Application method of intelligent tire embedded sensor

Abstract

The invention relates to the technical field of intelligent tire forming and manufacturing, and provides an intelligent tire embedded sensor application method which comprises the steps that a positioning sensor is automatically grabbed through an intelligent mechanical arm, rubber packaging is conducted, then the sensor is placed in a lining layer, and tire manufacturing information generated by a background control terminal in real time is received for intelligent tire forming. The method involves data processing, such as obtaining tire, road and vehicle travel history data, training a tire pressure monitoring model, and monitoring tire pressure in real time to ensure sensor accuracy. Meanwhile, a tire forming control model is trained based on historical data and tire data, tire manufacturing information is generated, and the tire forming process is optimized.

Description

Technical Field

[0001] The present invention relates to the technical field of intelligent tire molding and manufacturing, and in particular to an application method of an intelligent tire embedded sensor. Background Art

[0002] Existing tires are chemically synthesized from steel cords, cord fabrics, natural rubber, and various polymers. When a car is driving at high speed, the driver's perception of the outside world is transmitted through the car components. As a component in direct contact with the road surface, the safety of the tire directly determines the safety of the driver. Smart tires mean that the tire is no longer a simple rubber composite on the vehicle, but connects the data information in the vehicle with external users through a control system. Existing smart tires usually attach strain sensor chips to the inner cavity of the tire and bond the sensor to the tire with an adhesive. This method cannot meet the same life cycle as the tire, and the inner wall of the tire needs to be polished to prevent it from falling off during bonding, which affects the air tightness of the tire.

[0003] Smart tire embedded sensors are tire monitoring technologies that install sensors directly on the air nozzle inside the tire. They can accurately measure the air pressure and temperature inside the tire, with the error usually controlled within 0.1Bar and 2℃, and are not affected by external environmental factors. In addition, the embedded sensor installed inside the tire not only improves the stability of the measurement, but also enhances the anti-theft performance, because the sensor is not easily stolen or damaged unless the entire tire is removed. Summary of the invention

[0004] In view of the above technical problems, the present invention provides an application method of an intelligent tire embedded sensor, which is used to solve the problem that existing sensors are usually placed outside the tire cavity and combined with the tire by bonding. In view of the characteristics and molding methods of existing intelligent tires, they have the following disadvantages: First, the strain sensor is bonded to the inner wall of the tire by an adhesive. In order to ensure the firmness of the bonding, the oil film in the inner cavity of the tire needs to be processed, usually by grinding. This method easily leads to the destruction of the tire airtight layer, affecting the tire's air retention, and it is impossible to adaptively adjust the parameters of the manufacturing equipment according to the tire manufacturing requirements.

[0005] The present invention provides an application method of an intelligent tire embedded sensor, comprising: Step S101, automatically grabbing and positioning the sensor by an intelligent manipulator and positioning the rubber package sensor at the position of the inner lining layer; Step S102, the robot grabs the sensor and places it in the packaging rubber, and enters the laminator through the conveyor belt, and the rubber is attached to the upper and lower surfaces of the sensor, and the rubber-encapsulated sensor is formed under the lamination of the laminator; Step S103, the robot places the rubber-encapsulated sensor in the inner lining layer according to the position and spacing, and transmits it to the forming drum along with the feeding rack; Step S104, after the inner liner with the sensor is transferred to the forming drum, receiving the tire manufacturing information generated in real time by the background control terminal; Step S105, performing intelligent tire molding through a molding machine according to the tire manufacturing information generated in real time by the background control terminal.

[0006] Furthermore, in the method for applying a sensor embedded in a smart tire according to the present invention, step S104 includes: Obtaining tire operation history data, road condition history data and vehicle travel history data, where the tire operation history data includes tire pressure history data, tire temperature history data and tire wear degree history data; Data features are extracted from the road condition historical data to obtain a road condition feature data group, and a correspondence is established between the features of each road condition in the road condition feature data group and the tire operation historical data and the vehicle driving history data to obtain a road condition tire operation data group, and the road condition tire operation data group is marked with road conditions, and the tire operation data of each road condition in the road condition tire operation data group is used to train a random forest model to obtain a road intersection tire pressure monitoring model group.

[0007] Furthermore, in the method for applying a smart tire embedded sensor according to the present invention, the step S104 further includes: Real-time road image data is collected, features are extracted from the real-time road image data to obtain road condition image features, the road condition image features are matched with a road intersection tire pressure monitoring model group to obtain a road intersection tire pressure monitoring model corresponding to the road condition image features, and the road intersection tire pressure monitoring model corresponding to the road condition image features is used as the real-time road intersection tire pressure monitoring model.

[0008] Furthermore, in the method for applying a smart tire embedded sensor according to the present invention, the step S104 further includes: Substituting the real-time road image data into the real-time road intersection tire pressure monitoring model to obtain the tire pressure prediction monitoring result; Collect real-time tire pressure data and compare the real-time tire pressure data with the tire predicted pressure monitoring result. If the comparison result between the real-time tire pressure data and the tire predicted pressure monitoring result is within the preset error range, the monitoring result of the tire embedded sensor is accurate.

[0009] Furthermore, in the method for applying a smart tire embedded sensor according to the present invention, the step S104 further includes: When the monitoring result of the tire embedded sensor is accurate, the historical data generated by the data processing in step S104 is obtained to obtain the historical data to be analyzed, and the preset intelligent tire molding manufacturing parameters are also obtained; Collecting the identity information of the tire manufactured using the preset intelligent tire molding manufacturing parameters, matching the identity information of the tire manufactured using the preset intelligent tire molding manufacturing parameters with the historical data to be analyzed, and obtaining the tire information corresponding to the identity information of the tire manufactured using the preset intelligent tire molding manufacturing parameters; A correspondence is established between the characteristics of each road condition and the tire information corresponding to the tire identity information produced by the preset intelligent tire molding and manufacturing parameters, and tire data corresponding to each road condition of the tire identity is obtained, and the intelligent tire molding and manufacturing historical data are obtained. The random forest model is trained using the intelligent tire molding and manufacturing historical data and the tire data corresponding to each road condition of the tire identity to obtain a tire molding control model.

[0010] Furthermore, in the method for applying a smart tire embedded sensor according to the present invention, the step S104 further includes: Receive intelligent tire molding task information, which includes tire molding parameters and tire applicable road condition information, match the tire applicable road condition information with tire data corresponding to each road condition of the tire identity, and obtain tire data for each road condition corresponding to the tire applicable road condition information.

[0011] Furthermore, in the method for applying a smart tire embedded sensor according to the present invention, the step S104 further includes: The server side substitutes the tire data of each road condition corresponding to the applicable road condition information of the tire into the tire molding control model, generates real-time tire manufacturing information, and sends the real-time generated tire manufacturing information to the background control terminal.

[0012] Beneficial effects of the present invention: Beneficial effects of the present invention: The present invention relates to an embedded integration method of sensors to avoid damage to the tire structure, and based on the data generated by the smart tire during actual use, the smart tire molding process and steps involved in the present invention are optimized without affecting the conventional structure of the tire, thereby improving the adaptability of the smart tire to different road conditions. BRIEF DESCRIPTION OF THE DRAWINGS

[0013] In order to more clearly illustrate the technical solution of the present invention, the following is a brief introduction to the drawings required for use in the embodiments. Obviously, for ordinary technicians in this field, other drawings can be obtained based on the drawings without paying any creative labor.

[0014] Figure 1 A schematic flow chart of a method for applying a smart tire embedded sensor provided by the present invention.

[0015] Figure 2The present invention provides a basic schematic diagram of the common tire molding process.

[0016] Figure 3 A schematic diagram of the basic process of intelligent tire molding provided by the present invention. DETAILED DESCRIPTION

[0017] In order to make the purpose, technical solution and advantages of the present invention clearer, the technical solution of the present invention will be clearly and completely described in conjunction with the specific embodiments of the present invention and the corresponding drawings. 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. The technical solutions provided by the embodiments of the present invention are described in detail below in conjunction with the drawings.

[0018] In order to better understand the purpose of the present invention, the present invention is described in further detail below.

[0019] See also Figures 1 to 3 The present invention provides an application method of an intelligent tire embedded sensor, comprising: Step S101, automatically grabbing and positioning the sensor by an intelligent manipulator and positioning the rubber package sensor at the position of the inner lining layer; Step S102, the robot grabs the sensor and places it in the packaging rubber, and enters the laminator through the conveyor belt, and the rubber is attached to the upper and lower surfaces of the sensor, and the rubber-encapsulated sensor is formed under the lamination of the laminator; Step S103, the robot places the rubber-encapsulated sensor in the inner lining layer according to the position and spacing, and transmits it to the forming drum along with the feeding rack; Step S104, after the inner liner with the sensor is transferred to the forming drum, receiving the tire manufacturing information generated in real time by the background control terminal; Step S105, performing intelligent tire molding through a molding machine according to the tire manufacturing information generated in real time by the background control terminal.

[0020] Step S101: Automatically grab and position the sensor and position the rubber package sensor on the inner lining layer by the intelligent manipulator. The intelligent manipulator can accurately place the sensor on the predetermined inner lining layer position, providing an accurate basis for subsequent steps.

[0021] Step S102: The robot grabs the sensor and places it in the packaging rubber. The sensor enters the laminator through the conveyor belt. The upper and lower surfaces of the sensor are attached with rubber. The laminator presses the sensor to form a rubber-encapsulated sensor. Step S103: The robot places the rubber-encapsulated sensors in the inner lining layer according to the position and spacing, and transmits them to the forming drum along with the feeding rack. The sensors are arranged in the inner lining layer according to the preset position and spacing.

[0022] Step S104: After the inner liner with the sensor is transferred to the forming drum, the tire manufacturing information generated in real time by the background control terminal is received, including: obtaining tire operation history data, road condition history data and vehicle driving history data, and establishing a road condition tire operation data group for training the road intersection tire pressure monitoring model group.

[0023] Collect real-time road image data and match it with the corresponding road intersection tire pressure monitoring model to obtain the real-time road intersection tire pressure monitoring model. Substitute the real-time road image data into the real-time road intersection tire pressure monitoring model to obtain the tire pressure prediction monitoring results, and compare them with the real-time tire pressure data to verify the accuracy of the sensor monitoring results. When the sensor monitoring results are accurate, further use historical data and preset intelligent tire molding parameters to train the tire molding control model.

[0024] Step S105: According to the tire manufacturing information generated in real time by the background control terminal, the intelligent tire is molded by the molding machine. The molding machine is controlled according to the received tire manufacturing information to complete the molding process of the intelligent tire.

[0025] The intelligent tire embedded sensor application method provided by the present invention avoids the problem of tire air tightness damage caused by traditional bonding methods, and improves the air retention and safety of the tire. By embedding the sensor inside the tire, the stability and accuracy of the measurement are improved, and the anti-theft performance is enhanced.

[0026] The present invention can optimize the tire molding process according to the data generated by the smart tire during actual use, thereby improving the adaptability of the smart tire to different road conditions. It also provides the possibility of personalized customization, so that the tire can be adaptively adjusted according to different road conditions and vehicle driving requirements.

[0027] In summary, the intelligent tire embedded sensor application method provided by the present invention has significant technical advantages and practical application value.

[0028] Specifically, in the method for applying a smart tire embedded sensor according to the present invention, step S104 includes: Obtaining tire operation history data, road condition history data and vehicle travel history data, where the tire operation history data includes tire pressure history data, tire temperature history data and tire wear degree history data; Data features are extracted from the road condition historical data to obtain a road condition feature data group, and a correspondence is established between the features of each road condition in the road condition feature data group and the tire operation historical data and the vehicle driving history data to obtain a road condition tire operation data group, and the road condition tire operation data group is marked with road conditions, and the tire operation data of each road condition in the road condition tire operation data group is used to train a random forest model to obtain a road intersection tire pressure monitoring model group.

[0029] In the provided method for applying the embedded sensor in the smart tire, step S104 not only involves receiving the tire manufacturing information generated in real time by the background control terminal, but also includes data processing and model application process. The following is a detailed explanation of the specific technical content of step S104: After the inner liner with sensors is transferred to the forming drum, the system receives the tire production information generated in real time by the background control terminal. This step is not as simple as receiving information, it also includes a series of analysis, prediction and model application based on historical data and real-time data.

[0030] Tire operation history data: including tire pressure history data, tire temperature history data and tire wear history data, which reflect the performance of tires under different conditions of use. The data describes the various road conditions that the tires have experienced, such as road surface material, flatness, slope, etc. Vehicle driving history data includes driving speed, acceleration, braking frequency, etc., which are closely related to tire wear and performance changes.

[0031] Use the road condition feature data set (extracted from the road condition historical data) to establish a corresponding relationship with the tire operation historical data and vehicle driving historical data to form a road condition tire operation data set. Label the data for road conditions and use them to train the random forest model to finally obtain the road intersection tire pressure monitoring model set. These models can predict tire pressure changes based on different road conditions.

[0032] Collect real-time road image data and perform feature extraction to obtain road condition image features. Match these features in the road intersection tire pressure monitoring model group to find the tire pressure monitoring model that best suits the current road condition as the real-time road intersection tire pressure monitoring model.

[0033] Substitute the real-time road image data into the real-time road intersection tire pressure monitoring model to obtain the tire pressure prediction monitoring results. At the same time, collect the real-time tire pressure data and compare it with the prediction results. If the comparison result is within the preset error range, it is considered that the monitoring result of the tire embedded sensor is accurate.

[0034] When the monitoring results of the tire embedded sensor are accurate, the historical data generated by the previous data processing (historical data to be analyzed) and the preset intelligent tire molding parameters are used for further analysis. The identity information of the tires produced using the preset intelligent tire molding parameters is collected and matched in the historical data to be analyzed to find the corresponding tire information. The corresponding relationship between these tire identities and the historical tire data produced for each intelligent tire road molding condition is obtained. The random forest model is trained to obtain the tire molding control model. This model can generate the optimal tire manufacturing parameters based on road conditions and tire identity information.

[0035] Receive intelligent tire building task information, including tire building parameters and tire applicable road condition information.

[0036] The tire applicable road condition information is matched with the tire data corresponding to each road condition in the tire identity to obtain the corresponding tire data. The server side substitutes this data into the tire molding control model to generate real-time tire manufacturing information. This information is sent to the background control terminal to guide the molding process of the intelligent tire.

[0037] Step S104 not only involves receiving tire manufacturing information generated in real time by the background control terminal, but also includes a series of complex data collection, preprocessing, model training, real-time data analysis and prediction, verification, and tire molding control model training and application processes. These steps together constitute the core part of the intelligent tire embedded sensor application method, enabling the intelligent tire to generate the optimal tire manufacturing parameters according to different road conditions and tire identity information, thereby improving the adaptability and performance of the tire.

[0038] Specifically, in the method for applying a smart tire embedded sensor according to the present invention, the step S104 further includes: Real-time road image data is collected, features are extracted from the real-time road image data to obtain road condition image features, the road condition image features are matched with a road intersection tire pressure monitoring model group to obtain a road intersection tire pressure monitoring model corresponding to the road condition image features, and the road intersection tire pressure monitoring model corresponding to the road condition image features is used as the real-time road intersection tire pressure monitoring model.

[0039] Substituting the real-time road image data into the real-time road intersection tire pressure monitoring model to obtain the tire pressure prediction monitoring result; Collect real-time tire pressure data and compare the real-time tire pressure data with the tire predicted pressure monitoring result. If the comparison result between the real-time tire pressure data and the tire predicted pressure monitoring result is within the preset error range, the monitoring result of the tire embedded sensor is accurate.

[0040] When the monitoring result of the tire embedded sensor is accurate, the historical data generated by the data processing in step S104 is obtained to obtain the historical data to be analyzed, and the preset intelligent tire molding manufacturing parameters are also obtained; Collecting the identity information of the tire manufactured using the preset intelligent tire molding manufacturing parameters, matching the identity information of the tire manufactured using the preset intelligent tire molding manufacturing parameters with the historical data to be analyzed, and obtaining the tire information corresponding to the identity information of the tire manufactured using the preset intelligent tire molding manufacturing parameters; A correspondence is established between the characteristics of each road condition and the tire information corresponding to the tire identity information produced by the preset intelligent tire molding and manufacturing parameters, and tire data corresponding to each road condition of the tire identity is obtained, and the intelligent tire molding and manufacturing historical data are obtained. The random forest model is trained using the intelligent tire molding and manufacturing historical data and the tire data corresponding to each road condition of the tire identity to obtain a tire molding control model.

[0041] Receive intelligent tire molding task information, which includes tire molding parameters and tire applicable road condition information, match the tire applicable road condition information with tire data corresponding to each road condition of the tire identity, and obtain tire data for each road condition corresponding to the tire applicable road condition information.

[0042] The server side substitutes the tire data of each road condition corresponding to the applicable road condition information of the tire into the tire molding control model, generates real-time tire manufacturing information, and sends the real-time generated tire manufacturing information to the background control terminal.

[0043] The system collects tire operation history data, including tire pressure, temperature and wear history, reflecting the performance of tires under different conditions of use. At the same time, it collects road condition history data and obtains road condition feature data sets through feature extraction technology to describe the characteristics of different road conditions. Vehicle driving history data is also recorded, including speed, acceleration, etc., to reflect the driving conditions of the vehicle under specific road conditions.

[0044] Combine the road condition feature data set, tire operation history data and vehicle driving history data to establish a road condition tire operation data set, and mark each road condition. Use these data to train the random forest model to obtain the road intersection tire pressure monitoring model set, which can predict the tire pressure changes under specific road conditions.

[0045] Application of real-time road image data: Collect road image data in real time and extract its features. Match these features in the tire pressure monitoring model group at the road intersection to find the tire pressure monitoring model that best suits the current road conditions. Substitute the real-time road image data into the model to obtain the predicted monitoring results of the tire pressure. At the same time, collect the real-time tire pressure data of the tire and compare it with the predicted monitoring results. If the comparison result is within the preset error range, it is confirmed that the monitoring result of the tire embedded sensor is accurate.

[0046] When the sensor monitoring results are accurate, further analysis is performed using the historical data generated by the previous data processing and the preset intelligent tire molding parameters. The identity information of the tires manufactured using these parameters is collected and matched in the historical data to find the corresponding tire information. The characteristics of each road condition are corresponding to these tire information to obtain tire data corresponding to each road condition. The random forest model is trained using these data and the intelligent tire molding historical data to obtain the tire molding control model.

[0047] Receive intelligent tire molding task information, including tire molding parameters and tire applicable road condition information. Match the tire data corresponding to each road condition of the tire identity to find the tire data corresponding to the applicable road condition. The server side substitutes this data into the tire molding control model to generate real-time tire production information. This information is sent to the background control terminal to guide the molding process of the intelligent tire. Through the above steps, step S104 enables the intelligent tire to be customized according to specific road conditions and requirements, thereby significantly improving the applicability of the tire.

[0048] The present invention is applicable to an application method of embedded sensors in smart tires, which can realize embedded integration of sensor arrays without affecting the overall performance and appearance of the tire. The basic difference between smart tires and ordinary pneumatic tires is that smart tires contain sensors inside to collect tire information. Therefore, the process of embedded sensors should be considered in the molding of smart tires to improve the tire molding process.

[0049] The present invention combines the sensor with the inner liner, and adds a sensor adhesive half component during tire molding. Figure 2 .

[0050] Please refer to the basic process of intelligent tire molding Figure 3 The embedded sensor adopts the form of upper and lower adhesive attachment. The rubber encapsulated sensor rubber roll is used as the first step of tire molding. The array sensor is accurately combined with the tire inner liner through cutting and positioning by an intelligent robot. The inner liner with array sensors is transmitted to the molding drum through the material rack and conveyor belt for intelligent tire molding.

[0051] The specific operation method is as follows: an intelligent manipulator is used to automatically grasp the positioning sensor and position the rubber encapsulated sensor at the position of the inner liner; a feed rack is used to transfer the semi-components required for tire production to a forming drum; the inner liner is the innermost semi-component of the tire; the rubber encapsulated sensor is embedded in the inner liner; a forming drum is used for tire molding; a molding machine is a tire molding device; a laminating machine is a sensor rubber encapsulation device; encapsulated rubber is used to encapsulate the sensor; and a sensor is used to sense tire status information.

[0052] The robot grabs the sensor and places it in the packaging rubber, which enters the laminator along the conveyor belt. The rubber adheres to the upper and lower surfaces of the sensor, and the rubber-packaged sensor is formed under the lamination of the laminator.

[0053] The robot places the rubber-encapsulated sensor in the inner liner at a certain position and spacing, and transfers it to the forming drum along with the feeder. After the inner liner with the sensor is transferred to the forming drum, the intelligent tire is formed by the forming machine.

[0054] The technical solution of the present invention solves the problem of being unable to adaptively adjust the parameters of the manufacturing equipment according to the tire manufacturing requirements in the following ways: First, the tire manufacturing information generated in real time by the background control terminal is received by the building drum during the intelligent tire building process, ensuring the real-time and accuracy of the information. This information is obtained through comprehensive analysis of tire operation history data, road condition history data, and vehicle driving history data. The tire pressure monitoring model group at the road intersection is trained through the random forest model to generate tire manufacturing information.

[0055] Secondly, according to the tire manufacturing information generated in real time by the background control terminal, intelligent tire molding is carried out through the molding machine, realizing data-driven tire manufacturing and improving the adaptability and performance of the tire. Before the tire is molded, it will receive intelligent tire molding task information including tire molding parameters and tire applicable road condition information, and match it with the tire data corresponding to each road condition of the tire identity to ensure the adaptability of the tire molding parameters to the road conditions.

[0056] In addition, the random forest model is trained using the historical data of intelligent tire molding and the tire data corresponding to each road condition to obtain the tire molding control model, which provides data support and optimization direction for tire molding.

[0057] After the tire is formed, the real-time tire pressure data is collected and compared with the predicted tire pressure monitoring results to ensure the accuracy of the tire embedded sensor monitoring results. If the comparison result is within the preset error range, it means that the monitoring result is accurate.

[0058] Finally, through intelligent robotic cutting and positioning, the array sensor is precisely combined with the tire inner liner, which optimizes the tire molding process and improves the intelligence level and performance of the tire.

[0059] In summary, the technical solution of the present invention effectively solves the problem of being unable to adaptively adjust the production equipment parameters according to tire manufacturing requirements through data-driven tire molding, real-time monitoring and feedback, sensor embedding and molding optimization.

Claims

1. A method for applying a sensor embedded in a smart tire, characterized in that: include: Step S101, automatically grabbing and positioning the sensor by an intelligent manipulator and positioning the rubber package sensor at the position of the inner lining layer; Step S102, the robot grabs the sensor and places it in the packaging rubber, and enters the laminator through the conveyor belt, and the rubber is attached to the upper and lower surfaces of the sensor, and the rubber-encapsulated sensor is formed under the lamination of the laminator; Step S103, the robot places the rubber-encapsulated sensor in the inner lining layer according to the position and spacing, and transmits it to the forming drum along with the feeding rack; Step S104, after the inner liner with the sensor is transferred to the forming drum, receiving the tire manufacturing information generated in real time by the background control terminal; Step S105, performing intelligent tire molding through a molding machine according to the tire manufacturing information generated in real time by the background control terminal.

2. The method for applying a smart tire embedded sensor according to claim 1, characterized in that: The step S104 includes: Obtaining tire operation history data, road condition history data and vehicle travel history data, where the tire operation history data includes tire pressure history data, tire temperature history data and tire wear degree history data; Data features are extracted from the road condition historical data to obtain a road condition feature data group, and a correspondence is established between the features of each road condition in the road condition feature data group and the tire operation historical data and the vehicle driving history data to obtain a road condition tire operation data group, and the road condition tire operation data group is marked with road conditions, and the tire operation data of each road condition in the road condition tire operation data group is used to train a random forest model to obtain a road intersection tire pressure monitoring model group.

3. The method for applying a smart tire embedded sensor as claimed in claim 2, characterized in that: The step S104 further includes: Real-time road image data is collected, features are extracted from the real-time road image data to obtain road condition image features, the road condition image features are matched with a road intersection tire pressure monitoring model group to obtain a road intersection tire pressure monitoring model corresponding to the road condition image features, and the road intersection tire pressure monitoring model corresponding to the road condition image features is used as the real-time road intersection tire pressure monitoring model.

4. The method for applying a smart tire embedded sensor as claimed in claim 3, characterized in that: The step S104 further includes: Substituting the real-time road image data into the real-time road intersection tire pressure monitoring model to obtain the tire pressure prediction monitoring result; Collect real-time tire pressure data and compare the real-time tire pressure data with the tire predicted pressure monitoring result. If the comparison result between the real-time tire pressure data and the tire predicted pressure monitoring result is within the preset error range, the monitoring result of the tire embedded sensor is accurate.

5. The method for applying a smart tire embedded sensor as claimed in claim 4, characterized in that: The step S104 further includes: When the monitoring result of the tire embedded sensor is accurate, the historical data generated by the data processing in step S104 is obtained to obtain the historical data to be analyzed, and the preset intelligent tire molding manufacturing parameters are also obtained; Collecting the identity information of the tire manufactured using the preset intelligent tire molding manufacturing parameters, matching the identity information of the tire manufactured using the preset intelligent tire molding manufacturing parameters with the historical data to be analyzed, and obtaining the tire information corresponding to the identity information of the tire manufactured using the preset intelligent tire molding manufacturing parameters; A correspondence is established between the characteristics of each road condition and the tire information corresponding to the tire identity information produced by the preset intelligent tire molding and manufacturing parameters, and tire data corresponding to each road condition of the tire identity is obtained, and the intelligent tire molding and manufacturing historical data are obtained. The random forest model is trained using the intelligent tire molding and manufacturing historical data and the tire data corresponding to each road condition of the tire identity to obtain a tire molding control model.

6. The method for applying a smart tire embedded sensor according to claim 5, characterized in that: The step S104 further includes: Receive intelligent tire molding task information, which includes tire molding parameters and tire applicable road condition information, match the tire applicable road condition information with tire data corresponding to each road condition of the tire identity, and obtain tire data for each road condition corresponding to the tire applicable road condition information.

7. The method for applying a smart tire embedded sensor according to claim 6, characterized in that: The step S104 further includes: The server side substitutes the tire data of each road condition corresponding to the applicable road condition information of the tire into the tire molding control model, generates real-time tire manufacturing information, and sends the real-time generated tire manufacturing information to the background control terminal.

Images