Operation monitoring control system for glucosamine sulfate capsule preparation equipment

Abstract

The invention provides an operation monitoring control system for glucosamine sulfate capsule preparation equipment, and relates to the technical field of equipment operation monitoring. The system is 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; collecting real-time operation sound data of the production system, and extracting corresponding real-time voiceprint feature information and real-time operation parameter information; and monitoring and analyzing according to the real-time operation parameter information 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 system carries out timely and accurate equipment comprehensive fault analysis in combination with voiceprint feature data, realizes accurate and timely monitoring of comprehensive faults of a production system, and ensures efficient and orderly production.

Description

Technical Field

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

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

[0003] Currently, the production process of glucosamine sulfate is already very mature. During the production process, due to the influence of different factors, the parameters in the production process need to be reasonably regulated. At the same time, since the production equipment basically operates under long-term load, the fault monitoring of the equipment is a necessary means to ensure production efficiency. Currently, the fault monitoring of production equipment mainly focuses on a single equipment unit, and 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, designing an operation monitoring and control system for a glucosamine sulfate capsule preparation device, which conducts timely and accurate comprehensive equipment fault analysis by combining voiceprint feature data, realizes the accurate and timely monitoring of comprehensive faults in the production system, and ensures the efficient and orderly progress of production, is an urgent problem to be solved at present. Summary of the Invention

[0005] The purpose of the present invention is to provide an operation monitoring and control system for a glucosamine sulfate capsule preparation device. By obtaining the historical sound data of the production system, extracting the frequency-domain feature data of the production system under different operating parameters, establishing voiceprint feature data that can be used for subsequent comparative reference analysis, and at the same time extracting the frequency-domain features of the collected real-time sound data, and then comparing and analyzing them with the voiceprint feature data to determine abnormal voiceprint feature information, and then conducting a matching analysis based on the voiceprint feature data extracted under equipment faults to determine the faulty equipment in the current situation. Moreover, this positioning of equipment faults has a certain directionality in terms of fault categories, providing a data basis for subsequent equipment fault analysis guided by fault voiceprint features. Considering that the current production system is basically under automated control, the source of sound data is not overly complex, and the noise reduction and filtering processing do not pose a great difficulty, which can reasonably and efficiently extract feature data information and accurately judge the fault situation, effectively ensuring the efficient and orderly progress of production.

[0006] In a first aspect, the present invention provides an operation monitoring and control system for a preparation device of glucosamine sulfate capsules. The system is configured to obtain historical sound data of a production system, perform frequency-domain feature extraction based on operation parameters to 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; and perform monitoring and analysis according to the real-time operation parameter information, 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.

[0007] In the present invention, the system obtains historical sound data of the production system, extracts frequency-domain feature data of the production system under different operation parameters, establishes voiceprint feature data that can be used for subsequent comparative reference analysis. At the same time, it extracts the frequency-domain features of the collected real-time sound data, and then performs comparative analysis with the voiceprint feature data to determine abnormal voiceprint feature information. Then, it performs matching analysis based on the voiceprint feature data extracted under equipment failures, and further determines the equipment that has failed in the current situation. Moreover, this positioning of equipment failures has a certain directivity for the types of failures, providing a data basis for subsequent analysis of equipment failures guided by fault voiceprint features. Considering that the current production system is basically automated control, the source of sound data is not too complex, and the noise reduction and filtering processing do not pose great difficulties, which can reasonably and efficiently extract feature data information and accurately judge the fault situation, effectively ensuring the efficient and orderly progress of production.

[0008] As a possible implementation, obtaining historical sound data of the production system, performing frequency-domain feature extraction based on operation parameters to form operation voiceprint feature data includes: obtaining normal operation sound record 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 normal operation voiceprint feature data; obtaining fault operation sound record 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; and combining the normal operation voiceprint feature data and the fault operation voiceprint feature data to form operation voiceprint feature data.

[0009] In the present invention, the main purpose of obtaining the historical sound data of the production system is to acquire the voiceprint feature information generated by the production system under various different operating parameters, so as to provide real-time monitoring and comparison of the system equipment for subsequent production under the same operating parameters. Here, considering that the voiceprint feature data can reflect both the normal operation of the system equipment and the faulty operation of the system equipment, the extraction of the voiceprint feature information from the 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 faulty conditions.

[0010] As a possible implementation, obtain the normal operation sound record data corresponding to different operating parameters in the historical sound data, and perform frequency domain feature clustering extraction based on the operating parameters to form normal operation voiceprint feature data, including: determining all normal operation control parameters and normal environmental impact parameters corresponding to the production system; clustering the normal operation sound record information with the same ranges of all normal operation control parameters and all normal environmental impact parameters in the normal operation sound record data to form different parameter normal operation sound record information sets; performing frequency domain feature extraction on the different parameter normal operation sound record information sets to form corresponding parameter normal operation voiceprint feature information; and performing similarity analysis for the operating parameters based on the different parameter normal operation voiceprint feature information to form normal operation voiceprint feature data.

[0011] In the present invention, the extraction of voiceprint feature data corresponding to different operating parameters under normal operation is most important to consider the influence of different operating parameters on the operation of system equipment. It can be understood that under different operating parameters, the sound information emitted by the equipment operation is obviously different, such as the motor speed is different under non-rated state and rated state, and the rotation sound information emitted is also different. Therefore, to reasonably extract the voiceprint feature information of the production system, it is necessary to cluster the historical sound data according to the operating parameters. Considering the factors affecting the operation and production of system equipment is not only the parameters at the level of actively adjusting the operating control parameters of the equipment, but also the environmental parameters will also affect the operation of the equipment, such as humidity in the air, ambient temperature, air flow rate, etc. After all, humidity has an impact on the conductivity of the equipment, and ambient temperature and air flow rate have an impact on the heat exchange of the equipment and the initial startup energy, so the operating parameters comprehensively consider the operating control parameters and environmental impact parameters of the equipment. Of course, since the production process of glucosamine sulfate is very proficient, the specific operating control parameters and environmental impact parameters can be set manually or determined based on system collection. After clustering the normal operating 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, so as 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 operation time of the normal batch According to the total running time of 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 According to the parameters, all the valid frequency domain information of the normal batch voiceprints in the voice recording information runs normally Determine the corresponding parameter normal running voiceprint frequency domain range A n ; The parameter normal running voiceprint frequency domain range A n Corresponds to all the normal running control parameter ranges and environmental impact parameter ranges in the parameter normal running voice recording set to form the parameter normal running voiceprint feature information

[0013] In the present invention, for the parameter normal running voice recording information under the determined operating parameters, considering that the production quantity and duration are not necessarily fixed each time, it is reasonable and necessary to perform a frequency domain transformation on the voice information. This transformation can be a simple Fourier ratio transformation, or other deformed Fourier transforms that are targeted or more directional. Of course, it should be noted that the voiceprint feature data to be extracted in this application mainly refers to the voiceprint feature data after the system equipment has entered the normal rated operating state. Therefore, for the total operating duration, the data during the startup and shutdown processes need to be reasonably omitted to avoid affecting the accuracy of the feature information. Of course, the frequency domain data formed during the operation will also be limited within a reasonable frequency domain range due to the dynamic operating characteristics of the equipment itself. As long as the corresponding frequency range is extracted as the voiceprint feature information, it can fully and accurately reflect the normal operating condition of the equipment

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

[0015] In the present invention, for the analysis of similarity, it is mainly considered that when the integrated sound information of the system device can still show the same and intersecting characteristics under different operating parameters, it can be determined that the integrated sound information of the system device is also operating normally in the entire merged range. Therefore, it can be considered that the corresponding operating parameters have the same influence on the device.

[0016] As a possible implementation, obtain the fault operation sound record data corresponding to different operating parameters in the historical sound data, and perform frequency-domain feature clustering extraction based on the operating parameters to form fault operation voiceprint feature data, including: determining the fault operation control parameter range and fault environment influence parameter range corresponding to different fault operation sound record data to form fault operation parameter information; for different fault operation sound record data, determine the corresponding different fault operation sound record information; for different fault operation sound record information, extract the fault sound record information corresponding to the device with a fault under the total fault operation duration, and perform frequency-domain conversion to form the corresponding fault voiceprint frequency-domain feature information; according to the fault voiceprint frequency-domain feature information corresponding to different devices under the fault operation sound record data, determine the device parameter fault voiceprint frequency-domain feature range of different devices under the fault operation parameter information. k represents different fault operation sound record data, and i represents the numbers of different devices in the production system; aggregate the device parameter fault voiceprint frequency-domain feature ranges of different devices corresponding to all different fault operation control parameter ranges and fault environment influence parameter ranges. Form fault operation voiceprint feature data.

[0017] In the present invention, the voiceprint feature information is sensitive to the situation of device faults. Therefore, it is necessary to extract the voiceprint feature information for device fault operations using historical sound data. It should be noted that for the voiceprint feature information generated by device faults, on the one hand, it will also show different situations due to different operating parameters, and on the other hand, the voiceprint information of different device faults is different. Therefore, the comprehensively obtained sound data information can be the superposition of the sound information emitted during the faults of multiple devices. To accurately locate the faulty device, it is necessary to extract the sound information of individual faults for different devices. And in the case of obtaining the fault sound information of each device, backward tracing can be performed based on the comprehensive fault sound data to accurately determine the device with a fault.

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

[0019] In the present invention, after obtaining the voiceprint feature information of the production system under normal operation and fault conditions, it can be used as reference data for real-time comparative monitoring and analysis of the equipment operation of the production system. To complete the comparative monitoring and analysis, it is necessary to obtain the real-time running sound data of the production system. It is also necessary to determine the running parameter information and the corresponding voiceprint feature information under the current production state.

[0020] As a possible implementation, according to the real-time running parameter information, and combining the running voiceprint feature data and the real-time voiceprint feature information for monitoring and analysis, real-time running monitoring and analysis result data is formed, including: performing normal running monitoring and analysis according to the real-time running parameter information and the normal running voiceprint feature data to form normal running monitoring and analysis result data; performing fault location analysis according to the normal running detection and analysis result data and combining the fault running voiceprint feature data to form fault location monitoring and analysis result data.

[0021] In the present invention, by comparing and analyzing the real-time running parameter information with the normal running voiceprint feature data, the normal running voiceprint feature data applicable to the current production state can be determined, thus accurately completing the real-time monitoring and analysis of the operation state of the system equipment. The real-time monitoring and analysis are mainly divided into two parts, one is the analysis of running normality, and the other is the analysis of fault location in case of abnormal running.

[0022] As a possible implementation, the normal operation monitoring and analysis are carried out according to the real-time operation parameter information and the normal operation voiceprint feature data, and the normal operation monitoring and analysis result data are formed, including: according to the real-time operation parameter information, it is determined that different normal operation control parameter ranges all include the corresponding real-time operation control parameters, and different normal environment impact parameter ranges all include the corresponding real-time environment impact parameter ranges of the parameter normal operation voiceprint feature range; according to the real-time operation voiceprint feature information and the parameter normal operation voiceprint feature range, the normal operation monitoring and analysis are carried out in the following ways: 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 duration does not exceed the operation stability threshold range, the 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 duration exceeds the operation stability threshold range, the 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, the real-time operation fault result information is formed.

[0023] In the present invention, the monitoring and analysis of the normal operation mainly determine whether the real-time operation voiceprint feature information is within the parameter normal operation voiceprint feature range. There will be three situations in this analysis and comparison. One is that all are normal, the second is that there are certain operation fluctuations in the equipment, resulting in the judgment of unstable equipment operation in the analysis and comparison, and the third is the situation where the real-time operation voiceprint feature information completely does not belong to the parameter normal operation voiceprint feature range.

[0024] As a possible implementation, according to the normal operation detection and analysis result data, and combining with the fault operation voiceprint feature data, the fault location analysis is carried out, and the fault location monitoring and analysis result data are formed, 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 corresponding real-time environment impact parameter ranges of the equipment parameter fault voiceprint frequency domain feature range of different devices; when the normal operation monitoring and analysis result is the real-time operation unstable fault result information, the frequency domain value g(f) whose difference in the frequency domain cumulative amount within the real-time effective operation duration exceeds the operation stability threshold range is extracted, and the following fault location analysis is carried out: for the equipment parameter fault voiceprint frequency domain feature range of different devices If there is a constant term e i , so that from the equipment parameter fault voiceprint frequency domain feature range the extracted equipment parameter fault voiceprint frequency domain feature value satisfies: Then the extracted non-zero item equipment parameter fault voiceprint frequency domain feature value The corresponding device is determined as a faulty device, forming real-time operation unstable faulty device information; when the operation normality monitoring and analysis result is real-time operation fault result information, the following fault location analysis is carried out according to the real-time operation voiceprint feature information j(f): for the device parameter fault voiceprint frequency domain feature range of different devices If there is a constant term p i , so that the device parameter fault voiceprint frequency domain feature value extracted from the device parameter fault voiceprint frequency domain feature range satisfies: Then the device corresponding to the non-zero item device parameter fault voiceprint frequency domain feature value extracted is determined as a faulty device, forming real-time operation faulty device information.

[0025] In the present invention, for the fault location analysis, first, it is necessary to determine the work of locating the faulty device according to the operation normality monitoring and analysis result. Only when the operation normality monitoring and analysis result determines that there is a fault, the location of the faulty device starts. It can be understood that in the frequency domain range, the fault feature information of different devices can be superimposed to form the voiceprint feature information obtained in real time. Therefore, the device with a fault can be judged by the method planned based on the device parameter fault voiceprint frequency domain feature value, which has high accuracy.

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

[0027] The system obtains the historical sound data of the production system, extracts the frequency domain feature data of the production system under different operation parameters, establishes the voiceprint feature data that can be used for subsequent comparison and reference analysis, and at the same time extracts the frequency domain features of the collected real-time sound data, and then compares and analyzes them with the voiceprint feature data to determine the abnormal voiceprint feature information. Then, the matching analysis is carried out according to the voiceprint feature data extracted under the device fault, and further the device with a fault in the current situation is determined. And this location of the device fault has a certain directivity for the fault category, providing a data basis for guiding the analysis of the device fault based on the fault voiceprint feature in the future. Considering that the current production system is basically automated control, the source of the sound data will not be too complex, and the noise reduction and filtering processing does not have a large difficulty, which can reasonably and efficiently realize the extraction of the feature data information and the accurate judgment of the fault situation, and effectively ensure the efficient and orderly progress of production. Description of the Drawings

[0028] To more clearly illustrate the technical solutions of the embodiments of the present invention, the following will briefly introduce the drawings required for use in the embodiments of the present invention. It should be understood that the following drawings only show certain embodiments of the present invention and should not be regarded as limiting the scope. For those of ordinary skill in the art, without creative efforts, other related drawings can also be obtained based on these drawings.

[0029] Figure 1 It is a step diagram of the operation monitoring and control system for the preparation equipment of glucosamine sulfate capsules provided by the embodiments of the present invention;

[0030] Figure 2 It is a structural schematic diagram of the operation monitoring and control system for the preparation equipment of glucosamine sulfate capsules provided by the embodiments of the present invention. Detailed implementation manners

[0031] The following will describe the technical solutions in the embodiments of the present invention in conjunction with the drawings in the embodiments of the present invention.

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

[0033] Currently, the production process of glucosamine sulfate is already very mature. During the production process, due to the influence of different factors, the parameters in the production process need to be reasonably regulated. At the same time, since the production equipment basically operates under long-term load, the fault monitoring of the equipment is a necessary means to ensure production efficiency. Currently, the fault monitoring of production equipment mainly focuses on a single equipment unit, and 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] Reference Figures 1 to 2, an embodiment of the present invention provides an operation monitoring and control system for a preparation device of glucosamine sulfate capsules. The system obtains historical sound data of the production system, extracts frequency-domain characteristic data of the production system under different operating parameters, establishes voiceprint characteristic data that can be used for subsequent comparative reference analysis, and at the same time extracts the frequency-domain characteristics of the collected real-time sound data, and then compares and analyzes it with the voiceprint characteristic data to determine abnormal voiceprint characteristic information. Then, matching analysis is carried out according to the voiceprint characteristic data extracted under equipment failure, and the equipment that fails under the current situation is determined. Moreover, this positioning of equipment failure has a certain directivity for the type of failure, providing a data basis for subsequent analysis of equipment failure guided by the failure voiceprint characteristic. Considering that the current production system is basically automated control, the source of sound data is not too complex, and the noise reduction and filtering process is not too difficult, which can reasonably and efficiently extract characteristic data information and accurately judge the failure situation, effectively ensuring the efficient and orderly progress of production.

[0035] The operation monitoring and control system for the preparation device of glucosamine sulfate capsules is specifically configured in the following steps:

[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] Obtaining historical sound data of the production system, performing frequency-domain feature extraction based on operating parameters, and forming operating voiceprint feature data includes: obtaining normal operating sound record data corresponding to different operating parameters in the historical sound data, and performing frequency-domain feature clustering extraction based on operating parameters to form normal operating voiceprint feature data; obtaining failure operating sound record data corresponding to different operating parameters in the historical sound data, and performing frequency-domain feature clustering extraction based on operating parameters to form failure operating voiceprint feature data; combining the normal operating voiceprint feature data and the failure operating voiceprint feature data to form operating voiceprint feature data.

[0038] The main purpose of obtaining the historical sound data of the production system is to obtain the voiceprint characteristic information generated by the production system under various different operating parameters, so as to provide real-time monitoring and comparison of the system equipment for subsequent production under the same operating parameters. Here, considering that the voiceprint characteristic data can reflect both the normal operation situation and the failure operation situation of the system equipment, the extraction of the voiceprint characteristic information of the historical sound data mainly includes the voiceprint characteristic data corresponding to different operating parameters under normal operation and the voiceprint characteristic data corresponding to different operating parameters under failure.

[0039] Obtain the normal operation sound record data corresponding to different operating parameters in the historical sound data, and perform frequency-domain feature clustering extraction based on the operating parameters to form normal operation voiceprint feature data, including: determining all normal operation control parameters and normal environmental impact parameters corresponding to the production system; clustering the normal operation sound record information with the same ranges of all normal operation control parameters and all normal environmental impact parameters in the normal operation sound record data to form different parameter normal operation sound record information sets; performing frequency-domain feature extraction on different parameter normal operation sound record information sets to form corresponding parameter normal operation voiceprint feature information; performing similarity analysis for the operating parameters according to different parameter normal operation voiceprint feature information to form normal operation voiceprint feature data.

[0040] When extracting the voiceprint feature data corresponding to different operating parameters under normal operation conditions, the most important thing is to consider the impact of different operating parameters on the operation of system equipment. It can be understood that for different operating parameters, the sound information emitted by the equipment operation is significantly different. For example, the rotational speed of the motor is different under non-rated and rated states, and thus the emitted rotational sound information is also different. Therefore, to reasonably extract the voiceprint feature information of the production system, it is necessary to first cluster the historical sound data according to the operating parameters. The factors affecting the operation of system equipment and production are not only the parameters at the level of actively adjusting the operating control parameters of the equipment, but also the environmental parameters that will affect the equipment operation, such as the humidity in the air, the environmental temperature, the air flow rate, etc. After all, humidity affects the conductivity of the equipment, and environmental temperature and air flow rate affect the heat exchange and initial startup energy of the equipment. Therefore, the operating parameters comprehensively consider the operating control parameters of the equipment and environmental impact parameters. Of course, since the production process of glucosamine sulfate is very proficient, the specific operating control parameters and environmental impact parameters can be set artificially or determined based on system collection. After clustering the normal operation sound record data based on the operating parameters, accurate voiceprint feature information can be extracted. Considering that for system equipment, the comprehensive voiceprint feature information generated can normally be the state display formed by the combination of different equipment 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, so as to simplify the complexity of subsequent data analysis and comparison, and also ensure the accuracy of the analysis results.

[0041] Performing frequency-domain feature extraction on different parameter normal operation sound record information sets to form corresponding parameter normal operation voiceprint feature information, including: for different parameter normal operation sound record information sets, determining the normal batch sound record information and the total normal batch operation duration corresponding to different batches in the information set According to the total running duration of the normal batch Obtain the effective duration of the normal batch in the middle section And the effective duration of the normal batch The normal batch voice effective recording information in the corresponding normal batch voice recording information n represents the number of the normal running voice recording information set of different parameters, and m represents the number of different normal batch voice recording information corresponding to the normal running voice recording information set with the number n; for the normal batch voice effective recording information corresponding to different normal batch voice recording information Perform frequency domain transformation to form the corresponding normal batch voiceprint effective frequency domain information According to all the normal batch voiceprint effective frequency domain information in the normal running voice recording information set of parameters Determine the corresponding normal running voiceprint frequency domain range A of the parameters n ; The normal running voiceprint frequency domain range A of the parameters n Corresponds to all the normal running control parameter ranges and environmental impact parameter ranges in the normal running voice recording set of parameters to form the normal running voiceprint feature information of the parameters

[0042] For the normal running voice recording information of the parameters under the determined operating parameters, considering that the production volume and duration are not necessarily fixed each time, it is reasonable and necessary to perform frequency domain transformation on the voice information. This transformation can be through simple Fourier ratio transformation, or other deformed Fourier transformations that are targeted or more directional. Of course, it should be noted that the voiceprint feature data in the normal running state to be extracted in this application is mainly for the voiceprint feature data after the system equipment has entered the normal rated running state. Therefore, for the total running duration, it is necessary to reasonably omit the data of the startup and shutdown processes at the front and back to avoid affecting the accuracy of the feature information. Of course, the frequency domain data formed during the running process will also be limited within a reasonable frequency domain range due to the dynamic running characteristics of the equipment itself. As long as the corresponding frequency range is extracted as the voiceprint feature information, it can fully and accurately reflect the normal running condition of the equipment.

[0043] According to the different normal running voiceprint feature information of the parameters, perform similarity analysis on the operating parameters to form normal running voiceprint feature data, including: according to the different normal running voiceprint frequency domain ranges A in the normal running voiceprint feature information of the parameters n, perform the similarity analysis of operating parameters in the following manner; if there is any intersection in the normal operating voice frequency domains of different parameters, perform the union operation on the normal operating voice frequency domains of different parameters to form a new normal operating voice frequency domain of the parameter, and respectively merge the corresponding different normal operating control parameter ranges of the normal operating voice frequency domains of different parameters to form the corresponding normal operating control parameter range, and respectively merge the different normal environmental impact parameter ranges to form the corresponding normal environmental impact parameter range; obtain all the newly formed normal operating voice frequency ranges of the parameter, 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 voice frequency domains of the parameter that have not undergone the union operation, the corresponding normal operating control parameter ranges, and the normal environmental impact parameter ranges, to form the normal operating voice feature data.

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

[0045] Obtain the fault operating sound record data corresponding to different operating parameters in the historical sound data, and perform frequency domain feature clustering extraction based on the operating parameters to form the fault operating voice feature data, including: determining the fault operating control parameter range and the fault environmental impact parameter range corresponding to different fault operating sound record data to form the fault operating parameter information; for different fault operating sound record data, determining the corresponding different fault operating sound record information; for different fault operating sound record information, extracting the fault sound record information corresponding to the equipment with a fault under the total fault operating duration and performing frequency domain conversion to form the corresponding fault voice frequency domain feature information; according to the fault voice frequency domain feature information corresponding to different equipment under the fault operating sound record data, determining the equipment parameter fault voice frequency domain feature range corresponding to different equipment under the fault operating parameter information k represents different fault operating sound record data, and i represents the numbers of different equipment in the production system; aggregate the equipment parameter fault voice frequency domain feature ranges corresponding to different equipment under all different fault operating control parameter ranges and fault environmental impact parameter ranges Form the fault operating voice feature data.

[0046] The voiceprint feature information is sensitive to the situation of equipment failures. Therefore, it is necessary to extract the voiceprint feature information during the operation of the equipment failure using historical voice data. It should be noted that for the voiceprint feature information generated by equipment failures, on the one hand, it will also show different situations due to different operating parameters, and on the other hand, the voice information of different equipment failures is different. Therefore, the comprehensively obtained voice data information can be the superposition of the voice information emitted during the failures of multiple devices. To accurately locate the faulty equipment, it is necessary to extract the voice information of individual failures for different devices. And in the case of obtaining the voice information of each device failure, reverse tracing can be performed based on the comprehensive failure voice data to accurately determine the faulty equipment.

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

[0048] Collect the real-time operation voice data of the production system, and extract the corresponding real-time voiceprint feature information and real-time operation parameter information, including: Determine the real-time operation voice information corresponding to the real-time total operation duration according to the real-time operation voice data; Determine the real-time effective operation duration in the latter segment of the real-time total operation duration and the corresponding real-time effective operation voice information; Perform frequency domain conversion on the real-time effective operation voice information to form the corresponding real-time operation voiceprint feature information; Determine the corresponding real-time operation control parameter range and real-time environmental impact parameter range according to the real-time operation voice data to form real-time operation parameter information.

[0049] After obtaining the voiceprint feature information of the production system under normal operation and failure conditions, it can be used as reference data for real-time comparative monitoring and analysis of the equipment operation of the production system. To complete the comparative monitoring and analysis, it is necessary to obtain the real-time operation voice data of the production system. It is also necessary to determine the operation parameter information and the corresponding voiceprint feature information in the current production state.

[0050] S3: According to the real-time operation parameter information, and combine the operation voiceprint feature data and the real-time voiceprint feature information for monitoring and analysis to form real-time operation monitoring and analysis result data.

[0051] According to the real-time operation parameter information, and combine the operation voiceprint feature data and the real-time voiceprint feature information for monitoring and analysis to form real-time operation monitoring and analysis result data, including: Perform normal operation monitoring and analysis according to the real-time operation parameter information and the normal operation voiceprint feature data to form normal operation monitoring and analysis result data; According to the normal operation detection and analysis result data, and combine the faulty operation voiceprint feature data for fault location analysis to form fault location monitoring and analysis result data.

[0052] By comparing and analyzing the real-time operating parameter information with the normal operating voiceprint feature data, the normal operating voiceprint feature data applicable to the current production status can be determined, thus accurately completing the real-time monitoring and analysis of the operating status of system equipment. The real-time monitoring and analysis mainly consists of two parts. One is the analysis of the normality of operation, and the other is the analysis of fault location in case of abnormal operation.

[0053] Based on the real-time operating parameter information and the normal operating voiceprint feature data, the normality monitoring and analysis of operation is carried out to form the result data of the normality monitoring and analysis of operation, including: according to the real-time operating parameter information, it is determined that the corresponding real-time operating control parameters are included in different normal operating control parameter ranges, and the corresponding real-time environmental impact parameters are included in different normal environmental impact parameter ranges, and the voiceprint feature range of normal operation of parameters is obtained; according to the real-time operating voiceprint feature information and the voiceprint feature range of normal operation of parameters, the normality monitoring and analysis of operation is carried out in the following ways: if the real-time operating voiceprint feature information belongs to the voiceprint feature range of normal operation of parameters, and the difference in the frequency-domain cumulative amount within the real-time effective operating duration does not exceed the operating stability threshold range, the real-time operating normal result information is formed; if the real-time operating voiceprint feature information belongs to the voiceprint feature range of normal operation of parameters, but the difference in the frequency-domain cumulative amount within the real-time effective operating duration exceeds the operating stability threshold range, the real-time operating unstable fault result information is formed; if the real-time operating voiceprint feature information does not belong to the voiceprint feature range of normal operation of parameters, the real-time operating fault result information is formed.

[0054] The monitoring and analysis of the normality of operation is mainly to determine whether the real-time operating voiceprint feature information is within the voiceprint feature range of normal operation of parameters. There are three situations in this kind of analysis and comparison. One is that all are normal. The second is that there are certain fluctuations in the operation of the equipment, resulting in the judgment of unstable operation of the equipment in the analysis and comparison. The third is the situation where the real-time operating voiceprint feature information completely does not belong to the voiceprint feature range of normal operation of parameters.

[0055] Based on the result data of the normality detection and analysis of operation, and combined with the fault operating voiceprint feature data, the fault location analysis is carried out to form the result data of the fault location monitoring and analysis, including: according to the real-time operating parameter information, it is determined that the corresponding real-time operating control parameters are included in different fault operating control parameter ranges, and the corresponding real-time environmental impact parameters are included in different fault environmental impact parameter ranges, and the frequency-domain feature ranges of the equipment parameter faults of different devices are obtained; when the result of the normality monitoring and analysis of operation is the real-time operating unstable fault result information, the frequency-domain value g(f) whose difference in the frequency-domain cumulative amount within the real-time effective operating duration exceeds the operating stability threshold range is extracted, and the following fault location analysis is carried out: for the frequency-domain feature ranges of the equipment parameter faults of different devices If there is a constant term e i such that from the frequency-domain feature ranges of the equipment parameter faults of the voiceprint The eigenvalue of the fault sound frequency domain feature of the equipment parameters extracted Satisfy: Then the non-zero eigenvalue of the fault sound frequency domain feature of the equipment parameters extracted The corresponding equipment is determined as the faulty equipment, forming the information of the unstable faulty equipment in real-time operation; when the result of the normal operation monitoring analysis is the information of the real-time operation fault result, according to the real-time operation sound feature information j(f), the following fault location analysis is carried out: for the range of the fault sound frequency domain feature of the equipment parameters of different equipment If there is a constant term p i , such that the eigenvalue of the fault sound frequency domain feature of the equipment parameters extracted from the range of the fault sound frequency domain feature of the equipment parameters The eigenvalue of the fault sound frequency domain feature of the equipment parameters extracted Satisfy: Then the non-zero eigenvalue of the fault sound frequency domain feature of the equipment parameters extracted The corresponding equipment is determined as the faulty equipment, forming the information of the faulty equipment in real-time operation.

[0056] For the fault location analysis, it is first necessary to determine the work of locating the faulty equipment according to the result of the normal operation monitoring analysis. Only when the result of the normal operation monitoring analysis determines that there is a fault, the location of the faulty equipment begins. It can be understood that in the frequency domain, the fault feature information of different equipment can be superimposed to form the real-time obtained sound feature information. Therefore, the equipment with a fault can be judged by the method planned based on the eigenvalue of the fault sound frequency domain feature of the equipment parameters, with high accuracy.

[0057] The present invention also provides an intelligent course schedule information extraction system, which includes a data acquisition unit for acquiring the historical sound data and real-time operation sound data of the production system; a feature extraction unit for extracting features from the historical sound data collected by the data acquisition unit to form operation sound feature data, and extracting features from the real-time operation sound data to form real-time sound feature information and real-time operation parameter information; a monitoring and analysis unit for monitoring and analyzing the real-time operation parameter information, operation sound feature data and real-time sound 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 the equipment for preparing glucosamine sulfate capsules provided by the embodiments of the present invention are as follows:

[0059] The system obtains the historical sound data of the production system, extracts the frequency-domain characteristic data of the production system under different operating parameters, establishes the voiceprint characteristic data that can be used for subsequent comparative reference analysis. At the same time, it extracts the frequency-domain characteristics of the collected real-time sound data, and then conducts a comparative analysis with the voiceprint characteristic data to determine the abnormal voiceprint characteristic information. Then, it conducts a matching analysis based on the voiceprint characteristic data extracted under equipment failures, and further determines the equipment that has failed in the current situation. Moreover, this positioning of equipment failures has a certain directivity for the types of failures, providing a data basis for subsequent analysis of equipment failures guided by fault voiceprint characteristics. Considering that the current production system is basically automated control, the source of sound data is not overly complex, and the noise reduction and filtering process does not pose a great difficulty, which can reasonably and efficiently extract the characteristic data information and accurately judge the fault situation, effectively ensuring the efficient and orderly progress of production.

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

[0061] In addition, the specific indication method can also be various existing indication methods. For example, but not limited to, the above indication methods and their various combinations, etc. The specific details of various indication methods can refer to the prior art and will not be elaborated herein. As described above, for example, when it is necessary to indicate multiple pieces of information of the same type, there may be a situation where the indication methods of different pieces of information are different. In the specific implementation process, the required indication method can be selected according to specific needs, and the embodiments of the present application do not limit the selected indication method. In this way, the indication methods involved in the embodiments 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. Moreover, the sending periods and / or sending timings of these sub - information can be the same or different. The specific sending method is not limited in the embodiments of the present application. Among them, the sending periods and / or sending timings of these sub - information can be predefined, for example, predefined according to a protocol, or can be configured by the sending - end device by sending configuration information to the receiving - end device.

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

[0064] The "protocol" involved in the embodiments of the present application can refer to a protocol family in the communication field, a standard protocol with a frame structure similar to that of a protocol family, or a relevant protocol applied to a future communication system. 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 "when" all mean that the device will perform corresponding processing under a certain objective situation, which does not limit time, and does not require the device to have a judgment action when implemented, nor does it mean the existence of other limitations.

[0066] In the description of the embodiments of the present application, unless otherwise specified, " / " indicates that the objects associated before and after are in an "or" relationship. For example, A / B may represent A or B. The "and / or" in the embodiments of the present application is only a description of the association relationship of the associated objects, indicating that three relationships may exist. For example, A and / or B may represent: A exists alone, A and B exist simultaneously, and B exists alone. Here, A and B may be singular or plural. Also, in the description of the embodiments of the present application, unless otherwise specified, "a plurality of" means two or more than two. "At least one (item)" or its similar expression refers to any combination of these items, including any combination of a single item or plural items. For example, at least one (item) of a, b, or c may represent: a, b, c, a - b, a - c, b - c, or a - b - c, where a, b, c may be single or multiple. Additionally, for the convenience of clearly describing the technical solutions of the embodiments of the present application, in the embodiments of the present application, terms such as "first" and "second" are used to distinguish identical or similar items with basically the same functions and roles. Those skilled in the art can understand that terms such as "first" and "second" do not limit the quantity and execution order, and "first", "second", etc. do not necessarily mean different. At the same time, in the embodiments of the present application, words such as "exemplary" or "for example" are used to represent examples, illustrations, or explanations. Any embodiment or design solution described as "exemplary" or "for example" in the embodiments of the present application should not be construed as being more preferred or having more advantages than other embodiments or design solutions. Rather, the use of words such as "exemplary" or "for example" is intended to present relevant concepts in a specific manner 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 this processor may also be other general - purpose processors, digital signal processors (DSPs), application - specific integrated circuits (ASICs), field - programmable gate arrays (FPGAs), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. The general - purpose processor may be a microprocessor or this processor may also be 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 ROM (PROM), an erasable PROM (EPROM), an electrically erasable PROM (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 but not limitation, many forms of random access memory (RAM) are available, such as static RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), double data rate SDRAM (DDR SDRAM), enhanced SDRAM (ESDRAM), synchlink 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 combination thereof. 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 programs are loaded or executed on a computer, the processes or functions described in the embodiments of the present application are generated in whole or in part. The computer can be a general-purpose computer, a special-purpose computer, a computer network, or other programmable devices. The computer instructions can be stored in a computer-readable storage medium or transmitted from one computer-readable storage medium to another. 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 wired (such as infrared, wireless, microwave, etc.) means. 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 a data center that contains one or more collections of available media. The available medium can be a magnetic medium (such as a floppy disk, a hard disk, or a magnetic tape), an optical medium (such as 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" in this document is merely a description of the association relationship between associated objects, indicating that there can be three relationships. For example, A and / or B can represent: A exists alone, A and B exist simultaneously, and B exists alone. Here, A and B can be singular or plural. In addition, the character " / " in this document generally represents an "or" relationship between the associated objects before and after, but it may also represent an "and / or" relationship, which can be specifically understood with reference to the context.

[0071] In this application, "at least one" means one or more, and "a plurality" means two or more. "At least one of the following" or its similar expressions refer to any combination of these items, including any combination of single items or plural items. For example, at least one of a, b, or c can represent: a, b, c, a - b, a - c, b - c, or a - b - c, where a, b, and c can be single or multiple.

[0072] It should be understood that in various embodiments of the present application, the magnitudes of the sequence numbers of the above processes do not mean the order of execution. The order of execution of each process should be determined by its function and internal logic, and should not constitute any limitation to the implementation process of the embodiments of the present application.

[0073] Those of ordinary skill in the art can realize that the units and algorithm steps of each example described in combination with the embodiments disclosed herein can be implemented by electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are executed in a hardware or software manner depends on the specific application and design constraints of the technical solution. Professional technicians can use different methods to implement the described functions for each specific application, but such implementation should not be considered to exceed the scope of this application.

[0074] Those skilled in the art can 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 foregoing method embodiments and will not be elaborated herein.

[0075] In 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 illustrative. For example, the division of the units is only a logical function division. In actual implementation, there may be other division methods. For example, 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 displayed or discussed coupling or direct coupling or communication connection to each other can be through some interfaces. The indirect coupling or communication connection of the devices or units can be in an electrical, mechanical, or other form.

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

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

[0078] When the above-mentioned 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 this application, in essence, or the part that contributes to the prior art or a part of this technical solution can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions for causing a computer device (which may be a personal computer, a server, or a network device, etc.) to execute all or part of the steps of the methods described in various embodiments of this application. The foregoing storage medium includes: various media such as USB flash drives, mobile hard disks, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical discs that can store program codes.

[0079] As described above, the above are only specific embodiments of this application, but the protection scope of this application is not limited thereto. Any person skilled in the art within the technical scope disclosed in this application can easily think of changes or substitutions, which should all be covered within the protection scope of this application. Therefore, the protection scope of this application should be subject to the protection scope of the claims.

Claims

1. An operation monitoring and control system for a glucosamine sulfate capsule preparation device, characterized in that: is configured as: Obtain historical sound data of the production system, perform frequency domain feature extraction based on operating parameters, and form operating 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; Based on the real-time operation parameter information, monitoring and analysis are performed 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.

2. The operation monitoring and control system for the glucosamine sulfate capsule preparation equipment according to claim 1, characterized in that: The method of obtaining historical sound data of the production system and extracting frequency domain features based on operating parameters to form operating voiceprint feature data includes: Acquire normal 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 normal operation 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; The normal operation voiceprint feature data and the fault operation voiceprint feature data are collected to form the operation voiceprint feature data.

3. The operation monitoring and control system for the glucosamine sulfate capsule preparation equipment according to claim 2, characterized in that: The obtaining of normal 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 normal operation voiceprint feature data, includes: Determine all normal operating control parameters and normal environmental impact parameters corresponding to the production system; Clustering the normal operating sound recording information with the same normal operating control parameter range and the same normal environmental impact parameter range in the normal operating sound recording data to form different parameter normal operating sound recording information sets; Perform frequency domain feature extraction on different sets of normal operation sound record information of the parameters to form corresponding normal operation voiceprint feature information of the parameters; According to the normal operation voiceprint feature information of different parameters, a similarity analysis is performed on the operation parameters to form the normal operation voiceprint feature data.

4. The operation monitoring and control system for the glucosamine sulfate capsule preparation equipment according to claim 3, characterized in that: The extracting frequency domain features of different parameter normal operation sound record information sets to form corresponding parameter normal operation voiceprint feature information includes: For different sets of normal operation sound recording information of the parameters, the normal batch sound recording information and the total operation time of the normal batch corresponding to different batches in the information set are determined. According to the normal batch total running time 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 numbers of different sets of normal operation sound recording information of the parameters, and m represents the numbers of different sets of normal batch sound recording information corresponding to the normal operation sound recording information set of the parameters 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 A n ; The parameters are set to operate normally in the voiceprint frequency domain range A n Corresponding to all the normal operation control parameter ranges and the environmental impact parameter ranges under the parameter normal operation sound record set, the parameter normal operation voiceprint feature information is formed.

5. The operation monitoring and control system for the glucosamine sulfate capsule preparation equipment according to claim 4, characterized in that: The performing similarity analysis on the operating parameters according to the normal operating voiceprint feature information of different parameters to form the normal operating voiceprint feature data includes: According to the different parameters in the normal operation voiceprint feature information, the normal operation voiceprint frequency domain range A n , perform similarity analysis of operating parameters in the following ways; If there are any intersections between the frequency domain ranges of the normal operating voiceprints of different parameters, the frequency domain ranges of the normal operating voiceprints of different parameters are combined to form a new frequency domain range of the normal operating voiceprints of different parameters, and the different normal operating control parameter ranges corresponding to the frequency domain ranges of the normal operating voiceprints of different 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; The normal operating voiceprint frequency range of all the newly formed parameters after similarity analysis, the corresponding normal operating control parameter range and the normal environment impact parameter range, as well as the remaining normal operating voiceprint frequency domain range of the parameters that have not occurred and calculated, the corresponding normal operating control parameter range and the normal environment impact parameter range are obtained to form the normal operating voiceprint feature data.

6. The operation monitoring and control system for the glucosamine sulfate capsule preparation equipment according to claim 5, characterized in that: The acquiring of 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, includes: Determine the fault operation control parameter range and the fault environment impact parameter range corresponding to the different fault operation sound recording data to form fault operation parameter information; For different fault operation sound recording data, determining corresponding different fault operation sound recording information; For different fault operation sound recording information, extract the fault sound recording information corresponding to the faulty equipment under the total fault operation time, 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 serial number of different equipment in the production system; Collect all the equipment parameter fault soundprint frequency domain feature ranges of different equipment corresponding to the fault operation control parameter ranges and the fault environment impact parameter ranges The fault operation voiceprint feature data is formed.

7. The operation monitoring and control system for the glucosamine sulfate capsule preparation equipment according to claim 6, characterized in that: The collecting of the real-time operation sound data of the production system and extracting the corresponding real-time voiceprint feature information and real-time operation parameter information includes: Determine the real-time operation sound information corresponding to the real-time total operation time according to the real-time operation 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 running effective sound information; Performing frequency domain conversion on the real-time operation effective sound information to form corresponding real-time operation voiceprint feature information; According to the real-time operation sound data, a corresponding real-time operation control parameter range and a real-time environment impact parameter range are determined to form the real-time operation parameter information.

8. The operation monitoring and control system for the glucosamine sulfate capsule preparation equipment according to claim 7, characterized in that: The monitoring and analysis is performed according to 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, including: Performing operation normality monitoring and analysis according to the real-time operation parameter information and the normal operation voiceprint feature data to form operation normality monitoring and analysis result data; A fault location analysis is performed based on the normal operation detection and analysis result data and combined with the fault operation voiceprint feature data to form fault location monitoring and analysis result data.

9. The operation monitoring and control system for the glucosamine sulfate capsule preparation equipment according to claim 8, characterized in that: The performing operation normality monitoring and analysis according to the real-time operation parameter information and the normal operation voiceprint feature data to form operation normality monitoring and analysis result data includes: According to the real-time operation parameter information, it is determined that the different normal operation control parameter ranges all include the corresponding real-time operation control parameter, and the different normal environment impact parameter ranges all include the parameter normal operation voiceprint feature range corresponding to the real-time environment impact parameter range; According to the real-time operation voiceprint feature information and the parameter normal operation voiceprint feature range, the following operation normality monitoring and analysis is performed; If the real-time operation voiceprint feature information belongs to the parameter normal operation voiceprint feature range, and the difference in frequency domain accumulation within the real-time effective operation time does not exceed the operation stability threshold range, then the real-time normal operation 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 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 generated; If the real-time operation voiceprint feature information does not belong to the parameter normal operation voiceprint feature range, real-time operation fault result information is generated.

10. The operation monitoring and control system for the glucosamine sulfate capsule preparation equipment according to claim 9, characterized in that: The fault location analysis is performed based on the normal operation detection and analysis result data, and combined with the fault operation voiceprint feature data to form the 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 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, the frequency domain value g(f) whose difference in the 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 frequency domain characteristic range of the device parameter fault soundprint for different devices If there is a constant term e i , so that the frequency domain characteristics of the soundprint from the device parameter fault range The frequency domain eigenvalues ​​of the device parameter fault soundprint extracted from satisfy: The extracted non-zero item is the device parameter fault soundprint frequency domain eigenvalue The corresponding equipment is determined as a faulty equipment, and real-time unstable faulty equipment information is generated; When the operation normality monitoring and analysis result is the real-time operation fault result information, the following fault location analysis is performed according to the real-time operation voiceprint feature information j(f): The frequency domain characteristic range of device parameter fault soundprint for different devices If there is a constant term p i , so that the frequency domain characteristics of the soundprint from the device parameter fault range The frequency domain eigenvalues ​​of the device parameter fault soundprint extracted from satisfy: The extracted non-zero item is the device parameter fault soundprint frequency domain eigenvalue The corresponding device is determined as a faulty device, and real-time operation faulty device information is generated.

Images