Method and system for overheat protection of intelligent maturation equipment based on Internet of Things

Abstract

The present invention relates to the field of overheat protection, and discloses a method and system for overheat protection of intelligent aging equipment based on the Internet of Things. The method includes: determining the communication protocol of the aging equipment, building the Internet of Things network of the aging equipment; filtering the operation data to obtain the filtered operation data, performing wavelet transform on the filtered operation data, and obtaining the wavelet transform coefficient; extracting the operation characteristics of the aging equipment in the wavelet transform coefficient, building an overheat monitoring deep model of the aging equipment, training the overheat monitoring deep model, and obtaining the trained overheat monitoring deep model; calculating the deep model performance of the trained overheat monitoring deep model, and analyzing the operation thermal energy coefficient of the aging equipment; building the operation thermal energy development curve of the aging equipment, identifying the overheat coordinates of the aging equipment, building the overheat protection instruction of the aging equipment, and executing the overheat protection of the aging equipment based on the overheat protection instruction. The present invention can improve the effect of overheat protection of the aging equipment.

Description

Technical Field

[0001] The present invention relates to the field of overheat protection, and in particular to a method and system for overheat protection of intelligent aging equipment based on the Internet of Things. Background Art

[0002] Overheat protection of maturation equipment is a safety measure. It means that when the temperature exceeds a certain set threshold during the operation of the equipment, the corresponding protection function is automatically started. Overheat protection of maturation equipment can realize real-time monitoring, overheat protection and intelligent management of maturation equipment, improve equipment operation efficiency and service life, and reduce the risk of equipment failure.

[0003] At present, the overheating protection of the maturation equipment is mainly achieved by collecting the operation data of the maturation equipment to analyze whether overheating occurs. When overheating occurs, the equipment operation can be cut off, the equipment operation performance can be reduced, or the ventilation and heat dissipation conditions can be improved to prevent the equipment from operating abnormally or malfunctioning due to overheating. This method can only identify the situation where the maturation equipment has overheated, and cannot prevent overheating, resulting in the overheating protection of the maturation equipment not being intelligent enough. Summary of the invention

[0004] The present invention provides a method and system for overheat protection of intelligent aging equipment based on the Internet of Things, the main purpose of which is to improve the effect of overheat protection of aging equipment.

[0005] To achieve the above object, the present invention provides a method for overheating protection of intelligent aging equipment based on the Internet of Things, comprising:

[0006] Acquire aging equipment information of the aging equipment, determine the communication protocol of the aging equipment based on the aging equipment information, and build an Internet of Things network of the aging equipment based on the communication protocol;

[0007] Based on the Internet of Things network, the operation data of the aging equipment is collected, the operation data is filtered to obtain filtered operation data, and the filtered operation data is subjected to wavelet transformation to obtain wavelet transformation coefficients;

[0008] Extracting the operation characteristics of the aging equipment from the wavelet transform coefficients, constructing an overheat monitoring deep model for the aging equipment, and training the overheat monitoring deep model based on the filtered operation data and the operation characteristics to obtain a trained overheat monitoring deep model;

[0009] Calculating the deep model performance of the trained overheat monitoring deep model, and when the deep model performance meets the requirements, analyzing the operating thermal energy coefficient of the curing equipment using the trained overheat monitoring deep model;

[0010] Based on the operating thermal energy coefficient, an operating thermal energy development curve of the maturation equipment is constructed; based on the operating thermal energy development curve, an overheating coordinate of the maturation equipment is identified; based on the overheating coordinate, an overheating protection instruction of the maturation equipment is constructed; and based on the overheating protection instruction, overheating protection of the maturation equipment is executed.

[0011] Optionally, the step of constructing an Internet of Things network of the maturation equipment based on the communication protocol includes:

[0012] identifying protocol compatibility of the maturation device with the communication protocol;

[0013] Based on the protocol compatibility, screening out protocol-incompatible devices from the maturation devices;

[0014] Performing protocol conversion on the protocol-incompatible device to obtain a conversion protocol;

[0015] Based on the communication protocol and the conversion protocol, an Internet of Things network of the maturation equipment is constructed.

[0016] Optionally, filtering the operating data to obtain filtered operating data includes:

[0017] Determine a filter window for the operating data, and calculate a data average of the window operating data corresponding to the filter window;

[0018] Performing window movement on the filtering window to obtain a moving window;

[0019] Calculate the moving data average value of the moving window operation data corresponding to the moving window;

[0020] The data average value and the moving data average value are serialized to obtain filtered operating data of the operating data.

[0021] Optionally, calculating the data average of the window running data corresponding to the filtering window includes:

[0022] Determining a window size of the filtering window;

[0023] Based on the window size, marking the data quantity of the window operation data corresponding to the filter window;

[0024] Based on the window size and the amount of data, the data average value of the window operation data corresponding to the filter window is calculated using the following formula:

[0025]

[0026] Among them, L[a] represents the data average of the window running data corresponding to the filter window, a represents the data quantity of the window running data corresponding to the filter window, M represents the window size of the window corresponding to the filter window, and E[i] represents the i-th window running data of the filter window.

[0027] Optionally, performing a wavelet transform on the filtering operation data to obtain a wavelet transform coefficient includes:

[0028] Determining a wavelet basis function of the filtering operation data;

[0029] Determining the decomposition level of the wavelet basis function;

[0030] Based on the decomposition level, the wavelet basis function is used to perform wavelet transform on the filtering operation data to obtain the wavelet transform coefficients.

[0031] Optionally, extracting the operation characteristics of the aging equipment from the wavelet transform coefficients includes:

[0032] Calculating coefficient amplitudes of the wavelet transform coefficients;

[0033] Analyzing the energy distribution state of the wavelet transform coefficients based on the coefficient amplitudes;

[0034] Calculating the coefficient phase of the wavelet transform coefficient;

[0035] Analyzing the waveform state of the wavelet transform coefficient based on the coefficient phase;

[0036] Based on the energy distribution state and the waveform state, operation characteristics of the aging device in the wavelet transform coefficients are extracted.

[0037] Optionally, the calculating the coefficient amplitude of the wavelet transform coefficient comprises:

[0038] The coefficient amplitude of the wavelet transform coefficient is calculated using the following formula:

[0039] |C_{r,v}|=sqrt(Re(D_{r,v})^2+Im(D_{r,v})^2)

[0040] Where |C_{r,v}| represents the coefficient amplitude 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, and Im(D_{r,v} represents the imaginary part of the wavelet transform coefficient.

[0041] Optionally, the identifying the overheating coordinate of the curing equipment based on the operating heat energy development curve includes:

[0042] Based on the operation heat energy development curve and a preset equipment heat energy risk limit, marking a risk area in the operation heat energy development curve;

[0043] Identifying risk characteristics of the risk area;

[0044] Based on the risk characteristics and the risk area, identifying a thermal energy risk factor of the curing equipment;

[0045] Based on the thermal energy risk factor, an overheating coordinate of the ripening equipment is determined.

[0046] Optionally, the identifying the thermal energy risk coefficient of the curing equipment based on the risk characteristics and the risk area includes:

[0047] Based on the risk characteristics, marking the fluctuation peaks and fluctuation valleys of the risk area;

[0048] Based on the fluctuation peak value and the fluctuation valley value, the thermal energy risk coefficient of the aging equipment is calculated using the following formula:

[0049]

[0050] Wherein, τ represents the thermal energy risk coefficient of the aging equipment, H represents the height from the fluctuation peak value to the fluctuation valley value in the risk area of ​​the operation thermal energy development curve corresponding to the aging equipment, cos represents the operation thermal energy development curve corresponding to the aging equipment, ω represents the angular frequency of the operation thermal energy development curve corresponding to the aging equipment, B represents the fluctuation peak value in the risk area of ​​the operation thermal energy development curve corresponding to the aging equipment, O represents the fluctuation valley value in the risk area of ​​the operation thermal energy development curve corresponding to the aging equipment, G t It indicates the duration from the peak value to the valley value of fluctuation in the risk area of ​​the operating heat energy development curve of the curing equipment.

[0051] In order to solve the above problems, the present invention also provides a system for overheating protection of intelligent aging equipment based on the Internet of Things, the system comprising:

[0052] An Internet of Things network construction module, used to obtain the ripening equipment information of the ripening equipment, determine the communication protocol of the ripening equipment based on the ripening equipment information, and construct the Internet of Things network of the ripening equipment based on the communication protocol;

[0053] An operation data filtering module, used for collecting the operation data of the aging equipment based on the Internet of Things network, filtering the operation data to obtain filtered operation data, and performing wavelet transformation on the filtered operation data to obtain wavelet transformation coefficients;

[0054] A deep model training module, used for extracting the operation characteristics of the aging equipment from the wavelet transform coefficients, constructing an overheat monitoring deep model for the aging equipment, and training the overheat monitoring deep model based on the filtered operation data and the operation characteristics to obtain a trained overheat monitoring deep model;

[0055] A thermal energy coefficient output module, used to calculate the deep model performance of the trained overheat monitoring deep model, and when the deep model performance meets the requirements, use the trained overheat monitoring deep model to analyze the operating thermal energy coefficient of the maturation equipment;

[0056] The equipment overheat protection module is used to construct an operating heat energy development curve of the maturation equipment based on the operating heat energy coefficient, identify the overheat coordinates of the maturation equipment based on the operating heat energy development curve, construct an overheat protection instruction of the maturation equipment based on the overheat coordinates, and execute overheat protection of the maturation equipment based on the overheat protection instruction.

[0057] The embodiment of the present invention builds the Internet of Things network of the aging equipment based on the communication protocol, which can realize the real-time acquisition of the status information of the aging equipment, as well as the collection and analysis of data; the embodiment of the present invention improves the data quality by filtering the operation data to obtain filtered operation data; the embodiment of the present invention analyzes the signal on different time scales by performing wavelet transform on the filtered operation data to obtain wavelet transform coefficients; the embodiment of the present invention extracts the operation characteristics of the aging equipment from the wavelet transform coefficients, which can provide data basis for the real-time monitoring and fault diagnosis of the equipment operation status in the later stage. Furthermore, the embodiment of the present invention constructs the overheating monitoring depth model of the aging equipment, which can be used to perform intelligent monitoring of the aging equipment through the model. Measurement and analysis, further, the embodiment of the present invention trains the overheat monitoring deep model based on the filtered operation data and the operation characteristics, and the trained overheat monitoring deep model can improve the performance of the model and improve the accuracy of the analysis of the operation status of the maturation equipment. When the performance of the deep model meets the requirements, the embodiment of the present invention uses the trained overheat monitoring deep model to analyze the operation thermal energy coefficient of the maturation equipment, and can monitor the maturation equipment in real time. Finally, the embodiment of the present invention constructs the operation thermal energy development curve of the maturation equipment based on the operation thermal energy coefficient, which can more intuitively analyze the thermal energy conversion of the maturation equipment. Based on the overheat coordinates, the overheat protection instruction of the maturation equipment is constructed to achieve the overheat protection of the maturation equipment. Therefore, the method and system for overheat protection of intelligent maturation equipment based on the Internet of Things proposed by the present invention can improve the effect of overheat protection of maturation equipment. BRIEF DESCRIPTION OF THE DRAWINGS

[0058] Figure 1A schematic flow chart of a method for overheat protection of intelligent aging equipment based on the Internet of Things provided by one embodiment of the present invention;

[0059] Figure 2 A functional module diagram of a system for overheat protection of intelligent aging equipment based on the Internet of Things provided by an embodiment of the present invention;

[0060] Figure 3 A schematic diagram of the structure of an electronic device of a system for overheat protection of an IoT intelligent aging device provided by an embodiment of the present invention;

[0061] The realization of the purpose, functional features and advantages of the present invention will be further explained in conjunction with embodiments and with reference to the accompanying drawings. DETAILED DESCRIPTION

[0062] It should be understood that the specific embodiments described herein are only used to explain the present invention, and are not used to limit the present invention.

[0063] The embodiment of the present application provides a method for overheating protection of intelligent maturation equipment based on the Internet of Things. The execution subject of the method for overheating protection of intelligent maturation equipment based on the Internet of Things includes but is not limited to at least one of the electronic devices such as the server and the terminal that can be configured to execute the method provided by the embodiment of the present application. In other words, the method for overheating protection of intelligent maturation equipment based on the Internet of Things can be executed by software or hardware installed on the terminal device or the server device, and the software can be a blockchain platform. The server includes but is not limited to: a single server, a server cluster, a cloud server or a cloud server cluster, etc. The server can be an independent server, or it can 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.

[0064] Reference Figure 1 FIG. 1 is a flow chart of a method for overheat protection of an intelligent aging device based on the Internet of Things according to an embodiment of the present invention. In this embodiment, the method for overheat protection of an intelligent aging device based on the Internet of Things includes:

[0065] , obtaining aging equipment information of the aging equipment, determining the communication protocol of the aging equipment based on the aging equipment information, and building an Internet of Things network for the aging equipment based on the communication protocol.

[0066] In the embodiment of the present invention, the maturation equipment information refers to data describing the maturation equipment, such as equipment signal, equipment type, equipment power and other data.

[0067] Furthermore, the embodiment of the present invention determines the communication protocol of the maturation device based on the maturation device information, which can provide a basis for data communication of the later maturation device. The communication protocol refers to a network that ensures data transmission of the maturation device, such as MQTT, Modbus and other protocols. In detail, the communication protocol of the maturation device is determined mainly by analyzing the protocol that the maturation device supports the most as the target communication protocol.

[0068] Optionally, the embodiment of the present invention constructs an Internet of Things network of the maturation equipment based on the communication protocol to achieve real-time acquisition of status information of the maturation equipment, as well as data collection and analysis. The Internet of Things network refers to a network that connects various physical objects to the network through information sensing devices to achieve intelligent identification, positioning, tracking, monitoring and management.

[0069] As an embodiment of the present invention, the Internet of Things network of the matured equipment is constructed based on the communication protocol, including: identifying the protocol compatibility between the matured equipment and the communication protocol; based on the protocol compatibility, screening out protocol-incompatible devices from the matured equipment; performing protocol conversion on the protocol-incompatible devices to obtain a conversion protocol; and constructing the Internet of Things network of the matured equipment based on the communication protocol and the conversion protocol.

[0070] Among them, the protocol compatibility refers to whether the mature device supports the communication protocol. If it does not support the communication protocol, the protocol compatibility is protocol incompatibility; if it supports the communication protocol, the protocol compatibility is protocol compatibility. The protocol-incompatible device refers to the device whose protocol compatibility is protocol incompatibility. The conversion protocol refers to the new protocol after the protocol-incompatible device is converted through the gateway device.

[0071] S2. Based on the Internet of Things network, collect the operation data of the aging equipment, filter the operation data to obtain filtered operation data, and perform wavelet transform on the filtered operation data to obtain wavelet transform coefficients.

[0072] In the embodiment of the present invention, the operation data refers to a data set of the operation process of the maturation equipment.

[0073] The embodiment of the present invention can improve the data quality by filtering the operation data to obtain filtered operation data, wherein the filtered operation data refers to a data set after the operation data is filtered.

[0074] As an embodiment of the present invention, the filtering processing of the operating data to obtain filtered operating data includes: determining a filtering window of the operating data, calculating a data average of the window operating data corresponding to the filtering window; window moving the filtering window to obtain a moving window; calculating a moving data average of the moving window operating data corresponding to the moving window; and serializing the data average and the moving data average to obtain filtered operating data of the operating data.

[0075] Among them, the filtering window refers to a sliding window of fixed size, the window running data refers to the data set of the running data in the sliding window, the data average value refers to the data mean of the data contained in the sliding window, the moving window refers to the window after the filtering window is moved, and the moving data average value refers to the mean of the data in the moving window.

[0076] Optionally, as an optional embodiment of the present invention, the calculating the data average of the window running data corresponding to the filtering window includes: determining the window size of the filtering window; marking the number of data of the window running data corresponding to the filtering window based on the window size; and calculating the data average of the window running data corresponding to the filtering window using the following formula based on the window size and the number of data:

[0077]

[0078] Among them, L[a] represents the data average of the window running data corresponding to the filter window, a represents the data quantity of the window running data corresponding to the filter window, M represents the window size of the window corresponding to the filter window, and E[i] represents the i-th window running data of the filter window.

[0079] The embodiment of the present invention performs wavelet transform on the filtering operation data to obtain wavelet transform coefficients, which can analyze signals at different time scales. The wavelet transform coefficients refer to the characteristics of the signal at different frequency scales.

[0080] Optionally, as an embodiment of the present invention, the step of performing a wavelet transform on the filtered running data to obtain wavelet transform coefficients includes: determining a wavelet basis function of the filtered running data; determining a decomposition level of the wavelet basis function; and based on the decomposition level, performing a wavelet transform on the filtered running data using the wavelet basis function to obtain the wavelet transform coefficients.

[0081] Among them, the wavelet basis function refers to the function used as the basis in the wavelet transform. They 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 and other functions. The decomposition level refers to the different levels of decomposition of the filtered running data to obtain the characteristics of the signal at different frequency scales. Generally, the higher the decomposition level, the finer the time resolution and the coarser the frequency resolution.

[0082] S3. Extracting the operation characteristics of the aging equipment from the wavelet transform coefficients, constructing an overheat monitoring deep model for the aging equipment, and training the overheat monitoring deep model based on the filtered operation data and the operation characteristics to obtain a trained overheat monitoring deep model.

[0083] The embodiment of the present invention can provide data basis for real-time monitoring of the equipment operation status and fault diagnosis in the later stage by extracting the operation characteristics of the maturation equipment from the wavelet transform coefficients. The operation characteristics refer to the dynamic transformation characteristics of the operation process of the maturation equipment, such as the equipment operation status, fault type, fault degree and other characteristics.

[0084] As an embodiment of the present invention, the extracting the operating characteristics of the maturation equipment in the wavelet transform coefficients includes: calculating the coefficient amplitude of the wavelet transform coefficients; analyzing the energy distribution state of the wavelet transform coefficients based on the coefficient amplitude; calculating the coefficient phase of the wavelet transform coefficients; analyzing the waveform state of the wavelet transform coefficients based on the coefficient phase; and extracting the operating characteristics of the maturation equipment in the wavelet transform coefficients based on the energy distribution state and the waveform state.

[0085] Among them, the coefficient amplitude refers to the value that can reflect the energy size of the signal at a certain time and frequency, the energy distribution state refers to the energy distribution characteristics of the device under different operating 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 under different operating states.

[0086] Optionally, as an optional embodiment of the present invention, the calculating the coefficient amplitude of the wavelet transform coefficient includes: calculating the coefficient amplitude of the wavelet transform coefficient using the following formula:

[0087] |C_{r,v}|=sqrt(Re(D_{r,v})^2+Im(D_{r,v})^2)

[0088] Where |C_{r,v}| represents the coefficient amplitude 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, and Im(D_{r,v} represents the imaginary part of the wavelet transform coefficient.

[0089] The translation of the wavelet transform coefficient refers to the translation of the wavelet basis function by v units on the time axis, and the scale of the wavelet transform coefficient refers to the expansion degree of the wavelet basis function on the time axis.

[0090] Optionally, the calculation of the coefficient phase of the wavelet transform coefficient may be achieved by calculating the ratio of the real part and the imaginary part corresponding to the wavelet transform coefficient.

[0091] The embodiment of the present invention can intelligently monitor and analyze the aging equipment through the model by constructing the overheat monitoring deep model of the aging equipment. The overheat monitoring deep model refers to the initial model architecture for monitoring the operating state of the aging equipment. In detail, the overheat monitoring deep model can be constructed by a convolutional neural network, a recursive neural network, a long short-term memory network, etc.

[0092] Furthermore, the embodiment of the present invention trains the overheat monitoring deep model based on the filtered operating data and the operating characteristics, and the trained overheat monitoring deep model can improve the performance of the model and improve the accuracy of the analysis of the operating status of the maturation equipment. Among them, the trained overheat monitoring deep model refers to the model after the overheat monitoring deep model is trained with a large amount of data. The training of the overheat monitoring deep model refers to using the prepared data set and features to input the data into the selected deep learning model for training; during the training process, the model will learn how to predict the overheating state based on the operating data and features; during the training process, the model's hyperparameters, such as the learning rate, batch size, and number of iterations, need to be adjusted to improve the performance of the model.

[0093] S4. Calculate the deep model performance of the trained overheat monitoring deep model. When the deep model performance meets the requirements, use the trained overheat monitoring deep model to analyze the operating thermal energy coefficient of the curing equipment.

[0094] The embodiment of the present invention calculates the deep model performance of the trained overheat monitoring deep model to identify whether the trained overheat monitoring deep model meets the monitoring requirements, thereby improving the reliability of the model. The deep model performance refers to the monitoring effect of the trained overheat monitoring deep model on the maturation equipment.

[0095] Optionally, as an embodiment of the present invention, the calculating the deep model performance of the trained overheat monitoring deep model includes: marking the model training output results of the trained overheat monitoring deep model; identifying the accuracy of the model training output results; and based on the accuracy, calculating the deep model performance of the trained overheat monitoring deep model.

[0096] Among them, the model training output result refers to the result output by the trained overheat monitoring depth model during the training process, and the accuracy refers to the accuracy of the model training output result.

[0097] Furthermore, in the embodiment of the present invention, when the performance of the deep model meets the requirements, the operating thermal energy coefficient of the maturation equipment can be analyzed by using the trained overheat monitoring deep model to monitor the maturation equipment in real time, wherein the operating thermal energy coefficient refers to the degree of overheating of the maturation equipment during operation.

[0098] As an embodiment of the present invention, when the performance of the deep model meets the requirements, the operating thermal energy coefficient of the maturation equipment is analyzed using the trained overheat monitoring deep model, including: using the observation network of the trained overheat monitoring deep model to identify the operating observation value of the maturation equipment; using the gain network of the trained overheat monitoring deep model to analyze the state gain value of the maturation equipment; and calculating the operating thermal energy coefficient of the maturation equipment based on the operating observation value and the state gain value.

[0099] Among them, the observation network refers to a network that identifies the data value of the current operating data of the maturation equipment, the operating observation value refers to the state value of the maturation equipment operation process, such as position, speed, heat and other values, the gain network refers to a network that analyzes the development direction of the state of the maturation equipment, which can be achieved through the Kalman gain function, and the state gain value refers to the development direction of the state of the maturation equipment, such as changes in the degree of heat increase, speed change law and other changes.

[0100] S5. constructing an operating thermal energy development curve of the curing equipment based on the operating thermal energy coefficient, identifying overheating coordinates of the curing equipment based on the operating thermal energy development curve, constructing an overheating protection instruction of the curing equipment based on the overheating coordinates, and executing overheating protection of the curing equipment based on the overheating protection instruction.

[0101] In the embodiment of the present invention, based on the operating thermal energy coefficient, the operating thermal energy development curve of the aging equipment is constructed to more intuitively analyze the thermal energy conversion of the aging equipment. The operating thermal energy development curve refers to a relationship curve between the thermal energy of the aging equipment and time. The operating thermal energy development curve can be realized by a curve function.

[0102] In the embodiment of the present invention, the overheating coordinate refers to the coordinate of the curing equipment in the operating thermal energy development curve where the curing equipment has an overheating risk.

[0103] As an embodiment of the present invention, identifying the overheating coordinates of the maturation equipment based on the operating thermal energy development curve includes: marking the risk area in the operating thermal energy development curve based on the operating thermal energy development curve and a preset equipment thermal energy risk limit; identifying the risk characteristics of the risk area; identifying the thermal energy risk coefficient of the maturation equipment based on the risk characteristics and the risk area; and determining the overheating coordinates of the maturation equipment based on the thermal energy risk coefficient.

[0104] Among them, the equipment thermal energy risk limit refers to the limit value of the overheating phenomenon of the maturation equipment, the risk area refers to the curve segment of the continuous overheating in the operating thermal energy development curve, the risk characteristics refer to the characteristics of the risk area, such as the area range, the degree of regional fluctuation and other characteristics, and the thermal energy risk coefficient refers to the degree of risk caused by the risk area to the maturation equipment.

[0105] Optionally, as an optional embodiment of the present invention, the identifying the thermal energy risk coefficient of the maturation equipment based on the risk characteristics and the risk area includes: marking the fluctuation peak value and the fluctuation valley value of the risk area based on the risk characteristics; and calculating the thermal energy risk coefficient of the maturation equipment based on the fluctuation peak value and the fluctuation valley value using the following formula:

[0106]

[0107] Wherein, τ represents the thermal energy risk coefficient of the aging equipment, H represents the height from the fluctuation peak value to the fluctuation valley value in the risk area of ​​the operation thermal energy development curve corresponding to the aging equipment, cos represents the operation thermal energy development curve corresponding to the aging equipment, ω represents the angular frequency of the operation thermal energy development curve corresponding to the aging equipment, B represents the fluctuation peak value in the risk area of ​​the operation thermal energy development curve corresponding to the aging equipment, O represents the fluctuation valley value in the risk area of ​​the operation thermal energy development curve corresponding to the aging equipment, G t It indicates the duration from the peak value to the valley value of fluctuation in the risk area of ​​the operating heat energy development curve of the curing equipment.

[0108] In the embodiment of the present invention, based on the overheating coordinates, the overheating protection instruction of the maturation equipment is constructed to realize the overheating protection of the maturation equipment. The overheating protection instruction refers to an instruction that can regulate the maturation equipment for overheating protection, such as instructions for cutting off the operation of the equipment, reducing the operation performance of the equipment, or improving ventilation and heat dissipation. The overheating protection instruction for constructing the maturation equipment can be determined according to the thermal energy risk coefficient corresponding to the overheating coordinates. For example, when the thermal energy risk coefficient is low, the overheating protection instruction can be to reduce the operation performance of the equipment, and when the thermal energy risk coefficient is high, the overheating protection instruction can be to cut off the operation of the equipment.

[0109] Optionally, in the embodiment of the present invention, the overheating protection of the curing equipment can be implemented by executing the overheating protection of the curing equipment based on the overheating protection instruction.

[0110] The embodiment of the present invention builds the Internet of Things network of the aging equipment based on the communication protocol, which can realize the real-time acquisition of the status information of the aging equipment, as well as the collection and analysis of data; the embodiment of the present invention improves the data quality by filtering the operation data to obtain filtered operation data; the embodiment of the present invention analyzes the signal on different time scales by performing wavelet transform on the filtered operation data to obtain wavelet transform coefficients; the embodiment of the present invention extracts the operation characteristics of the aging equipment from the wavelet transform coefficients, which can provide data basis for the real-time monitoring and fault diagnosis of the equipment operation status in the later stage. Furthermore, the embodiment of the present invention constructs the overheating monitoring depth model of the aging equipment, which can be used to perform intelligent monitoring of the aging equipment through the model. Measurement and analysis, further, the embodiment of the present invention trains the overheat monitoring deep model based on the filtered operation data and the operation characteristics, and the trained overheat monitoring deep model can improve the performance of the model and improve the accuracy of the analysis of the operation status of the maturation equipment. When the performance of the deep model meets the requirements, the embodiment of the present invention uses the trained overheat monitoring deep model to analyze the operation thermal energy coefficient of the maturation equipment, and can monitor the maturation equipment in real time. Finally, the embodiment of the present invention constructs the operation thermal energy development curve of the maturation equipment based on the operation thermal energy coefficient, which can more intuitively analyze the thermal energy conversion of the maturation equipment. Based on the overheat coordinates, the overheat protection instruction of the maturation equipment is constructed to achieve the overheat protection of the maturation equipment. Therefore, the method for overheat protection of intelligent maturation equipment based on the Internet of Things proposed in the present invention can improve the effect of overheat protection of maturation equipment.

[0111] like Figure 2 , which is a functional module diagram of a system for overheating protection of intelligent aging equipment based on the Internet of Things provided by one embodiment of the present invention.

[0112] The system 200 for overheat protection of intelligent aging equipment based on the Internet of Things of the present invention can be installed in an electronic device. According to the functions to be implemented, the system 200 for overheat protection of intelligent aging equipment based on the Internet of Things can include an Internet of Things network construction module 201, an operation data filtering module 202, a deep model training module 203, a thermal energy coefficient output module 204 and an equipment overheat protection module 205. The module described in the present invention can also be called a unit, which refers to a series of computer program segments that can be executed by an electronic device processor and can complete fixed functions, which are stored in the memory of the electronic device.

[0113] In this embodiment, the functions of each module / unit are as follows:

[0114] The Internet of Things network construction module 201 is used to obtain the ripening equipment information of the ripening equipment, determine the communication protocol of the ripening equipment based on the ripening equipment information, and construct the Internet of Things network of the ripening equipment based on the communication protocol;

[0115] The operation data filtering module 202 is used to collect the operation data of the aging equipment based on the Internet of Things network, filter the operation data to obtain filtered operation data, and perform wavelet transformation on the filtered operation data to obtain wavelet transformation coefficients;

[0116] The deep model training module 203 is used to extract the operation characteristics of the aging equipment from the wavelet transform coefficients, build an overheat monitoring deep model for the aging equipment, and train the overheat monitoring deep model based on the filtered operation data and the operation characteristics to obtain a trained overheat monitoring deep model;

[0117] The thermal energy coefficient output module 204 is used to calculate the deep model performance of the trained overheat monitoring deep model, and when the deep model performance meets the requirements, the trained overheat monitoring deep model is used to analyze the operating thermal energy coefficient of the maturation equipment;

[0118] The equipment overheat protection module 205 is used to construct an operation heat energy development curve of the maturation equipment based on the operation heat energy coefficient, identify the overheat coordinates of the maturation equipment based on the operation heat energy development curve, construct an overheat protection instruction of the maturation equipment based on the overheat coordinates, and execute overheat protection of the maturation equipment based on the overheat protection instruction.

[0119] In detail, each module described in the system 200 for overheating protection of intelligent aging equipment based on the Internet of Things described in the embodiment of the present invention adopts the same technical means as the method for overheating protection of intelligent aging equipment based on the Internet of Things described in the accompanying drawings when used, and can produce the same technical effects, which will not be repeated here.

[0120] An embodiment of the present invention provides an electronic device for implementing a method for overheating protection of intelligent aging equipment based on the Internet of Things.

[0121] See also Figure 3 As shown, the electronic device may include a processor 30, a memory 31, a communication bus 32 and a communication interface 33, and may also include a computer program stored in the memory 31 and executable on the processor 30, such as a method program for overheat protection of intelligent aging equipment based on the Internet of Things.

[0122] In some embodiments, the processor may be composed of an integrated circuit, for example, a single packaged integrated circuit, or a plurality of packaged integrated circuits with the same or different functions, including one or more central processing units (CPUs), microprocessors, digital processing chips, graphics processors, and combinations of various control chips, etc. The processor is the control core (Control Unit) of the electronic device, and uses various interfaces and lines to connect various components of the entire electronic device, and executes or executes programs or modules stored in the memory (for example, executing programs based on overheat protection of IoT intelligent maturation equipment, etc.), and calls data stored in the memory to execute various functions of the electronic device and process data.

[0123] The memory includes at least one type of readable storage medium, and the readable storage medium includes flash memory, mobile hard disk, multimedia card, card-type memory (for example: SD or DX memory, etc.), magnetic memory, disk, optical disk, etc. The memory may be an internal storage unit of an electronic device in some embodiments, such as a mobile hard disk of the electronic device. The memory may also be an external storage device of an electronic device in other embodiments, such as a plug-in mobile hard disk, a smart memory card (Smart Media Card, SMC), a secure digital (Secure Digital, SD) card, a flash card (Flash Card), etc. equipped on the electronic device. Further, the memory may also include both an internal storage unit of the electronic device and an external storage device. The memory may not only be used to store application software and various types of data installed in the electronic device, such as the code of a program based on overheat protection of an IoT intelligent maturation device, but may also be used to temporarily store data that has been output or is to be output.

[0124] The communication bus may be a Peripheral Component Interconnect (PCI) bus or an Extended Industry Standard Architecture (EISA) bus, etc. The bus may be divided into an address bus, a data bus, a control bus, etc. The bus is configured to realize connection and communication between the memory and at least one processor, etc.

[0125] The communication interface is used for communication between the above-mentioned electronic device and other devices, including a network interface and a user interface. Optionally, the network interface may include a wired interface and / or a wireless interface (such as a WI-FI interface, a Bluetooth interface, etc.), which is generally 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)), and optionally, the user interface may also be a standard wired interface, a wireless interface. Optionally, in some embodiments, the display may be an LED display, a liquid crystal display, a touch-sensitive liquid crystal display, and an OLED (Organic Light-Emitting Diode, organic light-emitting diode) touch device, etc. Among them, the display may also be appropriately referred to as a display screen or a display unit, which is used to display information processed in the electronic device and to display a visual user interface.

[0126] For example, although not shown, the electronic device may also include a power source (such as a battery) for supplying power to each component. Preferably, the power source may be logically connected to the at least one processor through a power management system, so that the power management system can realize functions such as charging management, discharging management, and power consumption management. The power source may also include any components such as one or more DC or AC power sources, recharging systems, power failure detection circuits, power converters or inverters, and power status indicators. The electronic device may also include a variety of sensors, Bluetooth modules, Wi-Fi modules, etc., which will not be repeated here.

[0127] It should be understood that the embodiment is for illustration only and the scope of the patent application is not limited to this structure.

[0128] The program based on overheat protection of the IoT intelligent aging equipment stored in the memory of the electronic device is a combination of multiple instructions. When running in the processor, it can achieve:

[0129] Acquire aging equipment information of the aging equipment, determine the communication protocol of the aging equipment based on the aging equipment information, and build an Internet of Things network of the aging equipment based on the communication protocol;

[0130] Based on the Internet of Things network, the operation data of the aging equipment is collected, the operation data is filtered to obtain filtered operation data, and the filtered operation data is subjected to wavelet transformation to obtain wavelet transformation coefficients;

[0131] Extracting the operation characteristics of the aging equipment from the wavelet transform coefficients, constructing an overheat monitoring deep model for the aging equipment, and training the overheat monitoring deep model based on the filtered operation data and the operation characteristics to obtain a trained overheat monitoring deep model;

[0132] Calculating the deep model performance of the trained overheat monitoring deep model, and when the deep model performance meets the requirements, analyzing the operating thermal energy coefficient of the curing equipment using the trained overheat monitoring deep model;

[0133] Based on the operating thermal energy coefficient, an operating thermal energy development curve of the maturation equipment is constructed; based on the operating thermal energy development curve, an overheating coordinate of the maturation equipment is identified; based on the overheating coordinate, an overheating protection instruction of the maturation equipment is constructed; and based on the overheating protection instruction, overheating protection of the maturation equipment is executed.

[0134] Specifically, the specific implementation method of the processor for the above instructions can refer to the description of the relevant steps in the corresponding embodiment of the accompanying drawings, which will not be repeated here.

[0135] Furthermore, if the module / unit integrated in the electronic device is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. The computer-readable storage medium can be volatile or non-volatile. For example, the computer-readable medium may include: any entity or system capable of carrying the computer program code, a recording medium, a USB flash drive, a mobile hard disk, a magnetic disk, an optical disk, a computer memory, and a read-only memory (ROM).

[0136] The present invention further provides a computer-readable storage medium, wherein the computer-readable storage medium stores a computer program, and when the computer program is executed by a processor of an electronic device, the computer program can implement:

[0137] Acquire aging equipment information of the aging equipment, determine the communication protocol of the aging equipment based on the aging equipment information, and build an Internet of Things network of the aging equipment based on the communication protocol;

[0138] Based on the Internet of Things network, the operation data of the aging equipment is collected, the operation data is filtered to obtain filtered operation data, and the filtered operation data is subjected to wavelet transformation to obtain wavelet transformation coefficients;

[0139] Extracting the operation characteristics of the aging equipment from the wavelet transform coefficients, constructing an overheat monitoring deep model for the aging equipment, and training the overheat monitoring deep model based on the filtered operation data and the operation characteristics to obtain a trained overheat monitoring deep model;

[0140] Calculating the deep model performance of the trained overheat monitoring deep model, and when the deep model performance meets the requirements, analyzing the operating thermal energy coefficient of the curing equipment using the trained overheat monitoring deep model;

[0141] Based on the operating thermal energy coefficient, an operating thermal energy development curve of the maturation equipment is constructed; based on the operating thermal energy development curve, an overheating coordinate of the maturation equipment is identified; based on the overheating coordinate, an overheating protection instruction of the maturation equipment is constructed; and based on the overheating protection instruction, overheating protection of the maturation equipment is executed.

[0142] In the several embodiments provided by the present invention, it should be understood that the disclosed devices, systems and methods can be implemented in other ways. For example, the system embodiments described above are only schematic, for example, the division of the modules is only a logical function division, and there may be other division methods in actual implementation.

[0143] The modules described as separate components may or may not be physically separated, and the components shown as modules may or may not be physical units, that is, they may be located in one place or distributed on 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.

[0144] In addition, each functional module in each embodiment of the present invention may be integrated into one processing unit, each unit may exist physically separately, or two or more units may be integrated into one unit. The above-mentioned integrated unit may be implemented in the form of hardware or in the form of hardware plus software functional modules.

[0145] It is obvious to those skilled in the art that the present invention is not limited to the details of the above exemplary embodiments, and that the present invention can be implemented in other specific forms without departing from the spirit or essential characteristics of the present invention.

[0146] Therefore, no matter from which point of view, the embodiments should be regarded as illustrative and non-restrictive, and the scope of the present invention is limited by the appended claims rather than the above description, so it is intended that all changes falling within the meaning and scope of the equivalent elements of the claims are included in the present invention. Any attached figure mark in the claims should not be regarded as limiting the claims involved.

[0147] The embodiments of the present application can acquire and process relevant data based on artificial intelligence technology. Among them, artificial intelligence (AI) is the theory, method, technology and application system that uses digital computers or machines controlled by digital computers to simulate, extend and expand human intelligence, perceive the environment, acquire knowledge and use knowledge to obtain the best results.

[0148] In addition, it is clear that the word "comprising" does not exclude other units or steps, and the singular does not exclude the plural. Multiple units or systems stated in a system claim can also be implemented by one unit or system through software or hardware. The words first, second, etc. are used to indicate names, and do not indicate any particular order.

[0149] Finally, it should be noted that the above embodiments are only used to illustrate the technical solution of the present invention rather than to limit it. Although the present invention has been described in detail with reference to the preferred embodiments, those skilled in the art should understand that the technical solution of the present invention can be modified or replaced by equivalents without departing from the spirit and scope of the technical solution of the present invention.

Claims

1. A method for overheat protection of intelligent maturation equipment based on the Internet of Things, characterized in that: The method comprises: Acquire aging equipment information of the aging equipment, determine the communication protocol of the aging equipment based on the aging equipment information, and build an Internet of Things network of the aging equipment based on the communication protocol; Based on the Internet of Things network, the operation data of the aging equipment is collected, the operation data is filtered to obtain filtered operation data, and the filtered operation data is subjected to wavelet transformation to obtain wavelet transformation coefficients; Extracting the operation characteristics of the aging equipment from the wavelet transform coefficients, constructing an overheat monitoring deep model for the aging equipment, and training the overheat monitoring deep model based on the filtered operation data and the operation characteristics to obtain a trained overheat monitoring deep model; Calculating the deep model performance of the trained overheat monitoring deep model, and when the deep model performance meets the requirements, analyzing the operating thermal energy coefficient of the curing equipment using the trained overheat monitoring deep model; Based on the operation thermal energy coefficient, an operation thermal energy development curve of the maturation equipment is constructed; based on the operation thermal energy development curve, the overheating coordinates of the maturation equipment are identified; wherein, the identifying the overheating coordinates of the maturation equipment based on the operation thermal energy development curve includes: based on the operation thermal energy development curve and a preset equipment thermal energy risk limit, marking the risk area in the operation thermal energy development curve, identifying the risk characteristics of the risk area, and identifying the thermal energy risk coefficient of the maturation equipment based on the risk characteristics and the risk area; wherein, the identifying the thermal energy risk coefficient of the maturation equipment based on the risk characteristics and the risk area includes: based on the risk characteristics, marking the fluctuation peak value and the fluctuation valley value of the risk area, and calculating the thermal energy risk coefficient of the maturation equipment based on the fluctuation peak value and the fluctuation valley value using the following formula: Wherein, τ represents the thermal energy risk coefficient of the aging equipment, H represents the height from the fluctuation peak value to the fluctuation valley value in the risk area of ​​the operation thermal energy development curve corresponding to the aging equipment, cos represents the operation thermal energy development curve corresponding to the aging equipment, ω represents the angular frequency of the operation thermal energy development curve corresponding to the aging equipment, B represents the fluctuation peak value in the risk area of ​​the operation thermal energy development curve corresponding to the aging equipment, O represents the fluctuation valley value in the risk area of ​​the operation thermal energy development curve corresponding to the aging equipment, G t The duration from the fluctuation peak value to the fluctuation valley value in the risk area of ​​the operating thermal energy development curve corresponding to the maturation equipment is represented; based on the thermal energy risk coefficient, the overheating coordinate of the maturation equipment is determined; based on the overheating coordinate, an overheating protection instruction of the maturation equipment is constructed; and based on the overheating protection instruction, the overheating protection of the maturation equipment is executed.

2. The method for overheat protection of intelligent aging equipment based on the Internet of Things as claimed in claim 1 is characterized in that: The method of constructing the Internet of Things network of the maturation device based on the communication protocol includes: identifying protocol compatibility of the maturation device with the communication protocol; Based on the protocol compatibility, screening out protocol-incompatible devices from the maturation devices; Performing protocol conversion on the protocol-incompatible device to obtain a conversion protocol; Based on the communication protocol and the conversion protocol, an Internet of Things network of the maturation equipment is constructed.

3. The method for overheat protection of intelligent aging equipment based on the Internet of Things as claimed in claim 1 is characterized in that: The filtering the operation data to obtain filtered operation data includes: Determine a filter window for the operating data, and calculate a data average of the window operating data corresponding to the filter window; Performing window movement on the filtering window to obtain a moving window; Calculate the moving data average value of the moving window operation data corresponding to the moving window; The data average value and the moving data average value are serialized to obtain filtered operating data of the operating data.

4. The method for overheat protection of intelligent aging equipment based on the Internet of Things as claimed in claim 3 is characterized in that: The calculating the data average value of the window operation data corresponding to the filtering window includes: Determining a window size of the filtering window; Based on the window size, marking the data quantity of the window operation data corresponding to the filter window; Based on the window size and the amount of data, the data average value of the window operation data corresponding to the filter window is calculated using the following formula: Among them, L[a] represents the data average of the window running data corresponding to the filter window, a represents the data quantity of the window running data corresponding to the filter window, M represents the window size of the window corresponding to the filter window, and E[i] represents the i-th window running data of the filter window.

5. The method for overheat protection of intelligent aging equipment based on the Internet of Things as claimed in claim 1 is characterized in that: The step of performing wavelet transform on the filtering operation data to obtain wavelet transform coefficients includes: Determining a wavelet basis function of the filtering operation data; Determining the decomposition level of the wavelet basis function; Based on the decomposition level, the wavelet basis function is used to perform wavelet transform on the filtering operation data to obtain the wavelet transform coefficients.

6. The method for overheat protection of intelligent aging equipment based on the Internet of Things as claimed in claim 1 is characterized in that: The step of extracting the operation characteristics of the aging equipment from the wavelet transform coefficients includes: Calculating coefficient amplitudes of the wavelet transform coefficients; Analyzing the energy distribution state of the wavelet transform coefficients based on the coefficient amplitudes; Calculating the coefficient phase of the wavelet transform coefficients; Analyzing the waveform state of the wavelet transform coefficient based on the coefficient phase; Based on the energy distribution state and the waveform state, operation characteristics of the aging device in the wavelet transform coefficients are extracted.

7. The method for overheat protection of intelligent aging equipment based on the Internet of Things as claimed in claim 6 is characterized in that: The calculating the coefficient amplitude of the wavelet transform coefficient comprises: The coefficient amplitude of the wavelet transform coefficient is calculated 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 amplitude 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, and Im(D_{r,v} represents the imaginary part of the wavelet transform coefficient.

8. A system for overheat protection of intelligent maturation equipment based on the Internet of Things, characterized in that: The system is used to execute the method for overheat protection of intelligent maturation equipment based on the Internet of Things as described in any one of claims 1 to 7, and the system comprises: An Internet of Things network construction module, used to obtain the ripening equipment information of the ripening equipment, determine the communication protocol of the ripening equipment based on the ripening equipment information, and construct the Internet of Things network of the ripening equipment based on the communication protocol; An operation data filtering module, used for collecting the operation data of the aging equipment based on the Internet of Things network, filtering the operation data to obtain filtered operation data, and performing wavelet transformation on the filtered operation data to obtain wavelet transformation coefficients; A deep model training module, used for extracting the operation characteristics of the aging equipment from the wavelet transform coefficients, constructing an overheat monitoring deep model for the aging equipment, and training the overheat monitoring deep model based on the filtered operation data and the operation characteristics to obtain a trained overheat monitoring deep model; A thermal energy coefficient output module, used to calculate the deep model performance of the trained overheat monitoring deep model, and when the deep model performance meets the requirements, use the trained overheat monitoring deep model to analyze the operating thermal energy coefficient of the maturation equipment; The equipment overheat protection module is used to construct an operating heat energy development curve of the maturation equipment based on the operating heat energy coefficient, identify the overheat coordinates of the maturation equipment based on the operating heat energy development curve, construct an overheat protection instruction of the maturation equipment based on the overheat coordinates, and execute overheat protection of the maturation equipment based on the overheat protection instruction.

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