Dyeing machine online monitoring and single neuron PID temperature control system based on IOT gateway

Abstract

The invention relates to the field of dyeing machine on-line monitoring and temperature control, and discloses a dyeing machine on-line monitoring and single neuron PID temperature control system based on an IOT gateway. Comprising a measuring instrument and a control instrument; a physical layer; the application layer comprises a data acquisition card, the data acquisition card is connected with a gateway of the Internet of Things through an RS485 communication interface, and a MODBUS-RTU protocol is adopted; an Internet of Things gateway; the cloud server is deployed privately, is located in a local area network and is responsible for data storage, analysis and display; and the client comprises a computer and a mobile phone. Through the design of the side end equipment layer, the physical layer, the application layer, the Internet of Things gateway, the cloud server and the client, various physical state quantities, such as temperature, pressure, flow, pH value and liquid level, of the dyeing machine can be collected and transmitted in real time, so that an operator can find and process potential problems in time, accidental shutdown and quality fluctuation in the production process are avoided, and the production efficiency is improved. And the stability and controllability of the production process are improved.

Description

Technical Field

[0001] The present invention relates to the technical field of online monitoring and temperature control of dyeing machines, and in particular to an online monitoring and single neuron PID temperature control system for dyeing machines based on an IOT gateway. Background Art

[0002] Dyeing machines are essential equipment in the textile industry, and precise control of dyeing and finishing processes is crucial to improving the quality of textiles. With the rapid development of automation technology and computer technology, the printing and dyeing industry is moving towards intelligence. However, although my country has also conducted a series of explorations and attempts in the intelligent dyeing machine monitoring system, compared with advanced countries, there is still a certain gap in my country's dyeing machine monitoring system, which is mainly reflected in the limited monitoring range, poor real-time performance and low degree of intelligence. The dyeing effect of fabrics is mainly affected by temperature, which requires real-time control of the dye temperature in the dyeing machine dyeing tank in strict accordance with the process curve. Nowadays, most of the temperature controllers used in dyeing machines are traditional PID. Due to the nonlinearity, model structure parameter changes and large time lag of the temperature control object of the dyeing machine, the traditional PID algorithm is difficult to meet the industrial requirements of dyeing and finishing. Therefore, it is necessary to develop a system with a wide monitoring range, strong real-time performance and high control accuracy.

[0003] For example, the Chinese invention patent (application number 202410120384.4) involves a temperature control system and method for industrial equipment, which stores and displays the temperature of the industrial equipment and controls the temperature in intervals to achieve precise control of the equipment temperature. The entire system improves the efficiency, quality and accuracy of equipment temperature regulation in the industrial manufacturing process. However, the system has a low level of intelligence, the monitoring range is limited to the site, and the algorithm has poor adaptability.

[0004] Therefore, in order to solve the above problems, an online monitoring and single neuron PID temperature control system for dyeing machines based on IOT gateway is proposed. The development of IOT technology enables intelligent connection between devices, and realizes real-time monitoring and remote control of equipment status. In addition, the single neuron PID control technology combines the dynamic learning ability of the neural network and the precise adjustment characteristics of PID control, which is suitable for temperature control of industrial equipment such as dyeing machines. Summary of the invention

[0005] In view of the shortcomings of the prior art, the present invention provides an online monitoring and single neuron PID temperature control system for a dyeing machine based on an IOT gateway, which solves the problems in the prior art of low intelligence, limited monitoring range to the site, and poor algorithm adaptability.

[0006] To achieve the above objectives, the present invention is implemented through the following technical solutions: an online monitoring and single neuron PID temperature control system for a dyeing machine based on IOT gateway, comprising:

[0007] Edge equipment layer: including measuring instruments and control instruments, responsible for collecting various physical state quantities in the dyeing process and performing temperature control;

[0008] Physical layer; handles the transmission of analog signals and control signals;

[0009] Application layer: including data acquisition card, which is connected to the IoT gateway via RS485 communication interface and adopts MODBUS-RTU protocol;

[0010] IoT gateway: responsible for uploading data from the edge device layer to the cloud server through the network;

[0011] Cloud server: Private deployment, located in the local area network, responsible for data storage, analysis and display;

[0012] Client: including computers and mobile phones, through which the data and services provided by the cloud server are accessed to monitor and control the status of the dyeing machine.

[0013] Preferably, the edge equipment layer also includes a single neuron PID control unit, which is used to control the temperature of the dyeing process of the dyeing machine so that it heats up according to the temperature curve set by the user to ensure the dyeing time.

[0014] Preferably, the measuring instruments in the edge equipment layer include a temperature sensor, a pressure sensor, a flow sensor, a pH meter, and a liquid level sensor, and the control instruments include a PTC heater.

[0015] Preferably, the single neuron PID control unit includes an input layer, weights, a summer and an activation function. The input layer receives input signals from sensors or other controllers, each input signal is multiplied by the corresponding weight and then summed. The summer passes the result of the weighted summation to the activation function for processing, and finally generates an output signal.

[0016] Preferably, the single neuron PID control unit is deployed in a field monitoring center, an incremental PID controller is established in the field monitoring center, and a single neural network is added to the incremental PID controller to obtain a single neuron PID controller.

[0017] Preferably, the single neuron PID control system has three-dimensional inputs, namely, the instantaneous error e(t) between the set temperature T1 and the current temperature T2, the rate of change of the error Δe(t), and the rate of change of the error change rate Δ2e(t); the network of the single neuron PID has a one-dimensional output, which is the output increment Δu(k) of the incremental PID.

[0018] Preferably, the single neuron PID control system includes a single neuron PID model, which integrates the functions of a classic incremental PID controller, including three parts: proportional, integral and differential. The summer performs weighted summation and nonlinear processing on the input signal.

[0019] Preferably, the single neuron PID control system is divided into two parts: temperature acquisition and temperature control. In the temperature acquisition part, the temperature sensor is responsible for collecting the temperature of the dyeing tank of the dyeing machine. Its output signal is filtered and amplified by the temperature transmitter and then input into the data acquisition card for data acquisition.

[0020] Preferably, the data acquisition card converts the received control signal into a pulse width modulation signal and transmits it to a solid-state relay. The solid-state relay adjusts the power of the positive temperature coefficient heater according to the change of the pulse width modulation signal, thereby realizing accurate control of the temperature of the dyeing machine.

[0021] Preferably, the application layer mainly processes the signals received from the physical layer and converts them into digital signals for network transmission.

[0022] The present invention provides an online monitoring and single neuron PID temperature control system for dyeing machines based on IOT gateway. It has the following beneficial effects:

[0023] 1. The present invention can collect and transmit various physical state quantities of the dyeing machine, such as temperature, pressure, flow, pH value and liquid level, in real time through the design of the edge device layer, physical layer, application layer, Internet of Things gateway, cloud server and client. These data are transmitted to the cloud server through the Internet of Things gateway for storage, analysis and display. Users can view the operating status of the dyeing machine at any time through the client application, including real-time data, historical records and alarm information, so that operators can discover and deal with potential problems in time, avoid unexpected shutdowns and quality fluctuations in the production process, and improve the stability and controllability of the production process.

[0024] 2. The present invention adopts a single neuron PID controller, which monitors the temperature change of the dyeing machine in real time and performs precise control according to the temperature curve set by the user. The single neuron PID controller can generate a control signal increment Δu(k) based on the instantaneous error e(k), the error change rate Δe(k) and the rate of change of the error change rate Δ2e(k), and ensure the accuracy and real-time performance of the control signal through weight adaptive adjustment. This advanced temperature control technology performs particularly well in the heating and insulation process at different stages, can respond quickly to temperature changes, and keep the temperature stable within the set range, thereby ensuring precise temperature control during the dyeing process and improving dyeing quality and production efficiency.

[0025] 3. The present invention not only supports uploading data from the edge device layer to the cloud server, but also supports issuing control instructions from the cloud server to the edge device layer, realizing two-way communication, so that users can adjust the working parameters of the dyeing machine in real time through the client application, such as temperature, pressure, flow and liquid level, etc., thereby enhancing the flexibility and management efficiency of the production process. In addition, the system adopts a variety of security measures, including firewall configuration, access control list (ACL) setting, SSL encrypted communication, etc., to ensure that data is not stolen or tampered with during transmission, thereby protecting the user's production data and privacy. BRIEF DESCRIPTION OF THE DRAWINGS

[0026] Figure 1 It is a schematic diagram of the overall architecture of the full-process data collection and monitoring system of the present invention;

[0027] Figure 2 It is the operation diagram of dyeing machine online monitoring and single neuron PID temperature control system based on IoT gateway;

[0028] Figure 3 This is a schematic diagram of a single neuron PID structure;

[0029] Figure 4 This is a schematic diagram of a single neuron PID control system;

[0030] Figure 5 It is a schematic diagram of the heating curve planning. DETAILED DESCRIPTION

[0031] The following will be combined with the drawings in the specification of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0032] Example:

[0033] Please see attached Figure 1 -Attached Figure 2 The embodiment of the present invention provides an online monitoring and single neuron PID temperature control system for a dyeing machine based on an IOT gateway, characterized in that it includes:

[0034] Edge equipment layer: including measuring instruments and control instruments, responsible for collecting various physical state quantities in the dyeing process and performing temperature control;

[0035] Physical layer; handles the transmission of analog signals and control signals;

[0036] Application layer: including data acquisition card, which is connected to the IoT gateway through RS485 communication interface and adopts MODBUS-RTU protocol. The application layer mainly processes the signals received from the physical layer and converts them into digital signals for network transmission.

[0037] IoT gateway: responsible for uploading data from the edge device layer to the cloud server through the network;

[0038] Cloud server: Private deployment, located in the local area network, responsible for data storage, analysis and display;

[0039] Client: including computers and mobile phones, through which the data and services provided by the cloud server are accessed to monitor and control the status of the dyeing machine;

[0040] The edge device layer also includes a single neuron PID control unit, which is used to control the temperature of the dyeing process of the dyeing machine so that it heats up according to the temperature curve set by the user to ensure the dyeing time. The measuring instruments in the edge device layer include temperature sensors, pressure sensors, flow sensors, pH meters, and liquid level sensors. The control instruments include PTC heaters. The single neuron PID control unit includes an input layer, weights, summers, and activation functions. The input layer receives input signals from sensors or other controllers. Each input signal is multiplied by the corresponding weight and then summed. The summer passes the weighted summation result to the activation function for processing, and finally generates an output signal. The single neuron PID control unit is deployed in the on-site monitoring center and established in the on-site monitoring center. Incremental PID controller, and add a single neural network to the incremental PID controller to get a single neural PID controller. The single neural PID control system includes a single neural PID model. The model integrates the functions of the classic incremental PID controller, including three parts: proportional, integral and differential. The summer performs weighted summation and nonlinear processing on the input signal. The single neural PID control system is divided into two parts: temperature acquisition and temperature control. In the temperature acquisition part, the temperature sensor is responsible for collecting the temperature of the dyeing tank of the dyeing machine. After filtering and amplification by the temperature transmitter, its output signal is input into the data acquisition card for data acquisition. The data acquisition card converts the received control signal into a pulse width modulation signal and transmits it to the solid-state relay. The solid-state relay adjusts the power of the positive temperature coefficient heater according to the change of the pulse width modulation signal, thereby realizing precise control of the temperature of the dyeing machine.

[0041] The single neuron PID control system has three-dimensional inputs, namely the instantaneous error e(t) between the set temperature T1 and the current temperature T2, the rate of change of the error Δe(t), and the rate of change of the error change rate Δ2e(t); the network of the single neuron PID has a one-dimensional output, which is the output increment Δu(k) of the incremental PID.

[0042] The edge equipment includes various sensors, mainly temperature sensors, pressure sensors, flow sensors, pH meters and liquid level sensors. The temperature sensors are installed in key locations such as dyeing vats, heating devices and cooling systems to monitor the temperature changes of the dyeing liquid in real time. The output signal of the pressure sensor is 4-20mA. It is installed in the pipelines and pump stations of the dyeing machine to monitor the pressure changes of the dyeing liquid in the pipeline in real time. The output signal of the flow sensor is 4-20mA. It is installed at the inlet and outlet of the dyeing machine to monitor the flow changes of the dyeing liquid in real time. The output signal of the liquid level sensor is 4-20mA. It is installed in the dyeing vat of the dyeing machine to monitor the liquid level changes of the dyeing liquid in real time. The output signal of the pH meter is 4-20mA. It is installed in the dyeing tank to detect the pH value changes of the dyeing liquid in real time.

[0043] After the signals collected by the sensor are summarized by the data acquisition card, they communicate with the IOT gateway of Yunken Technology through the RS485 bus. The communication protocol adopts MODBUS-RTU, which is widely used in the industrial field and has stability and reliability. The IOT gateway is connected to the Ethernet through a network cable and uses the HTTP protocol for communication. The data transmission format is JSON, which is simple and clear, easy to parse and process, and suitable for cloud data transmission. The collected information such as pressure, liquid level, temperature, flow and pH value is uploaded to the cloud once per second, realizing real-time monitoring of the operating status of the dyeing machine. At the same time, the IOT gateway can also receive control instructions from the cloud server and transmit them to the data acquisition card to make the corresponding actuator act. This two-way communication mechanism ensures remote monitoring and control of the dyeing machine, providing an important guarantee for the flexibility and efficiency of the production process. By adopting such a communication method and data transmission mechanism, the system can realize efficient monitoring and control of the dyeing machine, providing users with a reliable production management solution. At the same time, the selection of communication protocols and the optimization of data transmission formats improve the stability and reliability of the system and ensure the safe transmission and accurate analysis of data.

[0044] In order to make the server run normally, select suitable hardware equipment to be used as a cloud server. Including high-performance server host, large-capacity hard disk drive and sufficient memory capacity, ensure that the selected hardware can meet the needs of dyeing machine data processing and storage, install Windows Server operating system on the server hardware, configure the server network to ensure that it can connect to the Internet and communicate with dyeing machines and other devices. Configure network settings, including IP address, subnet mask, default gateway, etc. Install necessary software for data processing and storage, including database management system MySQL, data analysis tool NumPy library, and software Zabbix for real-time monitoring and alarm. Implement necessary security settings, including firewall configuration, access control list (ACL) settings, encrypted communication, etc., to ensure that the server and the data stored therein are not subject to unauthorized access and attacks, and formulate backup and recovery strategies to ensure the security and reliability of dyeing machine data. Including regular data backup to external storage media network storage and establishing a disaster recovery plan. Optimize the performance of the server to ensure that it can handle a large amount of dyeing machine data and achieve rapid response, including optimizing database indexes, adjusting server configuration parameters, and using cache technology. Deploy monitoring tools to monitor the operating status and performance indicators of the server in real time. Regular system maintenance and updates are performed to ensure that the server is in optimal condition and potential faults and vulnerabilities are fixed in a timely manner.

[0045] Users such as mobile phones, tablets, field control terminals or computers access the network through Ethernet, 4G, 5G and WIFI. Users first need to register an account and establish a connection with the server through identity authentication after logging in. According to the user role and permissions, the server will verify the user's access request and ensure that only authorized users can access specific data and functions. Users access the remote control interface provided by the cloud server, which provides an interface for communication with the server, enabling users to view the real-time data, historical records, alarm information, etc. of the dyeing machine. The client application uses the HTTP communication protocol to exchange data with the server. The WebSocket real-time communication protocol is used for the transmission of real-time data. In order to protect the security of data, the encryption technology SSL protocol is usually used during the data transmission process to ensure that the data is not stolen or tampered with during the transmission process. Through remote control, users can adjust the working status of the dyeing machine at any time according to production needs, ensure the stability and controllability of the production process, and no longer be subject to the limitations of time and space. The remote control function is highly secure and reliable. The system uses encrypted communication and identity authentication technology to ensure the security and privacy protection of data transmission. At the same time, the system also has fault self-healing and backup recovery mechanisms.

[0046] Figure 2The operation diagram of the online monitoring and single neuron PID temperature control system of the dyeing machine based on IoT gateway is shown. The system obtains the production information of the dyeing machine group through various sensors, which usually include temperature sensors, pressure sensors, flow sensors, liquid level sensors and pH meters, etc. They summarize the production parameters in the form of analog signals to the data acquisition card. The data acquisition card uses the MODBUS-RTU protocol of the RS485 bus to transmit the collected data to the IoT gateway. As the data aggregation point, the IoT gateway is connected to the server through Ethernet. The server is responsible for processing, analyzing and storing the collected data. Through the processing of the server, the production data is stored in the database to provide support for subsequent data analysis and production process optimization. In this system, the client has the function of obtaining and modifying production parameters. Through the client, the operator can monitor the operating status of the dyeing machine in real time, adjust the production parameters to meet production needs, and realize flexible control and management of the production process. In addition, the on-site host computer terminal is also included in the system, and its main function is to monitor and control the dyeing machine in real time. These terminals are usually located at the production site, enabling operators to directly monitor the operating status of the dyeing machine and make necessary adjustments, thereby ensuring the stability and efficiency of the production process. For temperature control, the system uses a single neuron PID controller. The controller adjusts the temperature of the dyeing machine in real time based on the temperature curve set by the user to ensure accurate temperature control during the production process. The single neuron PID controller provides a reliable solution for temperature control of the dyeing machine with its fast response and stability. By integrating sensor technology, network communication technology and advanced control algorithms, the system realizes intelligent monitoring and precise control of the dyeing machine production process, providing users with a more efficient and reliable production management solution.

[0047] Figure 3 It is a single neuron PID structure, where r(k) is the expected value, i.e., the set temperature, e(k) is the difference between the expected value and the output temperature of the dyeing machine, w1'(k), w2'(k), w3'(k) are the connection weights of the single neuron, k is the gain of the system output variable, △u(k) is the input increment of the controlled object, u(k) is the total input of the controlled object, Z-1 represents the system delay, y(k) is the output of the controlled system, and after processing, e(k) is obtained to obtain x1(k), x2(k), x3(k), which are respectively expressed as:

[0048] x1(k)=e(k) (1)

[0049] x2(k)=Δe(k)=e(k)-e(k-1) (2)

[0050] x3(k)=Δ 2e(k)=e(k)-2e(k-1)+e(k-2) (3)

[0051] The neuron input is shown below:

[0052]

[0053] Since the sum of the input weights of a neuron is 1, the weights must be normalized. The formula is as follows:

[0054]

[0055] wi is the weight value updated by learning rules. At this time, the input control quantity of the controlled system can be written as:

[0056]

[0057] In order to make the single neuron control algorithm realize adaptive control, it is also necessary to introduce learning rules, whose main function is to make the control algorithm modify the weight according to the current state and enhance the control effect of the system. The present invention adopts the supervised Hebb learning rule, and its fitness function is expressed as:

[0058]

[0059] Where r(k) is the expected value. To minimize E, the gradient descent method is used to find the optimal weight value. The formula is as follows:

[0060]

[0061] Where η is the learning factor. Since the above learning rule does not weaken the process of connecting neurons, the formula becomes:

[0062] Δw i =η(y(k)-r(k))x i (k)u(k) (9)

[0063] In a large number of practical applications, engineers have found that the online adjustment of weights is mainly related to e(k) and e(k-1), so the weight formula can be changed to.

[0064] w i (k) = w i (k-1)+ηe(k)u(k)(2e(k)-e(k-1)) (10)

[0065] Figure 4The single neuron PID control system is demonstrated, which is mainly divided into two parts: temperature acquisition and temperature control. In the temperature acquisition part, the temperature sensor is responsible for collecting the temperature of the dyeing tank of the dyeing machine. Its output signal is filtered and amplified by the temperature transmitter and then input into the data acquisition card for data acquisition. The data acquisition card is connected to the server through the gateway, and the collected data is stored in the database after processing. In the temperature control part, the field control terminal requests the server for information such as the temperature of the dyeing machine. The server transmits the collected temperature data to the single neuron PID controller for processing. The single neuron PID controller adjusts the temperature of the dyeing machine in real time according to the preset control strategy, and outputs the control signal to the data acquisition card. The data acquisition card converts the received control signal into a PWM (pulse width modulation) signal and transmits it to the solid-state relay. The solid-state relay adjusts the power of the PTC (positive temperature coefficient) heater according to the change of the PWM signal, thereby realizing accurate control of the temperature of the dyeing machine. By integrating the two links of temperature acquisition and temperature control, the single neuron PID control system can realize accurate control of the temperature of the dyeing machine, ensure the stability and consistency of the temperature during the dyeing process, and provide reliable technical support for the optimization of the dyeing process.

[0066] The system operation process includes:

[0067] S1. Data acquisition: The sensors at the edge equipment layer collect various physical state quantities of the dyeing machine in real time, such as temperature, pressure, flow, pH value and liquid level. The collected analog signals are filtered and amplified by the transmitter and then transmitted to the data acquisition card.

[0068] S2. Data transmission, the data acquisition card transmits the processed signal to the IOT gateway through the RS485 bus and MODBUS-RTU protocol. The IOT gateway converts the data into JSON format and uploads it to the cloud server through Ethernet and HTTP protocol.

[0069] S3. Data processing and storage: The cloud server receives data and performs storage, analysis and display. The server stores data such as temperature, pressure, flow, pH value and liquid level in the MySQL database.

[0070] S4. Real-time monitoring and control; users access the cloud server through client applications (such as mobile phones, tablets or computers) to view the real-time data of the dyeing machine. The client application uses HTTP to exchange data with the server and uses WebSocket for real-time data transmission. The server generates a control signal through a single-neuron PID controller based on the temperature curve set by the user, and transmits the signal to the data acquisition card through the IOT gateway. The data acquisition card converts the control signal into a PWM signal and transmits it to the solid-state relay. The solid-state relay adjusts the power of the PTC heater according to the PWM signal to achieve precise temperature control.

[0071] In a specific embodiment

[0072] Figure 5 The temperature rise curve planning for an alkaline dyeing process is shown. This curve is designed to achieve precise temperature control of the solution in the dyeing machine to ensure the smooth progress of the dyeing process and the stability of the dyeing effect.

[0073] S1. Initial temperature stabilization, first, inject an appropriate amount of water into the dyeing machine and keep the temperature of the dyeing machine stable at 60°C. At this temperature, the auxiliaries and dyes are dissolved in water, and the dissolution process lasts for 20 minutes to ensure sufficient mixing and dissolution.

[0074] S2. Initial heating; then, start heating with an initial heating rate of 2°C / min until reaching 90°C.

[0075] S3. Mid-term heating: After reaching 90°C, change the heating rate to 1°C / min and continue heating until reaching 100°C.

[0076] S4. Rapid heating; after reaching 100°C, further increase the heating rate to 1.5°C / min until reaching 130°C.

[0077] S5. Keep warm: keep dyeing at 130℃ for 30 minutes to ensure full penetration of dye and dyeing effect.

[0078] S6. Cooling. Finally, the cooling process begins. The cooling rate is 1.5℃ / min until the temperature drops to 70℃. At 70℃, the washing process begins and the dyeing process is completed. The heating curve planning designed in this way can ensure the gradual rise and fall of the temperature during the dyeing process, so that the dye can fully penetrate and the dyeing effect is uniform and stable. At the same time, by controlling the heating rate and holding time at different stages, the needs of different dyeing processes can be met, and the dyeing efficiency and dyeing quality can be improved.

[0079] The system realizes comprehensive monitoring and precise control of the dyeing machine through multi-level architecture and IOT technology. The introduction of single neuron PID temperature control technology makes the temperature control more adaptive and precise, and can adapt to different working environments and process requirements. The system's two-way communication mechanism and advanced data processing technology not only provide real-time monitoring and remote control functions, but also ensure the security and reliability of data. The overall design provides users with an efficient and reliable production management solution, especially in complex and changeable dyeing processes.

[0080] Although embodiments of the present invention have been shown and described, it will be appreciated by those skilled in the art that various changes, modifications, substitutions and variations may be made to the embodiments without departing from the principles and spirit of the present invention, and that the scope of the present invention is defined by the appended claims and their equivalents.

Claims

1. A dyeing machine online monitoring and single neuron PID temperature control system based on IOT gateway, characterized in that: include: Edge equipment layer: including measuring instruments and control instruments, responsible for collecting various physical state quantities in the dyeing process and performing temperature control; Physical layer: handles the transmission of analog signals and control signals; Application layer: including data acquisition card, which is connected to the IoT gateway through RS485 communication interface and adopts MODBUS-RTU protocol; IoT Gateway: responsible for uploading data from the edge device layer to the cloud server through the network; Cloud server: Privately deployed, located in the local area network, responsible for data storage, analysis and display; Client: including computers and mobile phones, through which the data and services provided by the cloud server are accessed to monitor and control the status of the dyeing machine.

2. According to claim 1, a dyeing machine online monitoring and single neuron PID temperature control system based on IOT gateway is characterized in that: The edge device layer also includes a single neuron PID control unit, which is used to control the temperature of the dyeing machine during the dyeing process so that it heats up according to the temperature curve set by the user to ensure the dyeing time.

3. According to claim 1, a dyeing machine online monitoring and single neuron PID temperature control system based on IOT gateway is characterized in that: The measuring instruments in the edge equipment layer include temperature sensors, pressure sensors, flow sensors, pH meters, and liquid level sensors, and the control instruments include PTC heaters.

4. According to claim 2, a dyeing machine online monitoring and single neuron PID temperature control system based on IOT gateway is characterized in that: The single neuron PID control unit includes an input layer, a weight, a summer and an activation function. The input layer receives input signals from sensors or other controllers. Each input signal is multiplied by the corresponding weight and then summed. The summer passes the weighted summation result to the activation function for processing, and finally generates an output signal.

5. According to claim 2, a dyeing machine online monitoring and single neuron PID temperature control system based on IOT gateway is characterized in that: The single neuron PID control unit is deployed in a field monitoring center, an incremental PID controller is established in the field monitoring center, and a single neural network is added to the incremental PID controller to obtain a single neuron PID controller.

6. According to the IOT gateway-based dyeing machine online monitoring and single neuron PID temperature control system according to claim 1, it is characterized in that: The single neuron PID control system has three-dimensional inputs, namely, the instantaneous error e(t) between the set temperature T1 and the current temperature T2, the rate of change of the error Δe(t), and the rate of change of the error change rate Δ2e(t); the network of the single neuron PID has a one-dimensional output, which is the output increment Δu(k) of the incremental PID.

7. According to claim 1, a dyeing machine online monitoring and single neuron PID temperature control system based on IOT gateway is characterized in that: The single neuron PID control system includes a single neuron PID model, which integrates the functions of a classic incremental PID controller, including three parts: proportional, integral and differential. The summer performs weighted summation and nonlinear processing on the input signal.

8. According to the IOT gateway-based dyeing machine online monitoring and single neuron PID temperature control system according to claim 1, it is characterized in that: The single neuron PID control system is divided into two parts: temperature acquisition and temperature control. In the temperature acquisition part, the temperature sensor is responsible for collecting the temperature of the dyeing tank of the dyeing machine. Its output signal is filtered and amplified by the temperature transmitter and then input into the data acquisition card for data acquisition.

9. The online monitoring and single neuron PID temperature control system for dyeing machine based on IOT gateway according to claim 8 is characterized in that: The data acquisition card converts the received control signal into a pulse width modulation signal and transmits it to the solid-state relay. The solid-state relay adjusts the power of the positive temperature coefficient heater according to the change of the pulse width modulation signal, thereby realizing accurate control of the temperature of the dyeing machine.

10. The online monitoring and single neuron PID temperature control system for dyeing machine based on IOT gateway according to claim 1 is characterized in that: The application layer mainly processes the signals received from the physical layer and converts them into digital signals for network transmission.

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