CN117791477B - Intelligent curing equipment overheat protection method and system based on Internet of things - Google Patents
Intelligent curing equipment overheat protection method and system based on Internet of things Download PDFInfo
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Abstract
The invention relates to the field of overheat protection and discloses a method and a system for overheat protection of intelligent curing equipment based on the Internet of things, wherein the method comprises the steps of determining a communication protocol of the curing equipment and constructing an Internet of things network of the curing equipment; the method comprises the steps of filtering operation data to obtain filtering operation data, carrying out wavelet transformation on the filtering operation data to obtain wavelet transformation coefficients, extracting operation characteristics of curing equipment in the wavelet transformation coefficients, constructing an overheat monitoring depth model of the curing equipment, training the overheat monitoring depth model to obtain a training overheat monitoring depth model, calculating the performance of the depth model of the training overheat monitoring depth model, analyzing operation heat energy coefficients of the curing equipment, constructing an operation heat energy development curve of the curing equipment, identifying overheat coordinates of the curing equipment, constructing overheat protection instructions of the curing equipment, and executing overheat protection of the curing equipment based on the overheat protection instructions. The invention can improve the overheat protection effect on curing equipment.
Description
Technical Field
The invention relates to the field of overheat protection, in particular to an overheat protection method and system for intelligent curing equipment based on the Internet of things.
Background
The overheat protection of the curing equipment refers to the overheat protection of the curing equipment, which is a safety measure, and the overheat protection of the curing equipment can realize real-time monitoring, overheat protection and intelligent management of the curing equipment, improve the operation efficiency and the service life of the equipment and reduce the fault risk of the equipment when the temperature exceeds a set certain threshold value in the operation process of the equipment.
At present, overheat protection of curing equipment is mainly carried out by collecting operation data of the curing equipment to analyze whether overheat phenomenon occurs, when overheat phenomenon occurs, modes of cutting off operation of the equipment, reducing operation performance of the equipment or improving ventilation and heat dissipation conditions can be utilized to prevent abnormal operation or faults of the equipment caused by overheat.
Disclosure of Invention
The invention provides an intelligent curing equipment overheat protection method and system based on the Internet of things, and mainly aims to improve the overheat protection effect on curing equipment.
In order to achieve the above purpose, the invention provides a method for overheat protection of intelligent curing equipment based on the internet of things, which comprises the following steps:
Acquiring curing equipment information of curing equipment, determining a communication protocol of the curing equipment based on the curing equipment information, and constructing an Internet of things network of the curing equipment based on the communication protocol;
acquiring operation data of the curing equipment based on the Internet of things, filtering the operation data to obtain filtering operation data, and performing wavelet transformation on the filtering operation data to obtain wavelet transformation coefficients;
extracting operation characteristics of the curing equipment in the wavelet transformation coefficients, constructing an overheat monitoring depth model of the curing equipment, and training the overheat monitoring depth model based on the filtering operation data and the operation characteristics to obtain a training overheat monitoring depth model;
Calculating the performance of the depth model of the training overheat monitoring depth model, and analyzing the operation heat energy coefficient of the curing equipment by using the training overheat monitoring depth model when the performance of the depth model meets the requirement;
and constructing an operation heat energy development curve of the curing equipment based on the operation heat energy coefficient, identifying overheat coordinates of the curing equipment based on the operation heat energy development curve, constructing overheat protection instructions of the curing equipment based on the overheat coordinates, and executing overheat protection of the curing equipment based on the overheat protection instructions.
Optionally, the constructing the internet of things of the curing device based on the communication protocol includes:
Identifying a protocol compatibility of the curing device with the communication protocol;
screening protocol incompatible equipment from the curing equipment based on the protocol compatibility;
performing protocol conversion on the protocol incompatible equipment to obtain a conversion protocol;
And constructing an Internet of things network of the curing equipment based on the communication protocol and the conversion protocol.
Optionally, the filtering processing is performed on the operation data to obtain filtered operation data, including:
Determining a filtering window of the operation data, and calculating a data average value of window operation data corresponding to the filtering window;
window movement is carried out on the filtering window, and a moving window is obtained;
calculating a moving data average value of moving window operation data corresponding to the moving window;
and serializing the data average value and the moving data average value to obtain the filtering operation data of the operation data.
Optionally, the calculating the data average value of the window operation data corresponding to the filtering window includes:
Determining a window size of the filter window;
marking the data quantity of window operation data corresponding to the filtering window based on the window size;
Based on the window size and the data quantity, calculating a data average value of window operation data corresponding to the filter window by using the following formula:
Wherein, la represents the data average value of the window operation data corresponding to the filter window, a represents the data quantity of the window operation data corresponding to the filter window, M represents the window size of the window corresponding to the filter window, and Ei represents the ith window operation data of the filter window.
Optionally, the performing wavelet transformation on the filtering operation data to obtain wavelet transformation coefficients includes:
determining a wavelet basis function of the filter operation data;
Determining a decomposition level of the wavelet basis function;
and based on the decomposition level, performing wavelet transformation on the filtering operation data by utilizing the wavelet basis function to obtain the wavelet transformation coefficient.
Optionally, the extracting the operation characteristics of the curing device in the wavelet transform coefficient includes:
Calculating the coefficient amplitude of the wavelet transformation coefficient;
Analyzing an energy distribution state of the wavelet transform coefficient based on the coefficient amplitude;
calculating a coefficient phase of the wavelet transform coefficient;
analyzing a waveform state of the wavelet transform coefficients based on the coefficient phases;
and extracting the operation characteristics of the curing equipment in the wavelet transformation coefficients based on the energy distribution state and the waveform state.
Optionally, the calculating the coefficient magnitude of the wavelet transform coefficient includes:
Calculating the coefficient amplitude of the wavelet transform coefficient using the following formula:
|C_{r,v}|=sqrt(Re(D_{r,v})^2+Im(D_{r,v})^2)
Where |c_ { r, v } | represents the coefficient magnitude of the wavelet transform coefficient, d_ { r, v } represents the wavelet transform coefficient, r represents the scale of the wavelet transform coefficient, v represents the translation of the wavelet transform coefficient, re (d_ { r, v } represents the real part of the wavelet transform coefficient, im (d_ { r, v } represents the imaginary part of the wavelet transform coefficient).
Optionally, the identifying overheat coordinates of the curing device based on the operating thermal energy development curve includes:
Marking a risk area in the running heat energy development curve based on the running heat energy development curve and a preset equipment heat energy risk limit value;
identifying risk features of the risk area;
Identifying a thermal energy risk coefficient of the curing device based on the risk features and the risk areas;
And determining overheat coordinates of the curing equipment based on the thermal energy risk coefficient.
Optionally, the identifying a thermal energy risk coefficient of the curing device based on the risk features and the risk areas includes:
marking a fluctuation peak value and a fluctuation valley value of the risk area based on the risk characteristics;
Based on the fluctuation peak value and the fluctuation valley value, calculating a thermal energy risk coefficient of the curing device by using the following formula:
Wherein τ represents a thermal energy risk coefficient of the curing device, H represents a height from a fluctuation peak value to a fluctuation valley value of a risk region in a corresponding operation thermal energy development curve of the curing device, cos represents a corresponding operation thermal energy development curve of the curing device, ω represents an angular frequency of the corresponding operation thermal energy development curve of the curing device, B represents a fluctuation peak value of the risk region in the corresponding operation thermal energy development curve of the curing device, O represents a fluctuation valley value of the risk region in the corresponding operation thermal energy development curve of the curing device, and G t represents a duration from the fluctuation peak value to the fluctuation valley value of the risk region in the corresponding operation thermal energy development curve of the curing device.
In order to solve the above problems, the present invention further provides a system for overheat protection of intelligent curing equipment based on the internet of things, the system comprising:
The system comprises an Internet of things construction module, a management module and a management module, wherein the Internet of things construction module is used for acquiring curing equipment information of curing equipment, determining a communication protocol of the curing equipment based on the curing equipment information, and constructing an Internet of things of the curing equipment based on the communication protocol;
The operation data filtering module is used for acquiring the operation data of the curing equipment based on the Internet of things, filtering the operation data to obtain filtering operation data, and performing wavelet transformation on the filtering operation data to obtain wavelet transformation coefficients;
The depth model training module is used for extracting the operation characteristics of the curing equipment in the wavelet transformation coefficients, constructing an overheat monitoring depth model of the curing equipment, and training the overheat monitoring depth model based on the filtering operation data and the operation characteristics to obtain a training overheat monitoring depth model;
The thermal energy coefficient output module is used for calculating the performance of the depth model of the training overheat monitoring depth model, and analyzing the operation thermal energy coefficient of the curing equipment by using the training overheat monitoring depth model when the performance of the depth model meets the requirement;
And the equipment overheat protection module is used for constructing an operation heat energy development curve of the curing equipment based on the operation heat energy coefficient, identifying overheat coordinates of the curing equipment based on the operation heat energy development curve, constructing overheat protection instructions of the curing equipment based on the overheat coordinates, and executing overheat protection of the curing equipment based on the overheat protection instructions.
The embodiment of the invention can realize real-time acquisition of state information of the curing equipment and collection and analysis of data by constructing an Internet of things network of the curing equipment based on the communication protocol, can improve the data quality of the data by filtering the operation data to obtain the filtered operation data, can analyze signals on different time scales by wavelet transformation of the filtered operation data to obtain wavelet transformation coefficients, can provide data basis for real-time monitoring and fault diagnosis of the operation state of the curing equipment at a later stage by extracting the operation characteristics of the wavelet transformation coefficients, can further realize intelligent monitoring and analysis of the curing equipment by constructing a overheat monitoring depth model of the curing equipment, can further train the overheat monitoring depth model based on the filtered operation data and the operation characteristics to obtain the training overheat monitoring depth model, can improve the performance of the model and improve the accuracy of the analysis of the operation state of the curing equipment, can further realize the real-time analysis of the curing equipment based on the thermal energy coefficient by constructing the thermal energy coefficient of the curing equipment by utilizing the overheat monitoring depth model, the overheat protection instruction of the curing device can be constructed to realize overheat protection of the curing device. Therefore, the intelligent curing equipment overheat protection method and system based on the Internet of things can improve the overheat protection effect on the curing equipment.
Drawings
Fig. 1 is a schematic flow chart of a method for overheat protection of intelligent curing equipment based on internet of things according to an embodiment of the present invention;
fig. 2 is a functional block diagram of a system for overheat protection based on intelligent curing equipment of the internet of things according to an embodiment of the present invention;
Fig. 3 is a schematic structural diagram of an electronic device of a system for overheat protection based on an intelligent curing device of the internet of things according to an embodiment of the present invention;
The achievement of the objects, functional features and advantages of the present invention will be further described with reference to the accompanying drawings, in conjunction with the embodiments.
Detailed Description
It should be understood that the specific embodiments described herein are for purposes of illustration only and are not intended to limit the scope of the invention.
The embodiment of the application provides an intelligent curing equipment overheat protection method based on the Internet of things. The execution main body of the method for overheat protection of the intelligent curing equipment based on the Internet of things comprises at least one of electronic equipment, such as a server side, a terminal and the like, which can be configured to execute the method provided by the embodiment of the application. In other words, the method for overheat protection of the intelligent curing device based on the internet of things can be executed by software or hardware installed in a terminal device or a server device, and the software can be a blockchain platform. The server side comprises, but is not limited to, a single server, a server cluster, a cloud server or a cloud server cluster and the like. The server may be an independent server, or may be a cloud server that provides cloud services, cloud databases, cloud computing, cloud functions, cloud storage, network services, cloud communications, middleware services, domain name services, security services, content delivery networks (Content Delivery Network, CDN), and basic cloud computing services such as big data and artificial intelligence platforms.
Referring to fig. 1, a flow chart of a method for overheat protection of intelligent curing equipment based on the internet of things according to an embodiment of the present invention is shown. In this embodiment, the method for overheat protection of intelligent curing equipment based on the internet of things includes:
And acquiring curing equipment information of the curing equipment, determining a communication protocol of the curing equipment based on the curing equipment information, and constructing an Internet of things network of the curing equipment based on the communication protocol.
In the embodiment of the present invention, the curing device information refers to data describing the curing device, such as device signals, device types, device power, and the like.
Further, according to the embodiment of the invention, based on the curing device information, the communication protocol of the curing device is determined, so that a basis can be provided for data communication of the post curing device. The communication protocol refers to a network, such as MQTT, modbus, etc., for ensuring data transmission of the curing device. In detail, the determining the communication protocol of the curing device is mainly by analyzing the most supported protocol of the curing device as the target communication protocol.
Optionally, the embodiment of the present invention constructs the internet of things network of the curing device based on the communication protocol, so as to obtain the status information of the curing device in real time, and collect and analyze data. The Internet of things network is a network which is used for connecting various entity objects to the network through information sensing equipment and realizing intelligent identification, positioning, tracking, monitoring and management.
The method for constructing the Internet of things network of the curing equipment based on the communication protocol comprises the steps of identifying protocol compatibility of the curing equipment and the communication protocol, screening protocol incompatible equipment from the curing equipment based on the protocol compatibility, carrying out protocol conversion on the protocol incompatible equipment to obtain a conversion protocol, and constructing the Internet of things network of the curing equipment based on the communication protocol and the conversion protocol.
The protocol compatibility refers to whether the curing device supports the communication protocol or not, the communication protocol is not supported, the protocol compatibility is protocol incompatibility, the communication protocol is supported, the protocol compatibility is protocol compatibility, the protocol incompatibility device refers to the protocol compatibility is protocol incompatibility device, and the conversion protocol refers to a new protocol after the protocol incompatibility device performs protocol conversion through a gateway device.
S2, acquiring operation data of the curing equipment based on the Internet of things, performing filtering processing on the operation data to obtain filtering operation data, and performing wavelet transformation on the filtering operation data to obtain wavelet transformation coefficients.
In the embodiment of the invention, the operation data refers to a data set of the operation process of the curing equipment.
According to the embodiment of the invention, the operation data is filtered, so that the data quality of the data can be improved. The filtering operation data refers to a data set after the operation data is subjected to filtering processing.
The method comprises the steps of determining a filter window of running data, calculating a data average value of the window running data corresponding to the filter window, moving the filter window to obtain a moving window, calculating a moving data average value of the moving window running data corresponding to the moving window, and serializing the data average value and the moving data average value to obtain the filter running data of the running data.
The filtering window is a sliding window with a fixed size, the window operation data is a data set of the operation data in the sliding window, the data average value is a data average value of data contained in the sliding window, the moving window is a window after the filtering window is moved, and the moving data average value is a data average value in the moving window.
Optionally, as an optional embodiment of the present invention, the calculating the data average value of the window operation data corresponding to the filter window includes determining a window size of the filter window, marking a data amount of the window operation data corresponding to the filter window based on the window size, and calculating the data average value of the window operation data corresponding to the filter window based on the window size and the data amount by using the following formula:
Wherein, la represents the data average value of the window operation data corresponding to the filter window, a represents the data quantity of the window operation data corresponding to the filter window, M represents the window size of the window corresponding to the filter window, and Ei represents the ith window operation data of the filter window.
According to the embodiment of the invention, the wavelet transformation is carried out on the filtering operation data, so that the wavelet transformation coefficients are obtained, and signals can be analyzed on different time scales. Wherein the wavelet transform coefficients refer to characteristics of the signal at different frequency scales.
Optionally, as an embodiment of the present invention, the performing wavelet transformation on the filtering operation data to obtain wavelet transformation coefficients includes determining a wavelet basis function of the filtering operation data, determining a decomposition level of the wavelet basis function, and performing wavelet transformation on the filtering operation data by using the wavelet basis function based on the decomposition level to obtain the wavelet transformation coefficients.
Wherein the wavelet basis functions refer to functions used as basis in wavelet transforms, which are a series of wavelet functions that can be used to decompose signals, such as Haar wavelet, daubechies (dbN) wavelet, mexican Hat (mexh) wavelet, morlet wavelet, meyer wavelet, etc., and the decomposition levels refer to different levels of decomposition of the filtered operating data to obtain characteristics of the signals at different frequency scales, typically the higher the decomposition level, the finer the time resolution and the coarser the frequency resolution.
S3, extracting operation characteristics of the curing equipment in the wavelet transformation coefficients, constructing an overheat monitoring depth model of the curing equipment, and training the overheat monitoring depth model based on the filtering operation data and the operation characteristics to obtain a training overheat monitoring depth model.
According to the embodiment of the invention, the operation characteristics of the curing equipment in the wavelet transformation coefficients are extracted, so that a data basis can be provided for the real-time monitoring and fault diagnosis of the equipment operation state in the later period. Wherein, the operation characteristics refer to dynamic transformation characteristics of the operation process of the curing equipment, such as the operation state, the fault type, the fault degree and the like of the equipment.
As one embodiment of the invention, the extracting the operation characteristics of the curing equipment in the wavelet transformation coefficients comprises calculating coefficient amplitude values of the wavelet transformation coefficients, analyzing energy distribution states of the wavelet transformation coefficients based on the coefficient amplitude values, calculating coefficient phases of the wavelet transformation coefficients, analyzing waveform states of the wavelet transformation coefficients based on the coefficient phases, and extracting the operation characteristics of the curing equipment in the wavelet transformation coefficients based on the energy distribution states and the waveform states.
The coefficient amplitude refers to a value capable of reflecting the energy of a signal at a certain time and frequency, the energy distribution state refers to the energy distribution characteristics of the device at different operation states, the coefficient phase refers to the waveform change of the signal at a certain time and frequency, and the waveform state refers to the waveform characteristics of the device at different operation states.
Alternatively, as an optional embodiment of the present invention, the calculating the coefficient magnitudes of the wavelet transform coefficients includes calculating the coefficient magnitudes of the wavelet transform coefficients using the following formula:
|C_{r,v}|=sqrt(Re(D_{r,v})^2+Im(D_{r,v})^2)
Where |c_ { r, v } | represents the coefficient magnitude of the wavelet transform coefficient, d_ { r, v } represents the wavelet transform coefficient, r represents the scale of the wavelet transform coefficient, v represents the translation of the wavelet transform coefficient, re (d_ { r, v } represents the real part of the wavelet transform coefficient, im (d_ { r, v } represents the imaginary part of the wavelet transform coefficient).
The shift of the wavelet transform coefficient means that the wavelet base function is shifted by v units on a time axis, and the scale of the wavelet transform coefficient means the expansion degree of the wavelet base function on the time axis.
Alternatively, the calculating the coefficient phase of the wavelet transform coefficient may be implemented by calculating a ratio of the wavelet transform coefficient to the real and imaginary parts.
According to the embodiment of the invention, the overheating monitoring depth model of the curing equipment is constructed, so that the curing equipment can be intelligently monitored and analyzed through the model. The overheat monitoring depth model is an initial model framework for monitoring the operation state of the curing equipment. In detail, the overheat monitoring depth model may be constructed by a convolutional neural network, a recurrent neural network, a long-short-term memory network, or the like.
Further, according to the embodiment of the invention, based on the filtering operation data and the operation characteristics, the overheat monitoring depth model is trained, so that the performance of the model can be improved, and the accuracy of analyzing the operation state of the curing equipment can be improved by training the overheat monitoring depth model. The training overheat monitoring depth model is a model obtained by training the overheat monitoring depth model through a large amount of data. The training of the overheat monitoring depth model refers to using a prepared data set and features to input data into a selected depth learning model for training, wherein in the training process, the model learns how to predict overheat states according to operation data and features, and in the training process, super parameters of the model such as learning rate, batch size, iteration number and the like need to be adjusted so as to improve the performance of the model.
S4, calculating the performance of the depth model of the training overheat monitoring depth model, and analyzing the operation heat energy coefficient of the curing equipment by using the training overheat monitoring depth model when the performance of the depth model meets the requirements.
According to the embodiment of the invention, the performance of the depth model of the training overheat monitoring depth model is calculated, and whether the training overheat monitoring depth model meets the monitoring requirement can be identified, so that the reliability of the model is improved. The depth model performance refers to the monitoring effect of the training overheat monitoring depth model on the curing equipment.
Alternatively, as one embodiment of the present invention, the computing the depth model performance of the training overheat monitoring depth model includes marking model training output results of the training overheat monitoring depth model, identifying accuracy of the model training output results, and computing the depth model performance of the training overheat monitoring depth model based on the accuracy.
The model training output result refers to a result output by the training overheat monitoring depth model in a training process, and the accuracy refers to the accuracy degree of the model training output result.
Further, according to the embodiment of the invention, when the performance of the depth model meets the requirement, the training overheat monitoring depth model is utilized to analyze the operation heat energy coefficient of the curing equipment, so that the curing equipment can be monitored in real time. Wherein, the operation heat energy coefficient refers to the overheat degree of the curing equipment in the operation process.
As one embodiment of the invention, when the performance of the depth model meets the requirement, the operation heat energy coefficient of the curing equipment is analyzed by utilizing the training overheat monitoring depth model, and the method comprises the steps of utilizing an observation network of the training overheat monitoring depth model to identify an operation observation value of the curing equipment, utilizing a gain network of the training overheat monitoring depth model to analyze a state gain value of the curing equipment, and calculating the operation heat energy coefficient of the curing equipment based on the operation observation value and the state gain value.
The observation network is a network for identifying a data value of current operation data of the curing equipment, the operation observation value is a state value, such as position, speed, heat value, etc., of an operation process of the curing equipment, the gain network is a network for analyzing the development direction of the state of the curing equipment, and the state gain value is a change of the development direction, such as heat increment degree, speed change law, etc., of the state of the curing equipment through a Kalman gain function.
S5, constructing an operation heat energy development curve of the curing equipment based on the operation heat energy coefficient, identifying overheat coordinates of the curing equipment based on the operation heat energy development curve, constructing overheat protection instructions of the curing equipment based on the overheat coordinates, and executing overheat protection of the curing equipment based on the overheat protection instructions.
According to the embodiment of the invention, based on the operation heat energy coefficient, the operation heat energy development curve of the curing equipment is constructed, so that the heat energy conversion of the curing equipment can be more intuitively analyzed. Wherein the operating thermal energy development curve refers to a thermal energy versus time construction curve of the curing device. The operating thermal energy development curve may be implemented by a curve function.
In the embodiment of the present invention, the overheat coordinates refer to coordinates of the overheat risk of the curing device in the running heat energy development curve.
As one embodiment of the invention, the identification of the overheat coordinates of the curing device based on the operation thermal energy development curve comprises marking a risk area in the operation thermal energy development curve based on the operation thermal energy development curve and a preset device thermal energy risk limit value, identifying a risk feature of the risk area, identifying a thermal energy risk coefficient of the curing device based on the risk feature and the risk area, and determining the overheat coordinates of the curing device based on the thermal energy risk coefficient.
The thermal energy risk limit of the equipment refers to a limit value of an overheating phenomenon of the curing equipment, the risk area refers to a curve segment continuously overheated in the running thermal energy development curve, the risk feature refers to a feature of the risk area, such as an area range, an area fluctuation degree and the like, and the thermal energy risk coefficient refers to a degree of risk of the risk area on the curing equipment.
Optionally, as an optional embodiment of the present invention, the identifying the thermal energy risk coefficient of the curing device based on the risk feature and the risk region includes marking a fluctuation peak value and a fluctuation valley value of the risk region based on the risk feature, and calculating the thermal energy risk coefficient of the curing device based on the fluctuation peak value and the fluctuation valley value by using the following formula:
Wherein τ represents a thermal energy risk coefficient of the curing device, H represents a height from a fluctuation peak value to a fluctuation valley value of a risk region in a corresponding operation thermal energy development curve of the curing device, cos represents a corresponding operation thermal energy development curve of the curing device, ω represents an angular frequency of the corresponding operation thermal energy development curve of the curing device, B represents a fluctuation peak value of the risk region in the corresponding operation thermal energy development curve of the curing device, O represents a fluctuation valley value of the risk region in the corresponding operation thermal energy development curve of the curing device, and G t represents a duration from the fluctuation peak value to the fluctuation valley value of the risk region in the corresponding operation thermal energy development curve of the curing device.
According to the embodiment of the invention, based on the overheat coordinates, overheat protection of the curing equipment can be realized by constructing overheat protection instructions of the curing equipment. The overheat protection instruction refers to an instruction capable of controlling the curing device to perform overheat protection, for example, an instruction for cutting off the operation of the device, reducing the operation performance of the device, improving ventilation and heat dissipation, and the like. The overheat protection instruction for constructing the curing device may be determined according to the overheat coordinates corresponding to a thermal energy risk coefficient, for example, when the thermal energy risk coefficient is low, the overheat protection instruction may be to reduce the device operation performance, and when the thermal energy risk coefficient is high, the overheat protection instruction may be to cut off the device operation.
Optionally, the embodiment of the present invention may implement overheat protection of the curing device by performing overheat protection of the curing device based on the overheat protection instruction.
The embodiment of the invention can realize real-time acquisition of state information of the curing equipment and collection and analysis of data by constructing an Internet of things network of the curing equipment based on the communication protocol, can improve the data quality of the data by filtering the operation data to obtain the filtered operation data, can analyze signals on different time scales by wavelet transformation of the filtered operation data to obtain wavelet transformation coefficients, can provide data basis for real-time monitoring and fault diagnosis of the operation state of the curing equipment at a later stage by extracting the operation characteristics of the wavelet transformation coefficients, can further realize intelligent monitoring and analysis of the curing equipment by constructing a overheat monitoring depth model of the curing equipment, can further train the overheat monitoring depth model based on the filtered operation data and the operation characteristics to obtain the training overheat monitoring depth model, can improve the performance of the model and improve the accuracy of the analysis of the operation state of the curing equipment, can further realize the real-time analysis of the curing equipment based on the thermal energy coefficient by constructing the thermal energy coefficient of the curing equipment by utilizing the overheat monitoring depth model, the overheat protection instruction of the curing device can be constructed to realize overheat protection of the curing device. Therefore, the intelligent curing equipment overheat protection method based on the Internet of things can improve the overheat protection effect on the curing equipment.
Fig. 2 is a functional block diagram of a system for overheat protection based on intelligent curing equipment of the internet of things according to an embodiment of the present invention.
The system 200 for overheat protection based on the intelligent curing equipment of the Internet of things can be installed in electronic equipment. According to the implemented functions, the system 200 for overheat protection of intelligent curing equipment based on the internet of things may include an internet of things building module 201, an operation data filtering module 202, a depth model training module 203, a thermal energy coefficient output module 204 and an equipment overheat protection module 205. The module of the invention, which may also be referred to as a unit, refers to a series of computer program segments, which are stored in the memory of the electronic device, capable of being executed by the processor of the electronic device and of performing a fixed function.
In the present embodiment, the functions concerning the respective modules/units are as follows:
The internet of things building module 201 is configured to obtain curing device information of a curing device, determine a communication protocol of the curing device based on the curing device information, and build an internet of things of the curing device based on the communication protocol;
The operation data filtering module 202 is configured to collect operation data of the curing device based on the internet of things, perform filtering processing on the operation data to obtain filtered operation data, and perform wavelet transform on the filtered operation data to obtain wavelet transform coefficients;
the depth model training module 203 is configured to extract an operation characteristic of the curing device in the wavelet transform coefficient, construct an overheat monitoring depth model of the curing device, and train the overheat monitoring depth model based on the filtering operation data and the operation characteristic to obtain a training overheat monitoring depth model;
The thermal energy coefficient output module 204 is configured to calculate a depth model performance of the training overheat monitoring depth model, and analyze an operation thermal energy coefficient of the curing device by using the training overheat monitoring depth model when the depth model performance meets a requirement;
The device overheat protection module 205 is configured to construct an operation thermal energy development curve of the curing device based on the operation thermal energy coefficient, identify overheat coordinates of the curing device based on the operation thermal energy development curve, construct overheat protection instructions of the curing device based on the overheat coordinates, and execute overheat protection of the curing device based on the overheat protection instructions.
In detail, the modules in the system 200 for overheat protection based on the intelligent curing device of the internet of things in the embodiment of the present invention use the same technical means as the method for overheat protection based on the intelligent curing device of the internet of things in the drawings, and can produce the same technical effects, which are not described herein.
The embodiment of the invention provides electronic equipment for realizing an overheat protection method of intelligent curing equipment based on the Internet of things.
Referring to fig. 3, the electronic device may include a processor 30, a memory 31, a communication bus 32, and a communication interface 33, and may further include a computer program stored in the memory 31 and executable on the processor 30, such as a method program for overheat protection of an intelligent curing device based on the internet of things.
The processor may be formed by an integrated circuit in some embodiments, for example, a single packaged integrated circuit, or may be formed by a plurality of integrated circuits packaged with the same function or different functions, including one or more central processing units (Central Processing Unit, CPU), microprocessors, digital processing chips, graphics processors, and combinations of various control chips. The processor is a Control Unit (Control Unit) of the electronic device, connects various components of the entire electronic device using various interfaces and lines, and executes various functions of the electronic device and processes data by running or executing programs or modules stored in the memory (for example, executing programs based on overheat protection of the intelligent aging device of the internet of things, etc.), and calling data stored in the memory.
The memory includes at least one type of readable storage medium including flash memory, removable hard disk, multimedia card, card memory (e.g., SD or DX memory, etc.), magnetic memory, magnetic disk, optical disk, etc. The memory may in some embodiments be an internal storage unit of the electronic device, such as a mobile hard disk of the electronic device. The memory may also be an external storage device of the electronic device in other embodiments, such as a plug-in mobile hard disk, a smart memory card (SMART MEDIA CARD, SMC), a Secure Digital (SD) card, a flash memory card (FLASH CARD), etc. that are provided on the electronic device. Further, the memory may also include both internal storage units and external storage devices of the electronic device. The memory can be used for storing application software installed in the electronic equipment and various data, such as codes based on programs for overheat protection of the intelligent curing equipment based on the Internet of things, and can be used for temporarily storing data which are already output or are to be output.
The communication bus may be a peripheral component interconnect standard (PERIPHERAL COMPONENT INTERCONNECT, PCI) bus, or an extended industry standard architecture (Extended Industry Standard Architecture, EISA) bus, or the like. The bus may be classified as an address bus, a data bus, a control bus, etc. The bus is arranged to enable a connection communication between the memory and at least one processor or the like.
The communication interface is used for communication between the electronic equipment and other equipment, and comprises a network interface and a user interface. Optionally, the network interface may include a wired interface and/or a wireless interface (e.g., WI-FI interface, bluetooth interface, etc.), typically used to establish a communication connection between the electronic device and other electronic devices. The user interface may be a Display (Display), an input unit such as a Keyboard (Keyboard), or alternatively a standard wired interface, a wireless interface. Alternatively, in some embodiments, the display may be an LED display, a liquid crystal display, a touch-sensitive liquid crystal display, an OLED (Organic Light-Emitting Diode) touch, or the like. The display may also be referred to as a display screen or display unit, as appropriate, for displaying information processed in the electronic device and for displaying a visual user interface.
For example, although not shown, the electronic device may further include a power source (such as a battery) for powering the respective components, and preferably, the power source may be logically connected to the at least one processor through a power management system, so as to perform functions of charge management, discharge management, and power consumption management through the power management system. The power supply may also include one or more of any of a direct current or alternating current power supply, a recharging system, a power failure detection circuit, a power converter or inverter, a power status indicator, and the like. The electronic device may further include various sensors, bluetooth modules, wi-Fi modules, etc., which are not described herein.
It should be understood that the embodiments described are for illustrative purposes only and are not limited to this configuration in the scope of the patent application.
The program stored in the memory of the electronic device and based on overheat protection of the intelligent curing device of the internet of things is a combination of a plurality of instructions, and when running in the processor, the program can realize:
Acquiring curing equipment information of curing equipment, determining a communication protocol of the curing equipment based on the curing equipment information, and constructing an Internet of things network of the curing equipment based on the communication protocol;
acquiring operation data of the curing equipment based on the Internet of things, filtering the operation data to obtain filtering operation data, and performing wavelet transformation on the filtering operation data to obtain wavelet transformation coefficients;
extracting operation characteristics of the curing equipment in the wavelet transformation coefficients, constructing an overheat monitoring depth model of the curing equipment, and training the overheat monitoring depth model based on the filtering operation data and the operation characteristics to obtain a training overheat monitoring depth model;
Calculating the performance of the depth model of the training overheat monitoring depth model, and analyzing the operation heat energy coefficient of the curing equipment by using the training overheat monitoring depth model when the performance of the depth model meets the requirement;
and constructing an operation heat energy development curve of the curing equipment based on the operation heat energy coefficient, identifying overheat coordinates of the curing equipment based on the operation heat energy development curve, constructing overheat protection instructions of the curing equipment based on the overheat coordinates, and executing overheat protection of the curing equipment based on the overheat protection instructions.
Specifically, the specific implementation method of the above instruction by the processor may refer to descriptions of related steps in the corresponding embodiment of the drawings, which are not repeated herein.
Further, the electronic device integrated modules/units, if implemented in the form of software functional units and sold or used as stand-alone products, may be stored in a computer readable storage medium. The computer readable storage medium may be volatile or nonvolatile. For example, the computer readable medium may include any entity or system capable of carrying the computer program code, a recording medium, a U disk, a removable hard disk, a magnetic disk, an optical disk, a computer Memory, a Read-Only Memory (ROM).
The present invention also provides a computer readable storage medium storing a computer program which, when executed by a processor of an electronic device, can implement:
Acquiring curing equipment information of curing equipment, determining a communication protocol of the curing equipment based on the curing equipment information, and constructing an Internet of things network of the curing equipment based on the communication protocol;
acquiring operation data of the curing equipment based on the Internet of things, filtering the operation data to obtain filtering operation data, and performing wavelet transformation on the filtering operation data to obtain wavelet transformation coefficients;
extracting operation characteristics of the curing equipment in the wavelet transformation coefficients, constructing an overheat monitoring depth model of the curing equipment, and training the overheat monitoring depth model based on the filtering operation data and the operation characteristics to obtain a training overheat monitoring depth model;
Calculating the performance of the depth model of the training overheat monitoring depth model, and analyzing the operation heat energy coefficient of the curing equipment by using the training overheat monitoring depth model when the performance of the depth model meets the requirement;
and constructing an operation heat energy development curve of the curing equipment based on the operation heat energy coefficient, identifying overheat coordinates of the curing equipment based on the operation heat energy development curve, constructing overheat protection instructions of the curing equipment based on the overheat coordinates, and executing overheat protection of the curing equipment based on the overheat protection instructions.
In the several embodiments provided by the present invention, it should be understood that the disclosed apparatus, system and method may be implemented in other manners. For example, the system embodiments described above are merely illustrative, e.g., the division of the modules is merely a logical function division, and other manners of division may be implemented in practice.
The modules described as separate components may or may not be physically separate, and components shown as modules may or may not be physical units, may be located in one place, or may be distributed over multiple network units. 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 the embodiments of the present invention may be integrated in one processing unit, or each unit may exist alone physically, or two or more units may be integrated in one unit. The integrated units can be realized in a form of hardware or a form of hardware and a form of 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 spirit or essential characteristics thereof.
The present embodiments are, therefore, to be considered in all respects as illustrative and not restrictive, the scope of the invention being indicated by the appended claims rather than by the foregoing description, and all changes which come within the meaning and range of equivalency of the claims are therefore intended to be embraced therein. Any reference signs in the claims shall not be construed as limiting the claim concerned.
The embodiment of the application can acquire and process the related data based on the artificial intelligence technology. Wherein artificial intelligence (ARTIFICIAL INTELLIGENCE, AI) is the theory, method, technique, and application system that uses a digital computer or a digital computer-controlled machine to simulate, extend, and expand human intelligence, sense the environment, acquire knowledge, and use knowledge to obtain optimal results.
Furthermore, it is evident that the word "comprising" does not exclude other elements or steps, and that the singular does not exclude a plurality. Multiple units or systems as set forth in the system claims may also be implemented by means of one unit or system in software or hardware. The terms first, second, etc. are used to denote a name, but not any particular order.
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 (8)
1. The method for overheat protection of intelligent curing equipment based on the Internet of things is characterized by comprising the following steps of:
Acquiring curing equipment information of curing equipment, determining a communication protocol of the curing equipment based on the curing equipment information, and constructing an Internet of things network of the curing equipment based on the communication protocol;
acquiring operation data of the curing equipment based on the Internet of things, filtering the operation data to obtain filtering operation data, and performing wavelet transformation on the filtering operation data to obtain wavelet transformation coefficients;
extracting operation characteristics of the curing equipment in the wavelet transformation coefficients, constructing an overheat monitoring depth model of the curing equipment, and training the overheat monitoring depth model based on the filtering operation data and the operation characteristics to obtain a training overheat monitoring depth model;
Calculating the performance of the depth model of the training overheat monitoring depth model, and analyzing the operation heat energy coefficient of the curing equipment by using the training overheat monitoring depth model when the performance of the depth model meets the requirement;
Constructing an operation heat energy development curve of the curing equipment based on the operation heat energy coefficient, identifying overheat coordinates of the curing equipment based on the operation heat energy development curve, wherein the identifying overheat coordinates of the curing equipment based on the operation heat energy development curve comprises marking a risk area in the operation heat energy development curve based on the operation heat energy development curve and a preset equipment heat energy risk limit value, identifying a risk feature of the risk area, and identifying a heat energy risk coefficient of the curing equipment based on the risk feature and the risk area, wherein the identifying the heat energy risk coefficient of the curing equipment based on the risk feature and the risk area comprises marking a fluctuation peak value and a fluctuation valley value of the risk area based on the risk feature, and calculating the heat energy risk coefficient of the curing equipment based on the fluctuation peak value and the fluctuation valley value by using the following formula:
Wherein τ represents a thermal energy risk coefficient of the curing device, H represents a height from a fluctuation peak value to a fluctuation valley value of a risk region in a corresponding operation thermal energy development curve of the curing device, cos represents a corresponding operation thermal energy development curve of the curing device, ω represents an angular frequency of the corresponding operation thermal energy development curve of the curing device, B represents a fluctuation peak value of the risk region in the corresponding operation thermal energy development curve of the curing device, O represents a fluctuation valley value of the risk region in the corresponding operation thermal energy development curve of the curing device, G t represents a duration from the fluctuation peak value to the fluctuation valley value of the risk region in the corresponding operation thermal energy development curve of the curing device, a overheat coordinate of the curing device is determined based on the thermal energy risk coefficient, a overheat protection instruction of the curing device is constructed based on the overheat coordinate, and overheat protection of the curing device is executed based on the overheat protection instruction.
2. The method for overheat protection of intelligent curing equipment based on the internet of things according to claim 1, wherein the constructing the internet of things of the curing equipment based on the communication protocol comprises:
Identifying a protocol compatibility of the curing device with the communication protocol;
screening protocol incompatible equipment from the curing equipment based on the protocol compatibility;
performing protocol conversion on the protocol incompatible equipment to obtain a conversion protocol;
And constructing an Internet of things network of the curing equipment based on the communication protocol and the conversion protocol.
3. The method for overheat protection of intelligent curing equipment based on the internet of things according to claim 1, wherein the filtering the operation data to obtain the filtered operation data comprises:
Determining a filtering window of the operation data, and calculating a data average value of window operation data corresponding to the filtering window;
window movement is carried out on the filtering window, and a moving window is obtained;
calculating a moving data average value of moving window operation data corresponding to the moving window;
and serializing the data average value and the moving data average value to obtain the filtering operation data of the operation data.
4. The method for overheat protection based on the intelligent curing equipment of the internet of things according to claim 3, wherein the calculating the data average value of the window operation data corresponding to the filter window comprises:
Determining a window size of the filter window;
marking the data quantity of window operation data corresponding to the filtering window based on the window size;
Based on the window size and the data quantity, calculating a data average value of window operation data corresponding to the filter window by using the following formula:
Wherein, la represents the data average value of the window operation data corresponding to the filter window, a represents the data quantity of the window operation data corresponding to the filter window, M represents the window size of the window corresponding to the filter window, and Ei represents the ith window operation data of the filter window.
5. The method for overheat protection of intelligent curing equipment based on the internet of things according to claim 1, wherein the performing wavelet transformation on the filtering operation data to obtain wavelet transformation coefficients comprises:
determining a wavelet basis function of the filter operation data;
Determining a decomposition level of the wavelet basis function;
and based on the decomposition level, performing wavelet transformation on the filtering operation data by utilizing the wavelet basis function to obtain the wavelet transformation coefficient.
6. The method for overheat protection of intelligent curing equipment based on the internet of things according to claim 1, wherein the extracting the operation characteristics of the curing equipment in the wavelet transformation coefficients comprises:
Calculating the coefficient amplitude of the wavelet transformation coefficient;
Analyzing an energy distribution state of the wavelet transform coefficient based on the coefficient amplitude;
calculating a coefficient phase of the wavelet transform coefficient;
analyzing a waveform state of the wavelet transform coefficients based on the coefficient phases;
and extracting the operation characteristics of the curing equipment in the wavelet transformation coefficients based on the energy distribution state and the waveform state.
7. The method for overheat protection of intelligent curing equipment based on the internet of things according to claim 6, wherein the calculating the coefficient amplitude of the wavelet transformation coefficient comprises:
Calculating the coefficient amplitude of the wavelet transform coefficient using the following formula:
|C_{r,v}|=sqrt(Re(D_{r,v})^2+Im(D_{r,v})^2)
Where |c_ { r, v } | represents the coefficient magnitude of the wavelet transform coefficient, d_ { r, v } represents the wavelet transform coefficient, r represents the scale of the wavelet transform coefficient, v represents the translation of the wavelet transform coefficient, re (d_ { r, v } represents the real part of the wavelet transform coefficient, im (d_ { r, v } represents the imaginary part of the wavelet transform coefficient).
8. A system for overheat protection of an intelligent curing device based on the internet of things, which is used for executing the method for overheat protection of the intelligent curing device based on the internet of things according to any one of claims 1 to 7, the system comprising:
The system comprises an Internet of things construction module, a management module and a management module, wherein the Internet of things construction module is used for acquiring curing equipment information of curing equipment, determining a communication protocol of the curing equipment based on the curing equipment information, and constructing an Internet of things of the curing equipment based on the communication protocol;
The operation data filtering module is used for acquiring the operation data of the curing equipment based on the Internet of things, filtering the operation data to obtain filtering operation data, and performing wavelet transformation on the filtering operation data to obtain wavelet transformation coefficients;
The depth model training module is used for extracting the operation characteristics of the curing equipment in the wavelet transformation coefficients, constructing an overheat monitoring depth model of the curing equipment, and training the overheat monitoring depth model based on the filtering operation data and the operation characteristics to obtain a training overheat monitoring depth model;
The thermal energy coefficient output module is used for calculating the performance of the depth model of the training overheat monitoring depth model, and analyzing the operation thermal energy coefficient of the curing equipment by using the training overheat monitoring depth model when the performance of the depth model meets the requirement;
And the equipment overheat protection module is used for constructing an operation heat energy development curve of the curing equipment based on the operation heat energy coefficient, identifying overheat coordinates of the curing equipment based on the operation heat energy development curve, constructing overheat protection instructions of the curing equipment based on the overheat coordinates, and executing overheat protection of the curing equipment based on the overheat protection instructions.
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| CN116245357B (en) * | 2023-01-31 | 2023-09-22 | 南京工大金泓能源科技有限公司 | Fault diagnosis method and system for intelligent energy-saving cabinet |
| CN116345386A (en) * | 2023-03-29 | 2023-06-27 | 上海勤芸电子科技有限公司 | Overheat protection method for equipment in abnormal state and electronic equipment |
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| KR20020064209A (en) * | 2001-02-01 | 2002-08-07 | 캐논 가부시끼가이샤 | Abnormality detection method and protection apparatus |
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