A water pump fault prediction method and system based on big data

Abstract

The present application discloses a water pump fault prediction method and system based on big data, which relates to water pump fault prediction, and includes an acoustic wave information acquisition module, an acoustic wave safety factor analysis module, a first-level safety prediction module, an operation information detection module, a second-level safety prediction module, a comprehensive safety analysis prediction module and a database. The operation of the water pump is fully monitored by the acoustic wave information acquisition module and the operation information detection module, which solves the potential danger of the prediction result, reduces the one-sidedness and inaccuracy of the prediction result, and analyzes the first and second-level safety prediction coefficients by the first-level safety prediction module and the second-level safety prediction module, and finally analyzes the comprehensive prediction coefficient of the water pump, so that the prediction of the water pump fault is comprehensive, the accuracy of the water pump fault prediction is improved, and the prediction results and corresponding data are uploaded to a shared database, which promotes the development of water conservancy and hydropower technology.

Description

[0001] This application is a divisional application of the application filed on September 5, 2023, with application number 202311133593.4 and invention name “A method and system for predicting water pump failure based on big data”. Technical Field

[0002] The present application relates to the field of water pump fault prediction, and in particular to a water pump fault prediction method and system based on big data. Background Art

[0003] With the rapid progress of society and the vigorous development of science and technology, water conservancy and hydropower technology has also ushered in rapid development, which is related to people's daily production and life, highlighting the importance of water pumps in the development of water conservancy and hydropower technology. However, there are still the following deficiencies in the prediction of water pump failures;

[0004] When predicting water pump failure, the existing technology usually monitors and analyzes the flow rate of the water pump based on the work experience of the monitoring personnel, and predicts the water pump failure based on this, ignoring the prediction of potential water pump failure under constant flow rate, making the prediction result potentially dangerous, and increasing the one-sidedness and inaccuracy of the prediction result; the existing technology lacks the analysis of the changes in the internal information of the water pump when predicting water pump failure, making the prediction of water pump failure incomplete, indirectly affecting the normal daily production and life of the people, and inhibiting the development of water conservancy and hydropower technology. Summary of the invention

[0005] The purpose of the present invention is to provide a water pump fault prediction method and system based on big data to solve the problems raised in the above background technology.

[0006] To achieve the above object, the present invention provides the following technical solution: a water pump fault prediction system based on big data, comprising:

[0007] Acoustic wave information collection module: used to collect the acoustic waves of the target water pump during operation, and obtain the acoustic wave information of the target water pump at the end point of each operation sub-stage;

[0008] Acoustic wave safety factor analysis module: used to analyze the acoustic wave safety factor of the target water pump in each operating sub-stage according to the acoustic wave information of the end point of the target water pump in each operating sub-stage;

[0009] The first-level safety prediction module is used to evaluate and predict the first-level safety status of the target water pump during operation through the acoustic wave safety factor of the target water pump in each operating sub-stage;

[0010] Operation information detection module: used to detect and obtain the operation information of the target water pump;

[0011] The second level safety prediction module is used to evaluate and predict the second level safety status of the target water pump during operation based on the operation information of the target water pump;

[0012] Comprehensive safety analysis and prediction module: used to make a comprehensive prediction of the operation safety of the target water pump according to the first level safety state when the target water pump is running and the second level safety state when the target water pump is running;

[0013] Database: used to store the standard sound wave information of the target water pump when it is running corresponding to each flow rate of the water pump, and store the standard motor temperature, standard current average value and current change rate corresponding to each flow rate of the target water pump.

[0014] In a preferred solution of the present application, the specific implementation of the sound wave information collection module is as follows:

[0015] Dividing the operation time of the target water pump into various operation sub-stages according to preset time intervals;

[0016] The sound spectrometer preset inside the target water pump is used to collect and draw a sound spectrum diagram of the target water pump when it is running in real time;

[0017] By analyzing the sound spectrum of the water pump during operation, the acoustic wave information of the target water pump at the end of each operation sub-stage is obtained, including frequency, amplitude and harmonics. The frequency, amplitude and harmonics in the acoustic wave information of the target water pump at the end of each operation sub-stage are marked as a i 、f i 、j i , i=1,2,...k, i represents the end point number of each running sub-stage arranged in chronological order.

[0018] In a preferred solution of the present application, the specific implementation of the acoustic wave safety factor analysis module is as follows:

[0019] The flow rate of the target water pump is collected in real time by a flow rate flow meter preset in the target water pump and recorded as v;

[0020] Establish a data extraction relationship between the database and the acoustic safety factor analysis module, extract the standard acoustic wave information of the target water pump when it is running corresponding to each water pump flow rate stored in the database, and screen and obtain the standard acoustic wave information corresponding to the target water pump when it is running according to the flow rate v of the target water pump, including the standard frequency a′, the standard amplitude f′ and the standard harmonic j′;

[0021] The standard frequency a′, standard amplitude f′ and standard harmonic j′ corresponding to the operation of the target water pump and the frequency a in the acoustic wave information of the target water pump at the end point of each operation sub-stage are calculated. i , amplitude f i Harmonics i Substitute into the formula Get the acoustic safety factor S of the target water pump at the end point of each operation sub-stage i , denoted as the acoustic safety factor S of the target water pump in each operating sub-stage i , wherein α represents the preset sound wave frequency influence factor, β represents the preset sound wave amplitude influence factor, χ represents the preset harmonic generation factor, δ represents the preset harmonic weight coefficient, and ε represents the preset safety factor influence factor.

[0022] In a preferred solution of the present application, the specific implementation method of the first-level safety prediction coefficient analysis of the target water pump in the first-level safety prediction module is as follows:

[0023] By analyzing and screening the sound spectrogram of the target water pump in each operating sub-stage, the number of abnormal frequencies and the number of abnormal amplitudes in the sound spectrogram of the target water pump in each operating sub-stage are counted, and marked as the number of abnormal frequencies and the number of abnormal amplitudes in the sound spectrogram of the target water pump in each operating sub-stage m i 、n i ;

[0024] The acoustic safety factor S of the target water pump in each operating sub-stage i and the number of abnormal frequencies m in the sound spectrum of the target water pump in each operating sub-stage i and the number of abnormal amplitudes n i Substitute into the formula

[0025] The first-level safety prediction coefficient Q1 of the target water pump in the next time interval is obtained, which is recorded as the first-level safety prediction coefficient Q1 of the target water pump, where It is represented as the preset safety weight factor, and λ is represented as the preset first-level safety prediction impact factor.

[0026] In a preferred solution of the present application, the specific implementation method of the first-level safety prediction of the target water pump operation at the next time interval in the first-level safety prediction module is as follows:

[0027] The first-level safety prediction coefficient of the target water pump is compared and analyzed with the preset standard first-level safety prediction coefficient of the target water pump. If the first-level safety prediction coefficient of the target water pump is smaller than the preset standard first-level safety prediction coefficient of the target water pump, it means that the first-level prediction of the operation of the target water pump in the next time interval is safe. If the first-level safety prediction coefficient of the target water pump is larger than the preset standard first-level safety prediction coefficient of the target water pump, it means that the first-level prediction of the operation of the target water pump in the next time interval is dangerous. A warning command is issued to the control center, and the prediction results and corresponding data are transmitted to the shared database.

[0028] In a preferred solution of the present application, the specific implementation of the operation information detection module is as follows:

[0029] By performing real-time detection in a detection device preset in the target water pump, the operation information of the target water pump in each operation sub-stage is obtained, including the operation current information and motor temperature T of the target water pump in each operation sub-stage. i , the operating current information is the average current value and the corresponding current change rate p i .

[0030] In a preferred solution of the present application, the specific implementation of the second-level safety prediction coefficient of the target water pump in the second-level safety prediction module is as follows:

[0031] Establish a data extraction relationship between the database and the second-level safety prediction module, extract the standard motor temperature, standard current average value and current change rate corresponding to each flow rate of the target water pump stored in the database, and filter and obtain the standard motor temperature T of the target water pump through the flow rate v of the target water pump. i ′, standard current average value and standard current change rate p′ i ;

[0032] The motor temperature T of the target water pump in each operating sub-stage i , the average current value of each operation sub-stage and the current change rate p i The corresponding standard motor temperature T i ′, standard current average value and standard current change rate p′ i Substitute into the formula

[0033]

[0034] The second-level safety prediction coefficient Q2 of the target water pump in the next time interval is obtained, which is recorded as the second-level safety prediction coefficient Q2 of the target water pump, where It represents the preset motor temperature influence factor, θ represents the preset current influence factor, and ζ represents the preset second level safety influence factor.

[0035] In a preferred solution of the present application, the specific implementation method of the second-level safety prediction of the target water pump operation at the next time interval in the second-level safety prediction module is as follows:

[0036] The second-level safety prediction coefficient of the target water pump is compared and analyzed with the preset standard second-level safety prediction coefficient of the target water pump. If the second-level safety prediction coefficient of the target water pump is smaller than the preset standard second-level safety prediction coefficient of the target water pump, it means that the second-level prediction of the target water pump operation in the next time interval is safe. If the second-level safety prediction coefficient of the target water pump is larger than the preset standard second-level safety prediction coefficient of the target water pump, it means that the second-level prediction of the target water pump operation in the next time interval is dangerous. A warning command is issued to the control center, and the prediction results and corresponding data are transmitted to the shared database.

[0037] In the preferred solution of this application, the specific implementation of the comprehensive safety analysis and prediction module is as follows:

[0038] Substitute the second-level safety prediction coefficient Q2 of the target water pump and the first-level safety prediction coefficient Q1 of the target water pump into the formula The comprehensive prediction coefficient Q0 of the target water pump is obtained, where ξ represents the preset safety factor offset factor, and μ represents the preset comprehensive prediction coefficient influencing factor;

[0039] The comprehensive prediction coefficient Q0 of the target water pump is compared and analyzed with the preset standard comprehensive prediction coefficient of the target water pump. If the comprehensive prediction coefficient Q0 of the target water pump is less than the preset standard comprehensive prediction coefficient of the target water pump, it means that the comprehensive prediction of the operation of the target water pump in the next time interval is safe. If the comprehensive prediction coefficient Q0 of the target water pump is greater than the preset standard comprehensive prediction coefficient of the target water pump, it means that the comprehensive prediction of the operation of the target water pump in the next time interval is dangerous. An early warning command is issued to the control center, and the prediction results and corresponding data are transmitted to the shared database.

[0040] To achieve the above object, the present invention also provides the following technical solution: a water pump fault prediction method based on big data, comprising the following steps;

[0041] By collecting the sound waves of the target water pump during operation, the sound wave information of the target water pump at the end point of each operation sub-stage is obtained;

[0042] According to the acoustic wave information of the target water pump at the end point of each operation sub-stage, the acoustic wave safety factor of the target water pump in each operation sub-stage is analyzed;

[0043] The first-level safety state of the target water pump during operation is evaluated and predicted through the acoustic safety factor of the target water pump in each operating sub-stage;

[0044] Detect and obtain the operating information of the target water pump;

[0045] Through the operation information of the target water pump, the second level safety state of the target water pump during operation is evaluated and predicted;

[0046] The operation safety of the target water pump is comprehensively predicted according to the first-level safety state when the target water pump is running and the second-level safety state when the target water pump is running.

[0047] Compared with the prior art, the present invention has the following beneficial effects:

[0048] The present invention collects the acoustic wave information emitted when the water pump is running at a constant flow rate, analyzes the acoustic wave safety factor of the target water pump in each operating sub-stage, and comprehensively analyzes the first-level safety state of the target water pump when it is running, effectively predicts the potential failure of the water pump, solves the potential danger of the prediction result, and reduces the one-sidedness and inaccuracy of the prediction result;

[0049] The present invention monitors the operation information of the target water pump in each operation sub-stage through the operation information detection module, analyzes the second-level safety prediction coefficient of the target water pump, and conducts a comprehensive analysis in combination with the first-level safety prediction coefficient of the target water pump, so as to make the prediction of water pump failure comprehensive, improve the accuracy of water pump failure prediction, ensure the normal daily production and life of the people, upload the prediction results and corresponding data to the shared database, and promote the development of water conservancy and hydropower technology. BRIEF DESCRIPTION OF THE DRAWINGS

[0050] The present invention is further described using the accompanying drawings, but the embodiments in the accompanying drawings do not constitute any limitation to the present invention. A person skilled in the art can obtain other drawings based on the following drawings without creative work.

[0051] Figure 1 It is a schematic diagram of module connection of the present invention. DETAILED DESCRIPTION

[0052] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0053] See also Figure 1 The present invention provides a technical solution: a water pump fault prediction system based on big data, including an acoustic wave information acquisition module, an acoustic wave safety factor analysis module, a first-level safety prediction module, an operation information detection module, a second-level safety prediction module, a comprehensive safety analysis and prediction module and a database.

[0054] The acoustic wave information collection module is connected to the acoustic wave safety factor analysis module, the acoustic wave safety factor analysis module is connected to the first-level safety prediction module and the database, the first-level safety prediction module is connected to the comprehensive safety analysis and prediction module, the operation information detection module is connected to the second-level safety prediction module, and the second-level safety prediction module is connected to the comprehensive safety analysis and prediction module and the database.

[0055] The acoustic wave information collection module is used to collect the acoustic waves of the target water pump when it is running, and obtain the acoustic wave information of the target water pump at the end point of each operating sub-stage;

[0056] Furthermore, the specific implementation of the sound wave information collection module is as follows:

[0057] Dividing the operation time of the target water pump into various operation sub-stages according to preset time intervals;

[0058] The sound spectrometer preset inside the target water pump is used to collect and draw a sound spectrum diagram of the target water pump when it is running in real time;

[0059] By analyzing the sound spectrum of the water pump during operation, the acoustic wave information of the target water pump at the end of each operation sub-stage is obtained, including frequency, amplitude and harmonics. The frequency, amplitude and harmonics in the acoustic wave information of the target water pump at the end of each operation sub-stage are marked as a i 、f i 、j i , i=1,2,...k, i represents the end point number of each running sub-stage arranged in chronological order.

[0060] The acoustic wave safety factor analysis module is used to analyze the acoustic wave safety factor of the target water pump in each operation sub-stage according to the acoustic wave information of the end point of the target water pump in each operation sub-stage;

[0061] Furthermore, the specific implementation of the acoustic wave safety factor analysis module is as follows:

[0062] The flow rate of the target water pump is collected in real time by a flow rate flow meter preset in the target water pump and recorded as v;

[0063] Establish a data extraction relationship between the database and the acoustic safety factor analysis module, extract the standard acoustic wave information of the target water pump when it is running corresponding to each water pump flow rate stored in the database, and screen and obtain the standard acoustic wave information corresponding to the target water pump when it is running according to the flow rate v of the target water pump, including the standard frequency a′, the standard amplitude f′ and the standard harmonic j′;

[0064] The standard frequency a′, standard amplitude f′ and standard harmonic j′ corresponding to the operation of the target water pump and the frequency a in the acoustic wave information of the target water pump at the end point of each operation sub-stage are calculated. i , amplitude f iHarmonics i Substitute into the formula Get the acoustic safety factor S of the target water pump at the end point of each operation sub-stage i , denoted as the acoustic safety factor S of the target water pump in each operating sub-stage i , where α represents the preset sound wave frequency influence factor, β represents the preset sound wave amplitude influence factor, χ represents the preset harmonic generation factor, δ represents the preset harmonic weight coefficient, and ε represents the preset safety factor influence factor;

[0065] It should be noted that the standard frequency a′, standard amplitude f′ and standard harmonic j′ corresponding to the target water pump in operation in the formula and the frequency a in the acoustic wave information of the target water pump at the end point of each operation sub-stage are i , amplitude f i Harmonics i The smaller the difference, the greater the acoustic safety factor S of the target water pump in each operating sub-stage. i The smaller;

[0066] The frequency a in the acoustic information of the target pump at the end point of each operating sub-stage in the formula is i , amplitude f i There will be no mutual influence between them, frequency a i , amplitude f i will jointly harmonic j i Make an impact.

[0067] The first-level safety prediction module is used to evaluate and predict the first-level safety state of the target water pump during operation through the acoustic wave safety factor of the target water pump in each operating sub-stage;

[0068] Furthermore, the specific implementation of the first-level safety prediction coefficient analysis of the target water pump in the first-level safety prediction module is as follows:

[0069] By analyzing and screening the sound spectrogram of the target water pump in each operating sub-stage, the number of abnormal frequencies and the number of abnormal amplitudes in the sound spectrogram of the target water pump in each operating sub-stage are counted, and marked as the number of abnormal frequencies and the number of abnormal amplitudes in the sound spectrogram of the target water pump in each operating sub-stage m i 、n i ;

[0070] The acoustic safety factor S of the target water pump in each operating sub-stage i and the number of abnormal frequencies m in the sound spectrum of the target water pump in each operating sub-stage i and the number of abnormal amplitudes n i Substitute into the formula

[0071] The first-level safety prediction coefficient Q1 of the target water pump in the next time interval is obtained, which is recorded as the first-level safety prediction coefficient Q1 of the target water pump, where denoted as a preset safety weight factor, and λ denoted as a preset first-level safety prediction impact factor;

[0072] It should be noted that in the formula, when the target water pump has a sonic safety factor S in each operating sub-stage i and the number of abnormal frequencies m in the sound spectrum of the target water pump in each operating sub-stage i and the number of abnormal amplitudes n i The smaller the time is, the smaller the first-level safety prediction coefficient of the target water pump is, which means that the first-level prediction of the target water pump operation in the next time interval is safer;

[0073] The acoustic safety factor S of the target water pump in each operating sub-stage in the formula is i and the number of abnormal frequencies m in the sound spectrum of the target water pump in each operating sub-stage i and the number of abnormal amplitudes n i They will not affect each other.

[0074] Furthermore, the specific implementation method of the first-level safety prediction of the target water pump operation at the next time interval in the first-level safety prediction module is as follows:

[0075] The first-level safety prediction coefficient of the target water pump is compared and analyzed with the preset standard first-level safety prediction coefficient of the target water pump. If the first-level safety prediction coefficient of the target water pump is smaller than the preset standard first-level safety prediction coefficient of the target water pump, it means that the first-level prediction of the operation of the target water pump in the next time interval is safe. If the first-level safety prediction coefficient of the target water pump is larger than the preset standard first-level safety prediction coefficient of the target water pump, it means that the first-level prediction of the operation of the target water pump in the next time interval is dangerous. A warning command is issued to the control center, and the prediction results and corresponding data are transmitted to the shared database.

[0076] The operation information detection module is used to detect and obtain the operation information of the target water pump;

[0077] Furthermore, the specific implementation of the operation information detection module is as follows:

[0078] By performing real-time detection in a detection device preset in the target water pump, the operation information of the target water pump in each operation sub-stage is obtained, including the operation current information and motor temperature T of the target water pump in each operation sub-stage. i , the operating current information is the average current value and the corresponding current change rate p i .

[0079] The second level safety prediction module is used to evaluate and predict the second level safety state of the target water pump during operation based on the operation information of the target water pump;

[0080] Furthermore, the specific implementation of the second-level safety prediction coefficient of the target water pump in the second-level safety prediction module is as follows:

[0081] Establish a data extraction relationship between the database and the second-level safety prediction module, extract the standard motor temperature, standard current average value and current change rate corresponding to each flow rate of the target water pump stored in the database, and filter and obtain the standard motor temperature T of the target water pump through the flow rate v of the target water pump. i ′, standard current average value and standard current rate of change p′ i ;

[0082] The motor temperature T of the target water pump in each operating sub-stage i , the average current value of each operation sub-stage and the current change rate p′ i The corresponding standard motor temperature T i ′, standard current average value and the standard current change rate p′ into the formula

[0083]

[0084] The second-level safety prediction coefficient Q2 of the target water pump in the next time interval is obtained, which is recorded as the second-level safety prediction coefficient Q2 of the target water pump, where is represented by the preset motor temperature influence factor, θ is represented by the preset current influence factor, and ζ is represented by the preset second level safety influence factor;

[0085] It should be noted that in the formula, when the motor temperature T of the target water pump in each operating sub-stage is i , the average current value of each operation sub-stage and the current change rate p i The corresponding standard motor temperature T i ′, standard current average value and standard current change rate p′ i The smaller the difference is, the smaller the second-level safety prediction coefficient of the target water pump is, which means that the second-level prediction of the target water pump operation in the next time interval is safer;

[0086] The motor temperature T of the target water pump in each operating sub-stage in the formula is i , the average current value of each operation sub-stage and the current change rate p i They will not affect each other.

[0087] Furthermore, the specific implementation method of the second-level safety prediction of the target water pump operation at the next time interval in the second-level safety prediction module is as follows:

[0088] The second-level safety prediction coefficient of the target water pump is compared and analyzed with the preset standard second-level safety prediction coefficient of the target water pump. If the second-level safety prediction coefficient of the target water pump is smaller than the preset standard second-level safety prediction coefficient of the target water pump, it means that the second-level prediction of the target water pump operation in the next time interval is safe. If the second-level safety prediction coefficient of the target water pump is larger than the preset standard second-level safety prediction coefficient of the target water pump, it means that the second-level prediction of the target water pump operation in the next time interval is dangerous. A warning command is issued to the control center, and the prediction results and corresponding data are transmitted to the shared database.

[0089] The comprehensive safety analysis and prediction module is used to comprehensively predict the operation safety of the target water pump according to the first level safety state when the target water pump is running and the second level safety state when the target water pump is running;

[0090] Furthermore, the specific implementation of the comprehensive safety analysis and prediction module is as follows:

[0091] Substitute the second level safety prediction coefficient Q2 of the target water pump and the first level safety prediction coefficient Q1 of the target water pump into the formula The comprehensive prediction coefficient Q0 of the target water pump is obtained, where ξ represents the preset safety factor offset factor, and μ represents the preset comprehensive prediction coefficient influencing factor;

[0092] It should be noted that the smaller the second-level safety prediction coefficient Q2 of the target water pump and the first-level safety prediction coefficient Q1 of the target water pump in the formula, the smaller the comprehensive prediction coefficient of the target water pump, which means that the comprehensive prediction of the operation of the target water pump in the next time interval is safer;

[0093] In the formula, the second level safety prediction coefficient Q2 of the target water pump and the first level safety prediction coefficient Q1 of the target water pump will not affect each other;

[0094] The comprehensive prediction coefficient Q0 of the target water pump is compared and analyzed with the preset standard comprehensive prediction coefficient of the target water pump. If the comprehensive prediction coefficient Q0 of the target water pump is less than the preset standard comprehensive prediction coefficient of the target water pump, it means that the comprehensive prediction of the operation of the target water pump in the next time interval is safe. If the comprehensive prediction coefficient Q0 of the target water pump is greater than the preset standard comprehensive prediction coefficient of the target water pump, it means that the comprehensive prediction of the operation of the target water pump in the next time interval is dangerous. An early warning command is issued to the control center, and the prediction results and corresponding data are transmitted to the shared database.

[0095] The present invention also provides the following technical solution: A method for predicting water pump failure based on big data, characterized in that it comprises the following steps;

[0096] By collecting the sound waves of the target water pump during operation, the sound wave information of the target water pump at the end point of each operation sub-stage is obtained;

[0097] According to the acoustic wave information of the target water pump at the end point of each operation sub-stage, the acoustic wave safety factor of the target water pump in each operation sub-stage is analyzed;

[0098] The first-level safety state of the target water pump during operation is evaluated and predicted through the acoustic safety factor of the target water pump in each operating sub-stage;

[0099] Detect and obtain the operating information of the target water pump;

[0100] Through the operation information of the target water pump, the second level safety state of the target water pump during operation is evaluated and predicted;

[0101] The operation safety of the target water pump is comprehensively predicted according to the first-level safety state when the target water pump is running and the second-level safety state when the target water pump is running.

[0102] The above are all preferred embodiments of the present application, and the protection scope of the present application is not limited thereto. Therefore, any equivalent changes made according to the structure, shape, and principle of the present application should be included in the protection scope of the present application.

Claims

1. A water pump failure prediction system based on big data, characterized by: include: Acoustic wave information collection module: used to collect the acoustic waves of the target water pump during operation, and obtain the acoustic wave information of the target water pump at the end point of each operation sub-stage; Acoustic wave safety factor analysis module: used to analyze the acoustic wave safety factor of the target water pump in each operating sub-stage according to the acoustic wave information of the end point of the target water pump in each operating sub-stage; The first-level safety prediction module is used to evaluate and predict the first-level safety status of the target water pump during operation through the acoustic wave safety factor of the target water pump in each operating sub-stage; Operation information detection module: used to detect and obtain the operation information of the target water pump; The second level safety prediction module is used to evaluate and predict the second level safety status of the target water pump during operation based on the operation information of the target water pump; Comprehensive safety analysis and prediction module: used to make a comprehensive prediction of the operation safety of the target water pump according to the first level safety state when the target water pump is running and the second level safety state when the target water pump is running; Database: used to store the standard sound wave information of the target water pump when it is running corresponding to each flow rate of the water pump, and store the standard motor temperature, standard current average value and current change rate corresponding to each flow rate of the target water pump; The acoustic wave information acquisition module is connected to the acoustic wave safety factor analysis module, the acoustic wave safety factor analysis module is connected to the first-level safety prediction module and the database, the first-level safety prediction module is connected to the comprehensive safety analysis prediction module, the operation information detection module is connected to the second-level safety prediction module, and the second-level safety prediction module is connected to the comprehensive safety analysis prediction module and the database; The specific implementation of the sound wave information acquisition module is as follows: Dividing the operation time of the target water pump into various operation sub-stages according to preset time intervals; The sound spectrum diagram of the target water pump when it is running is collected and drawn in real time according to the sound spectrometer preset inside the target water pump; By analyzing the sound spectrum of the water pump during operation, the acoustic wave information of the target water pump at the end of each operation sub-stage is obtained, including frequency, amplitude and harmonics. The frequency, amplitude and harmonics in the acoustic wave information of the target water pump at the end of each operation sub-stage are marked as a i 、f i 、j i , i=1,2,...k, i represents the end point number of each running sub-stage arranged in chronological order; The specific implementation of the operation information detection module is as follows: By performing real-time detection in a detection device preset in the target water pump, the operation information of the target water pump in each operation sub-stage is obtained, including the operation current information and motor temperature T of the target water pump in each operation sub-stage. i , the operating current information is the average current value and the corresponding current change rate p i ; The specific implementation of the acoustic wave safety factor analysis module is as follows: The flow rate of the target water pump is collected in real time by a flow rate flow meter preset in the target water pump and recorded as v; Establish a data extraction relationship between the database and the acoustic safety factor analysis module, extract the standard acoustic wave information of the target water pump when it is running corresponding to each water pump flow rate stored in the database, and screen and obtain the standard acoustic wave information corresponding to the target water pump when it is running according to the flow rate v of the target water pump, including the standard frequency a′, the standard amplitude f′ and the standard harmonic j′; The standard frequency a′, standard amplitude f′ and standard harmonic j′ corresponding to the operation of the target water pump and the frequency a in the acoustic wave information of the target water pump at the end point of each operation sub-stage are calculated. i , amplitude f i Harmonics i Substitute into the formula Get the acoustic safety factor S of the target water pump at the end point of each operation sub-stage i , denoted as the acoustic safety factor S of the target water pump in each operating sub-stage i , where α represents the preset sound wave frequency influence factor, β represents the preset sound wave amplitude influence factor, χ represents the preset harmonic generation factor, δ represents the preset harmonic weight coefficient, and ε represents the preset safety factor influence factor; The specific implementation of the first-level safety prediction coefficient analysis of the target water pump in the first-level safety prediction module is as follows: By analyzing and screening the sound spectrogram of the target water pump in each operating sub-stage, the number of abnormal frequencies and the number of abnormal amplitudes in the sound spectrogram of the target water pump in each operating sub-stage are counted, and marked as the number of abnormal frequencies and the number of abnormal amplitudes in the sound spectrogram of the target water pump in each operating sub-stage m i 、n i ; The acoustic safety factor S of the target water pump in each operating sub-stage i and the number of abnormal frequencies m in the sound spectrum of the target water pump in each operating sub-stage i and the number of abnormal amplitudes n i Substitute into the formula The first-level safety prediction coefficient Q1 of the target water pump in the next time interval is obtained, which is recorded as the first-level safety prediction coefficient Q1 of the target water pump, where denoted as a preset safety weight factor, and λ denoted as a preset first-level safety prediction impact factor; The specific implementation of the second-level safety prediction coefficient of the target water pump in the second-level safety prediction module is as follows: Establish a data extraction relationship between the database and the second-level safety prediction module, extract the standard motor temperature, standard current average value and current change rate corresponding to each flow rate of the target water pump stored in the database, and filter and obtain the standard motor temperature T of the target water pump through the flow rate v of the target water pump. i ′, standard current average value and standard current change rate p i ′; The motor temperature T of the target water pump in each operating sub-stage i , the average current value of each operating sub-stage and the current change rate p i The corresponding standard motor temperature T i ′, standard current average value and standard current change rate p i Substitute ′ into the formula ， The second-level safety prediction coefficient Q2 of the target water pump in the next time interval is obtained, which is recorded as the second-level safety prediction coefficient Q2 of the target water pump, where is represented by the preset motor temperature influence factor, θ is represented by the preset current influence factor, and ζ is represented by the preset second level safety influence factor; The specific implementation of the comprehensive safety analysis and prediction module is as follows: Substitute the second-level safety prediction coefficient Q2 of the target water pump and the first-level safety prediction coefficient Q1 of the target water pump into the formula The comprehensive prediction coefficient Q0 of the target water pump is obtained, where ξ represents the preset safety factor offset factor, and μ represents the preset comprehensive prediction coefficient influencing factor; Compare and analyze the comprehensive prediction coefficient Q0 of the target water pump with the preset standard comprehensive prediction coefficient of the target water pump. If the comprehensive prediction coefficient Q0 of the target water pump is less than the preset standard comprehensive prediction coefficient of the target water pump, it means that the comprehensive prediction of the operation of the target water pump in the next time interval is safe. If the comprehensive prediction coefficient Q0 of the target water pump is greater than the preset standard comprehensive prediction coefficient of the target water pump, it means that the comprehensive prediction of the operation of the target water pump in the next time interval is dangerous. Send an early warning instruction to the control center, and transmit the prediction result and corresponding data to the shared database; The specific implementation of the second-level safety prediction of the target water pump operation in the next time interval in the second-level safety prediction module is as follows: The second-level safety prediction coefficient of the target water pump is compared and analyzed with the preset standard second-level safety prediction coefficient of the target water pump. If the second-level safety prediction coefficient of the target water pump is smaller than the preset standard second-level safety prediction coefficient of the target water pump, it means that the second-level prediction of the target water pump operation in the next time interval is safe. If the second-level safety prediction coefficient of the target water pump is larger than the preset standard second-level safety prediction coefficient of the target water pump, it means that the second-level prediction of the target water pump operation in the next time interval is dangerous. A warning command is issued to the control center, and the prediction results and corresponding data are transmitted to the shared database.

2. A prediction method for a water pump fault prediction system based on big data according to any one of claim 1, characterized in that: The steps include: By collecting the sound waves of the target water pump during operation, the sound wave information of the target water pump at the end point of each operation sub-stage is obtained; According to the acoustic wave information of the target water pump at the end point of each operation sub-stage, the acoustic wave safety factor of the target water pump in each operation sub-stage is analyzed; The first-level safety state of the target water pump during operation is evaluated and predicted through the acoustic safety factor of the target water pump in each operating sub-stage; Detect and obtain the operating information of the target water pump; Through the operation information of the target water pump, the second level safety state of the target water pump during operation is evaluated and predicted; The operation safety of the target water pump is comprehensively predicted according to the first-level safety state when the target water pump is running and the second-level safety state when the target water pump is running.

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