the acquisition processing parameters of each abnormal moment of each sensor of the air compressor in the preset time period before the current moment are obtained.

[0096] The beneficial effect of the above technology is: based on the acquisition parameters of all normal moments of each sensor of the air compressor in the preset time period before the current moment, the acquisition standard deviation, acquisition standard value and all left adjacent moments and all right adjacent moments of each abnormal moment of each sensor of the air compressor in the preset time period before the current moment are obtained, which facilitates the subsequent acquisition and processing parameters of each abnormal moment of each sensor of the air compressor in the preset time period before the current moment.

[0097] Example 5: Based on Example 4, a method for processing data for monitoring the operating status of an air compressor is provided, which obtains the acquisition processing parameters of each abnormal moment of each sensor of the air compressor in the preset time period before the current moment based on the acquisition standard deviation, acquisition standard value, and all left adjacent moments and all right adjacent moments of each abnormal moment of each sensor of the air compressor in the preset time period before the current moment, including:

[0098] ;

[0099] in, The collection and processing parameters of the currently calculated abnormal moment of the air compressor sensor in the preset time period before the current moment, The minimum value of the collected parameters of the currently calculated sensor of the air compressor at all left adjacent moments of the currently calculated abnormal moment in the preset time period before the current moment, The maximum value of the collected parameters of the currently calculated sensor of the air compressor at all left adjacent moments of the currently calculated abnormal moment in the preset time period before the current moment, The minimum value of the collected parameters of the currently calculated sensor of the air compressor at all right adjacent moments of the currently calculated abnormal moment in the preset time period before the current moment, is the maximum value of the acquisition parameters of the currently calculated sensor of the air compressor at all right adjacent moments of the currently calculated abnormal moment in the preset time period before the current moment, d is the acquisition standard value of the currently calculated sensor of the air compressor in the preset time period before the current moment, r is the acquisition standard deviation of the currently calculated sensor of the air compressor in the preset time period before the current moment, ln is the natural logarithm, and the value of the natural constant e is 2.718.

[0100] The beneficial effects of the above technology are: based on the collection parameters of each sensor of the air compressor at all moments in the preset time period before the current moment, the collection and processing parameters of each sensor of the air compressor at each abnormal moment in the preset time period before the current moment are obtained, and the parameters that can reflect the operating status of each sensor of the air compressor at each abnormal moment in the preset time period before the current moment are accurately predicted, thereby realizing the prediction of the operating status of each sensor of the air compressor at each abnormal moment in the preset time period before the current moment.

[0101] Example 6: Based on Example 3, a method for processing air compressor operating status monitoring data obtains a filling tree diagram of each missing moment in a preset time period before the current moment for each sensor of the air compressor based on each missing moment and all abnormal moments in a preset time period before the current moment, including:

[0102] Take each missing moment of each sensor of the air compressor in the preset time period before the current moment as the root node;

[0103] Among all normal moments of each sensor of the air compressor in the preset time period before the current moment, the normal moment with the longest time interval from each missing moment is used as the first construction moment of each missing moment, and the quotient of the time interval between each missing moment of each sensor of the air compressor in the preset time period before the current moment and the first construction moment of the corresponding missing moment and the preset time interval is used as the first construction layer number of the corresponding missing moment;

[0104] Take the normal moment whose quotient between the time interval from each missing moment and the preset time interval among all normal moments of the preset time period before the current moment for each sensor of the air compressor is 1 as the left child node of the first layer, take the normal moment whose quotient between the time interval from each missing moment and the preset time interval among all normal moments of the preset time period before the current moment for each sensor of the air compressor is 2 as the left child node of the second layer, continue to increase the quotient between the time interval from each missing moment and the preset time interval until the quotient between the time interval from each missing moment and the preset time interval is the same as the first construction layer number of the corresponding missing moment, and obtain the left child nodes of all layers;

[0105] Among all abnormal moments of each sensor of the air compressor in the preset time period before the current moment, the abnormal moment with the longest time interval from each missing moment is used as the second construction moment of each missing moment, and the quotient of the time interval between each missing moment of each sensor of the air compressor in the preset time period before the current moment and the second construction moment corresponding to the missing moment and the preset time interval is used as the second construction layer number corresponding to the missing moment;

[0106] Take the abnormal moment whose quotient value between the time interval from each missing moment and the preset time interval among all abnormal moments of the preset time period before the current moment for each sensor of the air compressor, which is 1, as the first-layer right child node; take the abnormal moment whose quotient value between the time interval from each missing moment and the preset time interval among all abnormal moments of the preset time period before the current moment for each sensor of the air compressor, which is 2, as the second-layer right child node; continue to increase the quotient value between the time interval from each missing moment and the preset time interval until the quotient value between the time interval from each missing moment and the preset time interval is the same as the second construction layer number of the corresponding missing moment, and obtain all-layer right child nodes;

[0107] The tree diagram composed of the root node, all layer left child nodes and all layer right child nodes corresponding to each missing moment of each sensor of the air compressor in the preset time period before the current moment is used as the filling tree diagram of the corresponding missing moment of the corresponding sensor of the air compressor in the preset time period before the current moment.

[0108] In this embodiment, the preset time interval is a preset time interval. The preset time interval in this application document is consistent with the time interval between two adjacent moments in a preset time period before the current moment.

[0109] In this embodiment, the combination is a method of combining the root node, all layer left child nodes and all layer right child nodes corresponding to each missing moment of each sensor of the air compressor in a preset time period before the current moment according to a preset construction model (a model preset to combine different types of nodes and construct a filled tree diagram) to construct a filled tree diagram.

[0110] The beneficial effect of the above technology is: according to each missing moment and all abnormal moments of each sensor of the air compressor in the preset time period before the current moment, the root node, all layer left child nodes and all layer right child nodes corresponding to each missing moment are obtained, and then the filling tree diagram of each missing moment of each sensor of the air compressor in the preset time period before the current moment is obtained. This embodiment gives in detail a specific method for constructing a filling tree diagram of each missing moment of each sensor of the air compressor in the preset time period before the current moment, which is convenient for the subsequent collection and calculation of filling parameters.

[0111] Example 7: Based on Example 6, a method for processing air compressor operating status monitoring data, based on a filling tree diagram of each missing moment of each sensor of the air compressor in a preset time period before the current moment, obtains collection filling parameters of each missing moment of each sensor of the air compressor in a preset time period before the current moment, including:

[0112] ;

[0113] in, The collection filling parameters for the currently calculated missing time of the air compressor's currently calculated sensor in the preset time period before the current moment, The minimum value of the acquisition parameters corresponding to all left child nodes of all layers in the filling tree diagram of the currently calculated missing moment of the air compressor sensor in the preset time period before the current moment, The maximum value of the acquisition parameters corresponding to all left child nodes of all layers in the filling tree diagram of the currently calculated missing moment of the air compressor sensor in the preset time period before the current moment, is the mean value of the acquisition parameters corresponding to all left child nodes of the i-th layer in the filling tree diagram of the currently calculated missing moment of the air compressor sensor in the preset time period before the current moment, The minimum value of the acquisition processing parameters corresponding to all right child nodes of all layers in the filling tree diagram of the currently calculated missing moment of the air compressor sensor in the preset time period before the current moment, The maximum value of the acquisition processing parameters corresponding to all right child nodes of all layers in the filling tree diagram of the currently calculated missing moment of the air compressor sensor in the preset time period before the current moment, is the mean of the acquisition and processing parameters corresponding to all the right child nodes of the i-th layer in the filling tree diagram of the currently calculated missing moment of the air compressor's currently calculated sensor in the preset time period before the current moment, min(p,q) is the minimum value between p and q, p is the total number of layers of left child nodes in the filling tree diagram of the currently calculated missing moment of the air compressor's currently calculated sensor in the preset time period before the current moment, q is the total number of layers of right child nodes in the filling tree diagram of the currently calculated missing moment of the air compressor's currently calculated sensor in the preset time period before the current moment, s is the number and value of all the left child nodes of the i-th layer and all the right child nodes of the i-th layer in the filling tree diagram of the currently calculated missing moment of the air compressor's currently calculated sensor in the preset time period before the current moment, ln is the natural logarithm, and the value of the natural constant e is 2.718.

[0114] The beneficial effects of the above technology are: based on the filling tree diagram of each missing moment of each sensor of the air compressor in the preset time period before the current moment, the collection filling parameters of each missing moment of each sensor of the air compressor in the preset time period before the current moment are obtained, and the parameters that can reflect the operating status of each sensor of the air compressor in each missing moment of the preset time period before the current moment are accurately predicted, which effectively fills the data gaps and further improves the integrity and accuracy of the monitoring data.

[0115] Example 8: Based on Example 3, a method for processing data for monitoring the operating status of an air compressor is provided. The method obtains a monitoring coefficient of each sensor of the air compressor at the current moment based on the acquisition and processing parameters of all abnormal moments of each sensor of the air compressor in a preset time period before the current moment and the acquisition and filling parameters of all missing moments. The method includes:

[0116] The average of the collected parameters of each sensor of the air compressor at all normal moments in the preset time period before the current moment is used as the first reference parameter of the corresponding sensor of the air compressor in the preset time period before the current moment;

[0117] The average value of the collected and processed parameters of each sensor of the air compressor at all abnormal moments in the preset time period before the current moment is used as the second reference parameter of the corresponding sensor of the air compressor in the preset time period before the current moment;

[0118] The average value of the collected filling parameters of each sensor of the air compressor at all missing moments in the preset time period before the current moment is used as the third reference parameter of the corresponding sensor of the air compressor in the preset time period before the current moment;

[0119] The average value of the first reference parameter, the second reference parameter and the third reference parameter of each sensor of the air compressor in a preset time period before the current moment is regarded as the monitoring coefficient of the corresponding sensor of the air compressor at the current moment.

[0120] The beneficial effects of the above technology are: based on the collection and processing parameters of all abnormal moments of each sensor of the air compressor in the preset time period before the current moment and the collection and filling parameters of all missing moments, the monitoring coefficient of each sensor of the air compressor at the current moment is obtained, and the degree of operation of each sensor of the air compressor at the current moment is quantified, providing a quantitative basis for evaluating the overall operating status of the air compressor.

[0121] Example 9: Based on Example 8, an air compressor operating status monitoring data processing method obtains the air compressor operating status monitoring result at the current moment based on the monitoring coefficients of all sensors of the air compressor at the current moment, including:

[0122] If the monitoring coefficient of a sensor of the air compressor at the current moment is not within the preset coefficient range of the corresponding sensor, the poor operating status will be regarded as the monitoring result of the air compressor operating status at the current moment; otherwise, the good operating status will be regarded as the monitoring result of the air compressor operating status at the current moment.

[0123] In this embodiment, the preset coefficient range of the sensor is a preset monitoring coefficient range when each sensor is in good operating condition, and each sensor corresponds to a preset coefficient range individually.

[0124] The beneficial effects of the above technology are: based on the monitoring coefficients and preset coefficient ranges of all sensors of the air compressor at the current moment, the air compressor operation status monitoring results at the current moment are accurately obtained, and in the event of sensor failure or insufficient accuracy, accurate acquisition of monitoring results is achieved.

[0125] Example 10: The present invention provides an air compressor operating status monitoring data processing system for executing any one of the air compressor operating status monitoring data processing methods in Examples 1 to 9, referring to Figure 2 ,include:

[0126] An analysis module is configured to obtain, based on the acquisition parameters of each sensor of the air compressor at each moment in a preset time period before the current moment, a data monitoring and determination result of each sensor of the air compressor at the current moment; if the data monitoring and determination result of a sensor of the air compressor at the current moment indicates that correction and filling is required, obtain all missing moments and all abnormal moments of each sensor of the air compressor in the preset time period before the current moment;

[0127] a processing module for obtaining, based on the collection parameters of each sensor of the air compressor at all moments in a preset time period before the current moment, collection and processing parameters of each sensor of the air compressor at each abnormal moment in the preset time period before the current moment;

[0128] a filling module, configured to obtain a filling tree diagram for each missing moment in the preset time period before the current moment for each sensor of the air compressor based on each missing moment and all abnormal moments in the preset time period before the current moment, and obtain collection filling parameters for each missing moment in the preset time period before the current moment for each sensor of the air compressor based on the filling tree diagram for each missing moment in the preset time period before the current moment;

[0129] The monitoring module is used to obtain the monitoring coefficient of each sensor of the air compressor at the current moment based on the collection and processing parameters of all abnormal moments of each sensor of the air compressor in a preset time period before the current moment and the collection and filling parameters of all missing moments, and obtain the air compressor operation status monitoring result at the current moment based on the monitoring coefficients of all sensors of the air compressor at the current moment.

[0130] The beneficial effects of the above technology are as follows: based on the collection parameters of each sensor of the air compressor at all moments in the preset time period before the current moment, the collection and processing parameters of each sensor of the air compressor at each abnormal moment in the preset time period before the current moment are obtained, and the parameters that can reflect the operating status of each sensor of the air compressor at each abnormal moment in the preset time period before the current moment are accurately predicted; based on each missing moment and all abnormal moments of each sensor of the air compressor in the preset time period before the current moment, a filling tree diagram of each missing moment of each sensor of the air compressor in the preset time period before the current moment is obtained, which is convenient for subsequent collection and filling parameter acquisition; based on the filling tree diagram of each missing moment of each sensor of the air compressor in the preset time period before the current moment, each sensor of the air compressor is obtained. By collecting and filling parameters of each missing moment of each sensor in the preset time period before the current moment, the parameters that can reflect the operating status of each sensor of the air compressor in each missing moment of the preset time period before the current moment are accurately predicted. Then, according to the collection and processing parameters of all abnormal moments of each sensor of the air compressor in the preset time period before the current moment and the collection and filling parameters of all missing moments, the monitoring coefficient of each sensor of the air compressor at the current moment is obtained, and the degree of the operating condition of each sensor of the air compressor at the current moment is quantified. Finally, according to the monitoring coefficients of all sensors of the air compressor at the current moment, the operating status monitoring result of the air compressor at the current moment is obtained, and in the case of sensor failure or insufficient accuracy, the monitoring result is accurately obtained.

[0131] Obviously, those skilled in the art can make various changes and modifications to the present invention without departing from the spirit and scope of the present invention, and the present invention is also intended to include these changes and modifications.

Claims

1. A method for processing air compressor operating status monitoring data, characterized in that: include: S1: Based on the acquisition parameters of each sensor of the air compressor at each moment in a preset time period before the current moment, obtain the data monitoring and judgment result of each sensor of the air compressor at the current moment. If the data monitoring and judgment result of a sensor of the air compressor at the current moment is that correction and filling is required, obtain all missing moments and all abnormal moments of each sensor of the air compressor in the preset time period before the current moment; S2: Based on the acquisition parameters of each sensor of the air compressor at all times within a preset time period before the current moment, obtain the acquisition processing parameters of each sensor of the air compressor at each abnormal moment in the preset time period before the current moment; S3: Based on each missing moment and all abnormal moments of each sensor of the air compressor in a preset time period before the current moment, a filling tree diagram of each missing moment of each sensor of the air compressor in a preset time period before the current moment is obtained, and based on the filling tree diagram of each missing moment of each sensor of the air compressor in a preset time period before the current moment, a collection filling parameter of each missing moment of each sensor of the air compressor in the preset time period before the current moment is obtained; S4: Based on the collection and processing parameters of all abnormal moments of each sensor of the air compressor in a preset time period before the current moment and the collection and filling parameters of all missing moments, the monitoring coefficient of each sensor of the air compressor at the current moment is obtained, and based on the monitoring coefficients of all sensors of the air compressor at the current moment, the air compressor operation status monitoring result at the current moment is obtained.

2. The air compressor operating status monitoring data processing method according to claim 1 is characterized in that: S1: Based on the acquisition parameters of each sensor of the air compressor at each moment in the preset time period before the current moment, the data monitoring and judgment result of each sensor of the air compressor at the current moment is obtained. If the data monitoring and judgment result of a sensor of the air compressor at the current moment is that correction and filling is required, all missing moments and all abnormal moments of each sensor of the air compressor in the preset time period before the current moment are obtained, including: Obtain the collection parameters of each sensor of the air compressor at each moment in a preset time period before the current moment; If each sensor of the air compressor fails to successfully collect the acquisition parameters at the current moment, the filling shall be corrected as the data monitoring and judgment result of the corresponding sensor of the air compressor at the current moment; If each sensor of the air compressor successfully collects the acquisition parameters at the current moment, and the acquisition parameters of the corresponding sensor of the air compressor at the current moment are greater than the product of the average value of the acquisition parameters of the corresponding sensor of the air compressor at all moments in the preset time period before the current moment and the preset multiple, the correction filling will be taken as the data monitoring judgment result of the corresponding sensor of the air compressor at the current moment; If a sensor of the air compressor has a data monitoring result at the current moment that determines that correction and filling are required, all missing moments and all abnormal moments of each sensor of the air compressor in a preset time period before the current moment are obtained.

3. The air compressor operating status monitoring data processing method according to claim 2 is characterized in that: If the data monitoring result of a sensor of the air compressor at the current moment is that correction filling is required, all missing moments and all abnormal moments of each sensor of the air compressor in the preset time period before the current moment are obtained, including: If the data monitoring result of a sensor of the air compressor at the current moment is that correction filling is required, it is determined whether each sensor of the air compressor has successfully collected the acquisition parameters at each moment in the preset time period before the current moment. If so, the corresponding moment is regarded as the time to be analyzed of the corresponding sensor of the air compressor in the preset time period before the current moment; otherwise, the corresponding moment is regarded as the missing moment of the corresponding sensor of the air compressor in the preset time period before the current moment; If the difference between the acquisition parameters of each sensor of the air compressor at each time to be analyzed in the preset time period before the current time and the acquisition parameters of all adjacent times corresponding to the time to be analyzed are all greater than the preset acquisition parameter threshold of the corresponding sensor, the corresponding time is regarded as the abnormal time of the corresponding sensor of the air compressor in the preset time period before the current time; All moments of each sensor of the air compressor in the preset time period before the current moment, except for missing moments and abnormal moments, are regarded as normal moments of the corresponding sensor of the air compressor in the preset time period before the current moment.

4. The air compressor operating status monitoring data processing method according to claim 3 is characterized in that: S2: Based on the acquisition parameters of each sensor of the air compressor at all times within a preset time period before the current moment, the acquisition processing parameters of each sensor of the air compressor at each abnormal moment in the preset time period before the current moment are obtained, including: The standard deviation of the acquisition parameters of each sensor of the air compressor at all normal times in the preset time period before the current time is regarded as the acquisition standard deviation of the corresponding sensor of the air compressor in the preset time period before the current time; The average of all normal acquisition parameters of each sensor of the air compressor in a preset time period before the current moment is regarded as the acquisition standard value of the corresponding sensor of the air compressor in the preset time period before the current moment; All normal moments before each abnormal moment in the time sequence of all moments of each sensor of the air compressor in the preset time period before the current moment are regarded as the left adjacent moments of the corresponding abnormal moment, and all normal moments after each abnormal moment in the time sequence of all moments of each sensor of the air compressor in the preset time period before the current moment are regarded as the right adjacent moments of the corresponding abnormal moment; Based on the collection standard deviation, collection standard value and all left adjacent moments and all right adjacent moments of each abnormal moment of each sensor of the air compressor in the preset time period before the current moment, the collection processing parameters of each sensor of the air compressor at each abnormal moment in the preset time period before the current moment are obtained.

5. The air compressor operating status monitoring data processing method according to claim 4 is characterized in that: Based on the acquisition standard deviation, acquisition standard value and all left adjacent moments and all right adjacent moments of each abnormal moment of each sensor of the air compressor in the preset time period before the current moment, the acquisition processing parameters of each abnormal moment of each sensor of the air compressor in the preset time period before the current moment are obtained, including: ; in, The collection and processing parameters of the currently calculated abnormal moment of the sensor of the air compressor in the preset time period before the current moment, is the minimum value of the collected parameters of the currently calculated sensor of the air compressor at all left adjacent moments of the currently calculated abnormal moment in the preset time period before the current moment, is the maximum value of the collected parameters of the currently calculated sensor of the air compressor at all left adjacent moments of the currently calculated abnormal moment in the preset time period before the current moment, is the minimum value of the collected parameters of the currently calculated sensor of the air compressor at all right adjacent moments of the currently calculated abnormal moment in the preset time period before the current moment, is the maximum value of the acquisition parameters of all right adjacent moments of the currently calculated abnormal moment of the currently calculated sensor of the air compressor in the preset time period before the current moment, d is the acquisition standard value of the currently calculated sensor of the air compressor in the preset time period before the current moment, r is the acquisition standard deviation of the currently calculated sensor of the air compressor in the preset time period before the current moment, ln is the natural logarithm, and the value of the natural constant e is 2.

718.

6. The air compressor operation status monitoring data processing method according to claim 3 is characterized in that: Based on each missing moment and all abnormal moments of each sensor of the air compressor in a preset time period before the current moment, a filling tree diagram of each missing moment of each sensor of the air compressor in a preset time period before the current moment is obtained, including: Take each missing moment of each sensor of the air compressor in a preset time period before the current moment as the root node; The normal moment with the longest time interval from each missing moment among all normal moments of each sensor of the air compressor in the preset time period before the current moment is regarded as the first construction moment of each missing moment, and the quotient of the time interval between each missing moment of each sensor of the air compressor in the preset time period before the current moment and the first construction moment of the corresponding missing moment and the preset time interval is regarded as the first construction layer number of the corresponding missing moment; Take the normal moment whose quotient value between the time interval from each missing moment and the preset moment interval among all normal moments of each sensor of the air compressor in the preset time period before the current moment is 1 as the left child node of the first layer, take the normal moment whose quotient value between the time interval from each missing moment and the preset moment interval among all normal moments of each sensor of the air compressor in the preset time period before the current moment is 2 as the left child node of the second layer, continue to increase the quotient value between the time interval from each missing moment and the preset moment interval until the quotient value between the time interval from each missing moment and the preset moment interval is the same as the first construction layer number of the corresponding missing moment, and obtain the left child nodes of all layers; Among all abnormal moments of each sensor of the air compressor in the preset time period before the current moment, the abnormal moment with the longest time interval from each missing moment is regarded as the second construction moment of each missing moment, and the quotient of the time interval between each missing moment of each sensor of the air compressor in the preset time period before the current moment and the second construction moment corresponding to the missing moment and the preset time interval is regarded as the second construction layer number corresponding to the missing moment; Take the abnormal moment whose quotient value between the time interval from each missing moment and the preset moment interval among all abnormal moments of each sensor of the air compressor in the preset time period before the current moment is 1 as the first-layer right child node, take the abnormal moment whose quotient value between the time interval from each missing moment and the preset moment interval among all abnormal moments of each sensor of the air compressor in the preset time period before the current moment is 2 as the second-layer right child node, continue to increase the quotient value between the time interval from each missing moment and the preset moment interval until the quotient value between the time interval from each missing moment and the preset moment interval is the same as the second construction layer number of the corresponding missing moment, and obtain all layers of right child nodes; The tree diagram composed of the root node, all layer left child nodes and all layer right child nodes corresponding to each missing moment of each sensor of the air compressor in the preset time period before the current moment is used as the filling tree diagram of the corresponding missing moment of the corresponding sensor of the air compressor in the preset time period before the current moment.

7. The air compressor operating status monitoring data processing method according to claim 6 is characterized in that: Based on the filling tree diagram of each missing moment of each sensor of the air compressor in the preset time period before the current moment, the acquisition filling parameters of each missing moment of each sensor of the air compressor in the preset time period before the current moment are obtained, including: ； in, The collection filling parameters of the currently calculated missing time of the air compressor's currently calculated sensor in the preset time period before the current time, The minimum value of the acquisition parameters corresponding to all left child nodes of all layers in the filling tree diagram of the currently calculated missing moment of the sensor of the air compressor in the preset time period before the current moment, The maximum value of the acquisition parameters corresponding to all left child nodes of all layers in the filling tree diagram of the currently calculated missing moment of the air compressor sensor in the preset time period before the current moment, is the mean value of the acquisition parameters corresponding to all left child nodes of the i-th layer in the filling tree diagram of the missing moment of the current calculation of the sensor of the air compressor in the preset time period before the current moment, The minimum value of the acquisition processing parameters corresponding to all right child nodes of all layers in the filling tree diagram of the currently calculated missing moment of the sensor of the air compressor in the preset time period before the current moment, The maximum value of the acquisition processing parameters corresponding to all right child nodes of all layers in the filling tree diagram of the currently calculated missing moment of the sensor of the air compressor in the preset time period before the current moment, is the mean of the acquisition and processing parameters corresponding to all the right child nodes of the i-th layer in the filling tree diagram of the currently calculated missing moment of the current calculation of the sensor of the air compressor in the preset time period before the current moment, min(p,q) is the minimum value between p and q, p is the total number of left child nodes in the filling tree diagram of the currently calculated missing moment of the current calculation of the sensor of the air compressor in the preset time period before the current moment, q is the total number of right child nodes in the filling tree diagram of the currently calculated missing moment of the current calculation of the sensor of the air compressor in the preset time period before the current moment, s is the number and value of all the left child nodes of the i-th layer and all the right child nodes of the i-th layer in the filling tree diagram of the currently calculated missing moment of the current calculation of the sensor of the air compressor in the preset time period before the current moment, ln is the natural logarithm, and the value of the natural constant e is 2.

718.

8. The air compressor operating status monitoring data processing method according to claim 3 is characterized in that: Based on the acquisition processing parameters of all abnormal moments of each sensor of the air compressor in the preset time period before the current moment and the acquisition filling parameters of all missing moments, the monitoring coefficient of each sensor of the air compressor at the current moment is obtained, including: The average of the collected parameters of each sensor of the air compressor at all normal times in the preset time period before the current time is used as the first reference parameter of the corresponding sensor of the air compressor in the preset time period before the current time; The average value of the collected processing parameters of each sensor of the air compressor at all abnormal moments in the preset time period before the current moment is used as the second reference parameter of the corresponding sensor of the air compressor in the preset time period before the current moment; The average value of the collected filling parameters of each sensor of the air compressor at all missing moments in the preset time period before the current moment is used as the third reference parameter of the corresponding sensor of the air compressor in the preset time period before the current moment; The average value of the first reference parameter, the second reference parameter and the third reference parameter of each sensor of the air compressor in a preset time period before the current moment is regarded as the monitoring coefficient of the corresponding sensor of the air compressor at the current moment.

9. The air compressor operating status monitoring data processing method according to claim 8, characterized in that: Based on the monitoring coefficients of all sensors of the air compressor at the current moment, the monitoring results of the air compressor operation status at the current moment are obtained, including: If the monitoring coefficient of a sensor of the air compressor at the current moment is not within the preset coefficient range of the corresponding sensor, the poor operating status is regarded as the monitoring result of the air compressor operating status at the current moment; otherwise, the good operating status is regarded as the monitoring result of the air compressor operating status at the current moment.

10. An air compressor operation status monitoring data processing system, characterized in that: A method for processing air compressor operating status monitoring data for executing any one of claims 1 to 9, comprising: An analysis module is used to obtain the data monitoring and judgment result of each sensor of the air compressor at the current moment based on the collection parameters of each sensor of the air compressor at each moment in a preset time period before the current moment, and if the data monitoring and judgment result of a sensor of the air compressor at the current moment is that correction and filling is required, all missing moments and all abnormal moments of each sensor of the air compressor in the preset time period before the current moment are obtained; A processing module, used to obtain the collection processing parameters of each sensor of the air compressor at each abnormal moment in the preset time period before the current moment based on the collection parameters of each sensor of the air compressor at all moments in the preset time period before the current moment; A filling module, for obtaining a filling tree diagram of each missing moment of each sensor of the air compressor in a preset time period before the current moment based on each missing moment and all abnormal moments of each sensor of the air compressor in a preset time period before the current moment, and obtaining a collection filling parameter of each missing moment of each sensor of the air compressor in the preset time period before the current moment based on the filling tree diagram of each missing moment of each sensor of the air compressor in the preset time period before the current moment; The monitoring module is used to obtain the monitoring coefficient of each sensor of the air compressor at the current moment based on the collection and processing parameters of all abnormal moments of each sensor of the air compressor in a preset time period before the current moment and the collection and filling parameters of all missing moments, and obtain the air compressor operation status monitoring result at the current moment based on the monitoring coefficients of all sensors of the air compressor at the current moment.

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