A big data-based dry vacuum pump vibration data analysis system and method

Abstract

The application discloses a kind of big data-based dry vacuum pump vibration data analysis system and method, belong to data analysis technical field.The system includes vacuum pump control module, data acquisition transmission module, database, model management analysis module, intelligent judgment module, early warning module and display module;The vacuum pump control module is used to start and close dry vacuum pump;The data acquisition transmission module is used to collect parameter data and vibration amplitude data;The database is used to store historical data;The model management analysis module is used to establish vibration analysis model, analyze the process that dry vacuum pump extracts gas in container, the influence of different parameter data on vibration amplitude;The intelligent judgment module is used to judge whether current dry vacuum pump exists fault risk;The early warning module is used for fault alarm;The display module is used to provide interactive platform, carries out digital display.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of data analysis, in particular to a dry vacuum pump vibration data analysis system and method based on big data. BACKGROUND

[0002] With the development of industrial automation and Internet of Things technology, the dry vacuum pump is widely used in many fields as an oil-free and pollution-free vacuum extraction equipment. When the dry vacuum pump is working, the negative pressure is formed in the pump cavity with the start of the screw inside the pump. Under the action of the pressure difference, the gas in the system to be extracted is sucked in and discharged, thereby creating a vacuum environment in the system to be extracted. By installing a monitoring device in the dry vacuum pump, abnormalities can be found at the early stage of equipment failure, and timely measures can be taken for maintenance to reduce the occurrence of safety risks. In the prior art, the fault judgment of the dry vacuum pump mostly depends on manual experience and preset rules, and lacks intelligent automatic diagnosis function, which has limitations. The vibration signals of the dry vacuum pump under different working conditions are difficult to further analyze, which easily leads to failure to identify or system misjudgment. SUMMARY

[0003] The present application aims to provide a dry vacuum pump vibration data analysis system and method based on big data to solve the problems in the background art.

[0004] In order to solve the above technical problems, the present application provides the following technical scheme: a dry vacuum pump vibration data analysis method based on big data, which comprises the following steps:

[0005] Step S1, starting the dry vacuum pump to extract the gas in the container, installing a pressure acquisition device at the gas inlet of the dry vacuum pump, and acquiring parameter data in the gas extraction process in the container; the parameter data includes the volume of the container and the change of the gas pressure at the gas inlet of the dry vacuum pump;

[0006] Step S2, determining the vibration data acquisition point of the dry vacuum pump, and collecting the vibration amplitude data at the vibration data acquisition point when the dry vacuum pump is working;

[0007] Step S3, storing the parameter data collected in step S1 and the vibration amplitude data collected in step S2 as historical data in the database, establishing a vibration analysis model according to the historical data stored in the database, and analyzing the influence of different parameter data on the vibration amplitude in the process of extracting the gas in the container by the dry vacuum pump;

[0008] Step S4, analyzing the real-time parameter data collected in step S1 and the real-time vibration amplitude data collected in step S2, judging whether the current dry vacuum pump has a risk of failure according to the analysis result of the vibration analysis model in step S3; if there is no risk of failure, continue to judge; if there is a risk of failure, perform failure alarm and send alarm information to the manager.

[0009] A dry vacuum pump vibration data analysis system based on big data, the system comprises a vacuum pump control module, a data acquisition and transmission module, a database, a model management and analysis module, an intelligent judgment module, an early warning module and a display module.

[0010] The vacuum pump control module is used to start and stop the dry vacuum pump; when the dry vacuum pump is started, the dry vacuum pump is controlled to extract the gas in the container.

[0011] The data acquisition and transmission module is used to install a pressure acquisition device at the gas inlet of the dry vacuum pump to collect parameter data during the extraction process of the gas in the container; the parameter data includes the volume of the container and the change of the gas pressure at the gas inlet of the dry vacuum pump; the vibration data acquisition point of the dry vacuum pump is determined to collect the vibration amplitude data at the vibration data acquisition point; the collected parameter data and vibration amplitude data are sent to the database and the intelligent judgment module.

[0012] The database is used to store the collected parameter data and vibration amplitude data as historical data and continuously update them.

[0013] The model management and analysis module is used to establish a vibration analysis model according to the historical data stored in the database, and analyze the influence of different parameter data on the vibration amplitude during the process of the dry vacuum pump extracting the gas in the container.

[0014] The intelligent judgment module is used to analyze the real-time parameter data and real-time vibration amplitude data, and judge whether the current dry vacuum pump has a risk of failure according to the analysis result of the vibration analysis model in the model management and analysis module; if there is no risk of failure, continue to judge; if there is a risk of failure, send a signal to the early warning module.

[0015] The early warning module is used to perform failure alarm and send alarm information to the manager.

[0016] The display module is used to provide an interactive platform for digital display.

[0017] An electronic device comprising a processor and a memory, wherein the memory stores a computer program that can be called by the processor;

[0018] The processor executes the above-mentioned big data-based dry vacuum pump vibration data analysis method by calling the computer program stored in the memory.

[0019] A computer-readable storage medium stores instructions that, when executed on a computer, cause the computer to perform a big data-based dry vacuum pump vibration data analysis method as described above.

[0020] Compared with the prior art, the present application has the beneficial effects that: by analyzing the influence of different parameter data on the vibration amplitude, the range of vacuum pump fault diagnosis is narrowed, and the accuracy of dry vacuum pump vibration data analysis is improved; by calculating the deviation value of the current dry vacuum pump vibration amplitude, it is judged whether the current dry vacuum pump has a fault risk, which reduces the interference of the instability of gas flow on the vacuum pump vibration data during the process of the dry vacuum pump extracting the gas in the container, thereby improving the vacuum pump fault diagnosis ability and reducing the occurrence of safety risks. BRIEF DESCRIPTION OF DRAWINGS

[0021] Fig. 1 is a step schematic diagram of a big data-based dry vacuum pump vibration data analysis method of the present application;

[0022] Fig. 2 is a structure schematic diagram of a big data-based dry vacuum pump vibration data analysis system of the present application. DETAILED DESCRIPTION

[0023] The technical solutions in the embodiments of the present application will be described clearly and completely below with reference to the drawings in the embodiments of the present application. Obviously, the described embodiments are only part of the embodiments of the present application, not all. Based on the embodiments in the present application, all other embodiments obtained by those skilled in the art without creative labor are within the scope of protection of the present application.

[0024] When the dry vacuum pump extracts the gas in the container, at a high gas extraction rate, the flow speed of the gas increases, the impact force of the gas flow increases, the contact force between the rotor and the pump shell increases, especially the outer edge part of the rotor, if the balance of the rotor is not good, these contact forces will produce a lot of vibration; in addition, when the gas pressure in the container decreases, the dilution degree of the gas increases, which will also cause the movement of the rotor to become unstable, thereby causing rotor vibration.

[0025] Please refer to Figs. 1-2 The present application provides technical solutions:

[0026] Please refer to Fig. 1 In this embodiment one, a big data-based dry vacuum pump vibration data analysis method is provided, which comprises the following steps:

[0027] Step S1, start the dry vacuum pump to extract the gas in the container, install a pressure acquisition device at the gas inlet of the dry vacuum pump, and acquire parameter data during the extraction of the gas in the container; the parameter data includes the volume of the container and the change of the gas pressure at the gas inlet of the dry vacuum pump.

[0028] Further, the gas outlet of the container is connected with the gas inlet of the dry vacuum pump, the target pressure to be reached by the gas in the container is determined before the operation of the dry vacuum pump, the gas pressure at the gas inlet of the dry vacuum pump is monitored by the pressure acquisition device when the dry vacuum pump extracts the gas in the container, and the vacuum pump is closed and the connection between the gas outlet of the container and the gas inlet of the dry vacuum pump is disconnected when the gas pressure at the gas inlet of the dry vacuum pump reaches the target pressure.

[0029] In the embodiment, the pressure acquisition device is a pressure sensor for acquiring the change of the gas pressure at the gas inlet of the dry vacuum pump; the change of the gas pressure in the container will cause the instability of the gas flow in the dry vacuum pump, thereby affecting the vibration amplitude of the vacuum pump, by acquiring the change of the gas pressure in the container, the influence of different gas extraction processes of the dry vacuum pump on the vibration data is analyzed, and the accuracy of the judgment of the vibration data by the system is improved.

[0030] Step S2, determine the vibration data acquisition point of the dry vacuum pump, and acquire the vibration amplitude data at the vibration data acquisition point when the dry vacuum pump is operated;

[0031] It should be noted that the vibration data is analyzed to determine whether the dry vacuum pump has a fault in advance, since the parts prone to failure in the dry vacuum pump are the rotor and the cavity, therefore, in the embodiment, the vibration data acquisition point is arranged at the position close to the rotor at the gas inlet of the dry vacuum pump, for reflecting the vibration induced by the gas pulsation, and the vibration amplitude data near the gas inlet of the dry vacuum pump is acquired by the vibration sensor.

[0032] Step S3, store the parameter data acquired in step S1 and the vibration amplitude data acquired in step S2 as historical data in a database, establish a vibration analysis model according to the historical data stored in the database, and analyze the influence of different parameter data on the vibration amplitude during the process of extracting the gas in the container by the dry vacuum pump;

[0033] Specifically, the method steps are as follows:

[0034] Step S31, store the acquired parameter data and vibration amplitude data as historical data in a database, and update the database; analyze the historical parameter data and historical vibration amplitude data of the dry vacuum pump during normal operation by calling the data from the database, determine the volume of the container according to the historical parameter data and determine different time stamps The gas pressure at the gas inlet of the dry vacuum pump ; according to the historical vibration amplitude data, determine different time stamps The vibration amplitude at the vibration data collection point ;

[0035] Step S32, according to the historical parameter data analysis result in step S31, calculate the extraction rate of the gas in the container at different time stamps ; : ; wherein, represents the time stamp The gas pressure at the gas inlet of the dry vacuum pump; represents to time step; represents the natural logarithm;

[0036] Step S33, according to the historical vibration amplitude data analysis result in step S31, calculate the vibration amplitude change amplitude at the vibration data collection point at different time stamps : : ; wherein, represents the time stamp The vibration amplitude at the vibration data collection point;

[0037] Step S34, establish a vibration analysis model, take the extraction rate of the gas in the container and the gas pressure at the gas inlet of the dry vacuum pump as influencing factors, and take the vibration amplitude change amplitude at the vibration data collection point as the influence value, analyze the influence of different parameter data on the vibration amplitude in the process of the dry vacuum pump extracting the gas in the container, according to the calculation formula:

[0038] ;

[0039] wherein, represents the extraction rate of the gas in the container; represents the gas pressure at the gas inlet of the dry vacuum pump; represents the influence degree of the extraction rate of the gas in the container on the vibration amplitude at the vibration data collection point; and both represent the influence degree of the gas pressure at the gas inlet of the dry vacuum pump on the vibration amplitude at the vibration data collection point; represents a constant; represents the base of the natural logarithm; represents the natural logarithm.

[0040] In this implementation, the working state of the dry vacuum pump is divided into normal operation and fault. Normal operation means that the vibration amplitude at the vibration data acquisition point of the dry vacuum pump is within the normal range, and the dry vacuum pump continues to operate. Fault means that the vibration amplitude at the vibration data acquisition point of the dry vacuum pump exceeds the normal range, and the dry vacuum pump stops working.

[0041] It should be noted that during the process of a dry vacuum pump extracting gas from a container, a high gas extraction rate will lead to increased vibration amplitude of the vacuum pump, while a low gas extraction rate will result in relatively stable vibration. When analyzing the impact of changes in gas pressure inside the container on the vibration amplitude of the dry vacuum pump, when the gas pressure inside the container is high, the density of gas molecules is high. When the gas inside the container is extracted, the force on the vacuum pump increases, thus leading to increased vibration amplitude near the rotor at the vacuum pump inlet. In this implementation, a vibration analysis model is established to... As The training parameters, As The training parameters, As The training parameters are substituted into the calculation formula in step S34 to perform fitting and determine... and The values ​​of the parameters are determined, and based on the fitting results, the influence of different parameter data on the vibration amplitude during the process of the dry vacuum pump extracting gas from the container is analyzed, thereby improving the accuracy of the vibration data analysis of the dry vacuum pump. The fitting methods include, but are not limited to, gradient descent, least squares, and ridge regression. By analyzing the influence of different parameter data on the vibration amplitude, the scope of vacuum pump fault diagnosis is narrowed, avoiding misjudgments of faults caused by the instability of gas during the process of the vacuum pump extracting gas from the container, and improving the accuracy of the vibration data analysis of the dry vacuum pump.

[0042] Step S4: Analyze the real-time parameter data collected in Step S1 and the real-time vibration amplitude data collected in Step S2. Based on the analysis results of the vibration analysis model in Step S3, determine whether there is a risk of failure in the current dry vacuum pump. If there is no risk of failure, continue the assessment. If there is a risk of failure, issue a fault alarm and send an alarm message to the management personnel.

[0043] Specifically, the steps are as follows:

[0044] Step S41: Analyze the collected real-time parameter data to determine the current gas pressure at the inlet of the dry vacuum pump. And determine the current gas extraction rate inside the container. ;Analyze the collected real-time vibration amplitude data to determine the amplitude change of vibration at the current vibration data collection point. , the deviation value of the current dry vacuum pump vibration amplitude is calculated :

[0045] ;

[0046] Step S42, historical parameter data and historical vibration amplitude data of the dry vacuum pump failure are called from the database for analysis; according to the historical parameter data, the gas pressure at the inlet and the extraction rate of the gas in the container when the dry vacuum pump fails are determined; according to the historical vibration amplitude data, the vibration amplitude change amplitude at the vibration data collection point when the dry vacuum pump fails is determined; according to the analysis results of the historical data of the dry vacuum pump failure called from the database, the deviation value of different vibration amplitudes is determined ; wherein, represents the amount of data of the dry vacuum pump failure analyzed from the database; a BP neural network is constructed, and is divided into a training set and a verification set , the training set is input into the BP neural network for training, the network weight and threshold value are trained through the back propagation algorithm, the verification set is cross-validated with the trained BP neural network, and the maximum deviation threshold value of the vibration amplitude when the dry vacuum pump fails is determined ;

[0047] Step S43, compare with to determine whether the current dry vacuum pump has a failure risk; when , the current dry vacuum pump does not have a failure risk, and the determination continues; when , the current dry vacuum pump has a failure risk, a failure alarm is given, and an alarm signal is sent to the management personnel.

[0048] It should be noted that by calculating the deviation value of the current dry vacuum pump vibration amplitude, the calculated deviation value is compared with the maximum deviation threshold value of the vibration amplitude when the dry vacuum pump fails, thereby reducing the interference of the instability of the gas flow on the vacuum pump vibration data during the process of the dry vacuum pump extracting the gas in the container, and improving the vacuum pump failure diagnosis capability and reducing the occurrence of safety risks.

[0049] Please refer to Fig. 2 , in this embodiment two: a dry vacuum pump vibration data analysis system based on big data is provided, which comprises a vacuum pump control module, a data acquisition and transmission module, a database, a model management and analysis module, an intelligent judgment module, an early warning module and a display module;

[0050] The vacuum pump control module is configured to start and stop the dry vacuum pump, and control the dry vacuum pump to extract the gas in the container when the dry vacuum pump is started.

[0051] The data acquisition and transmission module is configured to install a pressure acquisition device at the gas inlet of the dry vacuum pump to acquire parameter data during the extraction of the gas in the container, wherein the parameter data includes the volume of the container and the change of the gas pressure at the gas inlet of the dry vacuum pump, to determine a vibration data acquisition point of the dry vacuum pump to acquire vibration amplitude data at the vibration data acquisition point, and to send the acquired parameter data and vibration amplitude data to the database and the intelligent judgment module.

[0052] The database is configured to store the acquired parameter data and vibration amplitude data as historical data and continuously update the historical data.

[0053] The model management and analysis module is configured to establish a vibration analysis model according to the historical data stored in the database, and analyze the influence of different parameter data on the vibration amplitude during the extraction of the gas in the container by the dry vacuum pump.

[0054] The intelligent judgment module is configured to analyze the acquired real-time parameter data and real-time vibration amplitude data, to determine whether the dry vacuum pump has a fault risk according to the analysis result of the vibration analysis model in the model management and analysis module, to continue the determination if there is no fault risk, and to send a signal to the early warning module if there is a fault risk.

[0055] The early warning module is configured to perform fault alarm and send alarm information to the manager.

[0056] The display module is configured to provide an interactive platform and perform digital display.

[0057] Further, the model management and analysis module includes a historical data analysis unit and a model training unit.

[0058] The historical data analysis unit is configured to analyze the historical parameter data and historical vibration amplitude data of the dry vacuum pump during normal operation, to determine the volume of the container according to the historical parameter data, to determine the gas pressure at the gas inlet of the dry vacuum pump at different time stamps according to the historical parameter data, to calculate the extraction rate of the gas in the container at different time stamps according to the analysis result of the historical parameter data, to determine the vibration amplitude at the vibration data acquisition point at different time stamps according to the historical vibration amplitude data, and to calculate the change amplitude of the vibration amplitude at the vibration data acquisition point at different time stamps according to the analysis result of the historical vibration amplitude data.

[0059] The model training unit is configured to establish a vibration analysis model, take the extraction rate of the gas in the container and the gas pressure at the gas inlet of the dry vacuum pump as influencing factors, and take the vibration amplitude variation at the vibration data collection point as an influencing value, so as to analyze the influence of different parameter data on the vibration amplitude during the extraction of the gas in the container by the dry vacuum pump.

[0060] Further, the intelligent judgment module comprises a real-time data analysis unit, a threshold determination unit and a judgment unit.

[0061] The real-time data analysis unit is configured to analyze the collected real-time parameter data, determine the gas pressure at the gas inlet of the current dry vacuum pump and the extraction rate of the gas in the current container, analyze the collected real-time vibration amplitude data, determine the vibration amplitude variation at the vibration data collection point of the current dry vacuum pump, and calculate the deviation value of the vibration amplitude of the current dry vacuum pump.

[0062] The threshold determination unit is configured to analyze the historical parameter data and the historical vibration amplitude data when the dry vacuum pump fails by calling the data from a database, determine the gas pressure at the gas inlet and the extraction rate of the gas in the container when the dry vacuum pump fails according to the historical parameter data, determine the vibration amplitude variation at the vibration data collection point when the dry vacuum pump fails according to the historical vibration amplitude data, determine the deviation value of different vibration amplitudes according to the analysis result of the historical data when the dry vacuum pump fails, construct a BP neural network, divide the deviation value of different vibration amplitudes into a training set and a verification set, input the training set into the BP neural network for training, train the network weight and threshold value through a back propagation algorithm, cross-verify the verification set with the trained BP neural network, and determine the maximum deviation threshold of the vibration amplitude when the dry vacuum pump fails.

[0063] The judgment unit is configured to determine whether the current dry vacuum pump has a failure risk according to the deviation value of the vibration amplitude of the current dry vacuum pump and the maximum deviation threshold of the vibration amplitude when the dry vacuum pump fails, continue the judgment when the current dry vacuum pump does not have a failure risk, and send a signal to the early warning module when the current dry vacuum pump has a failure risk.

[0064] Further, the interactive platform is provided for displaying the gas pressure at the gas inlet of the current dry vacuum pump, the extraction rate of the gas in the current container, the vibration amplitude variation at the vibration data collection point of the current dry vacuum pump, and the deviation value of the vibration amplitude of the current dry vacuum pump.

[0065] In this embodiment,

[0066] The system is a dry vacuum pump vibration data analysis and failure early warning system. When the vacuum pump control module starts the dry vacuum pump, the dry vacuum pump extracts the gas in the container.

[0067] The data acquisition and transmission module acquires parameter data and vibration amplitude data at the vibration data acquisition point during the gas extraction process in the container; and sends the acquired parameter data and vibration amplitude data to the database and the intelligent judgment module;

[0068] The database stores the acquired parameter data and vibration amplitude data as historical data, and continuously updates them;

[0069] The historical data analysis unit in the model management and analysis module retrieves historical parameter data and historical vibration amplitude data when the dry vacuum pump is normally operated from the database for analysis, and sends the analysis result to the model training unit; the model training unit establishes a vibration analysis model to analyze the influence of different parameter data on the vibration amplitude during the process of extracting the gas in the container by the dry vacuum pump;

[0070] The real-time data analysis unit in the intelligent judgment module analyzes the acquired real-time parameter data and real-time vibration amplitude data, and sends the analysis result to the threshold determination unit; the threshold determination unit retrieves historical parameter data and historical vibration amplitude data when the dry vacuum pump is faulty from the database for analysis, and determines the maximum deviation threshold of the vibration amplitude when the dry vacuum pump is faulty; the judgment unit determines whether the current dry vacuum pump has a risk of failure according to the deviation value of the current vibration amplitude of the dry vacuum pump and the maximum deviation threshold of the vibration amplitude when the dry vacuum pump is faulty; when the current dry vacuum pump has a risk of failure, a signal is sent to the early warning module;

[0071] After receiving the signal sent by the intelligent judgment module, the early warning module alarms the failure, and sends alarm information to the manager;

[0072] The display module provides an interactive platform to digitally display the gas pressure at the gas inlet of the current dry vacuum pump, the extraction rate of the gas in the current container, the vibration amplitude change amplitude at the current vibration data acquisition point, and the deviation value of the vibration amplitude of the current dry vacuum pump.

[0073] In this embodiment three: an electronic device is provided, comprising a processor and a memory, wherein the memory stores a computer program that can be called by the processor;

[0074] The processor executes the steps of the above-mentioned dry vacuum pump vibration data analysis method based on big data by calling the computer program stored in the memory.

[0075] In the fourth embodiment: a computer readable storage medium is provided, which stores instructions, when the instructions are run on a computer, make the computer execute the steps of a big data-based dry vacuum pump vibration data analysis method as described above, to realize the following functions: determine the target pressure required to be reached by the gas in the container; collect parameter data during the extraction process of the gas in the container; collect vibration amplitude data at the vibration data collection point; establish a vibration analysis model to analyze the influence of different parameter data on the vibration amplitude during the process of extracting the gas in the container by the dry vacuum pump; and judge whether the current dry vacuum pump has a risk of failure.

[0076] The computer readable storage medium includes various storage program code media such as a U disk, a mobile hard disk, a read-only memory, a random access memory, a magnetic disk or an optical disk.

[0077] Finally, it should be noted that: the above only for the preferred embodiments of the present application, and not for the purpose of limiting the present application, although the foregoing embodiments of the present application have been described in detail, for those skilled in the art, it still can be modified, or part of the technical features of the equivalent replacement of the technical solutions recorded in the foregoing embodiments. Any modification, equivalent replacement, improvement, etc. within the spirit and principles of the present application shall be included within the scope of protection of the present application.

Claims

1. A big data based dry vacuum pump vibration data analysis method, characterized in that: The method comprises the following steps: Step S1, start the dry vacuum pump to extract the gas in the container, install a pressure acquisition device at the gas inlet of the dry vacuum pump, and acquire parameter data during the extraction of the gas in the container; the parameter data includes the volume of the container and the change of the gas pressure at the gas inlet of the dry vacuum pump; Step S2, determine the vibration data acquisition point of the dry vacuum pump, and acquire the vibration amplitude data at the vibration data acquisition point when the dry vacuum pump is operating; Step S3, store the parameter data acquired in step S1 and the vibration amplitude data acquired in step S2 as historical data in a database, establish a vibration analysis model according to the historical data stored in the database, and analyze the influence of different parameter data on the vibration amplitude during the extraction of the gas in the container by the dry vacuum pump; the specific process is as follows: Step S31, the collected parameter data and vibration amplitude data are stored as historical data in the database, and the database is updated; the historical parameter data and historical vibration amplitude data of the dry vacuum pump in normal operation are called from the database for analysis, according to the historical parameter data, the volume V of the container is determined, and the gas pressure P at the gas inlet of the dry vacuum pump at different time stamps t is determined t ; according to the historical vibration amplitude data, the vibration amplitude f at the vibration data collection point at different time stamps t is determined t ; Step S32, according to the historical parameter data analysis result in step S31, the extraction rate S of the gas in the container at different time stamps t is calculated t : wherein, P0 represents the gas pressure at the inlet of the dry vacuum pump at time stamp t0; Δt represents the time step from t0 to t; ln represents the natural logarithm; Step S33, according to the historical vibration amplitude data analysis result in step S31, the vibration amplitude change amplitude F of the vibration data collection point at different time stamp t is calculated t : wherein, represents the vibration amplitude of the vibration data collection point at time stamp t0. Step S34, establish the vibration analysis model, take the extraction rate of the gas in the container and the gas pressure at the gas inlet of the dry vacuum pump as influencing factors, take the vibration amplitude change amplitude at the vibration data acquisition point as an influence value, analyze the influence of different parameter data on the vibration amplitude during the extraction of the gas in the container by the dry vacuum pump, and calculate according to the formula: Wherein, x1 represents the extraction rate of the gas in the container; x2 represents the gas pressure at the gas inlet of the dry vacuum pump; a represents the influence degree of the extraction rate of the gas in the container on the vibration amplitude at the vibration data acquisition point; b1 and b2 both represent the influence degree of the gas pressure at the gas inlet of the dry vacuum pump on the vibration amplitude at the vibration data acquisition point; c represents a constant; e represents the base of natural logarithm; ln represents natural logarithm; Step S4, analyze the real-time parameter data acquired in step S1 and the real-time vibration amplitude data acquired in step S2, determine whether there is a risk of failure of the current dry vacuum pump according to the analysis result of the vibration analysis model in step S3, continue to determine if there is no risk of failure, and perform failure alarm and send alarm information to the management personnel if there is a risk of failure.

2. The big data based dry vacuum pump vibration data analysis method according to claim 1, characterized in that: The gas outlet of the container is connected with the gas inlet of the dry vacuum pump, the target pressure to be reached by the gas in the container is determined before the operation of the dry vacuum pump, the gas pressure at the gas inlet of the dry vacuum pump is monitored by the pressure acquisition device when the dry vacuum pump extracts the gas in the container, and the vacuum pump is closed and the connection between the gas outlet of the container and the gas inlet of the dry vacuum pump is disconnected when the gas pressure at the gas inlet of the dry vacuum pump reaches the target pressure.

3. The big data based dry vacuum pump vibration data analysis method according to claim 2, characterized in that: The method step of step S4 is as follows: Step S41, analyze the acquired real-time parameter data, determine the current gas pressure P0 at the gas inlet of the dry vacuum pump, and determine the current extraction rate S0 of the gas in the container; analyze the acquired real-time vibration amplitude data, determine the vibration amplitude change amplitude F0 at the current vibration data acquisition point, and calculate the deviation value E0 of the vibration amplitude of the current dry vacuum pump: Step S42, historical parameter data and historical vibration amplitude data when the dry vacuum pump fails are called from the database for analysis; according to the historical parameter data, the gas pressure at the gas inlet and the extraction rate of the gas in the container when the dry vacuum pump fails are determined; according to the historical vibration amplitude data, the vibration amplitude change amplitude at the vibration data collection point when the dry vacuum pump fails is determined; according to the analysis result of the historical data when the dry vacuum pump fails, the deviation values E1, E2,..., En of different vibration amplitudes are determined n ; wherein n represents the amount of data when the dry vacuum pump fails that is called from the database for analysis; a BP neural network is constructed, E1, E2,..., En is input into the BP neural network, the network weight and threshold value are trained through a back propagation algorithm, the validation set H2 is cross-validated with the trained BP neural network, and the maximum deviation threshold K of the vibration amplitude when the dry vacuum pump fails is determined. n The training set H1 and the validation set H2 are divided, the training set H1 is input into the BP neural network for training, the network weight and threshold value are trained through a back propagation algorithm, the validation set H2 is cross-validated with the trained BP neural network, and the maximum deviation threshold K of the vibration amplitude when the dry vacuum pump fails is determined. Step S43, compare K with E0, judge whether the current dry vacuum pump has a risk of failure; when E0K, the current dry vacuum pump has a risk of failure, a failure alarm is performed, and an alarm signal is sent to the manager.

4. A data analysis system implementing the big data based dry vacuum pump vibration data analysis method of any one of claims 1-3, characterized in that: The data analysis system comprises a vacuum pump control module, a data acquisition and transmission module, a database, a model management and analysis module, an intelligent judgment module, an early warning module and a display module. The vacuum pump control module is used for starting and stopping the dry vacuum pump; when the dry vacuum pump is started, the dry vacuum pump is controlled to extract the gas in the container; The data acquisition and transmission module is used for installing a pressure acquisition device at the gas inlet of the dry vacuum pump to acquire parameter data in the process of extracting the gas in the container; the parameter data comprises the volume of the container and the change of the gas pressure at the gas inlet of the dry vacuum pump; a vibration data acquisition point of the dry vacuum pump is determined to acquire vibration amplitude data at the vibration data acquisition point; and the acquired parameter data and vibration amplitude data are sent to the database and the intelligent judgment module; The database is used for storing the acquired parameter data and vibration amplitude data as historical data and continuously updating the historical data; The model management and analysis module is used for establishing a vibration analysis model according to the historical data stored in the database to analyze the influence of different parameter data on the vibration amplitude in the process of extracting the gas in the container by the dry vacuum pump; The intelligent judgment module is used for analyzing the acquired real-time parameter data and real-time vibration amplitude data, judging whether the current dry vacuum pump has a risk of failure according to the analysis result of the vibration analysis model in the model management and analysis module; if not, the judgment is continued; if yes, a signal is sent to the early warning module; The early warning module is used for performing a failure alarm and sending alarm information to the manager; The display module is used for providing an interactive platform for digital display.

5. The big data based dry vacuum pump vibration data analysis system of claim 4, wherein: The model management and analysis module comprises a historical data analysis unit and a model training unit; The historical data analysis unit is used for analyzing the historical parameter data and historical vibration amplitude data of the dry vacuum pump in normal operation from the database, determining the volume of the container according to the historical parameter data, determining the gas pressure at the gas inlet of the dry vacuum pump at different time stamps according to the historical parameter data analysis result, and calculating the extraction rate of the gas in the container at different time stamps according to the historical parameter data analysis result; The model training unit is used for establishing a vibration analysis model, taking the extraction rate of the gas in the container and the gas pressure at the gas inlet of the dry vacuum pump as influencing factors, taking the vibration amplitude change amplitude at the vibration data acquisition point as an influence value, and analyzing the influence of different parameter data on the vibration amplitude in the process of extracting the gas in the container by the dry vacuum pump. The intelligent judgment module comprises a real-time data analysis unit, a threshold determination unit and a judgment unit; 6. The big data based dry vacuum pump vibration data analysis system of claim 5, wherein: ​ The real-time data analysis unit is configured to analyze the collected real-time parameter data, determine the gas pressure at the gas inlet of the dry vacuum pump, and determine the extraction rate of the gas in the container; The real-time vibration amplitude data analysis unit is configured to analyze the collected real-time vibration amplitude data, determine the vibration amplitude variation amplitude at the vibration data collection point, and calculate the deviation value of the vibration amplitude of the dry vacuum pump; The threshold value determination unit is configured to analyze the historical parameter data and the historical vibration amplitude data when the dry vacuum pump fails, determine the gas pressure at the gas inlet and the extraction rate of the gas in the container when the dry vacuum pump fails according to the historical parameter data, determine the vibration amplitude variation amplitude at the vibration data collection point when the dry vacuum pump fails according to the historical vibration amplitude data, determine the deviation values of different vibration amplitudes according to the analysis results of the historical data when the dry vacuum pump fails, construct a BP neural network, divide the deviation values of different vibration amplitudes into a training set and a verification set, input the training set into the BP neural network for training, train the network weights and thresholds through a back propagation algorithm, cross-verify the verification set with the trained BP neural network, and determine the maximum deviation threshold of the vibration amplitude when the dry vacuum pump fails. The judgment unit is configured to determine whether the current dry vacuum pump has a failure risk according to the deviation value of the vibration amplitude of the current dry vacuum pump and the maximum deviation threshold of the vibration amplitude when the dry vacuum pump fails, continue the judgment when the current dry vacuum pump does not have a failure risk, and send a signal to the early warning module when the current dry vacuum pump has a failure risk.

7. A big data based dry vacuum pump vibration data analysis system as claimed in claim 6, wherein: The interactive platform is configured to display the gas pressure at the gas inlet of the current dry vacuum pump, the extraction rate of the gas in the container, the vibration amplitude variation amplitude at the vibration data collection point, and the deviation value of the vibration amplitude of the current dry vacuum pump.

8. An electronic device, comprising: The processor and the memory, wherein the memory stores a computer program that can be called by the processor; The processor calls the computer program stored in the memory to execute the dry vacuum pump vibration data analysis method based on big data according to any one of claims 1-3. The instructions are stored in the memory, and when the instructions are run on the computer, the computer executes the dry vacuum pump vibration data analysis method based on big data according to any one of claims 1-3.

9. A computer-readable storage medium, characterized in that: ​

Images