Remote temperature control method based on mold temperature controller

Abstract

The invention relates to the technical field of remote control of mold temperature controllers, and discloses a remote temperature control method based on a mold temperature controller, which comprises the following steps: S1, collecting real-time temperature data of the mold temperature controller through a temperature sensor, and transmitting the data to a control system; s2, in a control system, performing real-time analysis and prediction on the acquired temperature data by using an intelligent algorithm and a machine learning model, and generating an adjustment instruction; s3, the adjusting instruction is sent to the mold temperature controller, and heating and cooling parameters of the mold temperature controller are adjusted in real time; and S4, connecting the mold temperature controller and the control system to an Internet of Things (IoT) platform to realize data intercommunication and cooperative control between the mold temperature controller and other production equipment. Temperature data are analyzed and predicted in real time through an intelligent algorithm and a machine learning model, an adjusting instruction is generated, heating and cooling parameters of the mold temperature controller are rapidly adjusted, and high-precision temperature control is achieved.

Description

Technical Field

[0001] The invention relates to the technical field of remote control of mold temperature controllers, and in particular to a remote temperature control method based on a mold temperature controller. Background Art

[0002] Mold temperature controllers (mold temperature controllers) are widely used in industrial fields such as injection molding, die casting, extrusion molding and rubber molding to control the temperature of the mold to ensure the stability of the production process and product quality. The mold temperature controller adjusts the mold temperature through heating and cooling cycles, thereby reducing the internal stress and deformation of the product and improving the surface finish and dimensional stability of the product.

[0003] Traditional mold temperature controller control systems mostly use manual or semi-automatic methods to adjust temperature. Since traditional control systems rely on manual adjustments or simple feedback control by operators, the response speed of adjusting heating or cooling parameters after detecting temperature changes is slow, making it difficult to meet the requirements of high-precision control. At the same time, mold temperature controllers currently mostly use local control and lack networking capabilities, making remote monitoring and control impossible. This means that operators need to frequently perform on-site operations and maintenance, increasing labor costs and management complexity. Summary of the invention

[0004] In view of the deficiencies in the prior art, the present invention provides a remote temperature control method based on a mold temperature controller to solve the problems of slow response speed, insufficient data processing capability, difficulty in remote monitoring and safety in the temperature control process of the existing mold temperature controller.

[0005] To achieve the above objectives, the present invention is implemented through the following technical solutions: A remote temperature control method based on a mold temperature controller comprises the following steps:

[0006] S1, collect the real-time temperature data of the mold temperature controller through the temperature sensor and transmit the data to the control system;

[0007] S2. In the control system, intelligent algorithms and machine learning models are used to analyze and predict the collected temperature data in real time and generate adjustment instructions;

[0008] S3, sending the adjustment instruction to the mold temperature controller to adjust the heating and cooling parameters of the mold temperature controller in real time;

[0009] S4. Connect the mold temperature controller and control system to the Internet of Things (IoT) platform to achieve data intercommunication and collaborative control between the mold temperature controller and other production equipment;

[0010] S5. Through the Internet of Things platform, the collected temperature data and control instructions are transmitted to the cloud computing platform for data storage and further processing;

[0011] S6. Use the cloud computing platform to perform big data analysis on the stored temperature data and optimize the control strategy and parameter settings;

[0012] S7. Use edge computing technology on local devices to pre-process and calculate real-time data, reduce latency and improve system response speed;

[0013] S8. Ensure the physical security, network security and operational security of data transmission and equipment operation through multi-layer security protection measures;

[0014] S9. When an abnormal situation or emergency is detected, the remote emergency shutdown function supported by the Internet of Things platform and cloud computing platform is used to ensure the safety of equipment and personnel.

[0015] Preferably, the specific steps of step S1 include:

[0016] S11. Set the acquisition frequency of the temperature sensor to ensure the real-time and accuracy of the collected data;

[0017] S12. Regularly calibrate the temperature sensor to ensure the accuracy and reliability of the data;

[0018] S13, transmitting the temperature data to the control system in real time through the sensor interface module.

[0019] Preferably, the step S2 specifically includes:

[0020] S21. Use machine learning models to analyze the collected temperature data in real time and identify temperature change trends;

[0021] S22, based on the analysis results, using an intelligent algorithm to generate corresponding adjustment instructions to optimize the heating and cooling parameters of the mold temperature controller;

[0022] S23. Continuously update and optimize machine learning models to improve the accuracy of analysis and prediction.

[0023] Preferably, the step S4 specifically includes:

[0024] S41. Configure the data acquisition module of the IoT platform to ensure stable connection with the mold temperature controller and other production equipment;

[0025] S42, through a secure communication protocol, transmit the operation data and control instructions of the mold temperature controller to the Internet of Things platform in real time;

[0026] S43. Set data sharing rules on the IoT platform to achieve data interoperability and collaborative work between the mold temperature controller and other production equipment.

[0027] Preferably, step S8 specifically includes:

[0028] S81. Use encryption technology during data transmission to ensure data security and integrity;

[0029] S82. Configure an authentication mechanism to ensure that only authorized personnel can access and control the mold temperature controller;

[0030] S83. Protect network security through firewalls and virtual private networks (VPNs) to prevent unauthorized access;

[0031] S84. Set up an abnormality detection and alarm system to immediately issue a warning signal and trigger an emergency shutdown procedure when a security threat or abnormal situation is detected.

[0032] A remote temperature control system based on a mold temperature controller, comprising:

[0033] The temperature sensor is used to execute step S1 by collecting real-time temperature data of the mold temperature controller and transmitting the data to the control system;

[0034] A control system, configured to execute step S2, receive and process data from the temperature sensor, and generate adjustment instructions through an intelligent algorithm and a machine learning model;

[0035] A mold temperature controller is used to execute step S3, receive adjustment instructions from the control system, and perform heating and cooling operations;

[0036] The Internet of Things platform is used to execute step S4 to achieve data intercommunication and collaborative control of the mold temperature controller and other production equipment;

[0037] A cloud computing platform, used to execute step S5 and step S6, and perform data storage, analysis and further processing;

[0038] An edge computing device, used to execute step S7, perform data preprocessing and calculation locally, reduce latency and improve system response speed;

[0039] The multi-layer security protection module is used to execute step S8 to ensure the physical security, network security and operational security of data transmission and device operation.

[0040] Preferably, the temperature sensor comprises:

[0041] The acquisition frequency setting module is used to execute step S11 to set the acquisition frequency of the temperature sensor to ensure the real-time and accuracy of the data;

[0042] A calibration module, used to execute step S12, regularly calibrate the temperature sensor to ensure the accuracy and reliability of the data;

[0043] The sensor interface module is used to execute step S13 to transmit the temperature data to the control system in real time.

[0044] Preferably, the control system comprises:

[0045] A data analysis module, used to execute step S21, use a machine learning model to perform real-time analysis on the collected temperature data and identify temperature change trends;

[0046] An intelligent algorithm module, used to execute step S22, and generate corresponding adjustment instructions based on the analysis results to optimize the heating and cooling parameters of the mold temperature controller;

[0047] The model optimization module is used to execute step S23, continuously update and optimize the machine learning model, and improve the accuracy of analysis and prediction.

[0048] Preferably, the Internet of Things platform includes:

[0049] A data acquisition module is used to execute step S41 to ensure stable connection with the mold temperature controller and other production equipment;

[0050] A communication module, used to execute step S42, and transmit the operation data and control instructions of the mold temperature controller in real time using a secure communication protocol;

[0051] The data sharing module is used to execute step S43 to achieve data intercommunication and collaborative work between the mold temperature controller and other production equipment.

[0052] Preferably, the multi-layer security protection module includes:

[0053] A data encryption module is used to execute step S81, and use encryption technology during data transmission to ensure the security and integrity of the data;

[0054] An identity authentication module, used to execute step S82, configure an identity authentication mechanism to ensure that only authorized personnel can access and control the mold temperature controller;

[0055] A network security module, used to execute step S83, to protect network security through a firewall and a virtual private network (VPN) to prevent unauthorized access;

[0056] The abnormality detection and alarm module is used to execute step S84, and when a security threat or abnormal situation is detected, a warning signal is issued and an emergency shutdown procedure is triggered.

[0057] The present invention provides a remote temperature control method based on a mold temperature controller. It has the following beneficial effects:

[0058] 1. The present invention uses a temperature sensor to collect the temperature data of the mold temperature controller in real time and transmits the data to the control system to ensure the real-time and accuracy of the temperature data. It also uses intelligent algorithms and machine learning models to analyze and predict the temperature data in real time, generate adjustment instructions, and quickly adjust the heating and cooling parameters of the mold temperature controller to achieve high-precision temperature control.

[0059] 2. The present invention stores and further processes data through a cloud computing platform, uses big data analysis technology to perform in-depth analysis on the stored temperature data, optimizes control strategies and parameter settings, and uses edge computing technology on local devices to pre-process and calculate real-time data, reduce data transmission delays, and improve the real-time and response speed of the system.

[0060] 3. The present invention connects the mold temperature controller and the control system to the Internet of Things platform to achieve data intercommunication and collaborative control between the mold temperature controller and other production equipment, optimize the overall production process, support remote monitoring and control of the mold temperature controller through the Internet of Things platform and the cloud computing platform, reduce the on-site operation requirements of operators, and improve management efficiency.

[0061] 4. The present invention uses a predictive maintenance algorithm to predict the failure and maintenance requirements of the mold temperature controller by analyzing historical data and real-time data, generate a maintenance plan in advance, and reduce unplanned downtime. BRIEF DESCRIPTION OF THE DRAWINGS

[0062] Figure 1 It is a schematic diagram of the overall steps of the remote temperature control method of the present invention;

[0063] Figure 2 It is a schematic diagram of the overall layout of the remote temperature control system of the present invention;

[0064] Figure 3 It is a structural schematic diagram of the Chinese temperature sensor module of the present invention;

[0065] Figure 4 It is a structural schematic diagram of the control system module in the present invention;

[0066] Figure 5 It is a structural diagram of the Internet of Things platform module in the present invention;

[0067] Figure 6 It is a schematic diagram of the structure of the multi-layer security protection module in the present invention. DETAILED DESCRIPTION

[0068] 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.

[0069] Example:

[0070] Please refer to the attached Figure 1 The embodiment of the present invention provides a remote temperature control method based on a mold temperature controller, comprising the following steps:

[0071] Step S1: Collect the real-time temperature data of the mold temperature controller through the temperature sensor and transmit the data to the control system.

[0072] Step S11: Set the acquisition frequency of the temperature sensor to ensure the real-time and accuracy of the collected data. Specifically, the acquisition frequency can be adjusted according to the requirements of the production process, such as collecting once per second or once per minute, to ensure that sufficiently detailed data can be obtained in different production scenarios.

[0073] Step S12: Regularly calibrate the temperature sensor to ensure the accuracy and reliability of the data. Calibration can be performed using a standard temperature source or a known temperature environment to correct the measurement deviation of the sensor.

[0074] Step S13: The temperature data is transmitted to the control system in real time through the sensor interface module. The sensor interface module includes signal amplification and filtering functions to ensure the stability and accuracy of data transmission.

[0075] Step S2: In the control system, intelligent algorithms and machine learning models are used to perform real-time analysis and prediction on the collected temperature data to generate adjustment instructions.

[0076] Step S21: Use a machine learning model to analyze the collected temperature data in real time to identify the temperature change trend. The model may include a neural network, a regression model, etc., to analyze the data change trend in real time.

[0077] Step S22: Based on the analysis results, the intelligent algorithm is used to generate corresponding adjustment instructions to optimize the heating and cooling parameters of the mold temperature controller. The intelligent algorithm can use a PID control algorithm, an adaptive control algorithm, etc. to ensure the accuracy of the control.

[0078] Step S23: Continuously update and optimize the machine learning model to improve the accuracy of analysis and prediction. The model can be updated based on historical data and actual production data, and continuously trained and optimized.

[0079] Step S3: Send the adjustment instruction to the mold temperature controller to adjust the heating and cooling parameters of the mold temperature controller in real time. The generated adjustment instruction is sent to the heating and cooling units of the mold temperature controller through the control system to ensure that the mold temperature is within the set range.

[0080] Step S4: Connect the mold temperature controller and the control system to the Internet of Things (IoT) platform to achieve data intercommunication and collaborative control between the mold temperature controller and other production equipment.

[0081] Step S41: Configure the data acquisition module of the Internet of Things platform to ensure stable connection with the mold temperature controller and other production equipment. The module can be connected to the equipment via wired or wireless mode to ensure the stability and real-time performance of data transmission.

[0082] Step S42: Transmit the operation data and control instructions of the mold temperature controller to the IoT platform in real time through a secure communication protocol. The communication protocol may adopt HTTPS, MQTT, etc. to ensure the security of data transmission.

[0083] Step S43: Set data sharing rules on the IoT platform to achieve data intercommunication and collaborative work between the mold temperature controller and other production equipment. Data sharing rules can be configured according to the needs of different equipment to ensure data consistency and effectiveness during collaborative work.

[0084] Step S5: Through the Internet of Things platform, the collected temperature data and control instructions are transmitted to the cloud computing platform for data storage and further processing. The cloud computing platform provides large-capacity storage space and powerful computing power to ensure the safe storage and efficient processing of data.

[0085] Step S6: Use the cloud computing platform to perform big data analysis on the stored temperature data to optimize the control strategy and parameter settings. The cloud platform can use big data analysis technology to deeply mine historical data and optimize the control strategy of the mold temperature controller.

[0086] Step S7: Use edge computing technology on local devices to pre-process and calculate real-time data, reduce delays and improve system response speed. Edge computing devices process data close to the data source, reduce data transmission delays, and improve the real-time performance of the system.

[0087] Step S8: Ensure the physical security, network security and operational security of data transmission and device operation through multi-layer security protection measures.

[0088] Step S81: Encryption technology is used during data transmission to ensure data security and integrity. Encryption technology can use algorithms such as AES and RSA to prevent data from being stolen or tampered with during transmission.

[0089] Step S82: Configure an identity authentication mechanism to ensure that only authorized personnel can access and control the mold temperature controller. Identity authentication can use multi-factor authentication (such as password + SMS verification code) to ensure the security of the system.

[0090] Step S83: Protect network security through firewalls and virtual private networks (VPNs) to prevent unauthorized access. Firewalls can set access control policies, and VPNs can provide secure remote access channels.

[0091] Step S84: Set up an abnormality detection and alarm system to immediately send out a warning signal and trigger an emergency shutdown procedure when a security threat or abnormal situation is detected. The abnormality detection system can monitor data changes in real time and immediately trigger an alarm and shutdown procedure once an abnormal situation is found to ensure the safety of equipment and personnel.

[0092] Step S9: When an abnormal situation or emergency is detected, the remote emergency shutdown function supported by the Internet of Things platform and the cloud computing platform is used to ensure the safety of equipment and personnel. Emergency shutdown instructions are sent remotely through the cloud platform to quickly respond to abnormal situations and protect the safety of the production site.

[0093] Please refer to the attached Figure 2-Figure 6 As part of the present invention, this embodiment also provides a remote temperature control system based on a mold temperature controller, including the following components:

[0094] Temperature sensor: used to execute step S1, by collecting real-time temperature data of the mold temperature controller and transmitting the data to the control system.

[0095] The acquisition frequency setting module is used to execute step S11 to set the acquisition frequency of the temperature sensor to ensure the real-time and accuracy of the data.

[0096] Calibration module: used to execute step S12, regularly calibrate the temperature sensor to ensure the accuracy and reliability of the data.

[0097] Sensor interface module: used to execute step S13 and transmit temperature data to the control system in real time.

[0098] Control system: used to execute step S2, receive and process data from the temperature sensor, and generate adjustment instructions through intelligent algorithms and machine learning models.

[0099] Data analysis module: used to execute step S21, use the machine learning model to perform real-time analysis on the collected temperature data and identify the temperature change trend.

[0100] Intelligent algorithm module: used to execute step S22 and generate corresponding adjustment instructions based on the analysis results to optimize the heating and cooling parameters of the mold temperature controller.

[0101] Model optimization module: used to execute step S23, continuously update and optimize the machine learning model, and improve the accuracy of analysis and prediction.

[0102] Mold temperature controller: used to execute step S3, receive adjustment instructions from the control system, and perform heating and cooling operations.

[0103] Internet of Things platform: used to execute step S4 to achieve data intercommunication and collaborative control between the mold temperature controller and other production equipment.

[0104] Data acquisition module: used to execute step S41 to ensure stable connection with the mold temperature controller and other production equipment.

[0105] Communication module: used to execute step S42, and use a secure communication protocol to transmit the operating data and control instructions of the mold temperature controller in real time.

[0106] Data sharing module: used to execute step S43 to realize data communication and collaborative work between the mold temperature controller and other production equipment.

[0107] Cloud computing platform: used to execute step S5 and step S6, and perform data storage, analysis and further processing.

[0108] Edge computing device: used to execute step S7, perform data preprocessing and calculation locally, reduce latency and improve system response speed.

[0109] Multi-layer security protection module: used to execute step S8 to ensure the physical security, network security and operational security of data transmission and device operation.

[0110] Data encryption module: used to execute step S81, using encryption technology during data transmission to ensure data security and integrity.

[0111] Authentication module: used to execute step S82, configure the authentication mechanism to ensure that only authorized personnel can access and control the mold temperature controller.

[0112] Network security module: used to execute step S83, protect network security through firewalls and virtual private networks (VPN) to prevent unauthorized access.

[0113] Abnormality detection and alarm module: used to execute step S84, and when a security threat or abnormal situation is detected, a warning signal is issued and an emergency shutdown procedure is triggered.

[0114] 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 remote temperature control method based on mold temperature controller, It is characterized in that The following steps are included: S1, collect the real-time temperature data of the mold temperature controller through the temperature sensor and transmit the data to the control system; S2. In the control system, intelligent algorithms and machine learning models are used to analyze and predict the collected temperature data in real time and generate adjustment instructions; S3, sending the adjustment instruction to the mold temperature controller to adjust the heating and cooling parameters of the mold temperature controller in real time; S4. Connect the mold temperature controller and control system to the Internet of Things (IoT) platform to achieve data intercommunication and collaborative control between the mold temperature controller and other production equipment; S5. Through the Internet of Things platform, the collected temperature data and control instructions are transmitted to the cloud computing platform for data storage and further processing; S6. Use the cloud computing platform to perform big data analysis on the stored temperature data to optimize the control strategy and parameter settings; S7. Use edge computing technology on local devices to pre-process and calculate real-time data, reduce latency and improve system response speed; S8. Ensure the physical security, network security and operational security of data transmission and equipment operation through multi-layer security protection measures; S9. When an abnormal situation or emergency is detected, the remote emergency shutdown function supported by the Internet of Things platform and cloud computing platform is used to ensure the safety of equipment and personnel.

2. A remote temperature control method based on a mold temperature controller according to claim 1, It is characterized in that The specific steps of step S1 include: S11. Set the acquisition frequency of the temperature sensor to ensure the real-time and accuracy of the collected data; S12. Regularly calibrate the temperature sensor to ensure the accuracy and reliability of the data; S13, transmitting the temperature data to the control system in real time through the sensor interface module.

3. A remote temperature control method based on a mold temperature controller according to claim 1, It is characterized in that The step S2 specifically includes: S21. Use machine learning models to analyze the collected temperature data in real time and identify temperature change trends; S22, based on the analysis results, using an intelligent algorithm to generate corresponding adjustment instructions to optimize the heating and cooling parameters of the mold temperature controller; S23. Continuously update and optimize machine learning models to improve the accuracy of analysis and prediction.

4. A remote temperature control method based on a mold temperature controller according to claim 1, It is characterized in that The step S4 specifically includes: S41. Configure the data acquisition module of the Internet of Things platform to ensure stable connection with the mold temperature controller and other production equipment; S42, through a secure communication protocol, transmit the operation data and control instructions of the mold temperature controller to the Internet of Things platform in real time; S43. Set data sharing rules on the IoT platform to achieve data interoperability and collaborative work between the mold temperature controller and other production equipment.

5. A remote temperature control method based on a mold temperature controller according to claim 1, It is characterized in that Step S8 specifically includes: S81. Use encryption technology during data transmission to ensure data security and integrity; S82. Configure an authentication mechanism to ensure that only authorized personnel can access and control the mold temperature controller; S83. Protect network security through firewalls and virtual private networks (VPNs) to prevent unauthorized access; S84. Set up an abnormality detection and alarm system to immediately issue a warning signal and trigger an emergency shutdown procedure when a security threat or abnormal situation is detected.

6. A remote temperature control system based on a mold temperature controller, It is characterized in that A remote temperature control method based on a mold temperature controller according to any one of claims 1 to 5, comprising: The temperature sensor is used to execute step S1 by collecting real-time temperature data of the mold temperature controller and transmitting the data to the control system; A control system, configured to execute step S2, receive and process data from the temperature sensor, and generate adjustment instructions through an intelligent algorithm and a machine learning model; A mold temperature controller is used to execute step S3, receive adjustment instructions from the control system, and perform heating and cooling operations; The Internet of Things platform is used to execute step S4 to achieve data intercommunication and collaborative control of the mold temperature controller and other production equipment; A cloud computing platform, used to execute step S5 and step S6, and perform data storage, analysis and further processing; An edge computing device, used to execute step S7, perform data preprocessing and calculation locally, reduce latency and improve system response speed; The multi-layer security protection module is used to execute step S8 to ensure the physical security, network security and operational security of data transmission and device operation.

7. A remote temperature control system based on a mold temperature controller according to claim 6, It is characterized in that The temperature sensor comprises: The acquisition frequency setting module is used to execute step S11 to set the acquisition frequency of the temperature sensor to ensure the real-time and accuracy of the data; A calibration module, used to execute step S12, regularly calibrate the temperature sensor to ensure the accuracy and reliability of the data; The sensor interface module is used to execute step S13 to transmit the temperature data to the control system in real time.

8. A remote temperature control system based on a mold temperature controller according to claim 6, It is characterized in that The control system comprises: A data analysis module, used to execute step S21, use a machine learning model to perform real-time analysis on the collected temperature data and identify temperature change trends; An intelligent algorithm module, used to execute step S22, and generate corresponding adjustment instructions based on the analysis results to optimize the heating and cooling parameters of the mold temperature controller; The model optimization module is used to execute step S23, continuously update and optimize the machine learning model, and improve the accuracy of analysis and prediction.

9. A remote temperature control system based on a mold temperature controller according to claim 6, It is characterized in that The IoT platform includes: A data acquisition module is used to execute step S41 to ensure stable connection with the mold temperature controller and other production equipment; A communication module, used to execute step S42, and transmit the operation data and control instructions of the mold temperature controller in real time using a secure communication protocol; The data sharing module is used to execute step S43 to achieve data intercommunication and collaborative work between the mold temperature controller and other production equipment.

10. A remote temperature control system based on a mold temperature controller according to claim 6, It is characterized in that The multi-layer security protection module includes: A data encryption module is used to execute step S81, and use encryption technology during data transmission to ensure the security and integrity of the data; An identity authentication module, used to execute step S82, configure an identity authentication mechanism to ensure that only authorized personnel can access and control the mold temperature controller; A network security module, used to execute step S83, to protect network security through a firewall and a virtual private network (VPN) to prevent unauthorized access; The abnormality detection and alarm module is used to execute step S84, and when a security threat or abnormal situation is detected, a warning signal is issued and an emergency shutdown procedure is triggered.

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