An operation monitoring and control system for glucosamine sulfate capsule preparation equipment

Abstract

The present invention provides an operation monitoring and control system for glucosamine sulfate capsule preparation equipment, relating to the technical field of equipment operation monitoring. The system is configured to acquire historical sound data from the production system, perform frequency domain feature extraction based on operating parameters, and generate operation voiceprint feature data; collect real-time operation sound data from the production system and extract corresponding real-time voiceprint feature information and real-time operation parameter information; and perform monitoring and analysis based on the real-time operation parameter information and in combination with the operation voiceprint feature data and real-time voiceprint feature information to generate real-time operation monitoring and analysis result data. By combining voiceprint feature data, the system conducts timely and accurate comprehensive equipment fault analysis, enabling accurate and timely monitoring of comprehensive production system faults and ensuring efficient and orderly production.

Description

Technical Field

[0001] The present invention relates to the technical field of equipment operation monitoring, in particular to an operation monitoring and control system for glucosamine sulfate capsule preparation equipment. Background Art

[0002] Glucosamine sulfate is a natural amino monosaccharide and an important component necessary for the synthesis of proteoglycans in the matrix of human joint cartilage. The glucosamine sulfate capsules produced can be used to prevent and treat various types of osteoarthritis, such as osteoarthritis in the knee, hip, spine, shoulder, hand, wrist, ankle and other parts of the body, as well as systemic osteoarthritis.

[0003] Currently, the production process of glucosamine sulfate is very mature. Due to the influence of various factors during the production process, the parameters of the production process will be reasonably regulated. At the same time, since the production equipment basically operates under load for a long time, equipment fault monitoring is a necessary means to ensure production efficiency. At present, the fault monitoring of production equipment is still mainly targeted at a single piece of equipment. There is a lack of effective means for comprehensive fault analysis and judgment, which cannot fully ensure the reasonable and effective monitoring and control of the entire production system. This greatly reduces the control of production efficiency and is not conducive to the efficient production of glucosamine sulfate capsules.

[0004] Therefore, it is an urgent problem to be solved to design an operation monitoring and control system for glucosamine sulfate capsule preparation equipment, to conduct timely and accurate equipment comprehensive fault analysis by combining voiceprint feature data, to achieve accurate and timely monitoring of comprehensive faults in the production system, and to ensure efficient and orderly production. Summary of the Invention

[0005] The present invention aims to provide an operation monitoring and control system for glucosamine sulfate capsule production equipment. The system acquires historical sound data from the production system and extracts frequency domain feature data related to the production system under different operating parameters to establish voiceprint feature data for subsequent comparative reference analysis. Furthermore, the system extracts frequency domain features from the collected real-time sound data, which are then compared and analyzed with the voiceprint feature data to identify abnormal voiceprint feature information. A matching analysis is then performed based on the voiceprint feature data extracted during equipment failure to identify the currently faulty equipment. This fault location analysis provides a certain degree of fault classification, providing a data foundation for subsequent analysis of equipment failures based on fault voiceprint features. Given that current production systems are largely automated, the source of sound data is not overly complex, and noise reduction and filtering processing is not difficult. This system can efficiently and effectively extract feature data information and accurately determine fault conditions, effectively ensuring efficient and orderly production.

[0006] In a first aspect, the present invention provides an operation monitoring and control system for glucosamine sulfate capsule preparation equipment, the system being configured to obtain historical sound data of a production system, perform frequency domain feature extraction based on operation parameters, and form operation voiceprint feature data; collect real-time operation sound data of the production system, and extract corresponding real-time voiceprint feature information and real-time operation parameter information; perform monitoring and analysis based on the real-time operation parameter information and in combination with the operation voiceprint feature data and the real-time voiceprint feature information, and form real-time operation monitoring and analysis result data.

[0007] In the present invention, the system acquires historical sound data from the production system and extracts frequency domain feature data about the production system under different operating parameters to establish voiceprint feature data that can be subsequently compared and analyzed. At the same time, the system extracts frequency domain features from the collected real-time sound data, which are then compared and analyzed with the voiceprint feature data to determine abnormal voiceprint feature information. A matching analysis is then performed based on the voiceprint feature data extracted under equipment failure conditions to determine the equipment that has failed in the current situation. This location of equipment failures has a certain degree of fault category directionality, providing a data foundation for subsequent analysis of equipment failures based on fault voiceprint features. Considering that current production systems are essentially automated, the source of sound data is not overly complex, and noise reduction and filtering processing is not difficult. This system can reasonably and efficiently extract feature data information and accurately determine fault conditions, effectively ensuring efficient and orderly production.

[0008] As a possible implementation method, historical sound data of the production system is obtained, and frequency domain feature extraction based on operating parameters is performed to form operating voiceprint feature data, including: obtaining normal operating sound recording data corresponding to different operating parameters in the historical sound data, and performing frequency domain feature clustering extraction based on the operating parameters to form normal operating voiceprint feature data; obtaining fault operating sound recording data corresponding to different operating parameters in the historical sound data, and performing frequency domain feature clustering extraction based on the operating parameters to form fault operating voiceprint feature data; and combining normal operating voiceprint feature data and fault operating voiceprint feature data to form operating voiceprint feature data.

[0009] In this invention, the primary purpose of acquiring historical sound data from a production system is to obtain the voiceprint feature information generated by the production system under various operating parameters, thereby providing real-time monitoring and comparison of system equipment during subsequent production under the same operating parameters. Considering that voiceprint feature data can reflect both normal operation and faulty operation of system equipment, the voiceprint feature information extracted from historical sound data primarily includes voiceprint feature data corresponding to different operating parameters under normal operation and voiceprint feature data corresponding to different operating parameters under faulty operation.

[0010] As a possible implementation method, normal operating sound recording data corresponding to different operating parameters in historical sound data is obtained, and frequency domain feature clustering extraction based on the operating parameters is performed to form normal operating voiceprint feature data, including: determining all normal operating control parameters and normal environmental impact parameters corresponding to the production system; clustering normal operating sound recording information with the same normal operating control parameter range and all normal environmental impact parameter range in the normal operating sound recording data to form different parameter normal operating sound recording information sets; performing frequency domain feature extraction on different parameter normal operating sound recording information sets to form corresponding parameter normal operating voiceprint feature information; and performing similarity analysis on the operating parameters based on different parameter normal operating voiceprint feature information to form normal operating voiceprint feature data.

[0011] In the present invention, the extraction of voiceprint feature data corresponding to different operating parameters under normal operation crucially considers the impact of these operating parameters on the operation of the system equipment. It is understood that the sound information emitted by equipment operating under different operating parameters differs significantly. For example, the speed of a motor differs between rated and non-rated conditions, resulting in different rotational sound information. Therefore, to effectively extract voiceprint feature information from a production system, historical sound data must first be clustered according to operating parameters. Factors influencing system equipment operation and production include not only those parameters that are actively adjusted for operational control parameters, but also environmental factors such as humidity, ambient temperature, and air velocity. Humidity affects the conductivity of the equipment, while ambient temperature and air velocity affect heat exchange and initial startup energy. Therefore, the operating parameters comprehensively consider both the equipment's operational control parameters and environmental impact parameters. Of course, given the extensive expertise in the production process of glucosamine sulfate, specific operational control parameters and environmental impact parameters can be set manually or determined based on system data. After clustering the normal operation sound recording data based on the operating parameters, accurate voiceprint feature information can be extracted. Considering that for system equipment, the comprehensively generated voiceprint feature information can normally be a state display formed by the combination of different devices within a reasonable operating parameter range. Therefore, after obtaining the comprehensive voiceprint feature information, similarity analysis can also be performed to reasonably merge the voiceprint feature data to simplify the complexity of subsequent data analysis and comparison, and also ensure the accuracy of the analysis results.

[0012] As a possible implementation method, frequency domain feature extraction is performed on different parameter normal operation sound recording information sets to form corresponding parameter normal operation voiceprint feature information, including: for different parameter normal operation sound recording information sets, determining the normal batch sound recording information corresponding to different batches in the information set and the total running time of the normal batch Based on the total running time of a normal batch Get the effective duration of the normal batch in the middle section and the effective time of normal batches Normal batch sound valid recording information in the corresponding normal batch sound recording information n represents the number of different parameter normal operation sound recording information sets, m represents the number of different normal batch sound recording information corresponding to the parameter normal operation sound recording information set numbered n; the normal batch sound valid recording information corresponding to different normal batch sound recording information Perform frequency domain transformation to form the corresponding normal batch voiceprint effective frequency domain information All the normal batch voiceprint valid frequency domain information in the sound recording information set according to the normal operation parameters Determine the corresponding parameters for normal operation of the voiceprint frequency domain range A n ; Set the parameters to operate normally in the voiceprint frequency domain range A n Corresponding to all normal operation control parameter ranges and environmental impact parameter ranges under the parameter normal operation sound record set, the parameter normal operation voiceprint feature information is formed.

[0013] In the present invention, the sound recording information of normal operation of the parameters under the determined operating parameters takes into account that each production does not necessarily have a fixed output and duration. Therefore, it is reasonable and necessary to transform the sound information in the frequency domain. This change can be achieved through a simple Fourier ratio transform, or it can be in the form of other targeted or more directional Fourier transforms. Of course, it should be noted that the voiceprint feature data under normal operating conditions to be extracted in this application is mainly for the voiceprint feature data after the system equipment has entered the normal rated operating state. Therefore, for the total operating time, it is necessary to reasonably omit the data of the previous and subsequent startup and shutdown processes to avoid affecting the accuracy of the feature information. Of course, the frequency domain data generated during the operation process will also be limited to a reasonable frequency domain range due to the dynamic operation characteristics of the equipment itself. As long as the corresponding frequency range is extracted as voiceprint feature information, the normal operating status of the equipment can be fully and accurately reflected.

[0014] As a possible implementation method, according to different parameters of normal operation voiceprint feature information, similarity analysis of the operating parameters is performed to form normal operation voiceprint feature data, including: according to different parameters in the normal operation voiceprint feature information, the normal operation voiceprint frequency domain range A n , perform the following operating parameter similarity analysis; if there is any intersection of the normal operating voiceprint frequency domain ranges of different parameters, then the normal operating voiceprint frequency domain ranges of different parameters are combined to form a new normal operating voiceprint frequency domain range of parameters, and the different normal operating control parameter ranges corresponding to the different normal operating voiceprint frequency domain ranges of different parameters are respectively combined to form the corresponding normal operating control parameter range, and the different normal environmental impact parameter ranges are respectively combined to form the corresponding normal environmental impact parameter range; obtain all the newly formed normal operating voiceprint frequency ranges of parameters, the corresponding normal operating control parameter ranges and the normal environmental impact parameter ranges after the similarity analysis, as well as the remaining normal operating voiceprint frequency domain ranges of parameters that have not been combined, and form the normal operating voiceprint feature data.

[0015] In the present invention, the similarity analysis mainly considers that the comprehensive sound information of the system equipment can show the same and intersecting under different operating parameters, so it can be determined that the comprehensive sound information of the system equipment is also operating normally in the entire range after the merger, and therefore it can be considered that the corresponding operating parameters have the same influence on the equipment.

[0016] As a possible implementation method, fault operation sound recording data corresponding to different operating parameters in historical sound data is obtained, and frequency domain feature clustering extraction based on the operating parameters is performed to form fault operation soundprint feature data, including: determining the fault operation control parameter range and fault environment impact parameter range corresponding to different fault operation sound recording data to form fault operation parameter information; for different fault operation sound recording data, determining the corresponding different fault operation sound recording information; for different fault operation sound recording information, extracting the fault sound recording information corresponding to the total fault operation time of the faulty equipment, and performing frequency domain conversion to form the corresponding fault soundprint frequency domain feature information; according to the fault soundprint frequency domain feature information corresponding to different equipment under the fault operation sound recording data, determining the equipment parameter fault soundprint frequency domain feature range corresponding to the fault operation parameter information. k represents the sound recording data of different fault operations, i represents the number of different equipment in the production system; the frequency domain feature range of the equipment parameter fault soundprint corresponding to different equipment under all different fault operation control parameter ranges and fault environment impact parameter ranges is collected. Form fault operation voiceprint feature data.

[0017] In the present invention, voiceprint feature information is sensitive to equipment failures. Therefore, it is necessary to use historical sound data to extract voiceprint feature information for equipment failures. It should be noted that the voiceprint feature information generated by equipment failures will, on the one hand, show different situations due to different operating parameters, and on the other hand, the sound information of different equipment failures is different. Therefore, the sound data information obtained in combination can be the superposition of the sound information emitted when multiple devices fail. In order to accurately locate the faulty equipment, it is necessary to extract the sound information of individual faults of different equipment. In addition, when the fault sound information of each device is obtained, reverse tracing can be performed based on the comprehensive fault sound data to accurately determine the device where the fault occurred.

[0018] As a possible implementation method, real-time operation sound data of the production system is collected, and corresponding real-time voiceprint feature information and real-time operation parameter information are extracted, including: determining the real-time operation sound information corresponding to the real-time total operation time based on the real-time operation sound data; determining the real-time effective operation time and the corresponding real-time operation effective sound information of the latter part of the real-time total operation time; performing frequency domain conversion on the real-time operation effective sound information to form corresponding real-time operation voiceprint feature information; determining the corresponding real-time operation control parameter range and real-time environmental impact parameter range based on the real-time operation sound data to form real-time operation parameter information.

[0019] In the present invention, after obtaining voiceprint feature information for both normal and faulty production system operation, it can be used as reference data for real-time comparative monitoring and analysis of the production system's equipment operation. To complete this comparative monitoring and analysis, obtaining real-time operating sound data from the production system is essential. Similarly, determining the operating parameter information and corresponding voiceprint feature information for the current production state is also necessary.

[0020] As a possible implementation method, monitoring and analysis are performed based on real-time operation parameter information and combined with operation voiceprint feature data and real-time voiceprint feature information to form real-time operation monitoring and analysis result data, including: performing operation normality monitoring and analysis based on real-time operation parameter information and normal operation voiceprint feature data to form operation normality monitoring and analysis result data; performing fault location analysis based on operation normality detection and analysis result data and combined with fault operation voiceprint feature data to form fault location monitoring and analysis result data.

[0021] In this invention, by comparing and analyzing real-time operating parameter information with normal operating voiceprint signature data, the normal operating voiceprint signature data applicable to the current production status can be determined, thereby accurately completing real-time monitoring and analysis of the system equipment operating status. Real-time monitoring and analysis mainly consists of two parts: one is the analysis of normal operation, and the other is the analysis of fault location in the event of abnormal operation.

[0022] As a possible implementation method, operation normality monitoring and analysis is performed based on real-time operation parameter information and normal operation voiceprint feature data to form operation normality monitoring and analysis result data, including: based on the real-time operation parameter information, determining that different normal operation control parameter ranges all include corresponding real-time operation control parameters, and different normal environment impact parameter ranges all include parameter normal operation voiceprint feature ranges corresponding to real-time environment impact parameter ranges; based on the real-time operation voiceprint feature information and the parameter normal operation voiceprint feature range, performing operation normality monitoring and analysis in the following manner; if the real-time operation voiceprint feature information belongs to the parameter normal operation voiceprint feature range, and the difference in the frequency domain accumulation within the real-time effective operation time does not exceed the operation stability threshold range, then real-time operation normal result information is formed; if the real-time operation voiceprint feature information belongs to the parameter normal operation voiceprint feature range, but the difference in the frequency domain accumulation within the real-time effective operation time exceeds the operation stability threshold range, then real-time operation unstable fault result information is formed; if the real-time operation voiceprint feature information does not belong to the parameter normal operation voiceprint feature range, then real-time operation fault result information is formed.

[0023] In this invention, the monitoring and analysis of operational normality primarily determines whether the real-time voiceprint characteristics fall within the parameterized normal operational voiceprint characteristic range. This analysis and comparison can yield three scenarios: one in which everything is normal; one in which the equipment experiences certain operational fluctuations, resulting in an unstable equipment operation; and a third in which the real-time voiceprint characteristics fall completely outside the parameterized normal operational voiceprint characteristic range.

[0024] As a possible implementation method, fault location analysis is performed based on the operation normality detection analysis result data and combined with the fault operation voiceprint feature data to form fault location monitoring and analysis result data, including: according to the real-time operation parameter information, determining that different fault operation control parameter ranges all include the corresponding real-time operation control parameters, and different fault environment impact parameter ranges all include the equipment parameter fault voiceprint frequency domain feature ranges of different equipment corresponding to the real-time environment impact parameter range; when the operation normality monitoring and analysis result is real-time operation unstable fault result information, extract the frequency domain value g(f) whose frequency domain cumulative difference within the real-time effective operation time exceeds the operation stability threshold range, and perform the following fault location analysis: for the equipment parameter fault voiceprint frequency domain feature ranges of different equipment If there is a constant term e i , so that the frequency domain characteristic range of the fault soundprint from the device parameter The frequency domain eigenvalues ​​of the device parameter fault soundprint extracted from satisfy: The extracted non-zero equipment parameter fault soundprint frequency domain eigenvalues The corresponding device is determined to be a faulty device, and the real-time operation unstable fault device information is generated; when the operation normality monitoring analysis result is the real-time operation fault result information, the following fault location analysis is performed based on the real-time operation voiceprint feature information j(f): the frequency domain feature range of the device parameter fault voiceprint of different devices If there is a constant term p i , so that the frequency domain characteristic range of the fault soundprint from the device parameter The frequency domain eigenvalues ​​of the device parameter fault soundprint extracted from satisfy: The extracted non-zero equipment parameter fault soundprint frequency domain eigenvalues The corresponding device is determined to be a faulty device, and real-time operating faulty device information is generated.

[0025] In the present invention, fault location analysis first requires determining the need for faulty device location based on the results of normality monitoring and analysis. Only when the normality monitoring and analysis confirm the presence of a fault does fault location analysis begin. It is understood that within the frequency domain, fault signature information from different devices can be superimposed to form real-time voiceprint feature information. Therefore, by planning based on the frequency domain characteristic values ​​of device parameter fault voiceprints, faulty devices can be identified with high accuracy.

[0026] The beneficial effects of the operation monitoring and control system for glucosamine sulfate capsule preparation equipment provided by the present invention are:

[0027] The system acquires historical sound data from the production system and extracts frequency domain feature data about the production system under different operating parameters to establish voiceprint feature data for subsequent comparative reference analysis. It also extracts frequency domain features from the collected real-time sound data, which are then compared and analyzed with the voiceprint feature data to identify abnormal voiceprint feature information. A matching analysis is then performed based on the voiceprint feature data extracted under equipment failure conditions to identify the device that is currently faulty. This location of equipment failures is highly specific to the fault category, providing a data foundation for subsequent analysis of equipment failures based on fault voiceprint features. Considering that current production systems are largely automated, the source of sound data is not overly complex, and noise reduction and filtering processing is not difficult. This system can reasonably and efficiently extract feature data information and accurately determine fault conditions, effectively ensuring efficient and orderly production. BRIEF DESCRIPTION OF THE DRAWINGS

[0028] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the following briefly introduces the drawings required for use in the embodiments of the present invention. It should be understood that the following drawings only illustrate certain embodiments of the present invention and therefore should not be regarded as limiting the scope. For ordinary technicians in this field, other relevant drawings can be obtained based on these drawings without paying any creative work.

[0029] Figure 1 A step diagram of an operation monitoring and control system for a glucosamine sulfate capsule preparation device provided in an embodiment of the present invention;

[0030] Figure 2 This is a structural schematic diagram of an operation monitoring and control system for glucosamine sulfate capsule preparation equipment provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0031] The technical solutions in the embodiments of the present invention will be described below with reference to the accompanying drawings in the embodiments of the present invention.

[0032] Glucosamine sulfate is a natural amino monosaccharide and an important component necessary for the synthesis of proteoglycans in the matrix of human joint cartilage. The glucosamine sulfate capsules produced can be used to prevent and treat various types of osteoarthritis, such as osteoarthritis in the knee, hip, spine, shoulder, hand, wrist, ankle and other parts of the body, as well as systemic osteoarthritis.

[0033] Currently, the production process of glucosamine sulfate is very mature. Due to the influence of various factors during the production process, the parameters of the production process will be reasonably regulated. At the same time, since the production equipment basically operates under load for a long time, equipment fault monitoring is a necessary means to ensure production efficiency. At present, the fault monitoring of production equipment is still mainly targeted at a single piece of equipment. There is a lack of effective means for comprehensive fault analysis and judgment, which cannot fully ensure the reasonable and effective monitoring and control of the entire production system. This greatly reduces the control of production efficiency and is not conducive to the efficient production of glucosamine sulfate capsules.

[0034] refer to Figures 1 and 2An embodiment of the present invention provides an operation monitoring and control system for glucosamine sulfate capsule preparation equipment. The system acquires historical sound data from the production system and extracts frequency domain feature data related to the production system under different operating parameters to establish voiceprint feature data for subsequent comparative reference analysis. The system also extracts frequency domain features from the collected real-time sound data, which are then compared and analyzed with the voiceprint feature data to identify abnormal voiceprint feature information. A matching analysis is then performed based on the voiceprint feature data extracted under equipment failure conditions to identify the equipment that is currently experiencing a fault. This location of equipment faults is highly specific to the fault category, providing a data foundation for subsequent analysis of equipment faults based on fault voiceprint features. Considering that current production systems are essentially automated, the source of sound data is not overly complex, and noise reduction and filtering processing is not difficult. This system can reasonably and efficiently extract feature data information and accurately determine fault conditions, effectively ensuring efficient and orderly production.

[0035] The operation monitoring and control system for the glucosamine sulfate capsule preparation equipment is specifically configured as follows:

[0036] S1: Obtain historical sound data of the production system, perform frequency domain feature extraction based on operating parameters, and form operating voiceprint feature data.

[0037] Obtain historical sound data of the production system, perform frequency domain feature extraction based on operating parameters, and form operating voiceprint feature data, including: obtaining normal operating sound recording data corresponding to different operating parameters in the historical sound data, and performing frequency domain feature clustering extraction based on the operating parameters to form normal operating voiceprint feature data; obtaining fault operating sound recording data corresponding to different operating parameters in the historical sound data, and performing frequency domain feature clustering extraction based on the operating parameters to form fault operating voiceprint feature data; and combining normal operating voiceprint feature data and fault operating voiceprint feature data to form operating voiceprint feature data.

[0038] The main purpose of acquiring historical sound data from a production system is to obtain the voiceprint feature information generated by the production system under various operating parameters, so as to provide real-time monitoring and comparison of system equipment for subsequent production under the same operating parameters. Considering that voiceprint feature data can reflect both the normal operation and the faulty operation of system equipment, the voiceprint feature information extracted from historical sound data mainly includes the voiceprint feature data corresponding to different operating parameters under normal operation and the voiceprint feature data corresponding to different operating parameters under fault conditions.

[0039] Normal operating sound recording data corresponding to different operating parameters in historical sound data are obtained, and frequency domain feature clustering extraction based on the operating parameters is performed to form normal operating voiceprint feature data, including: determining all normal operating control parameters and normal environmental impact parameters corresponding to the production system; clustering normal operating sound recording information with the same range of all normal operating control parameters and all normal environmental impact parameters in the normal operating sound recording data to form different parameter normal operating sound recording information sets; performing frequency domain feature extraction on different parameter normal operating sound recording information sets to form corresponding parameter normal operating voiceprint feature information; performing similarity analysis on the operating parameters based on different parameter normal operating voiceprint feature information to form normal operating voiceprint feature data.

[0040] When extracting voiceprint feature data corresponding to different operating parameters under normal operation, it's crucial to consider how these parameters impact the operation of the system equipment. Understandably, the sound signatures emitted by equipment operating under different operating parameters differ significantly. For example, the speed of a motor differs between rated and non-rated conditions, resulting in different rotational sounds. Therefore, to effectively extract voiceprint feature information from a production system, historical sound data must be clustered according to operating parameters. Factors influencing system equipment operation and production include not only proactively adjusting equipment operating control parameters but also environmental factors such as humidity, ambient temperature, and air velocity. Humidity affects the device's conductivity, while ambient temperature and air velocity affect heat exchange and initial startup energy. Therefore, operating parameters comprehensively consider both the device's operating control parameters and environmental impact parameters. Of course, given the extensive expertise in the glucosamine sulfate production process, specific operating control parameters and environmental impact parameters can be set manually or determined based on system data. After clustering the normal operation sound recording data based on the operating parameters, accurate voiceprint feature information can be extracted. Considering that for system equipment, the comprehensively generated voiceprint feature information can normally be a state display formed by the combination of different devices within a reasonable operating parameter range. Therefore, after obtaining the comprehensive voiceprint feature information, similarity analysis can also be performed to reasonably merge the voiceprint feature data to simplify the complexity of subsequent data analysis and comparison, and also ensure the accuracy of the analysis results.

[0041] Perform frequency domain feature extraction on different parameter normal operation sound recording information sets to form corresponding parameter normal operation voiceprint feature information, including: for different parameter normal operation sound recording information sets, determine the normal batch sound recording information and the total operation time of the normal batch corresponding to the different batches in the information set. Based on the total running time of a normal batch Get the effective duration of the normal batch in the middle section and the effective time of normal batches Normal batch sound valid recording information in the corresponding normal batch sound recording information n represents the number of different parameter normal operation sound recording information sets, m represents the number of different normal batch sound recording information corresponding to the parameter normal operation sound recording information set numbered n; the normal batch sound valid recording information corresponding to different normal batch sound recording information Perform frequency domain transformation to form the corresponding normal batch voiceprint effective frequency domain information All the normal batch voiceprint valid frequency domain information in the sound recording information set according to the normal operation parameters Determine the corresponding parameters for normal operation of the voiceprint frequency domain range A n ; Set the parameters to operate normally in the voiceprint frequency domain range A n Corresponding to all normal operation control parameter ranges and environmental impact parameter ranges under the parameter normal operation sound record set, the parameter normal operation voiceprint feature information is formed

[0042] The sound recording information of normal operation of the parameters under the determined operating parameters, considering that each production does not necessarily have a fixed output and duration, it is reasonable and necessary to transform the sound information in the frequency domain. This change can be achieved through a simple Fourier ratio transform, or it can be in the form of other targeted or more directional Fourier transforms. Of course, it should be noted that the voiceprint feature data under normal operating conditions to be extracted in this application is mainly for the voiceprint feature data after the system equipment has entered the normal rated operating state. Therefore, for the total operating time, it is necessary to reasonably omit the data of the previous and subsequent startup and shutdown processes to avoid affecting the accuracy of the feature information. Of course, the frequency domain data generated during the operation process will also be limited to a reasonable frequency domain range due to the dynamic operation characteristics of the equipment itself. As long as the corresponding frequency range is extracted as voiceprint feature information, the normal operating status of the equipment can be fully and accurately reflected.

[0043] According to the normal operation voiceprint feature information of different parameters, similarity analysis of the operating parameters is performed to form normal operation voiceprint feature data, including: according to the normal operation voiceprint feature information of different parameters in the normal operation voiceprint frequency domain range A n, perform the following operating parameter similarity analysis; if there is any intersection of the normal operating voiceprint frequency domain ranges of different parameters, then the normal operating voiceprint frequency domain ranges of different parameters are combined to form a new normal operating voiceprint frequency domain range of parameters, and the different normal operating control parameter ranges corresponding to the different normal operating voiceprint frequency domain ranges of different parameters are respectively combined to form the corresponding normal operating control parameter range, and the different normal environmental impact parameter ranges are respectively combined to form the corresponding normal environmental impact parameter range; obtain all the newly formed normal operating voiceprint frequency ranges of parameters, the corresponding normal operating control parameter ranges and the normal environmental impact parameter ranges after the similarity analysis, as well as the remaining normal operating voiceprint frequency domain ranges of parameters that have not been combined, and form the normal operating voiceprint feature data.

[0044] The similarity analysis mainly considers that under different operating parameters, the comprehensive sound information of the system equipment can still show the same and intersection. It can be determined that the comprehensive sound information of the system equipment is also operating normally in the entire range after the merger. Therefore, it can be considered that the corresponding operating parameters have the same influence on the equipment.

[0045] Obtain fault operation sound recording data corresponding to different operation parameters in historical sound data, and perform frequency domain feature clustering extraction based on the operation parameters to form fault operation soundprint feature data, including: determining the fault operation control parameter range and fault environment impact parameter range corresponding to different fault operation sound recording data to form fault operation parameter information; for different fault operation sound recording data, determine the corresponding different fault operation sound recording information; for different fault operation sound recording information, extract the fault sound recording information corresponding to the total fault operation time of the faulty equipment, and perform frequency domain conversion to form the corresponding fault soundprint frequency domain feature information; according to the fault soundprint frequency domain feature information corresponding to different equipment under the fault operation sound recording data, determine the equipment parameter fault soundprint frequency domain feature range corresponding to the fault operation parameter information. k represents the sound recording data of different fault operations, i represents the number of different equipment in the production system; the frequency domain feature range of the equipment parameter fault soundprint corresponding to different equipment under all different fault operation control parameter ranges and fault environment impact parameter ranges is collected. Form fault operation voiceprint feature data.

[0046] Voiceprint feature information is sensitive to equipment failures. Therefore, it is necessary to use historical sound data to extract voiceprint feature information for equipment failures. It should be noted that the voiceprint feature information generated by equipment failures will show different situations due to different operating parameters. On the other hand, the sound information of different equipment failures is different. Therefore, the sound data information obtained in combination can be the superposition of the sound information emitted by multiple equipment failures. In order to accurately locate the faulty equipment, it is necessary to extract the sound information of individual faults of different equipment. In addition, when the fault sound information of each equipment is obtained, reverse tracing can be performed based on the comprehensive fault sound data to accurately determine the faulty equipment.

[0047] S2: Collect the real-time operation sound data of the production system and extract the corresponding real-time voiceprint feature information and real-time operation parameter information.

[0048] Real-time operation sound data of the production system is collected, and the corresponding real-time voiceprint feature information and real-time operation parameter information are extracted, including: determining the real-time operation sound information corresponding to the real-time total operation time based on the real-time operation sound data; determining the real-time effective operation time and the corresponding real-time operation effective sound information of the latter part of the real-time total operation time; performing frequency domain conversion on the real-time operation effective sound information to form the corresponding real-time operation voiceprint feature information; determining the corresponding real-time operation control parameter range and real-time environmental impact parameter range based on the real-time operation sound data to form the real-time operation parameter information.

[0049] After obtaining voiceprint feature information for both normal and faulty production system operation, it can be used as reference data for real-time comparative monitoring and analysis of equipment operation in the production system. To complete this comparative monitoring and analysis, obtaining real-time operating sound data from the production system is essential. It is also necessary to determine the operating parameters and corresponding voiceprint feature information under the current production state.

[0050] S3: Based on the real-time operation parameter information, combined with the operation voiceprint feature data and the real-time voiceprint feature information, monitoring and analysis are performed to form real-time operation monitoring and analysis result data.

[0051] Based on the real-time operation parameter information, and in combination with the operation voiceprint feature data and the real-time voiceprint feature information, monitoring and analysis are performed to form real-time operation monitoring and analysis result data, including: based on the real-time operation parameter information and the normal operation voiceprint feature data, operation normality monitoring and analysis result data are performed to form operation normality monitoring and analysis result data; based on the operation normality detection and analysis result data, and in combination with the fault operation voiceprint feature data, fault location analysis is performed to form fault location monitoring and analysis result data.

[0052] By comparing and analyzing real-time operating parameter information with normal operating voiceprint signature data, we can determine the normal operating voiceprint signature data applicable to the current production status, thereby accurately completing real-time monitoring and analysis of the system equipment operating status. Real-time monitoring and analysis is mainly divided into two parts: one is the analysis of normal operation, and the other is the analysis of fault location in the event of abnormal operation.

[0053] An operation normality monitoring and analysis is performed based on the real-time operation parameter information and the normal operation voiceprint feature data to form operation normality monitoring and analysis result data, including: based on the real-time operation parameter information, determining that different normal operation control parameter ranges all include the corresponding real-time operation control parameters, and that different normal environment impact parameter ranges all include the parameter normal operation voiceprint feature range corresponding to the real-time environment impact parameter range; based on the real-time operation voiceprint feature information and the parameter normal operation voiceprint feature range, an operation normality monitoring and analysis is performed in the following manner; if the real-time operation voiceprint feature information belongs to the parameter normal operation voiceprint feature range, and the difference in the frequency domain cumulative amount within the real-time effective operation time does not exceed the operation stability threshold range, then real-time operation normal result information is formed; if the real-time operation voiceprint feature information belongs to the parameter normal operation voiceprint feature range, but the difference in the frequency domain cumulative amount within the real-time effective operation time exceeds the operation stability threshold range, then real-time operation unstable fault result information is formed; if the real-time operation voiceprint feature information does not belong to the parameter normal operation voiceprint feature range, then real-time operation fault result information is formed.

[0054] Monitoring and analyzing operational normality primarily determines whether the real-time voiceprint signature information falls within the parameterized normal operating voiceprint signature range. This analysis and comparison can yield three scenarios: 1) normal operation; 2) fluctuations in device operation, resulting in an unstable device operation; and 3) the real-time voiceprint signature information falls completely outside the parameterized normal operating voiceprint signature range.

[0055] According to the operation normality detection analysis result data, and combined with the fault operation voiceprint feature data, fault location analysis is performed to form fault location monitoring and analysis result data, including: according to the real-time operation parameter information, it is determined that different fault operation control parameter ranges all include the corresponding real-time operation control parameters, and different fault environment impact parameter ranges all include the equipment parameter fault voiceprint frequency domain feature ranges of different equipment corresponding to the real-time environment impact parameter range; when the operation normality monitoring analysis result is real-time operation unstable fault result information, the frequency domain value g(f) whose difference in frequency domain cumulative amount within the real-time effective operation time exceeds the operation stability threshold range is extracted, and the following fault location analysis is performed: the equipment parameter fault voiceprint frequency domain feature ranges of different equipment If there is a constant term e i , so that the frequency domain characteristic range of the fault soundprint from the device parameter The frequency domain eigenvalues ​​of the device parameter fault soundprint extracted from satisfy: The extracted non-zero equipment parameter fault soundprint frequency domain eigenvalues The corresponding device is determined to be a faulty device, and the real-time operation unstable fault device information is generated; when the operation normality monitoring analysis result is the real-time operation fault result information, the following fault location analysis is performed based on the real-time operation voiceprint feature information j(f): the frequency domain feature range of the device parameter fault voiceprint of different devices If there is a constant term p i , so that the frequency domain characteristic range of the fault soundprint from the device parameter The frequency domain eigenvalues ​​of the device parameter fault soundprint extracted from satisfy: The extracted non-zero equipment parameter fault soundprint frequency domain eigenvalues The corresponding device is determined to be a faulty device, and real-time operating faulty device information is generated.

[0056] Fault location analysis first requires determining the need for faulty device location based on the results of normality monitoring and analysis. Only when normality monitoring and analysis confirm a fault is the fault location analysis initiated. It is understood that within the frequency domain, fault signature information from different devices can be superimposed to form real-time voiceprint feature information. Therefore, by planning based on the frequency domain characteristic values ​​of device parameter fault voiceprints, faulty devices can be identified with high accuracy.

[0057] The present invention also provides an intelligent course schedule information extraction system, which includes a data acquisition unit for acquiring historical sound data and real-time operation sound data of a production system; a feature extraction unit for performing feature extraction on the historical sound data collected by the data acquisition unit to form operation voiceprint feature data, and performing feature extraction on the real-time operation sound data to form real-time voiceprint feature information and real-time operation parameter information; a monitoring and analysis unit for monitoring and analyzing the real-time operation parameter information, operation voiceprint feature data and real-time voiceprint feature information formed by the feature extraction unit to form real-time operation monitoring and analysis result data.

[0058] In summary, the beneficial effects of the operation monitoring and control system for glucosamine sulfate capsule preparation equipment provided by the embodiments of the present invention are:

[0059] The system acquires historical sound data from the production system and extracts frequency domain feature data about the production system under different operating parameters to establish voiceprint feature data for subsequent comparative reference analysis. It also extracts frequency domain features from the collected real-time sound data, which are then compared and analyzed with the voiceprint feature data to identify abnormal voiceprint feature information. A matching analysis is then performed based on the voiceprint feature data extracted under equipment failure conditions to identify the device that is currently faulty. This location of equipment failures is highly specific to the fault category, providing a data foundation for subsequent analysis of equipment failures based on fault voiceprint features. Considering that current production systems are largely automated, the source of sound data is not overly complex, and noise reduction and filtering processing is not difficult. This system can reasonably and efficiently extract feature data information and accurately determine fault conditions, effectively ensuring efficient and orderly production.

[0060] In the embodiment of the present application, "indication" may include direct indication and indirect indication, and may also include explicit indication and implicit indication. The information indicated by a certain information is called information to be indicated. In the specific implementation process, there are many ways to indicate the information to be indicated, such as but not limited to, the information to be indicated can be directly indicated, such as the information to be indicated itself or the index of the information to be indicated. The information to be indicated can also be indirectly indicated by indicating other information, wherein there is an association relationship between the other information and the information to be indicated. It is also possible to indicate only a part of the information to be indicated, while the other parts of the information to be indicated are known or agreed in advance. For example, the indication of specific information can also be achieved by means of the arrangement order of each piece of information agreed in advance (such as specified in the protocol), thereby reducing the indication overhead to a certain extent. At the same time, the common parts of each piece of information can also be identified and indicated uniformly to reduce the indication overhead caused by indicating the same information separately.

[0061] In addition, the specific indication method can also be various existing indication methods, such as but not limited to the above-mentioned indication methods and various combinations thereof. The specific details of the various indication methods can be referred to the prior art and will not be repeated herein. As can be seen from the above, for example, when it is necessary to indicate multiple information of the same type, there may be a situation where the indication methods for different information are different. In the specific implementation process, the required indication method can be selected according to specific needs. The embodiment of the present application does not limit the selected indication method. In this way, the indication method involved in the embodiment of the present application should be understood to cover various methods that can enable the party to be indicated to obtain the information to be indicated.

[0062] It should be understood that the information to be indicated can be sent as a whole or divided into multiple sub-information and sent separately, and the sending period and / or sending time of these sub-information can be the same or different. The specific sending method is not limited in the embodiments of this application. The sending period and / or sending time of these sub-information can be predefined, for example, predefined according to a protocol, or can be configured by the transmitting device by sending configuration information to the receiving device.

[0063] "Pre-definition" or "pre-configuration" can be implemented by pre-saving corresponding codes, tables or other methods that can be used to indicate relevant information in the device, and the embodiments of the present application do not limit the specific implementation method. Among them, "saving" can mean saving in one or more memories. The one or more memories can be set separately or integrated in an encoder or decoder, a processor, or a communication device. The one or more memories can also be partially set separately and partially integrated in a decoder, a processor, or a communication device. The type of memory can be any form of storage medium, and the embodiments of the present application do not limit this.

[0064] The "protocol" involved in the embodiments of the present application may refer to a protocol family in the communication field, a standard protocol with a similar protocol family frame structure, or a related protocol used in future communication systems. The embodiments of the present application do not make specific limitations on this.

[0065] In the embodiments of the present application, descriptions such as "when...", "in the case of...", "if" and "if" all mean that the device will perform corresponding processing under certain objective circumstances. It does not limit the time, nor does it require the device to perform judgment actions when implemented, nor does it mean that there are other limitations.

[0066] In the description of the embodiments of the present application, unless otherwise specified, " / " indicates that the objects associated with each other are in an "or" relationship. For example, A / B can represent A or B. "And / or" in the embodiments of the present application is only a description of the association relationship of the associated objects, indicating that there can be three relationships. For example, A and / or B can represent: A exists alone, A and B exist at the same time, and B exists alone. A and B can be singular or plural. In addition, in the description of the embodiments of the present application, unless otherwise specified, "multiple" refers to two or more than two. "At least one of the following" or similar expressions refers to any combination of these items, including any combination of single or plural items. For example, at least one of a, b, or c can represent: a, b, c, ab, ac, bc, or abc, where a, b, and c can be single or multiple. In addition, in order to facilitate the clear description of the technical solutions of the embodiments of the present application, in the embodiments of the present application, words such as "first" and "second" are used to distinguish between identical or similar items with basically the same functions and effects. Those skilled in the art will understand that words such as "first" and "second" do not limit the quantity and execution order, and words such as "first" and "second" do not necessarily limit differences. At the same time, in the embodiments of the present application, words such as "exemplary" or "for example" are used to indicate examples, illustrations or explanations. Any embodiment or design described as "exemplary" or "for example" in the embodiments of the present application should not be interpreted as being more preferred or more advantageous than other embodiments or design. Specifically, the use of words such as "exemplary" or "for example" is intended to present related concepts in a concrete way for easy understanding.

[0067] It should be understood that the processor in the embodiments of the present application may be a central processing unit (CPU), and the processor may also be other general-purpose processors, digital signal processors (DSP), application-specific integrated circuits (ASIC), field programmable gate arrays (FPGA) or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. The general-purpose processor may be a microprocessor or any conventional processor, etc.

[0068] It should also be understood that the memory in the embodiments of the present application may be a volatile memory or a non-volatile memory, or may include both volatile and non-volatile memories. Among them, the non-volatile memory may be a read-only memory (ROM), a programmable read-only memory (PROM), an erasable programmable read-only memory (EPROM), an electrically erasable programmable read-only memory (EEPROM), or a flash memory. The volatile memory may be a random access memory (RAM), which is used as an external cache. By way of example and not limitation, many forms of random access memory (RAM) are available, such as static RAM (SRAM), dynamic random access memory (DRAM), synchronous DRAM (SDRAM), double data rate synchronous dynamic random access memory (DDR SDRAM), enhanced synchronous dynamic random access memory (ESDRAM), synchronous link DRAM (SLDRAM), and direct rambus RAM (DRRAM).

[0069] The above embodiments can be implemented in whole or in part by software, hardware (such as circuits), firmware or any other combination. When implemented using software, the above embodiments can be implemented in whole or in part in the form of a computer program product. The computer program product includes one or more computer instructions or computer programs. When the computer instructions or computer program are loaded or executed on a computer, the process or function described in the embodiment of the present application is generated in whole or in part. The computer can be a general-purpose computer, a special-purpose computer, a computer network, or other programmable device. The computer instructions can be stored in a computer-readable storage medium or transmitted from one computer-readable storage medium to another computer-readable storage medium. For example, the computer instructions can be transmitted from one website, computer, server or data center to another website, computer, server or data center via a wired (such as infrared, wireless, microwave, etc.) method. The computer-readable storage medium can be any available medium that can be accessed by a computer or a data storage device such as a server or data center that contains one or more available media sets. The available medium can be a magnetic medium (for example, a floppy disk, a hard disk, a tape), an optical medium (for example, a DVD), or a semiconductor medium. The semiconductor medium can be a solid-state drive.

[0070] It should be understood that the term "and / or" as used herein simply describes a relationship between associated objects, indicating that three possible relationships exist. For example, "A and / or B" can represent: A alone, A and B together, or B alone. A and B can be singular or plural. Furthermore, the character " / " as used herein generally indicates an "or" relationship between the associated objects, but it may also indicate an "and / or" relationship. For specific understanding, please refer to the context.

[0071] In this application, "at least one" means one or more, and "plurality" means two or more. "At least one of the following" or similar expressions refers to any combination of these items, including any combination of single or plural items. For example, at least one of a, b, or c can mean: a, b, c, ab, ac, bc, or abc, where a, b, and c can be single or plural.

[0072] It should be understood that in the various embodiments of the present application, the size of the serial numbers of the above-mentioned processes does not mean the order of execution. The execution order of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the embodiments of the present application.

[0073] Those skilled in the art will appreciate that the units and algorithm steps of each example described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are performed in hardware or software depends on the specific application and design constraints of the technical solution. Professional and technical personnel can use different methods to implement the described functions for each specific application, but such implementation should not be considered beyond the scope of this application.

[0074] Those skilled in the art will clearly understand that, for the convenience and brevity of description, the specific working processes of the systems, devices and units described above can refer to the corresponding processes in the aforementioned method embodiments and will not be repeated here.

[0075] In the several embodiments provided in this application, it should be understood that the disclosed systems, devices and methods can be implemented in other ways. For example, the device embodiments described above are merely schematic. For example, the division of the units is merely a logical function division. In actual implementation, there may be other division methods, such as multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be through some interfaces, indirect coupling or communication connection of devices or units, which can be electrical, mechanical or other forms.

[0076] The units described as separate components may or may not be physically separate, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed across multiple network units. Some or all of these units may be selected to achieve the purpose of this embodiment according to actual needs.

[0077] In addition, each functional unit in each embodiment of the present application may be integrated into one processing unit, or each unit may exist physically separately, or two or more units may be integrated into one unit.

[0078] If the functions are implemented in the form of software functional units and sold or used as independent products, they can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present application, or the part that contributes to the prior art, or the part of the technical solution, can be embodied in the form of a software product. The computer software product is stored in a storage medium and includes several instructions for enabling a computer device (which can be a personal computer, a server, or a network device, etc.) to execute all or part of the steps of the method described in each embodiment of the present application. The aforementioned storage medium includes various media that can store program codes, such as a USB flash drive, a mobile hard disk, a read-only memory (ROM), a random access memory (RAM), a magnetic disk or an optical disk.

[0079] The above description is merely a specific embodiment of the present application, but the scope of protection of the present application is not limited thereto. Any changes or substitutions that can be easily conceived by a person skilled in the art within the technical scope disclosed in this application should be included in the scope of protection of this application. Therefore, the scope of protection of this application should be based on the scope of protection of the claims.

Claims

1. An operation monitoring and control system for glucosamine sulfate capsule preparation equipment, characterized in that: Configured to: Obtain historical sound data from the production system, perform frequency domain feature extraction based on operating parameters, and generate operational voiceprint feature data; Collecting real-time operating sound data of the production system and extracting corresponding real-time voiceprint feature information and real-time operating parameter information; Performing monitoring and analysis based on the real-time operation parameter information and in combination with the operation voiceprint feature data and the real-time voiceprint feature information to form real-time operation monitoring and analysis result data; The historical sound data of the production system is obtained, and frequency domain feature extraction based on operating parameters is performed to form operating voiceprint feature data, including: Acquiring normal operating sound recording data corresponding to different operating parameters in the historical sound data, and performing frequency domain feature clustering extraction based on the operating parameters to form normal operating voiceprint feature data; Acquire fault operation sound recording data corresponding to different operation parameters in the historical sound data, and perform frequency domain feature clustering extraction based on the operation parameters to form fault operation voiceprint feature data; Aggregating the normal operation voiceprint feature data and the fault operation voiceprint feature data to form the operation voiceprint feature data; Acquiring normal operating sound recording data corresponding to different operating parameters in the historical sound data, and performing frequency domain feature clustering extraction based on the operating parameters to form normal operating voiceprint feature data, including: Determine all normal operating control parameters and normal environmental impact parameters corresponding to the production system; Clustering normal operating sound recording information with the same normal operating control parameter ranges and the same normal environmental impact parameter ranges in the normal operating sound recording data to form different parameter normal operating sound recording information sets; Performing frequency domain feature extraction on different sets of sound recording information of normal operation of the parameters to form corresponding normal operation voiceprint feature information of the parameters; Performing similarity analysis on the operating parameters based on the normal operating voiceprint feature information of different parameters to form the normal operating voiceprint feature data; Performing frequency domain feature extraction on different sets of the parameter normal operation sound recording information to form corresponding parameter normal operation voiceprint feature information, including: For different sets of normal operation sound recording information of the parameters, determine the normal batch sound recording information and the total operation time of the normal batch corresponding to the different batches in the information set ; According to the total running time of the normal batch , get the effective duration of the normal batch in the middle section and the normal batch validity period Normal batch sound valid recording information in the corresponding normal batch sound recording information , n represents the number of different sets of normal parameter operation sound recording information, and m represents the number of different normal batch sound recording information corresponding to the normal parameter operation sound recording information set numbered n; The normal batch sound valid recording information corresponding to the normal batch sound recording information Perform frequency domain transformation to form the corresponding normal batch voiceprint effective frequency domain information ; All the normal batch voiceprint valid frequency domain information in the normal operation sound recording information set according to the parameters , determine the corresponding parameters for normal operation of the voiceprint frequency domain range ; The parameters are operated normally within the voiceprint frequency domain Corresponding to all the normal operation control parameter ranges and the environmental impact parameter ranges in the parameter normal operation sound record set, forming the parameter normal operation voiceprint feature information; Based on the normal operation voiceprint feature information of different parameters, similarity analysis is performed on the operation parameters to form the normal operation voiceprint feature data, including: According to the different frequency domain ranges of the normal operation voiceprints in the normal operation voiceprint feature information of the parameters , perform similarity analysis of operating parameters in the following ways; If any of the different normal operating voiceprint frequency domain ranges of the parameters intersect, the different normal operating voiceprint frequency domain ranges of the parameters are combined to form a new normal operating voiceprint frequency domain range of the parameters, and the different normal operating control parameter ranges corresponding to the different normal operating voiceprint frequency domain ranges of the parameters are respectively combined to form a corresponding normal operating control parameter range, and the different normal environment impact parameter ranges are respectively combined to form a corresponding normal environment impact parameter range; Obtaining all newly formed parameter normal operation voiceprint frequency ranges, the corresponding normal operation control parameter ranges, and the normal environment impact parameter ranges after similarity analysis, as well as the remaining parameter normal operation voiceprint frequency domain ranges, the corresponding normal operation control parameter ranges, and the normal environment impact parameter ranges that have not occurred and calculated, to form the normal operation voiceprint feature data; Acquiring fault operation sound recording data corresponding to different operation parameters in the historical sound data, and performing frequency domain feature clustering extraction based on the operation parameters to form fault operation voiceprint feature data, including: determining the fault operation control parameter ranges and the fault environment impact parameter ranges corresponding to the different fault operation sound recording data to form fault operation parameter information; Determining corresponding different fault operation sound recording information for different fault operation sound recording data; For different fault operation sound recording information, extract the fault sound recording information corresponding to the total fault operation time of the faulty equipment, and perform frequency domain conversion to form corresponding fault soundprint frequency domain feature information; According to the fault soundprint frequency domain feature information corresponding to different devices under the fault operation sound recording data, determine the device parameter fault soundprint frequency domain feature range of different devices corresponding to the fault operation parameter information , k represents different fault operation sound recording data, i represents the number of different equipment in the production system; Collect all the different fault operation control parameter ranges and the fault environment impact parameter ranges corresponding to the device parameter fault soundprint frequency domain feature ranges of different devices , forming the fault operation voiceprint feature data; Collecting real-time operating sound data of the production system and extracting corresponding real-time voiceprint feature information and real-time operating parameter information, including: Determining the real-time running sound information corresponding to the real-time total running time according to the real-time running sound data; Determine the real-time effective running time of the latter segment of the real-time total running time and the corresponding real-time effective running sound information; Performing frequency domain conversion on the real-time running effective sound information to form corresponding real-time running voiceprint feature information; Determine the corresponding real-time operation control parameter range and real-time environment impact parameter range based on the real-time operation sound data to form the real-time operation parameter information: Performing operation normality monitoring and analysis based on the real-time operation parameter information and the normal operation voiceprint feature data to generate operation normality monitoring and analysis result data; According to the real-time operation parameter information, determining that different normal operation control parameter ranges all include the corresponding real-time operation control parameter, and different normal environment impact parameter ranges all include the parameter normal operation voiceprint feature range corresponding to the real-time environment impact parameter range; Based on the real-time operation voiceprint feature information and the parameter normal operation voiceprint feature range, perform the following operation normality monitoring and analysis; If the real-time operation voiceprint feature information falls within the parameter normal operation voiceprint feature range, and the difference in frequency domain cumulative amount within the real-time effective operation time does not exceed the operation stability threshold range, then the real-time operation normal result information is generated; If the real-time operation voiceprint feature information falls within the parameter normal operation voiceprint feature range, but the difference in frequency domain cumulative amount within the real-time effective operation time exceeds the operation stability threshold range, then real-time operation unstable fault result information is generated; If the real-time operation voiceprint feature information does not fall within the parameter normal operation voiceprint feature range, generating real-time operation fault result information; Performing fault location analysis based on the normal operation detection and analysis result data and in combination with the fault operation voiceprint feature data to form fault location monitoring and analysis result data; According to the real-time operation parameter information, it is determined that different fault operation control parameter ranges all include the corresponding real-time operation control parameters, and different fault environment impact parameter ranges all include the device parameter fault soundprint frequency domain feature ranges of different devices corresponding to the real-time environment impact parameter ranges; When the operation normality monitoring and analysis result is the real-time operation unstable fault result information, extract the frequency domain value whose difference in the frequency domain cumulative amount within the real-time effective operation time exceeds the operation stability threshold range , and perform the following fault location analysis: The frequency domain characteristic range of the device parameter fault soundprint for different devices , if there is a constant term , so that the frequency domain characteristics of the fault soundprint from the device parameters range The frequency domain eigenvalues ​​of the device parameter fault soundprint extracted from satisfy: , then the extracted non-zero item is the device parameter fault soundprint frequency domain eigenvalue The corresponding device is determined to be a faulty device, generating real-time unstable fault device information; When the operation normality monitoring and analysis result is the real-time operation fault result information, according to the real-time operation voiceprint feature information , perform the following fault location analysis: Frequency domain characteristic range of device parameter fault soundprint for different devices , if there is a constant term , so that the frequency domain characteristics of the fault soundprint from the device parameters range The frequency domain eigenvalues ​​of the device parameter fault soundprint extracted from satisfy: , then the extracted non-zero item is the device parameter fault soundprint frequency domain eigenvalue The corresponding device is determined to be a faulty device, and real-time operating faulty device information is generated.

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