CN116339253A - Intelligent mechanical production monitoring management and control system based on Internet of things - Google Patents
Intelligent mechanical production monitoring management and control system based on Internet of things Download PDFInfo
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Abstract
The invention discloses an intelligent mechanical production monitoring and controlling system based on the Internet of things, and belongs to the technical field of intelligent mechanical production; by carrying out modularized treatment on different areas of intelligent mechanical production, reliable data support can be provided for carrying out differentiated analysis on the production of different subsequent mechanical equipment; the temperature monitoring table and the loudness monitoring table are constructed by the real-time temperature and the real-time loudness of the monitoring statistics to preprocess the data, so that the change of the corresponding data item can be intuitively obtained, and reliable data support can be provided for the abnormal analysis of the subsequent data item; the method and the device are used for solving the technical problems that in the existing scheme, monitoring and analysis cannot be carried out on the production states of all mechanical equipment in the intelligent mechanical production process from different dimensions, and the production of the mechanical equipment is dynamically controlled by integrating analysis results of different dimensions, so that the overall effect of monitoring and controlling the intelligent mechanical production is poor.
Description
Technical Field
The invention relates to the technical field of intelligent mechanical production, in particular to an intelligent mechanical production monitoring and controlling system based on the Internet of things.
Background
Along with the rapid development of the intellectualization of mechanical manufacture, all host factories in the engineering machinery industry are carrying out transformation and upgrading so as to aim at improving the lean level of production, an intelligent production workshop is created, the production efficiency is improved by configuring nonstandard automation equipment, and the degree of manual intervention in the production process is reduced.
The existing intelligent mechanical production monitoring and controlling scheme has certain defects in implementation, namely periodic or irregular inspection of the loss of mechanical equipment is implemented by means of manual experience, and the running state of the mechanical equipment is imaged and intelligently analyzed through imaging equipment, so that faults can be automatically identified and positioned; the existing anomalies are output through data monitoring and single comparison by different sensors; however, monitoring and analysis cannot be performed on the production states of all the mechanical devices in the intelligent mechanical production process from different dimensions, and the analysis results of different dimensions are integrated to dynamically control the production of the mechanical devices, so that the overall effect of monitoring and controlling the intelligent mechanical production is poor.
Disclosure of Invention
The invention aims to provide an intelligent mechanical production monitoring and controlling system based on the Internet of things, which is used for solving the technical problem that in the existing scheme, monitoring and analysis cannot be carried out on the production state of each mechanical device in the intelligent mechanical production process from different dimensions, and the production of the mechanical device is dynamically controlled by integrating analysis results of different dimensions, so that the overall effect of intelligent mechanical production monitoring and controlling is poor.
The aim of the invention can be achieved by the following technical scheme:
an intelligent mechanical production monitoring management and control system based on the internet of things, comprising:
the monitoring and dividing module is used for carrying out modularization processing on different areas of intelligent mechanical production and carrying out data statistics of different dimensions to obtain a divided monitoring set;
the processing analysis module is used for preprocessing and analyzing various data of the partition monitoring centralized monitoring statistics, and comprises the following steps:
establishing a temperature monitoring table and a loudness monitoring table according to the temperature monitoring data and the loudness monitoring data; connecting the real-time temperature in the temperature monitoring table to obtain a monitored temperature curve, and connecting the real-time loudness in the loudness monitoring table to obtain a monitored loudness curve; respectively matching the monitored temperature curve and the monitored loudness curve with a preset temperature warning value and a preset loudness warning value;
if the monitored temperature curve does not exceed the temperature warning value and the monitored loudness curve does not exceed the loudness warning value, generating a normal signal, and monitoring the production state of the corresponding mechanical equipment according to the normal signal;
if the monitored temperature curve exceeds the temperature warning value or the monitored loudness curve exceeds the loudness warning value, generating an early warning signal;
the abnormality analysis module is used for analyzing and evaluating the production state of the corresponding mechanical equipment according to the early warning signal to obtain first state analysis data containing abnormality duration and first, second and third different signals, and uploading the first state analysis data to the database and the cloud platform;
the production evaluation module is used for monitoring and evaluating the working states of different mechanical equipment after overhaul to obtain second state analysis data comprising the production state estimation degree and the first, second and third biological signals, and uploading the second state analysis data to the database and the cloud platform;
and the production control module is used for combining the first state analysis data and the second state analysis data to dynamically control the production of each mechanical device in the intelligent mechanical production process in sequence.
Preferably, the working steps of the monitoring and dividing module include:
marking and dividing the area where the mechanical equipment is located according to the functions of intelligent mechanical production, and simultaneously matching the functions of intelligent mechanical production with a function weight table pre-stored in a database to obtain corresponding function weights and associating the corresponding function weights with the divided area;
the method comprises the steps of monitoring and counting real-time temperature and real-time loudness through a sensor on preset monitoring points on mechanical equipment, and arranging and combining the monitoring points according to time sequence to obtain temperature monitoring data and loudness monitoring data;
and forming a division monitoring set by the plurality of divided areas and corresponding functional weights, temperature monitoring data and loudness monitoring data, and uploading the division monitoring set to a database and a cloud platform.
Preferably, temperature monitoring data and loudness monitoring data are acquired, real-time temperature in the temperature monitoring data and real-time loudness in the loudness monitoring data are respectively set as ordinate, and meanwhile, real-time Beijing time is set as abscissa to establish a temperature monitoring table and a loudness monitoring table.
Preferably, the working steps of the anomaly analysis module include:
counting the total times of the monitoring temperature curve exceeding the temperature warning value and marking the total times as a first warning total times WZ; and counting the total times of the monitoring loudness curve exceeding the loudness warning value and marking the total times as a second warning total times XZ;
counting the total duration of the monitoring temperature curve exceeding the temperature warning value each time and marking the total duration as the temperature warning duration WS; and counting the total duration of the loudness curve exceeding the duration of the loudness warning value each time and marking the duration as the loudness warning duration XS;
counting the maximum difference value between the monitored temperature curve and the temperature warning value after the monitored temperature curve exceeds the temperature warning value each time, and marking the maximum difference value as a temperature difference value WC; and counting and monitoring the maximum difference value between the loudness curve and the loudness warning value after the loudness curve exceeds the loudness warning value each time, and marking the maximum difference value as a loudness difference value XC;
and extracting the numerical values of all the marked data, integrating the numerical values in parallel, and obtaining the abnormal duration YCD corresponding to the corresponding mechanical equipment through calculation.
Preferably, the calculation formula of the abnormality duration YCD is:
wherein c1, c2, c3 and c4 are preset different proportion coefficients, and c3 is more than 0 and less than c1 and c4 is more than 0 and less than c2; ZQ is the corresponding function weight of the mechanical equipment;
when the production state of the corresponding mechanical equipment is analyzed according to the abnormal persistence, a corresponding abnormal persistence threshold value is obtained according to the function weight of the mechanical equipment, and the abnormal persistence threshold value are matched to obtain a first differential signal, a second differential signal and a third differential signal.
Preferably, if the anomaly duration is less than the anomaly duration threshold, generating a first alien signal; if the abnormality duration is not less than the abnormality duration threshold and is not greater than Y of the abnormality duration threshold, Y is a real number greater than one hundred, generating a second differential signal; if the anomaly duration is greater than Y of the anomaly duration threshold, a third differential signal is generated.
Preferably, the working steps of the production assessment module comprise:
marking the time after the mechanical equipment is overhauled as first time, counting the time length of the mechanical equipment after the mechanical equipment is started to work according to the first time, and marking the time length as the total working time length GS;
counting the total number of parts produced by the mechanical equipment in the total working time and marking the total number as BZ;
and extracting the numerical values of all the marked data, integrating the numerical values in parallel, and obtaining the production-like estimated degree SZD corresponding to the mechanical equipment through calculation.
Preferably, the calculation formula of the productive state estimation SZD is:
SZD＝zQ×(g1×CS+g2×BZ+a)
wherein g1 and g2 are preset different proportion coefficients, and g1 is more than 1 and g2 is more than 1; alpha is an error compensation factor;
when the production state of the corresponding mechanical equipment is analyzed according to the production state estimation degree, a corresponding production state estimation range is obtained according to the functional weight of the mechanical equipment, and the production state estimation degree is matched with the production state estimation range to obtain a first production signal, a second production signal and a third production signal.
Preferably, if the production state estimate is smaller than the minimum value of the production state estimate range, generating a first production state signal; generating a second raw signal if the production state estimation degree is not less than the minimum value of the production state estimation range and not greater than the maximum value of the production state estimation range; if the production state estimation degree is larger than the maximum value of the production state estimation range, a third production state signal is generated.
Preferably, the production of each machine in the intelligent machine production process is dynamically managed, including preparing to perform inspection maintenance on the slightly abnormal machine, and immediately performing inspection maintenance on the moderately abnormal machine.
Compared with the prior art, the invention has the beneficial effects that:
1. according to the invention, by carrying out modularization processing on different areas of intelligent mechanical production, reliable data support can be provided for carrying out differentiation analysis on the production implementation of different subsequent mechanical equipment; the temperature monitoring table and the loudness monitoring table are constructed by the real-time temperature and the real-time loudness of the monitoring statistics to preprocess the data, so that the change of the corresponding data item can be intuitively obtained, and reliable data support can be provided for the abnormal analysis of the subsequent data item.
2. According to the invention, the abnormal persistence is obtained by integrating various data in the aspect of mechanical production, and the production state of mechanical production can be integrally analyzed and evaluated from the aspect of abnormality based on the abnormal persistence; the production state of the mechanical equipment is analyzed and evaluated from the operation aspect through the production state estimation, so that the abnormality of the mechanical equipment can be found out in time and efficiently, and the alarm prompt can be given, and the overall effect of the monitoring and analysis of the mechanical equipment in the operation aspect can be effectively improved.
3. According to the invention, the production states of the mechanical equipment are integrally evaluated by combining the monitoring and integrating results in different aspects, so that the accuracy of monitoring and analyzing the mechanical equipment can be effectively improved, and different inspection and maintenance schemes can be timely and efficiently implemented on the mechanical equipment with different abnormal degrees.
Drawings
The invention is further described below with reference to the accompanying drawings.
Fig. 1 is a block diagram of an intelligent mechanical production monitoring and controlling system based on the internet of things.
Detailed Description
The following description of the embodiments of the present invention will be made clearly and completely with reference to the accompanying drawings, in which it is apparent that the embodiments described are only some embodiments of the present invention, but not all embodiments. All other embodiments, which can be made by those skilled in the art based on the embodiments of the invention without making any inventive effort, are intended to be within the scope of the invention.
Example 1
As shown in fig. 1, the invention relates to an intelligent mechanical production monitoring and controlling system based on the internet of things, which comprises a monitoring and dividing module, a processing and analyzing module, an anomaly analyzing module, a database and a cloud platform;
the monitoring and dividing module is used for carrying out modularization processing on different areas of intelligent mechanical production and carrying out data statistics of different dimensions to obtain a divided monitoring set; comprising the following steps:
marking and dividing the area where the mechanical equipment is located according to the functions of intelligent mechanical production, and simultaneously matching the functions of intelligent mechanical production with a function weight table pre-stored in a database to obtain corresponding function weights and associating the corresponding function weights with the divided area;
the function weight table comprises a plurality of different functions and corresponding function weights, wherein the different functions are preset with a corresponding function weight, and the different function weights can be customized according to actual conditions; roles include, but are not limited to, shipping, extruding, drilling, sanding, and polishing;
the method comprises the steps of monitoring and counting real-time temperature and real-time loudness through a sensor on preset monitoring points on mechanical equipment, and arranging and combining the monitoring points according to time sequence to obtain temperature monitoring data and loudness monitoring data; the sensor may be a temperature sensor and a loudness sensor;
it should be noted that, in the embodiment of the invention, whether the mechanical equipment is abnormal or not is only monitored and analyzed based on the temperature data item and the loudness data item, and in fact, other data items can be monitored and counted as required and fused for processing and analysis;
the partitioned areas and the corresponding functional weights, temperature monitoring data and loudness monitoring data form a partitioned monitoring set, and the partitioned monitoring set is uploaded to a database and a cloud platform;
in the embodiment of the invention, by carrying out modularized treatment on different areas of intelligent mechanical production, reliable data support can be provided for carrying out differentiated analysis on the production of different subsequent mechanical equipment; in addition, the method is different from the prior art in that the method is divided manually or the occupied area is divided, and in the embodiment of the invention, the accuracy and the diversity of the division can be effectively improved by dividing the land by the corresponding functions of the mechanical equipment.
The processing analysis module is used for preprocessing and analyzing various data of the partition monitoring centralized monitoring statistics, and comprises the following steps:
acquiring temperature monitoring data and loudness monitoring data, respectively setting real-time temperature in the temperature monitoring data and real-time loudness in the loudness monitoring data as ordinate, and simultaneously setting real-time Beijing time as abscissa to establish a temperature monitoring table and a loudness monitoring table;
connecting the real-time temperature in the temperature monitoring table to obtain a monitored temperature curve, and connecting the real-time loudness in the loudness monitoring table to obtain a monitored loudness curve;
respectively matching the monitored temperature curve and the monitored loudness curve with a preset temperature warning value and a preset loudness warning value; the temperature alert value and the loudness alert value may be obtained based on big data of the machine production;
if the monitored temperature curve does not exceed the temperature warning value and the monitored loudness curve does not exceed the loudness warning value, generating a normal signal, and monitoring the production state of the corresponding mechanical equipment according to the normal signal;
if the monitored temperature curve exceeds the temperature warning value or the monitored loudness curve exceeds the loudness warning value, generating an early warning signal, and analyzing and evaluating the production state of the corresponding mechanical equipment according to the early warning signal.
In the embodiment of the invention, the temperature monitoring table and the loudness monitoring table are constructed by the real-time temperature and the real-time loudness of the monitoring statistics to preprocess the data, so that the change of the corresponding data item can be intuitively obtained, and reliable data support can be provided for the abnormal analysis of the subsequent data item.
The abnormality analysis module is used for analyzing and evaluating the production state of the corresponding mechanical equipment according to the early warning signal, and comprises the following steps:
counting the total times of the monitoring temperature curve exceeding the temperature warning value and marking the total times as a first warning total times WZ; and counting the total times of the monitoring loudness curve exceeding the loudness warning value and marking the total times as a second warning total times XZ;
counting the total duration of the monitoring temperature curve exceeding the temperature warning value each time and marking the total duration as the temperature warning duration WS; and counting the total duration of the loudness curve exceeding the duration of the loudness warning value each time and marking the duration as the loudness warning duration XS; the units of the temperature warning duration and the loudness warning duration are minutes;
counting the maximum difference value between the monitored temperature curve and the temperature warning value after the monitored temperature curve exceeds the temperature warning value each time, and marking the maximum difference value as a temperature difference value WC; and counting and monitoring the maximum difference value between the loudness curve and the loudness warning value after the loudness curve exceeds the loudness warning value each time, and marking the maximum difference value as a loudness difference value XC;
extracting the numerical values of all the marked data, integrating the numerical values in parallel, and obtaining the abnormal persistence YCD corresponding to the corresponding mechanical equipment through calculation; the calculation formula of the anomaly duration YCD is:
wherein c1, c2, c3 and c4 are preset different proportion coefficients, and c3 is more than 0 and less than c1 and less than c4 and less than c2, c1 can take on a value of 2.437, c2 can take on a value of 4.653, c3 can take on a value of 1.325 and c4 can take on a value of 3.641; ZQ is the corresponding function weight of the mechanical equipment;
the abnormal persistence is a numerical value for integrating various data of the mechanical equipment during production to integrally evaluate the production state thereof; the larger the abnormality duration is, the more abnormal the production state of the corresponding mechanical equipment is;
when the production state of the corresponding mechanical equipment is analyzed according to the abnormality persistence, the corresponding abnormality persistence threshold is obtained according to the function weight of the mechanical equipment, and the abnormality persistence is matched with the abnormality persistence threshold;
if the abnormality duration is smaller than the abnormality duration threshold, judging that the production state of the corresponding mechanical equipment is normal and generating a first differential signal;
if the abnormality duration is not less than the abnormality duration threshold and is not greater than Y of the abnormality duration threshold, wherein Y is a real number greater than one hundred, judging that the production state of the corresponding mechanical equipment is slightly abnormal and generating a second differential signal, and marking the corresponding mechanical equipment as first equipment according to the second differential signal;
if the abnormality duration is greater than Y of the abnormality duration threshold, judging that the production state of the corresponding mechanical equipment is moderately abnormal and generating a third differential signal, and marking the corresponding mechanical equipment as second equipment according to the third differential signal;
the abnormal duration and the corresponding first, second and third aliquoting signals form first state analysis data, and the first state analysis data is uploaded to a database and a cloud platform.
In the embodiment of the invention, the abnormal persistence is obtained by integrating various data in the mechanical production aspect, the production state of the mechanical production can be analyzed and evaluated integrally from the abnormal aspect based on the abnormal persistence, and compared with the comparison and output of single data items in the existing scheme, the embodiment of the invention can effectively improve the integral effect of the abnormal aspect on the production state analysis of the mechanical production.
Example two
On the basis of the first embodiment, the method further comprises the following steps:
the production evaluation module is used for monitoring and evaluating the working states of different mechanical equipment after overhaul and comprises the following components:
marking the time after the mechanical equipment is overhauled as first time, wherein the unit of the first time is hour, counting the time length of the mechanical equipment after the mechanical equipment is started to work according to the first time, and marking the time length as the total working time length GS; the unit of the total working time length is hours;
counting the total number of parts produced by the mechanical equipment in the total working time and marking the total number as BZ;
extracting the numerical values of all the marked data, integrating the numerical values in parallel, and obtaining the production-like estimated degree SZD corresponding to the mechanical equipment through calculation; the calculation formula of the production state estimation degree SZD is as follows:
SZD＝ZQ×(g1×CS+g2×BZ+a)
wherein g1 and g2 are preset different proportion coefficients, g1 is more than 1 and less than g2, g1 can take the value of 1.563, and g2 can take the value of 3.217; alpha is an error compensation factor and can take a value of 1.3275; the error compensation factor is used for improving the accuracy of the production state estimation calculation, can be obtained by training the existing production big data, and can be customized according to experience;
when the production state of the corresponding mechanical equipment is analyzed according to the production state estimation, the corresponding production state estimation range is obtained according to the function weight of the mechanical equipment, and the production state estimation is matched with the production state estimation range;
if the production state estimation degree is smaller than the minimum value of the production state estimation range, judging that the production state of the corresponding mechanical equipment is normal and generating a first life signal;
if the production state estimation degree is not smaller than the minimum value of the production state estimation range and not larger than the maximum value of the production state estimation range, judging that the production state of the corresponding mechanical equipment is slightly abnormal, generating a second raw signal, and marking the corresponding mechanical equipment as third equipment according to the second raw signal;
if the production state estimation degree is larger than the maximum value of the production state estimation range, judging that the production state of the corresponding mechanical equipment is moderately abnormal, generating a third raw signal, and marking the corresponding mechanical equipment as fourth equipment according to the third raw signal;
the production state estimation degree, the corresponding first, second and third physiological signals form second state analysis data, and the second state analysis data is uploaded to a database and a cloud platform.
In the embodiment of the invention, the production state of the mechanical equipment is analyzed and evaluated from the operation aspect through the production state estimation by carrying out monitoring statistics on various data after the overhaul of the mechanical equipment and integrating the production state estimation in parallel, so that the abnormality of the mechanical equipment and the alarm prompt can be timely and efficiently found, and the overall effect of the monitoring and analysis of the mechanical equipment in the operation aspect can be effectively improved.
Example III
On the basis of the first embodiment and the second embodiment, the method further comprises the following steps:
the production control module is used for combining the first state analysis data and the second state analysis data to dynamically control the production of each mechanical device in the intelligent mechanical production process in sequence; comprising the following steps:
when the production of each mechanical device in the intelligent mechanical production process is dynamically controlled in sequence, traversing the first state analysis data and the second state analysis data respectively;
if the second differential signal or the second raw signal exists in the traversing results of the first state analysis data and the second state analysis data, preparing to execute inspection maintenance on the corresponding mechanical equipment;
if the third differential signal or the third raw signal exists in the traversing results of the first state analysis data and the second state analysis data, immediately performing inspection maintenance on the corresponding mechanical equipment.
In the embodiment of the invention, the production states of the mechanical equipment are integrally evaluated by combining the results of monitoring and integrating in different aspects, so that the accuracy of monitoring and analyzing the mechanical equipment can be effectively improved, and different inspection and maintenance schemes can be timely and efficiently implemented on the mechanical equipment with different abnormal degrees.
It should be noted that, the formulas related in the foregoing are all formulas with dimensions removed and numerical values calculated, and are a formula closest to the actual situation obtained by software simulation by collecting a large amount of data, and the scaling factor in the formulas and each preset threshold value in the analysis process are set by those skilled in the art according to the actual situation or obtained by simulation of a large amount of data.
In the several embodiments provided by the present invention, it should be understood that the disclosed system may be implemented in other ways. For example, the above-described embodiments of the invention are merely illustrative, and for example, the division of modules is merely a logical function division, and other manners of division may be implemented in practice.
The modules illustrated as separate components may or may not be physically separate, and components shown as modules may or may not be physical modules, i.e., may be located in one place, or may be distributed over a plurality of network modules. Some or all of the modules may be selected according to actual needs to achieve the purpose of the solution of this embodiment.
In addition, each functional module in each embodiment of the present invention may be integrated into one processing module, or each module may exist alone physically, or two or more modules may be integrated into one module. The integrated modules may be implemented in hardware or in hardware plus software functional modules.
It will be evident to those skilled in the art that the invention is not limited to the details of the foregoing illustrative embodiments, and that the present invention may be embodied in other specific forms without departing from the essential characteristics thereof.
Finally, it should be noted that the above-mentioned embodiments are merely for illustrating the technical solution of the present invention and not for limiting the same, and although the present invention has been described in detail with reference to the preferred embodiments, it should be understood by those skilled in the art that modifications and equivalents may be made to the technical solution of the present invention without departing from the spirit and scope of the technical solution of the present invention.
Claims (10)
1. Intelligent mechanical production monitoring management and control system based on thing networking, its characterized in that includes: the monitoring and dividing module is used for carrying out modularization processing on different areas of intelligent mechanical production and carrying out data statistics of different dimensions to obtain a divided monitoring set;
the processing analysis module is used for preprocessing and analyzing various data of the partition monitoring centralized monitoring statistics, and comprises the following steps:
establishing a temperature monitoring table and a loudness monitoring table according to the temperature monitoring data and the loudness monitoring data; connecting the real-time temperature in the temperature monitoring table to obtain a monitored temperature curve, and connecting the real-time loudness in the loudness monitoring table to obtain a monitored loudness curve; respectively matching the monitored temperature curve and the monitored loudness curve with a preset temperature warning value and a preset loudness warning value;
if the monitored temperature curve does not exceed the temperature warning value and the monitored loudness curve does not exceed the loudness warning value, generating a normal signal, and monitoring the production state of the corresponding mechanical equipment according to the normal signal;
if the monitored temperature curve exceeds the temperature warning value or the monitored loudness curve exceeds the loudness warning value, generating an early warning signal;
the abnormality analysis module is used for analyzing and evaluating the production state of the corresponding mechanical equipment according to the early warning signal to obtain first state analysis data containing abnormality duration and first, second and third different signals, and uploading the first state analysis data to the database and the cloud platform;
the production evaluation module is used for monitoring and evaluating the working states of different mechanical equipment after overhaul to obtain second state analysis data comprising the production state estimation degree and the first, second and third biological signals, and uploading the second state analysis data to the database and the cloud platform;
and the production control module is used for combining the first state analysis data and the second state analysis data to dynamically control the production of each mechanical device in the intelligent mechanical production process in sequence.
2. The intelligent mechanical production monitoring and control system based on the internet of things according to claim 1, wherein the working steps of the monitoring and dividing module comprise:
marking and dividing the area where the mechanical equipment is located according to the functions of intelligent mechanical production, and simultaneously matching the functions of intelligent mechanical production with a function weight table pre-stored in a database to obtain corresponding function weights and associating the corresponding function weights with the divided area;
the method comprises the steps of monitoring and counting real-time temperature and real-time loudness through a sensor on preset monitoring points on mechanical equipment, and arranging and combining the monitoring points according to time sequence to obtain temperature monitoring data and loudness monitoring data;
and forming a division monitoring set by the plurality of divided areas and corresponding functional weights, temperature monitoring data and loudness monitoring data, and uploading the division monitoring set to a database and a cloud platform.
3. The intelligent mechanical production monitoring and controlling system based on the internet of things according to claim 1, wherein temperature monitoring data and loudness monitoring data are acquired, real-time temperature in the temperature monitoring data and real-time loudness in the loudness monitoring data are respectively set as ordinate, and meanwhile real-time Beijing time is set as abscissa to establish a temperature monitoring table and a loudness monitoring table.
4. The intelligent mechanical production monitoring and controlling system based on the internet of things according to claim 1, wherein the working steps of the anomaly analysis module comprise:
counting the total times of the monitoring temperature curve exceeding the temperature warning value and marking the total times as a first warning total times WZ; and counting the total times of the monitoring loudness curve exceeding the loudness warning value and marking the total times as a second warning total times XZ;
counting the total duration of the monitoring temperature curve exceeding the temperature warning value each time and marking the total duration as the temperature warning duration WS; and counting the total duration of the loudness curve exceeding the duration of the loudness warning value each time and marking the duration as the loudness warning duration XS;
counting the maximum difference value between the monitored temperature curve and the temperature warning value after the monitored temperature curve exceeds the temperature warning value each time, and marking the maximum difference value as a temperature difference value WC; and counting and monitoring the maximum difference value between the loudness curve and the loudness warning value after the loudness curve exceeds the loudness warning value each time, and marking the maximum difference value as a loudness difference value XC;
and extracting the numerical values of all the marked data, integrating the numerical values in parallel, and obtaining the abnormal duration YCD corresponding to the corresponding mechanical equipment through calculation.
5. The intelligent mechanical production monitoring and controlling system based on the internet of things according to claim 4, wherein the calculation formula of the abnormal duration YCD is:
wherein c1, c2, c3 and c4 are preset different proportion coefficients, and c3 is more than 0 and less than c1 and c4 is more than 0 and less than c2; ZQ is the corresponding function weight of the mechanical equipment;
when the production state of the corresponding mechanical equipment is analyzed according to the abnormal persistence, a corresponding abnormal persistence threshold value is obtained according to the function weight of the mechanical equipment, and the abnormal persistence threshold value are matched to obtain a first differential signal, a second differential signal and a third differential signal.
6. The intelligent machine production monitoring and control system based on the internet of things according to claim 5, wherein if the anomaly persistence is less than an anomaly persistence threshold value, generating a first differential signal; if the abnormality duration is not less than the abnormality duration threshold and is not greater than Y of the abnormality duration threshold, Y is a real number greater than one hundred, generating a second differential signal; if the anomaly duration is greater than Y of the anomaly duration threshold, a third differential signal is generated.
7. The intelligent mechanical production monitoring and control system based on the internet of things according to claim 1, wherein the working steps of the production evaluation module include:
marking the time after the mechanical equipment is overhauled as first time, counting the time length of the mechanical equipment after the mechanical equipment is started to work according to the first time, and marking the time length as the total working time length GS;
counting the total number of parts produced by the mechanical equipment in the total working time and marking the total number as BZ;
and extracting the numerical values of all the marked data, integrating the numerical values in parallel, and obtaining the production-like estimated degree SZD corresponding to the mechanical equipment through calculation.
8. The intelligent mechanical production monitoring and controlling system based on the internet of things according to claim 7, wherein the calculation formula of the production state estimation SZD is:
SZD＝ZQ×(g1×GS+g2×BZ+α)
wherein g1 and g2 are preset different proportion coefficients, and g1 is more than 1 and g2 is more than 1; alpha is an error compensation factor;
when the production state of the corresponding mechanical equipment is analyzed according to the production state estimation degree, a corresponding production state estimation range is obtained according to the functional weight of the mechanical equipment, and the production state estimation degree is matched with the production state estimation range to obtain a first production signal, a second production signal and a third production signal.
9. The intelligent machine production monitoring and control system based on the internet of things according to claim 8, wherein if the production state estimation is smaller than the minimum value of the production state estimation range, generating a first production state signal; generating a second raw signal if the production state estimation degree is not less than the minimum value of the production state estimation range and not greater than the maximum value of the production state estimation range; if the production state estimation degree is larger than the maximum value of the production state estimation range, a third production state signal is generated.
10. The intelligent machine production monitoring and control system based on the internet of things according to claim 1, wherein the dynamic control of the production of each machine in the intelligent machine production process comprises the preparation of performing inspection maintenance on slightly abnormal machine equipment and the immediate performance of inspection maintenance on slightly abnormal machine equipment.
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Cited By (2)
| Publication number | Priority date | Publication date | Assignee | Title |
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| CN117094603A (en) * | 2023-10-17 | 2023-11-21 | 山东卫康生物医药科技有限公司 | Intelligent control-based automatic management system for medical functional food production line |
| CN117309042A (en) * | 2023-09-08 | 2023-12-29 | 纬创软件(武汉)有限公司 | Intelligent manufacturing data real-time monitoring method and system based on Internet of things technology |
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Cited By (3)
| Publication number | Priority date | Publication date | Assignee | Title |
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| CN117309042A (en) * | 2023-09-08 | 2023-12-29 | 纬创软件(武汉)有限公司 | Intelligent manufacturing data real-time monitoring method and system based on Internet of things technology |
| CN117094603A (en) * | 2023-10-17 | 2023-11-21 | 山东卫康生物医药科技有限公司 | Intelligent control-based automatic management system for medical functional food production line |
| CN117094603B (en) * | 2023-10-17 | 2024-01-05 | 山东卫康生物医药科技有限公司 | Intelligent control-based automatic management system for medical functional food production line |
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