Computer data intelligent processing method

Abstract

The invention relates to the technical field of data processing, in particular to a computer data intelligent processing method, which comprises the following steps: S1, acquiring operation data of equipment and oil data of the equipment; s2, analyzing the running state of the equipment; s3, an equipment pressure fluctuation coefficient is constructed according to the equipment pressure collected in the monitoring period, and the fluctuation state of the equipment pressure is analyzed according to the construction result of the equipment pressure fluctuation coefficient; s4, analyzing the oil state of the equipment; s5, the state of the equipment is analyzed according to the analysis result of the operation state of the equipment in each monitoring period in the management period, the analysis result of the fluctuation state of the pressure of the equipment in each monitoring period and the analysis result of the oil state of the equipment; and S6, adjusting the state analysis process of the equipment in the management period according to the operation duration of the equipment collected in the management period and the lubricating oil moisture of the equipment. According to the invention, the processing efficiency of computer data is improved.

Description

Technical Field

[0001] The present invention relates to the technical field of data processing, and in particular to a computer data intelligent processing method. Background Art

[0002] In modern industrial production, stable operation and efficient performance of equipment are key factors to ensure production efficiency and economic benefits. The operating status of equipment is not only affected by the external environment, but also closely related to the internal operating mechanism and the state of the lubrication system. Especially for equipment such as industrial pumps, which are subject to a large amount of mechanical loads and dynamic performance requirements, it is particularly important to accurately monitor their operating status and oil quality.

[0003] Chinese patent publication number CN118101445A discloses an industrial computer information intelligent processing method and system, which relates to the field of data processing technology, and classifies and preprocesses industrial computers; sends the labeled data information to the gateway device and the cloud platform respectively according to the real-time labeling; comprehensively organizes the received data through the cloud platform to obtain comprehensive data, generates a task processing chain for the data processing task, obtains abnormal nodes, calculates data abnormal values, obtains replacement node information according to the data abnormal values, calculates data normal values, obtains replacement nodes according to the data normal values, and generates a task replacement chain. The present invention realizes comprehensive management and efficient processing of industrial computer data, improves the quality and efficiency of data processing, and provides strong support for the stable operation and data analysis of industrial computers; it can be seen that when processing industrial computer data, the scheme only analyzes the quality of abnormal data, and there is a problem of low computer data processing efficiency. Summary of the invention

[0004] The object of the present invention is to provide a computer data intelligent processing method to solve at least one of the problems existing in the prior art.

[0005] To achieve the above object, the present invention adopts the following technical solutions:

[0006] A computer data intelligent processing method, comprising:

[0007] Step S1: Collecting equipment operation data and equipment oil data;

[0008] Step S2: constructing a temperature state index according to the equipment operating temperature collected during the monitoring period, constructing a vibration state index according to the equipment vibration intensity collected during the monitoring period, constructing a current state index according to the equipment current collected during the monitoring period, and analyzing the operating state of the equipment according to the temperature state index, vibration state index and current state index constructed during the monitoring period;

[0009] Step S3: constructing an equipment pressure fluctuation coefficient according to the equipment pressure collected during the monitoring period, and analyzing the fluctuation state of the equipment pressure according to the construction result of the equipment pressure fluctuation coefficient;

[0010] Step S4: analyzing the oil state of the equipment according to the lubricating oil viscosity and lubricating oil iron content of the equipment collected during the management period;

[0011] Step S5: analyzing the state of the equipment according to the analysis results of the operating state of the equipment in each monitoring period within the management period, the analysis results of the fluctuation state of the pressure of the equipment in each monitoring period, and the analysis results of the oil state of the equipment;

[0012] Step S6: Adjust the analysis process of the equipment status within the management period according to the operation time of the equipment and the water content of the lubricating oil of the equipment collected within the management period.

[0013] Furthermore, the method for analyzing the operating status of the device in step S2 includes:

[0014] Step S21: analyzing the temperature status of the device and constructing a temperature status index according to the analysis results;

[0015] Step S22: analyzing the vibration state of the equipment and constructing a vibration state index according to the analysis result;

[0016] Step S23: analyzing the current state of the device and constructing a current state index according to the analysis result;

[0017] Step S24: analyzing the operating status of the equipment according to the temperature status index, vibration status index and current status index constructed during the monitoring period;

[0018] In step S21, when analyzing the temperature state of the device and constructing the temperature state index, the temperature state of the device is analyzed according to the device operating temperature collected during the monitoring period. The temperature state of the device includes normal and abnormal. If the temperature state of the device in the current monitoring period is normal, the temperature state index is set to WT1; if the temperature state of the device in the current monitoring period is abnormal, the temperature state index is set to WT2;

[0019] When analyzing the vibration state of the equipment, the step S22 compares the equipment vibration intensity z0 collected during the monitoring period with the preset vibration intensity z1 to analyze the abnormality of the equipment vibration intensity, which includes normal and abnormal. The equipment vibration state is analyzed according to the analysis result of the abnormality of the equipment vibration intensity during the monitoring period, which includes normal and abnormal. If the equipment vibration state in the current monitoring period is normal, the vibration state index is set to ZT1; if the equipment vibration state in the current monitoring period is abnormal, the vibration state index is set to ZT2.

[0020] Furthermore, in step S23, when analyzing the device current state, the device current a0 collected during the monitoring period is compared with each preset current to analyze the abnormality of the device current, and the abnormality of the device current includes normal and abnormal; the device current state is analyzed according to the analysis result of the abnormality of the device current during the monitoring period, and the device current state includes normal and abnormal. If the device current state in the current monitoring period is normal, the current state index is set to DT1; if the device current state in the current monitoring period is abnormal, the current state index is set to DT2;

[0021] The step S24 analyzes the operating state of the equipment according to the temperature state index, vibration state index and current state index constructed during the monitoring period. The operating state of the equipment includes normal operation and abnormal operation.

[0022] Furthermore, in step S3, when analyzing the fluctuation state of the equipment pressure based on the equipment pressure collected during the monitoring period, an equipment pressure fluctuation coefficient B is constructed based on the equipment pressure collected during the monitoring period, and the equipment pressure fluctuation coefficient B is compared with a preset fluctuation threshold value B1 to analyze the fluctuation state of the equipment pressure, and the fluctuation state of the equipment pressure includes a stable state and an abnormal state.

[0023] Furthermore, the method for analyzing the oil state of the equipment in step S4 includes:

[0024] Step S41: analyzing the abnormality of the equipment lubricating oil viscosity according to the equipment lubricating oil viscosity collected during the management period, and constructing a viscosity abnormality coefficient according to the analysis result;

[0025] Step S42: analyzing the abnormality of the iron content of the equipment lubricating oil according to the iron content of the equipment lubricating oil collected during the management period, and constructing an iron content abnormality coefficient according to the analysis result;

[0026] Step S43: analyzing the oil state of the equipment according to the construction results of the viscosity abnormal coefficient and the iron content abnormal coefficient within the management period;

[0027] When analyzing the abnormality of the equipment lubricating oil viscosity, the step S41 compares the equipment lubricating oil viscosity n0 collected during the management period with the preset viscosity n1 to analyze the abnormality of the lubricating oil viscosity. The abnormality of the lubricating oil viscosity includes normal and abnormal. If the equipment lubricating oil viscosity in the current management period is abnormal, the viscosity abnormality coefficient is set to ND1; if the equipment lubricating oil viscosity in the current management period is normal, the viscosity abnormality coefficient is set to ND2.

[0028] Furthermore, in step S42, when analyzing the abnormality of the iron content of the equipment lubricating oil, the iron content h0 of the equipment lubricating oil collected during the management period is compared with the preset iron content h1 to analyze the abnormality of the iron content of the equipment lubricating oil. The abnormality of the iron content of the equipment lubricating oil includes normal and abnormal. If the iron content of the equipment lubricating oil in the current management period is normal, the iron content abnormality coefficient is set to TY1; if the iron content of the equipment lubricating oil in the current management period is abnormal, the iron content abnormality coefficient is set to TY2.

[0029] Furthermore, the analysis process of the oil state of the equipment in step S33 is as follows:

[0030] If w1×abnormal viscosity coefficient+w2×abnormal iron content coefficient≤RT, the oil status of the equipment in the current management cycle is determined to be normal;

[0031] If w1×abnormal viscosity coefficient+w2×abnormal iron content coefficient>RT, the oil state of the equipment in the current management cycle is determined to be abnormal;

[0032] Among them, w1 is the viscosity weight, w2 is the iron content weight, and RT is the preset abnormal coefficient.

[0033] Furthermore, the analysis process of the device status in step S5 is as follows:

[0034] If the oil status of the equipment in the current management cycle is normal, when [q1×r1+q2×r2] / r0≤YC, the equipment status of the current management cycle is judged to be normal; when [q1×r1+q2×r2] / r0>YC, the equipment status of the current management cycle is judged to be faulty;

[0035] If the oil state of the equipment in the current management cycle is abnormal, the state of the equipment in the current management cycle is determined to be a fault state;

[0036] Among them, q1 is the operating status weight, q2 is the pressure fluctuation weight, r1 is the number of monitoring cycles in which the operating status of the equipment is abnormal operation within the management cycle, r2 is the number of monitoring cycles in which the fluctuation status of the equipment pressure is abnormal within the management cycle, r0 is the number of monitoring cycles within the management cycle, and YC is the preset status threshold.

[0037] Furthermore, the method for adjusting the analysis process of the device status in step S6 includes:

[0038] Step S61: analyzing the usage status of the equipment according to the operation time of the equipment collected during the management period, and adjusting the analysis process of the status of the equipment during the management period according to the usage status of the equipment;

[0039] Step S62: adjusting the analysis process of the equipment usage status according to the lubricating oil moisture of the equipment collected during the management period;

[0040] When adjusting the analysis process of the status of the equipment within the management cycle, step S61 compares the operating time yt0 of the equipment collected during the management cycle with the preset operating time yt1 to analyze the usage status of the equipment. The usage status of the equipment includes normal and abnormal. If the usage status of the equipment in the current management cycle is abnormal, the preset status threshold is adjusted to YC1.

[0041] Furthermore, in the step S62, when adjusting the analysis process of the equipment's usage status, the lubricating oil moisture f0 of the equipment collected during the management period is compared with the preset moisture f1 to analyze the abnormality of the equipment's lubricating oil moisture. The abnormality of the equipment's lubricating oil moisture includes normal and abnormal. If the equipment's lubricating oil moisture is abnormal in the current management period, the preset operating time is adjusted to yt1'.

[0042] The beneficial effects of the present invention are as follows: through systematic data collection and analysis, including equipment operation data and oil data, the equipment operation status is monitored in real time, potential problems are discovered in time, thereby reducing unexpected failures and downtime; secondly, status indexes (such as temperature status index, vibration status index and current status index) are constructed, and through comprehensive analysis of these indexes, maintenance personnel can more scientifically evaluate the overall operation status of the equipment. At the same time, through the analysis of equipment operation time and lubricating oil moisture, the maintenance strategy can be dynamically adjusted to make it more flexible and effective, thereby improving the processing efficiency of computer data. BRIEF DESCRIPTION OF THE DRAWINGS

[0043] In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the following briefly introduces the drawings required for use in the description of the embodiments. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work.

[0044] Figure 1 Schematic diagram of the flow of the computer data intelligent processing method of this embodiment.

[0045] Figure 2 Schematic diagram of the flow of the method for analyzing the operating status of the device in this embodiment.

[0046] Figure 3 Schematic diagram of the flow of the method for analyzing the oil state of the equipment in this embodiment.

[0047] Figure 4 It is a flowchart of the adjustment method of the analysis process of the status of the device in this embodiment. DETAILED DESCRIPTION

[0048] In order to more clearly illustrate the present invention, the present invention is further described below in conjunction with preferred embodiments and accompanying drawings. Similar components in the accompanying drawings are represented by the same reference numerals. It should be understood by those skilled in the art that the content specifically described below is illustrative rather than restrictive, and should not be used to limit the scope of protection of the present invention.

[0049] It should be noted that, although the terms first, second, third, etc. may be used to describe in the embodiments of the present application, these descriptions should not be limited to these terms. These terms are only used to distinguish the descriptions. For example, without departing from the scope of the embodiments of the present application, the first may also be referred to as the second, and similarly, the second may also be referred to as the first.

[0050] See also Figure 1 As shown, it is a flow chart of the computer data intelligent processing method of this embodiment, and the method includes:

[0051] Step S1: Collecting the operation data of the equipment and the oil data of the equipment, wherein the operation data of the equipment includes the operation temperature of the equipment, the vibration intensity of the equipment, the pressure of the equipment, the current of the equipment and the operation time of the equipment, and the oil data of the equipment includes the viscosity of the lubricating oil of the equipment, the iron content of the lubricating oil of the equipment and the moisture content of the lubricating oil of the equipment; the equipment pressure is the pressure at the inlet of the equipment, and the equipment is an industrial pump; it can be understood that, in this embodiment, no specific limitation is made on the collection method of the operation data of the equipment and the oil data of the equipment, and those skilled in the art can freely set it, as long as the collection method of the operation data of the equipment and the oil data of the equipment is satisfied, wherein the operation data of the equipment can be collected through various intelligent sensors, the viscosity of the lubricating oil can be measured by a viscometer to measure the viscosity of the oil sample, the iron content of the lubricating oil of the equipment can be tested by an oil analysis laboratory, and the iron content in the oil can be determined by spectral analysis, and the moisture content of the lubricating oil of the equipment can be detected by a moisture detector (such as a coulometric moisture meter) to detect the moisture content in the oil.

[0052] Please continue reading Figure 1 As shown, the computer data intelligent processing method also includes:

[0053] Step S2: construct a temperature state index based on the equipment operating temperature collected during the monitoring period, construct a vibration state index based on the equipment vibration intensity collected during the monitoring period, construct a current state index based on the equipment current collected during the monitoring period, and analyze the equipment's operating state based on the temperature state index, vibration state index and current state index constructed during the monitoring period.

[0054] It is understandable that the present embodiment does not specifically limit the setting of the monitoring period, and those skilled in the art can freely set it as long as the setting requirements of the monitoring period are met, wherein the monitoring period can be set to 1 minute, 2 minutes, etc.

[0055] See also Figure 2 As shown, it is a flow chart of the method for analyzing the operating status of the device in this embodiment, including:

[0056] Step S21: Analyze the temperature status of the device and construct a temperature status index based on the analysis results.

[0057] Specifically, in step S21 of this embodiment, when analyzing the temperature state of the device and constructing the temperature state index, the temperature state of the device is analyzed according to the device operating temperature collected during the monitoring period, and the temperature state index is constructed according to the temperature state, and the temperature state is converted into an index, so that the health status of the device is more intuitive and convenient for monitoring and analysis. The analysis process of the temperature state and the construction process of the temperature state index are as follows:

[0058] If t0≤t1, it is determined that the temperature status of the equipment in the current monitoring period is normal, and the temperature status index is set to WT1, and WT1=0;

[0059] If t0>t1, it is determined that the temperature state of the device in the current monitoring period is abnormal, and the temperature state index is set to WT2, and WT2=(t0-t1) / t1 is set, where t0 is the average device operating temperature collected during the monitoring period, and t1 is the preset temperature threshold.

[0060] It is understandable that the present embodiment does not specifically limit the setting of the preset temperature threshold, and those skilled in the art can freely set it as long as the setting requirements of the preset temperature threshold are met, wherein the optimal value of t1 is 65 degrees Celsius.

[0061] Please continue reading Figure 2 As shown, the method for analyzing the operating status of the device also includes:

[0062] Step S22: Analyze the vibration state of the equipment and construct a vibration state index based on the analysis results.

[0063] Specifically, in step S22 of this embodiment, when analyzing the vibration state of the device, the device vibration intensity z0 collected during the monitoring period is compared with the preset vibration intensity z1 to analyze the abnormality of the device vibration intensity. If z0≤z1, the device vibration intensity is determined to be normal; if z0>z1, the device vibration intensity is determined to be abnormal.

[0064] The vibration state of the equipment is analyzed according to the analysis results of the abnormality of the vibration intensity of the equipment during the monitoring period. If i 1 / i 0≤α, the vibration state of the equipment in the current monitoring period is determined to be normal, and the vibration state index is set to ZT1, and ZT1=0; if i1 / i 0>α, the vibration state of the equipment in the current monitoring period is determined to be abnormal, and the vibration state index is set to ZT2, and ZT2=exp{lg(i 1 / i0-α)}; by analyzing the vibration intensity, the equipment structure and operation problems can be discovered in time, and by establishing the vibration state index, the abnormal state of the equipment can be quantitatively evaluated;

[0065] Among them, i1 is the duration of abnormal vibration intensity of the equipment during the monitoring period, i0 is the duration of the monitoring period, and α is the first preset abnormal proportion.

[0066] It can be understood that, in this embodiment, there is no specific limitation on the setting of the preset vibration intensity and the first preset abnormality ratio, and those skilled in the art can set them freely as long as the setting requirements of the preset vibration intensity and the first preset abnormality ratio are met, wherein the optimal value of z1 is 0.5g, and the optimal value of α is 0.05.

[0067] Please continue reading Figure 2 As shown, the method for analyzing the operating status of the device also includes:

[0068] Step S23: Analyze the current state of the device and construct a current state index based on the analysis result.

[0069] Specifically, in step S23 of this embodiment, when analyzing the device current state, the device current a0 collected during the monitoring period is compared with each preset current to analyze the abnormality of the device current. If a1≤a0≤a2, the device current is determined to be normal. If a0<a1 or a0>a2, the device current is determined to be abnormal. a1 is the first preset current, a2 is the second preset current, and a1<a2;

[0070] The device current state is analyzed according to the analysis result of the abnormality of the device current in the monitoring period. If i 2 / i 0≤β, the device current state in the current monitoring period is determined to be normal, and the current state index is set to DT1, and DT1=0; if i 2 / i0>β, the device current state in the current monitoring period is determined to be abnormal, and the current state index is set to DT2, and DT2=exp[3×(i2 / i 0-β)-3]; by monitoring the current change, problems such as poor energy efficiency or abnormal operation can be discovered. Timely discovery of current abnormalities can enhance the safety of the equipment and reduce the risk of overload;

[0071] Wherein, i 2 is the duration of abnormal current of the equipment within the monitoring period, and β is the second preset abnormal proportion.

[0072] It can be understood that, in this embodiment, the setting of the preset current and the second preset abnormality ratio is not specifically limited, and those skilled in the art can set them freely, as long as the setting requirements of the preset vibration intensity and the first preset abnormality ratio are met, wherein the optimal value of a1 is the equipment rated current × 0.7, the optimal value of a2 is the equipment rated current, and the optimal value of β is 0.05.

[0073] Please continue reading Figure 2 As shown, the method for analyzing the operating status of the device also includes:

[0074] Step S24: analyzing the operating status of the equipment according to the temperature status index, vibration status index and current status index constructed during the monitoring period.

[0075] Specifically, the analysis process of the operating status of the device in step S24 of this embodiment is as follows:

[0076] If x1×temperature state index+x2×vibration state index+x3×current state index≤u0, the operation state of the equipment in the current monitoring period is determined to be normal operation;

[0077] If x1×temperature state index+x2×vibration state index+x3×current state index>u0, the operation state of the equipment in the current monitoring period is judged to be abnormal operation; by comprehensively evaluating multiple state indexes, it is helpful to fully understand the operation health of the equipment, improve the scientific nature of maintenance decisions, and improve the processing efficiency of computer data;

[0078] Among them, x1 is the temperature weight, x2 is the vibration weight, x3 is the current weight, x1+x2+x3=1, and u0 is the preset operating state threshold.

[0079] It can be understood that, in this embodiment, there is no specific limitation on the setting of each weight and the preset operating status threshold, and those skilled in the art can set it freely as long as the setting requirements of each weight and the preset operating status threshold are met, among which the optimal value of x1 is 0.4, the optimal value of x2 is 0.2, the optimal value of x3 is 0.4, and the optimal value of u0 is 0.18.

[0080] Please continue reading Figure 1 As shown, the computer data intelligent processing method also includes:

[0081] Step S3: constructing an equipment pressure fluctuation coefficient according to the equipment pressure collected during the monitoring period, and analyzing the fluctuation state of the equipment pressure according to the construction result of the equipment pressure fluctuation coefficient.

[0082] Specifically, in step S3 of this embodiment, when analyzing the fluctuation state of the equipment pressure according to the equipment pressure collected during the monitoring period, the equipment pressure fluctuation coefficient B is constructed according to the equipment pressure collected during the monitoring period, and is set as follows:

[0083] Among them, Yj is the jth equipment pressure data collected during the monitoring period, Y0 is the average value of the equipment pressure data collected during the monitoring period, and J is the number of equipment pressure data collected during the monitoring period;

[0084] The equipment pressure fluctuation coefficient B is compared with the preset fluctuation threshold value B1 to analyze the fluctuation state of the equipment pressure. If B≤B1, the fluctuation state of the equipment pressure in the current monitoring period is determined to be a stable state; if B>B1, the fluctuation state of the equipment pressure in the current monitoring period is determined to be an abnormal state. By constructing the pressure fluctuation coefficient, a quantitative indicator of equipment operation stability is provided to help managers understand the steady state of equipment operation, thereby improving the accuracy of equipment status analysis and further improving the efficiency of computer data processing.

[0085] It can be understood that the present embodiment does not specifically limit the setting of the preset fluctuation threshold, and those skilled in the art can freely set it as long as the setting requirements of the preset fluctuation threshold are met, wherein the optimal value of B1 is 0.15.

[0086] Please continue reading Figure 1 As shown, the computer data intelligent processing method also includes:

[0087] Step S4: Analyze the oil status of the equipment according to the lubricating oil viscosity and lubricating oil iron content of the equipment collected during the management period.

[0088] Specifically, in this embodiment, the lubricating oil viscosity and lubricating oil iron content of the equipment are collected at the end of the management cycle. It can be understood that in this embodiment, no specific limitation is made to the setting of the management cycle, and technical personnel in this field can set it freely, as long as the setting requirements of the management cycle are met. Among them, the setting of the management cycle should be greater than the setting of the monitoring cycle, and the management cycle can be set to 15 days, 20 days, etc.

[0089] See also Figure 3 As shown, it is a flow chart of the method for analyzing the oil state of the equipment in this embodiment, including:

[0090] Step S41: Analyze the abnormality of the equipment lubricating oil viscosity according to the equipment lubricating oil viscosity collected during the management period, and construct a viscosity abnormality coefficient according to the analysis result.

[0091] Specifically, in step S41 of this embodiment, when analyzing the abnormality of the equipment lubricating oil viscosity, the lubricating oil viscosity n0 of the equipment collected during the management period is compared with the preset viscosity n1 to analyze the abnormality of the lubricating oil viscosity. If n0<n1, it is determined that the equipment lubricating oil viscosity in the current management period is abnormal, and the viscosity abnormality coefficient is set to ND1, and ND1 is set to (n1-n0) / n1; if n0≥n1, it is determined that the equipment lubricating oil viscosity in the current management period is normal, and the viscosity abnormality coefficient is set to ND2, and ND2 is set to 0; by monitoring the viscosity, the applicability of the oil can be evaluated, and viscosity abnormalities can be discovered in time, which can avoid equipment damage caused by poor lubrication.

[0092] It can be understood that the setting of the preset viscosity is not specifically limited in this embodiment, and those skilled in the art can freely set it as long as the setting requirements of the preset viscosity are met, wherein the optimal value of n1 is 80 cSt.

[0093] Please continue reading Figure 3 As shown, the method for analyzing the oil state of the equipment also includes:

[0094] Step S42: Analyze the abnormality of the iron content of the equipment lubricating oil according to the iron content of the equipment lubricating oil collected during the management period, and construct an iron content abnormality coefficient according to the analysis result.

[0095] Specifically, in step S42 of this embodiment, when analyzing the abnormality of the iron content of the equipment lubricating oil, the iron content h0 of the equipment lubricating oil collected during the management period is compared with the preset iron content h1 to analyze the abnormality of the iron content of the equipment lubricating oil to reflect the wear of the mechanical parts. If h0≤h1, it is determined that the iron content of the equipment lubricating oil in the current management period is normal, and the iron content abnormality coefficient is set to TY1, and TY1=0; if h0>h1, it is determined that the iron content of the equipment lubricating oil in the current management period is abnormal, and the iron content abnormality coefficient is set to TY2, and the setting is:

[0096] TY2=[(h0-h1) / (h0+h1)] / {1+[(h0-h1) / (h0+h1)] 2}; By monitoring the iron content in the equipment lubricating oil to reflect the wear of mechanical parts, it helps to identify potential faults. By constructing an iron content abnormality coefficient to quantify the abnormal degree of iron content in the equipment lubricating oil, the accuracy of equipment oil status analysis is improved.

[0097] It is understandable that the setting of the preset iron content is not specifically limited in this embodiment, and those skilled in the art can freely set it as long as the setting requirements of the preset iron content are met, wherein the optimal value of h1 is 200 ppm.

[0098] Please continue reading Figure 3 As shown, the method for analyzing the oil state of the equipment also includes:

[0099] Step S43: Analyze the oil status of the equipment according to the construction results of the viscosity abnormality coefficient and the iron content abnormality coefficient within the management period.

[0100] Specifically, the analysis process of the oil state of the equipment in step S33 of this embodiment is as follows:

[0101] If w1×abnormal viscosity coefficient+w2×abnormal iron content coefficient≤RT, the oil status of the equipment in the current management cycle is determined to be normal;

[0102] If w1×abnormal viscosity coefficient+w2×abnormal iron content coefficient>RT, the oil status of the equipment in the current management cycle is determined to be abnormal; through the analysis of viscosity and iron content, the health status of the oil can be more comprehensively evaluated to ensure the reliable operation of the equipment, while improving the accuracy of equipment status analysis, thereby improving the processing efficiency of computer data;

[0103] Wherein, w1 is the viscosity weight, w2 is the iron content weight, w1+w2=1, and RT is the preset abnormal coefficient.

[0104] It can be understood that, in this embodiment, there is no specific limitation on the setting of each weight and the preset abnormality coefficient, and those skilled in the art can set it freely, as long as the setting requirements of each weight and the preset abnormality coefficient are met. Among them, the best value of w1 is 0.4, the best value of w2 is 0.6, and the best value of RT is 0.15.

[0105] Please continue reading Figure 1 As shown, the computer data intelligent processing method also includes:

[0106] Step S5: Analyze the state of the equipment according to the analysis results of the running state of the equipment within the management period, the analysis results of the fluctuation state of the pressure of the equipment, and the analysis results of the oil state of the equipment.

[0107] Specifically, the process of analyzing the status of the device in step S5 of this embodiment is as follows:

[0108] If the oil status of the equipment in the current management cycle is normal, when [q1×r1+q2×r2] / r0≤YC, the equipment status of the current management cycle is judged to be normal; when [q1×r1+q2×r2] / r0>YC, the equipment status of the current management cycle is judged to be faulty;

[0109] If the oil status of the equipment in the current management cycle is abnormal, the equipment status in the current management cycle is determined to be a faulty state; through comprehensive analysis of various aspects of the equipment (oil, operating status, pressure fluctuations), it is possible to quickly identify potential faults and take response measures, and provide a reference basis for equipment maintenance and resource allocation, optimize maintenance plans and reduce downtime;

[0110] Among them, q1 is the operating status weight, q2 is the pressure fluctuation weight, q1>q2, r1 is the number of monitoring cycles in which the operating status of the equipment is abnormal operation within the management cycle, r2 is the number of monitoring cycles in which the fluctuation status of the equipment pressure is abnormal within the management cycle, r0 is the number of monitoring cycles within the management cycle, and YC is the preset status threshold.

[0111] It can be understood that the present embodiment does not specifically limit the setting of each weight and the preset state threshold, and those skilled in the art can freely set them as long as the setting requirements of each weight and the preset state threshold are met, among which the optimal value of q1 is 1.4, the optimal value of q2 is 0.9, and the optimal value of YC is 0.1.

[0112] Please continue reading Figure 1 As shown, the computer data intelligent processing method also includes:

[0113] Step S6: Adjust the analysis process of the equipment status within the management period according to the operation time of the equipment and the water content of the lubricating oil of the equipment collected within the management period.

[0114] Specifically, the operating time of the equipment collected during the management period is the total operating time of the equipment at the end of the management period.

[0115] See also Figure 4 As shown, it is a flow chart of the adjustment method of the analysis process of the state of the device in this embodiment, including:

[0116] Step S61: Analyze the usage status of the equipment according to the operation time of the equipment collected during the management period, and adjust the analysis process of the status of the equipment during the management period according to the usage status of the equipment.

[0117] Specifically, in step S61 of this embodiment, when adjusting the analysis process of the status of the equipment within the management period, the operation time yt0 of the equipment collected during the management period is compared with the preset operation time yt1 to analyze the usage status of the equipment. The analysis of the status of the equipment can be adjusted according to the usage of the equipment. If yt0≤yt1, the usage status of the equipment in the current management period is determined to be normal and no adjustment is made; if yt0>yt1, the usage status of the equipment in the current management period is determined to be abnormal, and the preset status threshold is adjusted to YC1, and YC1=YC×exp[(yt1-yt0) / yt1] is set; by matching and adjusting the analysis process according to the actual operating time of the equipment, the accuracy of the status analysis of the equipment is improved, thereby improving the processing efficiency of computer data.

[0118] It is understandable that the present embodiment does not specifically limit the setting of the preset operating time, and those skilled in the art can freely set it as long as the setting requirements of the preset operating time are met, wherein the optimal value of yt1 is 5000h.

[0119] Please continue reading Figure 4 As shown, the method for adjusting the analysis process of the device status also includes:

[0120] Step S62: The analysis process of adjusting the use status of the equipment according to the moisture content of the lubricating oil of the equipment collected during the management period.

[0121] Specifically, in step S62 of this embodiment, when adjusting the analysis process of the equipment's usage status, the moisture content f0 of the equipment's lubricating oil collected during the management period is compared with the preset moisture content f1 to analyze the abnormality of the equipment's lubricating oil moisture content, so as to dynamically adjust the analysis process of the equipment's usage status. If f0≤f1, it is determined that the moisture content of the equipment's lubricating oil in the current management period is normal and no adjustment is made; if f0>f1, it is determined that the moisture content of the equipment's lubricating oil in the current management period is abnormal, and the preset operating time is adjusted to yt1', and yt1'=yt1×[1-(f0-f1) / f1] is set; by monitoring the moisture content, it is ensured that the lubricating oil is in good working condition to avoid the risks of emulsification and corrosion, and at the same time, the analysis process of the equipment's usage status is dynamically adjusted to make the analysis of the equipment status more flexible, thereby improving the processing efficiency of computer data.

[0122] It can be understood that the setting of the preset moisture content is not specifically limited in this embodiment, and those skilled in the art can freely set it as long as the setting requirements of the preset moisture content are met, wherein the optimal value of f1 is 1000 ppm.

[0123] Specifically, the computer data intelligent processing method described in this embodiment is applied to the processing of industrial equipment data. By integrating multiple monitoring methods and intelligent analysis, the scientificity and efficiency of equipment management are improved, and a solution with great reference value is provided for the maintenance and management of industrial equipment.

[0124] Obviously, the above embodiments of the present invention are merely examples for clearly illustrating the present invention, and are not limitations on the embodiments of the present invention. For those skilled in the art, other different forms of changes or modifications can be made based on the above description. It is impossible to list all the embodiments here. All obvious changes or modifications derived from the technical solution of the present invention are still within the protection scope of the present invention.

Claims

1. A computer data intelligent processing method, characterized in that: include: Step S1: Collecting equipment operation data and equipment oil data; Step S2: constructing a temperature state index according to the equipment operating temperature collected during the monitoring period, constructing a vibration state index according to the equipment vibration intensity collected during the monitoring period, constructing a current state index according to the equipment current collected during the monitoring period, and analyzing the operating state of the equipment according to the temperature state index, vibration state index and current state index constructed during the monitoring period; Step S3: constructing an equipment pressure fluctuation coefficient according to the equipment pressure collected during the monitoring period, and analyzing the fluctuation state of the equipment pressure according to the construction result of the equipment pressure fluctuation coefficient; Step S4: analyzing the oil state of the equipment according to the lubricating oil viscosity and lubricating oil iron content of the equipment collected during the management period; Step S5: analyzing the state of the equipment according to the analysis results of the operating state of the equipment in each monitoring period within the management period, the analysis results of the fluctuation state of the pressure of the equipment in each monitoring period, and the analysis results of the oil state of the equipment; Step S6: Adjust the analysis process of the equipment status within the management period according to the operation time of the equipment and the water content of the lubricating oil of the equipment collected within the management period.

2. The computer data intelligent processing method according to claim 1, characterized in that: The method for analyzing the operating status of the device in step S2 includes: Step S21: analyzing the temperature status of the device and constructing a temperature status index according to the analysis results; Step S22: analyzing the vibration state of the equipment and constructing a vibration state index according to the analysis result; Step S23: analyzing the current state of the device and constructing a current state index according to the analysis result; Step S24: analyzing the operating status of the equipment according to the temperature status index, vibration status index and current status index constructed during the monitoring period; In step S21, when analyzing the temperature state of the device and constructing the temperature state index, the temperature state of the device is analyzed according to the device operating temperature collected during the monitoring period. The temperature state of the device includes normal and abnormal. If the temperature state of the device in the current monitoring period is normal, the temperature state index is set to WT1; if the temperature state of the device in the current monitoring period is abnormal, the temperature state index is set to WT2; When analyzing the vibration state of the equipment, the step S22 compares the equipment vibration intensity z0 collected during the monitoring period with the preset vibration intensity z1 to analyze the abnormality of the equipment vibration intensity, which includes normal and abnormal. The equipment vibration state is analyzed according to the analysis result of the abnormality of the equipment vibration intensity during the monitoring period, which includes normal and abnormal. If the equipment vibration state in the current monitoring period is normal, the vibration state index is set to ZT1; if the equipment vibration state in the current monitoring period is abnormal, the vibration state index is set to ZT2.

3. The computer data intelligent processing method according to claim 2, characterized in that: When analyzing the device current state, the step S23 compares the device current a0 collected during the monitoring period with each preset current to analyze the abnormality of the device current, which includes normal and abnormal. The device current state is analyzed according to the analysis result of the abnormality of the device current during the monitoring period, which includes normal and abnormal. If the device current state is normal during the current monitoring period, the current state index is set to DT1; if the device current state is abnormal during the current monitoring period, the current state index is set to DT2. The step S24 analyzes the operating state of the equipment according to the temperature state index, vibration state index and current state index constructed during the monitoring period. The operating state of the equipment includes normal operation and abnormal operation.

4. The computer data intelligent processing method according to claim 3, characterized in that: When analyzing the fluctuation state of the equipment pressure according to the equipment pressure collected during the monitoring period, the step S3 constructs the equipment pressure fluctuation coefficient B according to the equipment pressure collected during the monitoring period, and compares the equipment pressure fluctuation coefficient B with the preset fluctuation threshold value B1 to analyze the fluctuation state of the equipment pressure, and the fluctuation state of the equipment pressure includes a stable state and an abnormal state.

5. The computer data intelligent processing method according to claim 4, characterized in that: The method for analyzing the oil state of the equipment in step S4 comprises: Step S41: analyzing the abnormality of the equipment lubricating oil viscosity according to the equipment lubricating oil viscosity collected during the management period, and constructing a viscosity abnormality coefficient according to the analysis result; Step S42: analyzing the abnormality of the iron content of the equipment lubricating oil according to the iron content of the equipment lubricating oil collected during the management period, and constructing an iron content abnormality coefficient according to the analysis result; Step S43: analyzing the oil state of the equipment according to the construction results of the viscosity abnormal coefficient and the iron content abnormal coefficient within the management period; When analyzing the abnormality of the equipment lubricating oil viscosity, the step S41 compares the equipment lubricating oil viscosity n0 collected during the management period with the preset viscosity n1 to analyze the abnormality of the lubricating oil viscosity. The abnormality of the lubricating oil viscosity includes normal and abnormal. If the equipment lubricating oil viscosity in the current management period is abnormal, the viscosity abnormality coefficient is set to ND1; if the equipment lubricating oil viscosity in the current management period is normal, the viscosity abnormality coefficient is set to ND2.

6. The computer data intelligent processing method according to claim 5, characterized in that: When analyzing the abnormality of the iron content of the equipment lubricating oil, the step S42 compares the iron content h0 of the equipment lubricating oil collected during the management period with the preset iron content h1 to analyze the abnormality of the iron content of the equipment lubricating oil. The abnormality of the iron content of the equipment lubricating oil includes normal and abnormal. If the iron content of the equipment lubricating oil in the current management period is normal, the iron content abnormality coefficient is set to TY1; if the iron content of the equipment lubricating oil in the current management period is abnormal, the iron content abnormality coefficient is set to TY2.

7. The computer data intelligent processing method according to claim 6, characterized in that: The analysis process of the oil state of the equipment in step S33 is as follows: If w1×abnormal viscosity coefficient+w2×abnormal iron content coefficient≤RT, the oil status of the equipment in the current management cycle is determined to be normal; If w1×abnormal viscosity coefficient+w2×abnormal iron content coefficient>RT, the oil state of the equipment in the current management cycle is determined to be abnormal; Among them, w1 is the viscosity weight, w2 is the iron content weight, and RT is the preset abnormal coefficient.

8. The computer data intelligent processing method according to claim 7, characterized in that: The analysis process of the device status in step S5 is as follows: If the oil status of the equipment in the current management cycle is normal, when [q1×r1+q2×r2] / r0≤YC, the equipment status of the current management cycle is judged to be normal; when [q1×r1+q2×r2] / r0>YC, the equipment status of the current management cycle is judged to be faulty; If the oil state of the equipment in the current management cycle is abnormal, the state of the equipment in the current management cycle is determined to be a fault state; Among them, q1 is the operating status weight, q2 is the pressure fluctuation weight, r1 is the number of monitoring cycles in which the operating status of the equipment is abnormal operation within the management cycle, r2 is the number of monitoring cycles in which the fluctuation status of the equipment pressure is abnormal within the management cycle, r0 is the number of monitoring cycles within the management cycle, and YC is the preset status threshold.

9. The computer data intelligent processing method according to claim 8, characterized in that: The method for adjusting the analysis process of the device status in step S6 includes: Step S61: analyzing the usage status of the equipment according to the operation time of the equipment collected during the management period, and adjusting the analysis process of the status of the equipment during the management period according to the usage status of the equipment; Step S62: adjusting the analysis process of the equipment usage status according to the lubricating oil moisture of the equipment collected during the management period; When adjusting the analysis process of the status of the equipment within the management cycle, step S61 compares the operating time yt0 of the equipment collected during the management cycle with the preset operating time yt1 to analyze the usage status of the equipment. The usage status of the equipment includes normal and abnormal. If the usage status of the equipment in the current management cycle is abnormal, the preset status threshold is adjusted to YC1.

10. The computer data intelligent processing method according to claim 9, characterized in that: In the step S62, when adjusting the analysis process of the equipment's usage status, the lubricant oil moisture f0 of the equipment collected during the management period is compared with the preset moisture f1 to analyze the abnormality of the equipment's lubricant oil moisture. The abnormality of the equipment's lubricant oil moisture includes normal and abnormal. If the equipment's lubricant oil moisture is abnormal in the current management period, the preset operating time is adjusted to yt1'.

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