CN115390513A - Remote intelligent monitoring system of automatic laminating machine - Google Patents
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Abstract
The invention discloses a remote intelligent monitoring system of an automatic laminating machine, which belongs to the field of laminating machines and is used for solving the problems that the laminating effect of a product needs to be checked manually in real time and the abnormal operation condition cannot be perceived by the automatic laminating machine.
Description
Technical Field
The invention belongs to the field of laminating machines, relates to a remote monitoring technology, and particularly relates to a remote intelligent monitoring system of an automatic laminating machine.
Background
The laminating machine comprises large mechanical parts such as an unreeling device, a gluing device, a conveying and pressurizing device, a driving motor and the like. The full-automatic laminating machine is mainly designed aiming at the production characteristics of small displays and small touch control components, is one of necessary equipment for producing liquid crystal displays, and is used for attaching polaroids on the front and back surfaces of a formed liquid crystal glass substrate according to polarization angles in the post-process production of liquid crystal display. The film/B glass substrate can be used for pressure-sensitive bonding of various materials such as an acrylic panel and the like, and has the advantages of high efficiency, convenience in alignment, high product yield and the like.
Among the prior art, automatic rigging machine needs artifical real-time observation and looks over the laminating effect of product when using, when spending a large amount of manpowers, still can't ensure the laminating effect and the laminating efficiency of product, also can't discover the unusual operation conditions of automatic rigging machine simultaneously, for this reason, we provide a remote intelligent monitoring system of automatic rigging machine.
Disclosure of Invention
Aiming at the defects in the prior art, the invention aims to provide a remote intelligent monitoring system of an automatic laminating machine.
The technical problem to be solved by the invention is as follows:
how to realize the remote intelligent monitoring to the automatic laminating machine based on laminating efficiency and operation condition.
The purpose of the invention can be realized by the following technical scheme:
a remote intelligent monitoring system of an automatic laminating machine comprises a user terminal, a model matching module, a data acquisition module, an operation analysis module, a monitoring judgment module, a labeling analysis module and a server, wherein the user terminal is used for inputting the model of the laminating machine and sending the model to the server, and the server sends the model to the model matching module;
the model matching module is in data connection with a database, and standard operating parameters of different models of laminating machines are stored in the database; the model matching module obtains standard operating parameters of the laminating machine according to model matching, and sends the standard operating parameters to the server, and the server sends the standard operating parameters to the operation analysis module and the labeling analysis module;
the data acquisition module is used for acquiring real-time operation parameters of the laminating machine and sending the real-time operation parameters to the server, and the server sends the real-time operation parameters to the operation analysis module and the labeling analysis module;
the operation analysis module is used for analyzing the operation condition of the laminating machine, analyzing to obtain an operation power deviation value of the laminating machine in a remote monitoring time period and feeding the operation power deviation value back to the server, and the server sends the operation power deviation value of the laminating machine in the remote monitoring time period to the monitoring judgment module;
the labeling analysis module is used for analyzing labeling work of the laminator, analyzing to obtain a labeling difference value of the laminator in a remote monitoring time period and feeding the labeling difference value back to the server, and the server sends the labeling difference value of the laminator in the remote monitoring time period to the monitoring judgment module;
the monitoring and judging module is used for carrying out remote monitoring and judging on the working condition of the laminating machine, and a working abnormal signal or a working normal signal generated by working is fed back to the server;
if the server receives the abnormal working signal, the abnormal working signal is forwarded to the user terminal;
and if the server receives the working normal signal, no operation is performed.
Further, the standard operation parameters comprise a standard operation power interval and a standard labeling number interval;
the real-time operation parameters comprise real-time operation power and real-time label number.
Further, the analysis process of the operation analysis module is specifically as follows:
the method comprises the following steps: marking the laminating machine as u, u =1,2, … …, and z are positive integers; setting a remote monitoring time period of the laminating machine, and setting a plurality of time points Tut, t =1,2, … …, x and x are positive integers in the remote monitoring time period, wherein t represents the number of the time points;
step two: obtaining the operating power of the laminating machine at each time point, and marking the operating power at each time point as GL Tut ；
Step three: counting the number of the time points, and recording the number of the time points as the number of time points DSu;
by the formula
Calculating to obtain the average running power JGLu of the laminating machine in the remote monitoring time period:
step four: comparing the average running power of the laminating machine in a remote monitoring period with the standard running power;
if the average running power is in the standard running power interval, calibrating the average running power as the standard running power;
if the average running power is in the standard running power interval, calibrating the standard running power interval as the standard running power;
step five: calculating the difference value of the operating power of the laminating machine at each time point and the standard operating power to obtain the operating power difference GLC of the laminating machine at each time point Tut ；
Step six: and adding and summing the running power difference at each time point and dividing the sum by the number of the time points to obtain a running power deviation value GPCu of the laminating machine in a remote monitoring period.
Further, the analysis process of the labeling analysis module is as follows:
step S1: performing time interval segmentation on the remote monitoring time interval, wherein the time interval segmentation takes minutes as a unit to obtain a plurality of minute time intervals Fui, i =1,2, … …, v and v are positive integers, and i represents the number of the minute time intervals;
step S2: the starting time of each minute period is used as the labeling starting counting time, and the ending time of each minute period is used as the labeling ending counting time;
and step S3: obtaining the labeling number of each minute period, and marking the labeling number as TB Fui (ii) a Counting the number of the minute time periods, and recording the number of the minute time periods as the time period number SSu;
and step S4: by the formula
Calculating to obtain labeling average number JTBu of the laminating machine in a remote monitoring time period;
step S5: and calculating the difference value between the labeling average number of the laminator and the standard labeling number interval in the remote monitoring time period to obtain the labeling difference value TBCu of the laminator in the remote monitoring time period.
Further, the calculation process of the labeling difference value of the laminating machine in the remote monitoring time period is as follows:
calculating a difference value of the labeling average JTBu of the laminator in the remote monitoring period and the upper limit value of the standard labeling number interval, calculating a difference value of the labeling average JTBu of the laminator in the remote monitoring period and the lower limit value of the standard labeling number interval, and adding the two groups of difference values to obtain an average value to obtain a labeling difference value TBCu.
Further, the working process of the monitoring and determining module is as follows:
step SS1: acquiring an operating power deviation value GPCu and a labeling difference value TBCu of the laminating machine in a remote monitoring period;
step SS2: acquiring an operating power deviation threshold value and a labeling difference threshold value stored in a server;
if the running power deviation value is greater than or equal to the running power deviation threshold value or the labeling difference value is greater than or equal to the labeling difference threshold value, generating a working abnormal signal;
if the operation power deviation value is smaller than the operation power deviation threshold value and the labeling difference value is smaller than the labeling difference threshold value, entering the next step;
and step SS3: calculating by a formula to obtain an abnormal value YCu of the laminating machine in a remote monitoring time period;
and step SS4: if the abnormal value of the laminating machine in the remote monitoring time period is larger than or equal to the abnormal threshold, generating a working abnormal signal;
and if the abnormal value of the laminating machine in the remote monitoring time period is smaller than the abnormal threshold, generating a normal working signal.
Further, a calculation formula of an abnormal value of the laminating machine in the remote monitoring time period is as follows:
YCu = GPCu × α + TBCu × β; in the formula, both alpha and beta are weight coefficients with fixed values, and the values of both alpha and beta are greater than zero.
Compared with the prior art, the invention has the beneficial effects that:
when the automatic laminating machine is remotely monitored, a user terminal inputs the model of the laminating machine, the model matching module obtains standard operating parameters of the laminating machine according to model matching, the operating condition of the laminating machine is analyzed through the operation analysis module, the operating power deviation value of the laminating machine in a remote monitoring time period is obtained through analysis, labeling work of the laminating machine is analyzed through the labeling analysis module, the labeling difference value of the laminating machine in the remote monitoring time period is analyzed, the operating power deviation value and the labeling difference value of the laminating machine in the remote monitoring time period are sent to the monitoring judgment module, the monitoring judgment module conducts remote monitoring judgment on the working condition of the laminating machine, and a working abnormal signal or a working normal signal is judged to be generated.
Drawings
In order to facilitate understanding for those skilled in the art, the present invention will be further described with reference to the accompanying drawings.
FIG. 1 is an overall system block diagram of the present invention.
Detailed Description
The technical solutions of the present invention will be described clearly and completely with reference to the following embodiments, and it should be understood that the described embodiments are only a part of the embodiments of the present invention, and not all of the embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
Referring to fig. 1, a remote intelligent monitoring system for an automatic laminating machine includes a user terminal, a model matching module, a data acquisition module, an operation analysis module, a monitoring determination module, a labeling analysis module, and a server;
before remote monitoring, the user terminal is used for registering a login system after personal information is input by a worker and sending the personal information to a server; the personal information comprises the name of a worker, the mobile phone number of real-name authentication and the like;
meanwhile, the server is in signal connection with a plurality of laminating machines;
during remote monitoring, the user terminal is used for inputting the model of the laminating machine and sending the model to the server, and the server sends the model to the model matching module;
the model matching module is in data connection with a database, and standard operating parameters of different models of laminating machines are stored in the database;
specifically, the standard operating parameters include a standard operating power interval, a standard labeling number interval, and the like;
the model matching module obtains standard operating parameters of the laminating machine according to model matching, and sends the standard operating parameters to the server, and the server sends the standard operating parameters to the operation analysis module and the labeling analysis module;
the data acquisition module is used for acquiring real-time operation parameters of the laminating machine and sending the real-time operation parameters to the server, and the server sends the real-time operation parameters to the operation analysis module and the labeling analysis module;
specifically, the real-time operation parameters include real-time operation power, real-time labeling number, and the like;
the operation analysis module is used for analyzing the operation condition of the laminating machine, and the analysis process is as follows:
the method comprises the following steps: marking the laminating machine as u, u =1,2, … …, z and z are positive integers; setting a remote monitoring time period of the laminating machine, and setting a plurality of time points Tut, t =1,2, … …, x and x are positive integers in the remote monitoring time period, wherein t represents the number of the time points;
step two: obtaining the operating power of the laminating machine at each time point, and marking the operating power at each time point as GL Tut ；
Step three: counting the number of the time points, and recording the number of the time points as the number of time points DSu;
by the formula
Calculating to obtain the average running power JGLu of the laminating machine in the remote monitoring time period:
step four: comparing the average running power of the laminating machine in a remote monitoring period with the standard running power;
if the average running power is in the standard running power interval, calibrating the average running power as the standard running power;
if the average running power is in the standard running power interval, calibrating the standard running power interval as the standard running power;
specifically, when the standard operating power interval is defined as the standard operating power, the upper limit value and the lower limit value of the standard operating power interval are added and averaged, and the averaged value is taken as the standard operating power;
step five: calculating the difference value of the operating power of the laminating machine at each time point and the standard operating power to obtain the operating power difference GLC of the laminating machine at each time point Tut ；
Step six: adding and summing the running power difference at each time point and dividing the sum by the number of the time points to obtain a running power deviation value GPCu of the laminating machine in a remote monitoring period, wherein the calculation formula is as follows:
GPCu=（GLC Tu1 +GLC Tu2 +……+GLC Tux ）/DSu；
the operation analysis module feeds back an operation power deviation value GPCu of the laminating machine in a remote monitoring time period to the server, and the server sends the operation power deviation value GPCu of the laminating machine in the remote monitoring time period to the monitoring judgment module;
the labeling analysis module is used for analyzing labeling work of the laminating machine, and the analysis process specifically comprises the following steps:
step S1: performing time interval segmentation on the remote monitoring time interval, wherein the time interval segmentation takes minutes as a unit to obtain a plurality of minute time intervals Fui, i =1,2, … …, v and v are positive integers, and i represents the number of the minute time intervals;
step S2: the starting time of each minute period is used as the labeling starting counting time, and the ending time of each minute period is used as the labeling ending counting time;
it can be understood that the labeling end count time of the previous minute period is the labeling start count time of the next minute period;
and step S3: obtaining the labeling number of each minute period and marking the labeling number as TB Fui (ii) a Counting the number of the minute time periods, and recording the number of the minute time periods as the time period number SSu;
and step S4: by the formula
Calculating to obtain labeling average number JTBu of the laminating machine in a remote monitoring time period;
step S5: calculating the difference value between the labeling average number of the laminator and the standard labeling number interval in the remote monitoring period to obtain the labeling difference value TBCu of the laminator in the remote monitoring period, specifically:
calculating a difference value of the labeling average JTBu of the laminating machine in a remote monitoring period and an upper limit value of a standard labeling number interval, calculating a difference value of the labeling average JTBu of the laminating machine in the remote monitoring period and a lower limit value of the standard labeling number interval, and adding and averaging the two groups of difference values to obtain a labeling difference value TBCu;
the labeling analysis module feeds back a labeling difference value TBCu of the laminator in a remote monitoring time period to the server, and the server sends the labeling difference value TBCu of the laminator in the remote monitoring time period to the monitoring judgment module;
the monitoring and judging module is used for combining the running power deviation value and the labeling difference value of the laminating machine in the remote monitoring time period to remotely monitor and judge the working condition of the laminating machine, and the working process is as follows:
step SS1: obtaining the running power deviation value GPCu and the labeling difference value TBCu of the laminating machine in the remote monitoring period through the calculation;
step SS2: acquiring an operating power deviation threshold value and a labeling difference threshold value which are stored in a server;
if the running power deviation value is greater than or equal to the running power deviation threshold value or the labeling difference value is greater than or equal to the labeling difference threshold value, generating a working abnormal signal;
if the operation power deviation value is smaller than the operation power deviation threshold value and the labeling difference value is smaller than the labeling difference threshold value, entering the next step;
step SS3: calculating an abnormal value YCu of the laminating machine in a remote monitoring time period through a formula YCu = GPCu multiplied by alpha + TBCu multiplied by beta; in the formula, α and β are both weight coefficients with fixed values, and the values of α and β are both greater than zero:
and step SS4: if the abnormal value of the laminating machine in the remote monitoring time period is larger than or equal to the abnormal threshold, generating a working abnormal signal;
if the abnormal value of the laminating machine in the remote monitoring time period is smaller than the abnormal threshold, generating a normal working signal;
the monitoring and judging module feeds back the working abnormal signal or the working normal signal to the server;
if the server receives the abnormal working signal, the abnormal working signal is forwarded to the user terminal;
and if the server receives the working normal signal, no operation is performed.
A remote intelligent monitoring system of an automatic laminating machine comprises a user terminal, a server, a model matching module, a database and a server, wherein the model of the laminating machine is input by the user terminal and is sent to the server during remote monitoring, the server sends the model to the model matching module, the model matching module is in data connection with the database, standard operating parameters of laminating machines of different models are stored in the database, the model matching module obtains the standard operating parameters of the laminating machine according to model matching and sends the standard operating parameters to the server, and the server sends the standard operating parameters to an operation analysis module and a labeling analysis module;
the real-time operation parameters of the laminating machine are acquired through the data acquisition module and are sent to the server, and the server sends the real-time operation parameters to the operation analysis module and the labeling analysis module;
analyzing the operation condition of the laminating machine through an operation analysis module, marking the laminating machine as u, setting a remote monitoring time period of the laminating machine, setting a plurality of time points Tut in the remote monitoring time period, then taking the operation power of the laminating machine at each time point, and marking the operation power of each time point as GL Tut Meanwhile, counting the number of time points, recording the number of the time points as the number of time points DSu, and combining a formula
Calculating to obtain the average running power JGLu of the laminating machine in a remote monitoring period, comparing the average running power of the laminating machine in the remote monitoring period with the standard running power, calibrating the average running power as the standard running power if the average running power is in a standard running power interval, calibrating the standard running power interval as the standard running power if the average running power is in the standard running power interval, calculating the difference value between the running power of the laminating machine at each time point and the standard running power, and obtaining the running power difference GLC of the laminating machine at each time point Tut The running power difference at each time point is added and summed and divided by the number of time points to obtain a running power deviation value GPCu of the laminating machine in the remote monitoring time period, the running analysis module feeds the running power deviation value GPCu of the laminating machine in the remote monitoring time period back to the server, and the server sends the running power deviation value GPCu of the laminating machine in the remote monitoring time period to the monitoring judgment module;
analyzing the labeling work of the laminating machine through a labeling analysis module, performing time interval segmentation on the remote monitoring time interval, wherein the time interval segmentation takes minutes as a unit to obtain a plurality of minute time intervals Fui, the starting time of each minute time interval is used as the labeling starting counting time, the ending time of each minute time interval is used as the labeling ending counting time, and then the labeling number TB of each minute time interval is obtained Fui Counting the number of minute periods, and comparing the minute periodsThe number of (2) is recorded as the number of time segments SSu, and is combined with the formula
Calculating to obtain labeling mean number JTBu of the laminating machine in a remote monitoring time period, calculating a difference value between the labeling mean number of the laminating machine in the remote monitoring time period and a standard labeling number interval to obtain a labeling difference value TBCu of the laminating machine in the remote monitoring time period, feeding the labeling difference value TBCu of the laminating machine in the remote monitoring time period back to a server by a labeling analysis module, and sending the labeling difference value TBCu of the laminating machine in the remote monitoring time period to a monitoring judgment module by the server;
the method comprises the steps that a monitoring judging module carries out remote monitoring judgment on the working condition of the laminating machine by combining an operating power deviation value and a labeling difference value of the laminating machine in a remote monitoring time period, an operating power deviation value GPCu and a labeling difference value TBCU of the laminating machine in the remote monitoring time period are obtained, then an operating power deviation threshold value and a labeling difference threshold value stored in a server are obtained, if the operating power deviation value is larger than or equal to the operating power deviation threshold value or the labeling difference value is larger than or equal to the labeling difference threshold value, a working abnormal signal is generated, if the operating power deviation value is smaller than the operating power deviation threshold value and the labeling difference value is smaller than the labeling difference threshold value, an abnormal value YCu of the laminating machine in the remote monitoring time period is obtained through calculation of a formula YCu = GPCu x alpha + TBCu x beta, if the abnormal value of the laminating machine in the remote monitoring time period is larger than or equal to the abnormal threshold value, a working abnormal signal is generated, if the abnormal value of the laminating machine in the remote monitoring time period is smaller than or the abnormal threshold value, a working normal signal is generated, the monitoring judging module feeds back the working abnormal signal to the server, if the server receives the working abnormal signal, a user terminal forwards the abnormal signal to the server, and the server does not carry out the abnormal operation if the abnormal signal.
The above formulas are all dimensionless values and calculated, the formula is a formula for obtaining the latest real situation by collecting a large amount of data and performing software simulation, the preset parameters in the formula are set by the technical personnel in the field according to the actual situation, the weight coefficient and the scale coefficient are specific values obtained by quantifying each parameter, so that the subsequent comparison is convenient, and the proportional relation between the parameters and the quantified values can be obtained as long as the proportional relation between the parameters and the quantified values is not influenced.
The preferred embodiments of the invention disclosed above are intended to be illustrative only. The preferred embodiments are not intended to be exhaustive or to limit the invention to the precise forms disclosed. Obviously, many modifications and variations are possible in light of the above teaching. The embodiments were chosen and described in order to best explain the principles of the invention and the practical application, to thereby enable others skilled in the art to best understand the invention for and utilize the invention. The invention is limited only by the claims and their full scope and equivalents.
Claims (7)
1. The remote intelligent monitoring system for the automatic laminating machine is characterized by comprising a user terminal, a model matching module, a data acquisition module, an operation analysis module, a monitoring judgment module, a labeling analysis module and a server, wherein the user terminal is used for inputting the model of the laminating machine and sending the model to the server, and the server sends the model to the model matching module;
the model matching module is in data connection with a database, and standard operating parameters of different models of laminating machines are stored in the database; the model matching module obtains standard operating parameters of the laminating machine according to model matching, and sends the standard operating parameters to the server, and the server sends the standard operating parameters to the operation analysis module and the labeling analysis module;
the data acquisition module is used for acquiring real-time operation parameters of the laminating machine and sending the real-time operation parameters to the server, and the server sends the real-time operation parameters to the operation analysis module and the labeling analysis module;
the operation analysis module is used for analyzing the operation condition of the laminating machine, analyzing to obtain an operation power deviation value of the laminating machine in a remote monitoring time period and feeding the operation power deviation value back to the server, and the server sends the operation power deviation value of the laminating machine in the remote monitoring time period to the monitoring judgment module;
the labeling analysis module is used for analyzing labeling work of the laminator, analyzing to obtain a labeling difference value of the laminator in a remote monitoring time period and feeding the labeling difference value back to the server, and the server sends the labeling difference value of the laminator in the remote monitoring time period to the monitoring judgment module;
the monitoring and judging module is used for carrying out remote monitoring and judging on the working condition of the laminating machine, and a working abnormal signal or a working normal signal generated by working is fed back to the server;
if the server receives the abnormal working signal, the abnormal working signal is forwarded to the user terminal;
and if the server receives the working normal signal, no operation is performed.
2. The remote intelligent monitoring system for the automatic laminating machine according to claim 1, wherein the standard operation parameters comprise a standard operation power interval and a standard labeling number interval;
the real-time operation parameters comprise real-time operation power and real-time label number.
3. The remote intelligent monitoring system for the automatic laminating machine according to claim 1, wherein the analysis process of the operation analysis module is as follows:
the method comprises the following steps: marking the laminating machine as u, u =1,2, … …, z and z are positive integers; setting a remote monitoring time period of the laminating machine, and setting a plurality of time points Tut, t =1,2, … …, x and x are positive integers in the remote monitoring time period, wherein t represents the number of the time points;
step two: obtaining the operating power of the laminating machine at each time point, and marking the operating power at each time point as GL Tut ；
Step three: counting the number of the time points, and recording the number of the time points as the number of time points DSu;
by the formula
Calculating to obtain the distance of the laminating machineAverage operating power JGLu in the monitoring period:
step four: comparing the average running power of the laminating machine in a remote monitoring period with the standard running power;
if the average running power is in the standard running power interval, calibrating the average running power as the standard running power;
if the average running power is in the standard running power interval, calibrating the standard running power interval as the standard running power;
step five: calculating the difference value of the operating power of the laminating machine at each time point and the standard operating power to obtain the operating power difference GLC of the laminating machine at each time point Tut ；
Step six: and adding and summing the running power difference at each time point, and dividing the sum by the number of the time points to obtain a running power deviation value GPCu of the laminating machine in the remote monitoring period.
4. The remote intelligent monitoring system for the automatic laminating machine according to claim 3, wherein the analysis process of the labeling analysis module is as follows:
step S1: performing time interval segmentation on the remote monitoring time interval, wherein the time interval segmentation takes minutes as a unit to obtain a plurality of minute time intervals Fui, i =1,2, … …, v and v are positive integers, and i represents the number of the minute time intervals;
step S2: the starting time of each minute period is used as the labeling starting counting time, and the ending time of each minute period is used as the labeling ending counting time;
and step S3: obtaining the labeling number of each minute period and marking the labeling number as TB Fui (ii) a Counting the number of the minute time periods, and recording the number of the minute time periods as the time period number SSu;
and step S4: by the formula
Calculating to obtain labeling average number JTBu of the laminating machine in a remote monitoring time period;
step S5: and calculating the difference value between the labeling average number of the laminator and the standard labeling number interval in the remote monitoring time period to obtain the labeling difference value TBCu of the laminator in the remote monitoring time period.
5. The remote intelligent monitoring system for the automatic laminating machine according to claim 4, wherein the calculation process of the labeling difference value of the laminating machine in the remote monitoring period is as follows:
calculating a difference value of the labeling average JTBu of the laminating machine in the remote monitoring time period and an upper limit value of a standard labeling number interval, calculating a difference value of the labeling average JTBu of the laminating machine in the remote monitoring time period and a lower limit value of the standard labeling number interval, and adding and averaging the two groups of difference values to obtain a labeling difference value TBCu.
6. The remote intelligent monitoring system for the automatic laminating machine according to claim 4, wherein the working process of the monitoring and judging module is as follows:
step SS1: acquiring an operating power deviation value GPCu and a labeling difference value TBCu of the laminating machine in a remote monitoring period;
step SS2: acquiring an operating power deviation threshold value and a labeling difference threshold value stored in a server;
if the running power deviation value is greater than or equal to the running power deviation threshold value or the labeling difference value is greater than or equal to the labeling difference threshold value, generating a working abnormal signal;
if the operation power deviation value is smaller than the operation power deviation threshold value and the labeling difference value is smaller than the labeling difference threshold value, entering the next step;
and step SS3: calculating an abnormal value YCu of the laminating machine in a remote monitoring time period through a formula;
and step SS4: if the abnormal value of the laminating machine in the remote monitoring time period is larger than or equal to the abnormal threshold, generating a working abnormal signal;
and if the abnormal value of the laminating machine in the remote monitoring time period is smaller than the abnormal threshold, generating a normal working signal.
7. The remote intelligent monitoring system for the automatic laminating machine according to claim 6, wherein a calculation formula of an abnormal value of the laminating machine in the remote monitoring time period is as follows:
YCu = GPCu × α + TBCu × β; in the formula, both alpha and beta are weight coefficients with fixed values, and the values of both alpha and beta are greater than zero.
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