CN115407731A - Production line working state monitoring and fault early warning system and method - Google Patents
Production line working state monitoring and fault early warning system and method Download PDFInfo
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Abstract
The invention provides a system and a method for monitoring the working state of a production line and early warning faults, wherein the system for monitoring the working state of the production line and early warning faults comprises the following steps: the data acquisition module is used for collecting and storing the operation data of the production line; the state monitoring module is used for preprocessing the running data of the production line on the edge side computing equipment, judging whether the production line is abnormal or not according to the processing result, uploading the preprocessed data to the cloud for deep processing and judging whether the production line is abnormal or not according to the processing result; the anomaly alarm module is used for sending an anomaly alarm when the edge side computing device and/or the cloud end judge that the production line is abnormal; and the abnormal autonomous analysis module is used for analyzing the abnormality generated by the production line and taking a countermeasure for eliminating the abnormality.
Description
Technical Field
The invention relates to the technical field of safety of industrial control equipment, in particular to a production line working state monitoring and fault early warning system based on edge cloud cooperation and a production line working state monitoring and fault early warning method based on edge cloud cooperation.
Background
The production line is widely used in the industrial field as an important carrier for industrial production. A large amount of mechanical equipment is arranged on a production line, each process link is buckled, and the abnormity of any workpiece and equipment can cause the shutdown of the whole production line, so that the major economic loss is caused, and the monitoring of the working state of the production line and the fault early warning are very important.
The current production line intelligent remote monitoring system generally directly uploads the operation data of equipment to the cloud end, and then the operation data is processed by the cloud end, and a processing result is returned to the monitoring system. Because the data is not uploaded after being preprocessed, the problems of large calculation load and slow data transmission in the transmission, calculation and storage processes of the data can be caused, and the real-time performance of fault early warning can not be ensured.
Disclosure of Invention
The invention aims to solve the technical problems and provides a production line working state monitoring and fault early warning system and method based on edge cloud cooperation, which can intelligently monitor the working state of a production line, effectively improve the real-time performance of fault early warning, and can autonomously analyze abnormal conditions and take countermeasures.
The technical scheme adopted by the invention is as follows:
a production line working state monitoring and fault early warning system based on edge cloud cooperation comprises: the data acquisition module is used for collecting and storing the operation data of the production line; the state monitoring module is used for preprocessing the running data of the production line on edge side computing equipment, judging whether the production line is abnormal or not according to a processing result, uploading the preprocessed data to a cloud end for deep processing and judging whether the production line is abnormal or not according to the processing result; the abnormity alarm module is used for sending an abnormity alarm when the edge side computing device and/or the cloud end judges that the production line is abnormal; and the abnormal autonomous analysis module is used for analyzing the abnormality of the production line and taking a countermeasure for removing the abnormality.
The operational data of the production line comprises: the production beat of the production line; temperature, pressure, torque and vibration conditions of wearing parts and important driving equipment on the production line; and operating a working log by the human-computer interface.
The data acquisition module is specifically used for monitoring and calculating the production beat through time, and respectively acquiring the temperature, the pressure, the torque and the vibration condition through a temperature sensor, a pressure sensor, a torque sensor and a vibration sensor.
The data acquisition module analyzes the action completion time and the workpiece in-place time of each station of the production line so as to realize time monitoring.
The edge side computing device is a PLC, and the PLC is specifically used for analyzing the operation data of the production line to obtain the current change trend information of the data, judging whether the production line is abnormal or not according to the current change trend information of the data, uploading the current change trend information of the data to the cloud for deep processing, and judging whether the production line is abnormal or not according to a processing result.
The cloud is specifically used for predicting future change trend information of data through a BP neural network prediction model and judging whether the production line is abnormal or not according to the future change trend information of the data.
The abnormal alarm module is specifically configured to receive alarm information of the edge-side computing device or the cloud and send an alarm when the edge-side computing device or the cloud determines that the data change trend is abnormal.
The abnormal autonomous analysis module is specifically used for analyzing abnormal reasons and abnormal time points and taking countermeasures for eliminating the abnormal conditions in the software control layer.
A production line working state monitoring and fault early warning method based on edge cloud cooperation comprises the following steps: collecting and storing the operation data of the production line; preprocessing the operation data of the production line on edge side computing equipment, judging whether the production line is abnormal or not according to a processing result, uploading the preprocessed data to a cloud for deep processing, and judging whether the production line is abnormal or not according to the processing result; when the edge side computing device and/or the cloud end judge that the production line is abnormal, an abnormal alarm is sent out; and analyzing the abnormality of the production line and taking a countermeasure for eliminating the abnormality.
The invention has the beneficial effects that:
according to the invention, the operation data of the production line is preprocessed and judged whether the operation data is abnormal or not through the edge side computing device and the cloud end, and the abnormal condition is autonomously analyzed through the abnormal autonomous analysis module, and a countermeasure for removing the abnormality is taken in time for the abnormal condition, so that the working state of the production line can be intelligently monitored, the real-time performance of fault early warning is effectively improved, and the abnormal condition can be autonomously analyzed and a countermeasure can be taken.
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Fig. 1 is a schematic block diagram of a production line working state monitoring and fault early warning system based on edge cloud coordination according to an embodiment of the present invention;
fig. 2 is a schematic flow chart of a production line working state monitoring and fault early warning method based on edge cloud coordination according to an embodiment of the present invention.
Detailed Description
The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious 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.
As shown in fig. 1, the production line working state monitoring and fault early warning system based on edge cloud coordination according to the embodiment of the present invention includes: the system comprises a data acquisition module 10, a state monitoring module 20, an abnormity alarm module 30 and an abnormity autonomous analysis module 40. The data acquisition module 10 is used for collecting and storing the operation data of the production line; the state monitoring module 20 is configured to preprocess the operation data of the production line on the edge-side computing device, determine whether the production line is abnormal according to a processing result, upload the preprocessed data to the cloud for deep processing, and determine whether the production line is abnormal according to the processing result; the anomaly alarm module 30 is configured to send an anomaly alarm when the edge side computing device and/or the cloud determines that the production line is abnormal; the anomaly automatic analysis module 40 is used for analyzing anomalies occurring in the production line and taking countermeasures for removing the anomalies.
In one embodiment of the present invention, the operation data of the production line collected by the data collection module 10 may include: the production rhythm of the production line, the temperature, pressure, torque and vibration conditions of wearing parts and important driving equipment on the production line and a human-computer interface operation work log.
In one embodiment of the present invention, the production cycle time of the production line can be used as an important criterion for determining whether the working state of the production line is abnormal. For example, if the tact of one production line is 1 minute to output x products, the tact of the production line is collected and stored by the data collection module 10. If the production line outputs x products in 2 minutes, the working state of the production line can be judged to be abnormal.
In an embodiment of the present invention, the data acquisition module 10 is specifically configured to calculate the tact through time monitoring, and respectively acquire the temperature, pressure, torque and vibration conditions through a temperature sensor, a pressure sensor, a torque sensor and a vibration sensor.
In one embodiment of the present invention, the data collection module 10 performs time monitoring by analyzing the completion time of the operation of each station and the in-place time of the workpiece in the production line. Wherein, whether the station action is finished, whether the work piece targets in place can be judged by using the ON time of the sensor.
In an embodiment of the present invention, the status monitoring module 20 is configured to preprocess the operation data of the production line on the edge-side computing device, determine whether the production line is abnormal according to a processing result, upload the preprocessed data to the cloud for deep processing, and determine whether the production line is abnormal according to the processing result. The edge measurement computing equipment is equipment on one side close to an object or a data source, an open platform integrating network, storage, computation and application core capabilities is adopted, connection is established with a sensor on a production line through WIFI, and preprocessing and analysis of collected data are achieved on the edge side.
In an embodiment of the present invention, the edge-side computing device is a PLC, and the PLC is specifically configured to analyze operation data of the production line to obtain current variation trend information of the data, determine whether the production line is abnormal according to the current variation trend information of the data, upload the current variation trend information of the data to a cloud for deep processing, and determine whether the production line is abnormal according to a processing result. The PLC is a programmable logic controller, and is an electronic system specially used for digital operation in an industrial environment. Specifically, the circuit module of each sensor sends the sensor value to the edge side computing device PLC through a 4G or 5G or wireless communication mode, and after receiving the data, the PLC analyzes the action completion time of each station and the ON time of the sensor for measuring whether the workpiece is in place, calculates the beat of each process and the beat of the whole production line, obtains a waveform diagram through data processing and analysis, monitors the signal characteristics, and extracts important data information, such as upper and lower extreme values of the waveform. By analyzing the data deviation of a plurality of groups of production beats, the PLC automatically sets the threshold range in the current production environment, and when the data change trend after the preprocessing of the edge side computing device exceeds the threshold range, the current production line is abnormal, and then the abnormal alarm module 30 is entered.
In an embodiment of the invention, the cloud end is specifically configured to predict future change trend information of the data through the BP neural network prediction model, and determine whether the production line is abnormal according to the future change trend information of the data. Specifically, the cloud receives data preprocessed by the edge-side computing device and performs deep processing, inputs a model established by the cloud to set a threshold range according to running data obtained by waveform circulation of previous cycles, analyzes and compares whether the data after the deep processing exceeds the threshold range, and if the data exceeds the threshold range, indicates that the current production line is abnormal, and then the data enters the abnormal alarm module 30. The cloud end carries out deep processing on data by adopting a main mode of combining wavelet transformation and a BP neural network to realize data analysis and prediction, and the method specifically comprises the following operations: firstly, performing six-layer decomposition on mixed signal data by utilizing wavelets, performing signal-noise separation and feature extraction, then extracting approximation coefficients and detail coefficients of wavelet packets from low frequency to high frequency, and taking wavelet analysis as a preprocessing means of a neural network to provide feature vectors for the neural network, training and completing diagnosis. The BP neural network is divided into three layers, namely an input layer, a hidden layer and an output layer, firstly, deep learning training samples are carried out, and the method specifically comprises the following steps: firstly, initializing a grid, setting an error function, and setting a calculation precision value and a maximum learning frequency; step two, randomly selecting the a-th input sample and the corresponding expected output; thirdly, calculating the input and the output of each neuron of the hidden layer; fourthly, calculating partial derivatives of the error function to each neuron of the output layer by utilizing the expected output and the actual output of the network; fifthly, correcting the connection weight by utilizing the partial derivative of each neuron of the output layer and the output of each neuron of the hidden layer; sixthly, correcting the connection weight by utilizing the partial derivative of each neuron of the hidden layer and the input of each neuron of the input layer; step seven, calculating a global error; and step eight, judging whether the grid error meets the requirement. And then, applying the trained model to a data automation test to reflect conditions such as result trend, bug number, quality problem and the like. And finally, the obtained data is programmed into a sample to update the model at regular time.
In an embodiment of the present invention, the anomaly alarm module 30 is specifically configured to receive alarm information of the edge-side computing device and/or the cloud and send an alarm when the edge-side computing device and/or the cloud determines that the data change trend is abnormal. Specifically, when the edge-side computing device determines that the data variation trend is abnormal, or the cloud determines that the data variation trend is abnormal, or the edge-side computing device and the cloud determine that the data variation trend is abnormal at the same time, alarm information is sent to the abnormality alarm module 30. Although it can be determined that the production beat is not abnormal only when the processing results of the edge-side computing device and the cloud are normal, the next production beat can be processed and determined as long as the data preprocessed by the edge-side computing device is determined to be normal, and the determination result of the cloud does not need to be waited.
In an embodiment of the present invention, the anomaly autonomic analysis module 40 is specifically configured to analyze an anomaly cause and an anomaly time point, and take countermeasures for removing an anomaly at a software control level. Specifically, if the data obtained by preprocessing the edge-side computing device and/or the data obtained by deep cloud processing exceed the threshold range, the anomaly alarm module 30 is triggered, at this time, the anomaly autonomic analysis module 40 automatically skips to the changed time point by searching the time point when the rising edge of the bit or the word becomes a specific value, confirms the fault time point, automatically finds the soft element part, the production beat and the human-computer interface operation working log related to the fault, finds the abnormal soft element, calls the reason for the alarm of the soft element after analysis, and takes a countermeasure for removing the anomaly in the software control layer. If the fault cannot be solved through the abnormal autonomous analysis module 40, such as the case of workpiece failure, the manual processing is waited. The PLC is equivalent to a power-on signal when the rising edge or the word becomes a specific value, namely, an instantaneous signal is given at the moment when the rising edge or the word becomes the specific value, and the instantaneous signal is equivalent to the power-on signal. For example, by looking for the rising edge of the bit at the x time point, the abnormal autonomous analysis module 40 automatically jumps to the x time point and confirms the fault, and takes a countermeasure of removing the abnormality by looking for the related abnormal soft element. The soft elements are devices with certain functions in the PLC, and the devices comprise electronic circuits, registers and storage units.
According to the edge cloud cooperation-based production line working state monitoring and fault early warning system, the edge side computing device and the cloud end are used for preprocessing the operation data of the production line and judging whether the operation data are abnormal or not, and measures for eliminating the abnormal conditions are taken in time by autonomously analyzing the abnormal conditions, so that the working state of the production line can be intelligently monitored, the real-time performance of fault early warning is effectively improved, and the abnormal conditions can be autonomously analyzed and taken.
Corresponding to the embodiment, the invention further provides a production line working state monitoring and fault early warning method based on edge cloud cooperation.
As shown in fig. 2, a method for monitoring the working state of a production line and warning faults based on edge cloud cooperation comprises the following steps:
s1, collecting and storing operation data of the production line.
In one embodiment of the invention, the operational data of the production line comprises: the production rhythm of the production line, the temperature, pressure, torque and vibration conditions of wearing parts and important driving equipment on the production line and a human-computer interface operation work log. The conditions of the vulnerable parts and important driving equipment on the production line can be monitored by sensors, and particularly can comprise a temperature sensor, a pressure sensor, a torque sensor and a vibration sensor.
In one embodiment of the invention, the production tact of the production line may be calculated by time monitoring. The time monitoring is realized by analyzing the action completion time and the workpiece in-place time of each station of the production line. The ON time of the sensor can be used for judging whether station action is finished or not and whether a workpiece is in place or not.
S2, preprocessing the operation data of the production line on edge side computing equipment, judging whether the production line is abnormal or not according to a processing result, uploading the preprocessed data to a cloud end for deep processing, and judging whether the production line is abnormal or not according to the processing result;
in an embodiment of the invention, the edge measurement computing device is a PLC, after receiving the operation data of the production line, the PLC analyzes the action completion time of each station and the ON time of the sensor that measures whether the workpiece is in place, calculates the beat of each process and the beat of the whole production line, and obtains a waveform diagram through data processing and analysis, and by analyzing the data deviation of a plurality of groups of production beats, the PLC automatically sets a threshold range in the current production environment, and when the data change trend preprocessed by the edge side computing device exceeds the threshold range, determines that the current production line is abnormal. The running data of the production line is firstly preprocessed on the edge side computing device, and the purpose is to avoid the problems of large computing load and slow data transmission in the data transmission, calculation and storage processes.
In one embodiment of the invention, the preprocessed data on the edge side computing device is uploaded to the cloud for deep processing. The cloud end carries out deep processing on the data, adopts the combination of wavelet transformation and a BP neural network to realize data analysis and prediction, uses wavelet analysis as a preprocessing means of the neural network, provides characteristic vectors for the neural network, trains and completes diagnosis. The BP neural network is divided into three layers, namely an input layer, a hidden layer and an output layer, and firstly deep learning training samples are carried out, wherein the method comprises the following specific steps: firstly, initializing a grid, setting an error function, and setting a calculation precision value and a maximum learning frequency; step two, randomly selecting the a-th input sample and the corresponding expected output; thirdly, calculating the input and the output of each neuron of the hidden layer; fourthly, calculating partial derivatives of the error function to each neuron of the output layer by utilizing the expected output and the actual output of the network; fifthly, correcting the connection weight by utilizing the partial derivative of each neuron of the output layer and the output of each neuron of the hidden layer; sixthly, correcting the connection weight by utilizing the partial derivative of each neuron of the hidden layer and the input of each neuron of the input layer; step seven, calculating a global error; and eighthly, judging whether the grid errors meet the requirements or not. And then, applying the trained model to a data automation test to reflect conditions such as result trend, bug number, quality problems and the like, for example, whether a production line is normal, whether a production program has bugs, the number of bugs, whether the quality of produced products is over-critical or not and the like. And finally, the obtained data is programmed into a sample to update the model at regular time.
And S3, when the edge side computing equipment and/or the cloud end judge that the production line is abnormal, an abnormal alarm is sent out.
In an embodiment of the present invention, although it can be determined that the beat is not abnormal only when the processing results of the edge-side computing device and the cloud are both normal, the processing can be started and the next beat can be determined only when the data preprocessed by the edge-side computing device is determined to be normal, and there is no need to wait for the determination result of the cloud.
And S4, analyzing the abnormity generated by the production line and taking a countermeasure for eliminating the abnormity.
In one embodiment of the invention, if the production line is abnormal, the time point of the change is automatically jumped to by searching the rising edge of the bit or the time point of the word changing to the specific value, the time point of the fault is confirmed, the soft element part, the production beat and the man-machine interface operation working log related to the fault are automatically found, the abnormal soft element is found, the reason of the alarm of the soft element is called after the analysis, and the countermeasure for removing the abnormality is taken on the software control level. If the fault cannot be solved through the abnormal autonomous analysis module 40, such as the case of workpiece failure, the manual processing is waited.
According to the edge cloud cooperation-based production line working state monitoring and fault early warning system, the edge side computing device and the cloud end are used for preprocessing the operation data of the production line and judging whether the operation data are abnormal or not, and measures for eliminating the abnormal conditions are taken in time by autonomously analyzing the abnormal conditions, so that the working state of the production line can be intelligently monitored, the real-time performance of fault early warning is effectively improved, and the abnormal conditions can be autonomously analyzed and taken.
In the description of the present invention, the terms "first" and "second" are used for descriptive purposes only and are not to be construed as indicating or implying relative importance or implying any number of technical features indicated. Thus, a feature defined as "first" or "second" may explicitly or implicitly include one or more of that feature. The meaning of "plurality" is two or more unless specifically limited otherwise.
In the present invention, unless otherwise expressly stated or limited, the terms "mounted," "connected," "secured," and the like are to be construed broadly and can, for example, be fixedly connected, detachably connected, or integrally formed; can be mechanically or electrically connected; either directly or indirectly through intervening media, either internally or in any other relationship. The specific meanings of the above terms in the present invention can be understood by those skilled in the art according to specific situations.
In the present invention, unless expressly stated or limited otherwise, the first feature "on" or "under" the second feature may be directly contacting the second feature or the first and second features may be indirectly contacting each other through intervening media. Also, a first feature "on," "over," and "above" a second feature may be directly or diagonally above the second feature, or may simply indicate that the first feature is at a higher level than the second feature. A first feature being "under," "below," and "beneath" a second feature may be directly under or obliquely under the first feature, or may simply mean that the first feature is at a lesser elevation than the second feature.
In the description herein, references to the description of the term "one embodiment," "some embodiments," "an example," "a specific example," or "some examples," etc., mean that a particular feature, structure, material, or characteristic described in connection with the embodiment or example is included in at least one embodiment or example of the invention. In this specification, the schematic representations of the terms used above are not necessarily intended to refer to the same embodiment or example. Furthermore, the particular features, structures, materials, or characteristics described may be combined in any suitable manner in any one or more embodiments or examples. Furthermore, various embodiments or examples and features of different embodiments or examples described in this specification can be combined and combined by one skilled in the art without contradiction.
Any process or method descriptions in flow charts or otherwise described herein may be understood as representing modules, segments, or portions of code which include one or more executable instructions for implementing specific logical functions or steps in the process, and alternate implementations are included within the scope of the preferred embodiment of the present invention in which functions may be executed out of order from that shown or discussed, including substantially concurrently or in reverse order, depending on the functionality involved, as would be understood by those reasonably skilled in the art of the present invention.
The logic and/or steps represented in the flowcharts or otherwise described herein, such as an ordered listing of executable instructions that can be considered to implement logical functions, can be embodied in any computer-readable medium for use by or in connection with an instruction execution system, apparatus, or device, such as a computer-based system, processor-containing system, or other system that can fetch the instructions from the instruction execution system, apparatus, or device and execute the instructions. For the purposes of this description, a "computer-readable medium" can be any means that can contain, store, communicate, propagate, or transport the program for use by or in connection with the instruction execution system, apparatus, or device. More specific examples (a non-exhaustive list) of the computer-readable medium would include the following: an electrical connection (electronic device) having one or more wires, a portable computer diskette (magnetic device), a Random Access Memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber device, and a portable compact disc read-only memory (CDROM). Additionally, the computer-readable medium could even be paper or another suitable medium upon which the program is printed, as the program can be electronically captured, via for instance optical scanning of the paper or other medium, then compiled, interpreted or otherwise processed in a suitable manner if necessary, and then stored in a computer memory.
It should be understood that portions of the present invention may be implemented in hardware, software, firmware, or a combination thereof. In the above embodiments, various steps or methods may be implemented in software or firmware stored in a memory and executed by a suitable instruction execution system. For example, if implemented in hardware, as in another embodiment, any one or combination of the following technologies, which are well known in the art, may be used: a discrete logic circuit having a logic gate circuit for implementing a logic function on a data signal, an application specific integrated circuit having an appropriate combinational logic gate circuit, a Programmable Gate Array (PGA), a Field Programmable Gate Array (FPGA), or the like.
It will be understood by those skilled in the art that all or part of the steps carried by the method for implementing the above embodiments may be implemented by hardware related to instructions of a program, which may be stored in a computer readable storage medium, and when the program is executed, the program includes one or a combination of the steps of the method embodiments.
In addition, functional units in the embodiments of the present invention may be integrated into one processing module, or each unit may exist alone physically, or two or more units are integrated into one module. The integrated module can be realized in a hardware mode, and can also be realized in a software functional module mode. The integrated module, if implemented in the form of a software functional module and sold or used as a separate product, may also be stored in a computer-readable storage medium.
Although embodiments of the present invention have been shown and described above, it is understood that the above embodiments are exemplary and should not be construed as limiting the present invention, and that variations, modifications, substitutions and alterations can be made to the above embodiments by those of ordinary skill in the art within the scope of the present invention.
Claims (9)
1. The utility model provides a production line operating condition monitoring and trouble early warning system based on limit cloud is collaborative which characterized in that includes:
the data acquisition module is used for collecting and storing the operation data of the production line;
the state monitoring module is used for preprocessing the running data of the production line on edge side computing equipment, judging whether the production line is abnormal or not according to a processing result, uploading the preprocessed data to a cloud end for deep processing, and judging whether the production line is abnormal or not according to the processing result;
the abnormity warning module is used for sending an abnormity warning when the edge side computing device and/or the cloud end judges that the production line is abnormal;
and the abnormal autonomous analysis module is used for analyzing the abnormality of the production line and taking a countermeasure for removing the abnormality.
2. The edge cloud coordination-based production line working state monitoring and fault early warning system as claimed in claim 1, wherein the operation data of the production line comprises: the production beat of the production line; temperature, pressure, torque and vibration conditions of wearing parts and important driving equipment on the production line; and operating a working log by the human-computer interface.
3. The production line working state monitoring and fault early warning system based on edge cloud cooperation as claimed in claim 2, wherein the data acquisition module is specifically configured to calculate the production takt through time monitoring, and to acquire the temperature, the pressure, the torque and the vibration conditions through a temperature sensor, a pressure sensor, a torque sensor and a vibration sensor, respectively.
4. The system for monitoring the working state and early warning of the fault of the production line based on the cooperation of the edge cloud and the cloud computing as claimed in claim 3, wherein the data acquisition module analyzes the action completion time and the workpiece in-place time of each station of the production line so as to realize time monitoring.
5. The system for monitoring the working state of the production line and early warning the failure based on the cooperation of the edge cloud and the cloud according to any one of claims 1 to 4, wherein the edge-side computing device is a PLC, the PLC is specifically configured to analyze operation data of the production line to obtain current change trend information of the data, judge whether the production line is abnormal according to the current change trend information of the data, upload the current change trend information of the data to the cloud for deep processing, and judge whether the production line is abnormal according to a processing result by the cloud.
6. The system for monitoring the working state of the production line and early warning the fault based on the edge cloud cooperation as claimed in claim 5, wherein the cloud end is specifically configured to predict future change trend information of data through a BP neural network prediction model, and determine whether the production line is abnormal or not according to the future change trend information of the data.
7. The system for monitoring the working state of the production line and early warning the failure based on the edge cloud cooperation as claimed in claim 6, wherein the abnormality alarming module is specifically configured to receive the alarming information of the edge-side computing device and/or the cloud and issue an alarm when the edge-side computing device and/or the cloud determines that the data change trend is abnormal.
8. The system for monitoring the working state of the production line and early warning the faults based on the edge cloud cooperation as claimed in claim 7, wherein the abnormal autonomous analysis module is specifically configured to analyze an abnormal reason and an abnormal time point, and take a countermeasure for removing the abnormality in a software control layer.
9. A production line working state monitoring and fault early warning method based on edge cloud cooperation is characterized by comprising the following steps:
collecting and storing the operation data of the production line;
preprocessing the operation data of the production line on edge side computing equipment, judging whether the production line is abnormal or not according to a processing result, uploading the preprocessed data to a cloud for deep processing, and judging whether the production line is abnormal or not according to the processing result;
when the edge side computing device and/or the cloud end judge that the production line is abnormal, an abnormal alarm is sent out;
and analyzing the abnormality of the production line and taking a countermeasure for eliminating the abnormality.
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Cited By (1)
| Publication number | Priority date | Publication date | Assignee | Title |
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| CN117420811A (en) * | 2023-12-19 | 2024-01-19 | 武汉佰思杰科技有限公司 | Production line quality monitoring method and system for automatic production |
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Cited By (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN117420811A (en) * | 2023-12-19 | 2024-01-19 | 武汉佰思杰科技有限公司 | Production line quality monitoring method and system for automatic production |
| CN117420811B (en) * | 2023-12-19 | 2024-03-08 | 武汉佰思杰科技有限公司 | Production line quality monitoring method and system for automatic production |
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