Embedded RFID tag processing supervision method and system for military tires

Abstract

The invention discloses a military tire embedded RFID tag processing supervision method and system, and relates to the technical field of tag processing supervision, and the method comprises the steps: based on the stress distribution characteristics of the contact surface of a tire body and a mold, recognizing the location which affects the RFID processing quality, continuously comparing the actual process parameter of each production station with a preset target value, and determining the RFID processing quality of the tire body; calculating the real-time deviation between the two stations, introducing an adaptive dynamic threshold mechanism, automatically adjusting the allowable fluctuation range of each process parameter, monitoring the temperature difference between the production stations in real time, and activating a heat path regulation and control strategy when detecting that the heat conduction influence exists between the adjacent stations; the function sensitive area is automatically used as a target area with the highest supervision priority, system resources are deployed in a centralized mode, and process parameter control and an abnormal response strategy are implemented. The supervision system implements a process parameter control and exception response strategy, not only optimizes the utilization efficiency of system resources, but also improves the supervision strength and exception handling capability of a key area.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of label processing supervision, in particular to a method and system for processing supervision of embedded RFID labels for military tires. BACKGROUND

[0002] In the manufacturing process of military tires, the processing quality of embedded RFID labels directly affects the informatization management and use safety of the tires, and this processing link involves multi-station cooperation, complex process parameter control and multi-physical field interference. The traditional supervision method is difficult to realize real-time sensing of the processing state, accurate regulation of the process parameters and effective suppression of thermal coupling and other interference factors, resulting in difficulty in guaranteeing the consistency and reliability of the embedded RFID labels. In view of the special requirements of military tires for high precision and high stability of embedded RFID labels, a multi-dimensional sensing, dynamic regulation and targeted optimization supervision method and system for the whole process of multi-station processing are needed. SUMMARY

[0003] The purpose of the present application is to provide a method and system for processing supervision of embedded RFID labels for military tires to solve the problems in the background art.

[0004] To achieve the above-mentioned purpose, the present application provides the following technical scheme: a method for processing supervision of embedded RFID labels for military tires, the supervision method comprising the following steps: S1: Multi-station process parameter configuration and multi-dimensional sensing of production state S1.1 Station partition and individualized process configuration The processing flow of the embedded RFID labels for military tires is divided into several production stations according to function and spatial distribution, and each production station receives the processing parameter instructions issued from the front-end configuration terminal; S1.2 Process data acquisition and regional feature recognition After acquiring multi-dimensional data, the function-sensitive area which has an impact on the RFID processing quality is identified based on the stress distribution characteristics of the tire body and the mold contact surface, and is marked as a function-sensitive area; S2: Dynamic regulation of processing parameters based on real-time feedback and suppression of thermal interference S2.1 Parameter deviation identification and adaptive threshold management The actual process parameters of each production station are continuously compared with the preset target value, the real-time deviation between the two is calculated, an adaptive dynamic threshold mechanism is introduced, and the allowed fluctuation range of each process parameter is automatically adjusted according to the tire rubber type, environmental temperature and humidity, and production rhythm variables; S2.2 Suppression and structure regulation of thermal coupling between adjacent stations The temperature difference between the production stations is monitored in real time, and when it is detected that there is a thermal conduction influence between adjacent stations, the thermal path regulation strategy is activated; S3: Functional zone supervision and processing quality optimization S3.1 Targeted control of functional sensitive zones Automatically prioritize functional sensitive zones as the highest priority target area for supervision, concentrate system resources, implement process parameter control and abnormal response strategies.

[0005] Preferably, in step S2.2, the temperature difference between production stations is monitored in real time, and when it is detected that there is a heat conduction effect between adjacent stations, the heat path regulation strategy is activated, including the following steps: Start the embedded inflatable thermal insulation structure, which is pre-embedded inside the mold wall body and penetrates along the overall thickness direction of the mold. When the temperature difference is detected to be out of limit, the inflation regulation mechanism is automatically triggered to prolong the heat diffusion path and reduce the degree of thermal coupling between production stations. Time-sharing vulcanization strategy is adopted to avoid multiple stations entering abnormal state simultaneously and causing regional heat aggregation effect.

[0006] Preferably, in step S2.1, parameter deviation identification and adaptive threshold management further include the following steps: Once the parameter deviation of a certain production station exceeds the corresponding dynamic threshold, automatically trigger the local processing parameter correction mechanism, including adjusting the heating timing of the heating unit of the production station, delaying or advancing the vulcanization time.

[0007] Preferably, in step S2.1, the actual process parameters of each production station are continuously compared with the preset target values, the real-time deviation between them is calculated, and an adaptive dynamic threshold mechanism is introduced. According to the tire rubber type, environmental temperature and humidity, and production rhythm variables, the allowed fluctuation range of each process parameter is automatically adjusted, including the following steps: Periodically obtain the current actual process parameter values of the production station, and compare them with the preset target values set based on tire model, rubber system, and process route elements, and then calculate the real-time deviation of each parameter; After obtaining the real-time deviation of each process parameter, a multi-factor influence weight distribution and hierarchical adjustment processing logic is used to calculate the dynamic threshold range of each process parameter; For each process parameter, set a basic allowed deviation, according to the rubber type of the tire being processed at the current station, query the predefined rubber-parameter sensitivity mapping table to obtain the sensitivity level of the rubber to the target parameter, and distribute the first-level weight accordingly; Combine the real-time collected environmental temperature and humidity data, and calculate the second-level environmental correction factor through the pre-set environmental condition influence factor model; According to the current production rhythm, introduce a rhythm adjustment coefficient to dynamically scale the allowed deviation range by the third level; Combine the above three levels of weight and correction factor to finally generate the dynamic threshold range of the current production station.

[0008] Preferably, in step S2.2, the temperature difference between production workstations is monitored in real time. When heat conduction is detected between adjacent workstations, a heat path control strategy is activated, including the following steps: To obtain the temperature difference between adjacent production stations and to comprehensively assess the processing interference risk caused by the coupling of mold structure; When heat conduction between adjacent workstations is detected and the impact has reached a critical condition that endangers the performance of RFID tags, the thermal path control strategy is automatically activated, including dynamic expansion control of the embedded inflatable thermal insulation structure: A time-sharing vulcanization strategy is adopted to stagger the vulcanization stages of adjacent workstations in terms of timing.

[0009] Preferably, in step S1.1, the workstation zoning and personalized process configuration further include the following steps: Each production station is used for a corresponding tire or a corresponding processing step, and supports differentiated parameter configuration for different models, materials and process requirements; The processing parameter instructions include vulcanization temperature curve, embedding pressure threshold, RFID chip tolerance range, and heating time.

[0010] Preferably, the functionally sensitive area includes: The actual embedding location of the RFID and its surrounding area; Areas within the mold that are temperature-sensitive, pressure-concentrated, and have complex material flow characteristics; Structural areas that affect RFID communication performance, durability, and subsequent reading stability.

[0011] Preferably, in step S3: functional area supervision and processing quality optimization, the following steps are also included: S3.2 Processing Quality Assessment After each production station completes processing, the status of the RFID tag is evaluated. The evaluation includes whether the communication is normal, whether the signal strength meets the standard, and whether the physical position and orientation of the chip meet the design requirements.

[0012] Preferably, the multidimensional data includes real-time temperature information of the mold cavity, the inside of the rubber material, and the surrounding RFID via a temperature monitoring unit; The stress distribution at the contact surface between the tire blank and the mold, as well as the local pressure changes during the RFID embedding process, are collected by a pressure sensor array. During processing, a preliminary assessment of RFID communication performance is conducted, including signal readability and chip response status.

[0013] This application also provides a monitoring system for the processing of embedded RFID tags in military tires, including: Data acquisition and location identification module: The processing flow of military tire embedded RFID tags is divided into several production stations according to function and spatial distribution. Each production station receives processing parameter instructions from the front-end configuration terminal. After collecting multi-dimensional data, based on the stress distribution characteristics of the contact surface between the tire body and the mold, the location that affects the RFID processing quality is identified and marked as a functionally sensitive area. Adaptive adjustment and strategy output module: continuously compares the actual process parameters of each production station with the preset target value, calculates the real-time deviation between the two, introduces an adaptive dynamic threshold mechanism, and automatically adjusts the allowable fluctuation range of various process parameters according to tire rubber type, ambient temperature and humidity and production cycle variables; monitors the temperature difference between production stations in real time, and activates the heat path control strategy when heat conduction influence between adjacent stations is detected. Management module: Automatically identifies functionally sensitive areas as the highest priority target areas for supervision, centrally allocates system resources, and implements process parameter control and anomaly response strategies.

[0014] The technical effects and advantages provided by the present invention in the above technical solution are as follows: This invention uses functionally sensitive areas as the highest priority target areas for supervision, and centrally allocates system resources to implement process parameter control and anomaly response strategies. This not only optimizes the utilization efficiency of system resources, but also significantly improves the supervision of key areas and the ability to handle anomalies. As a result, it improves the overall embedding quality, consistency and reliability of RFID tags, meeting the high standards required for embedded information components in military tires.

[0015] This invention enables each production station to receive customized processing parameter instructions based on its specific function and spatial distribution by dividing the processing flow into workstations and configuring personalized processes. This achieves precise control and efficient collaboration in the processing process, effectively avoiding label embedding quality problems caused by parameter mismatch or inconsistent processes between workstations.

[0016] This invention introduces a real-time feedback mechanism, continuously comparing the deviation between actual process parameters and preset target values. Combined with adaptive dynamic threshold management, it can automatically adjust the allowable fluctuation range of process parameters according to variables such as tire rubber type, ambient temperature and humidity, and production cycle time, ensuring that processing parameters are always within the optimal control range, thus greatly improving process stability and adaptability.

[0017] This invention addresses the potential thermal coupling interference between adjacent workstations by monitoring the temperature difference in real time and activating a thermal path control strategy. This effectively suppresses the adverse effects of heat conduction on processing quality, further ensuring the stability and consistency of the processing environment. Attached Figure Description

[0018] To more clearly illustrate the technical solutions in the embodiments of this application or the prior art, the drawings used in the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments recorded in this invention. For those skilled in the art, other drawings can be obtained based on these drawings.

[0019] Figure 1 This is a mind map of the overall regulatory method and system of the present invention.

[0020] Figure 2 This is a framework diagram of the monitoring system of the present invention. Detailed Implementation

[0021] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, the technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0022] Example: This example provides a method for monitoring the processing of embedded RFID tags in military tires. Please refer to [link / reference]. Figure 1 As shown, the regulatory approach includes the following steps: S1: Multi-station process parameter configuration and multi-dimensional perception of production status S1.1 Workstation Zoning and Personalized Process Configuration For the processing flow of embedded RFID tags for military tires, the overall processing flow is divided into several production stations according to function and spatial distribution. Each production station is responsible for the corresponding tire or corresponding processing step, and supports differentiated parameter configuration for different models, materials and process requirements. It receives processing parameter instructions from the front-end configuration terminal, covering key process indicators such as vulcanization temperature curve, embedding pressure threshold, RFID chip tolerance range, and heating time. S1.2 Comprehensive Process Data Acquisition and Regional Feature Identification The temperature monitoring unit acquires real-time temperature information of the mold cavity, the inside of the rubber material, and the surrounding area of ​​the RFID; the pressure sensor array collects the stress distribution of the contact surface between the tire blank and the mold, and the local pressure changes during the RFID embedding process; the RFID communication performance is initially evaluated during processing, such as signal readability and chip response status; the above multi-dimensional data are collected simultaneously, and based on the stress distribution characteristics of the contact surface between the tire blank and the mold, the key areas that have the most significant impact on the RFID processing quality are identified, namely the functionally sensitive areas that need to be focused on in subsequent supervision and control.

[0023] S2: Dynamic control of processing parameters and suppression of thermal interference based on real-time feedback S2.1 Parameter Deviation Identification and Adaptive Threshold Management The system continuously compares the actual process parameters (such as temperature, pressure, embedding depth, etc.) of each production station with the preset target values, calculating the real-time deviation between the two. For example, the temperature deviation ΔT = |target temperature - measured temperature|, and the pressure deviation ΔP = |set pressure - current pressure|. An adaptive dynamic threshold mechanism is introduced to automatically adjust the allowable fluctuation range (i.e., dynamic threshold) of various process parameters according to the tire rubber type, ambient temperature and humidity, and production cycle variables to adapt to the changing actual production conditions. Once the parameter deviation of a certain station exceeds the corresponding dynamic threshold, a local processing parameter correction mechanism is automatically triggered, including: adjusting the heating sequence of the heating unit of that production station; delaying or advancing the vulcanization time to ensure that the RFID chip is in the optimal working window. S2.2 Suppression of Thermal Coupling Between Adjacent Workstations and Structural Control Real-time monitoring of actual parameter differences between adjacent production stations, with particular attention to: temperature differences between production stations (e.g., ΔT_adj ≥ set temperature difference limit); processing interference risks caused by structural coupling between production stations; and activation of thermal path control strategies when significant heat conduction between adjacent stations is detected that jeopardizes RFID tag performance. Specific measures include: An embedded inflatable thermal insulation structure is activated. This structure is pre-embedded inside the mold wall and extends along the overall thickness of the mold. When an excessive temperature difference is detected, an expansion control mechanism (such as gas filling) is automatically triggered, thereby extending the heat diffusion path and increasing the equivalent heat transfer resistance to reduce the degree of thermal coupling between production stations. A time-sharing vulcanization strategy is adopted to avoid multiple stations from simultaneously entering a high-temperature and high-pressure state, which could lead to regional heat accumulation. S3: Functional Location Supervision and Processing Quality Optimization S3.1 Functional Sensitive Area Targeted Control The system automatically identifies functionally sensitive areas as the highest priority target areas for supervision, centrally allocates system resources, implements stricter process parameter control and anomaly response strategies, and improves the overall consistency and reliability of processing. Functionally sensitive areas typically include: The actual embedding location of the RFID and its surrounding area; The area in the mold that is most sensitive to temperature, most concentrated in pressure, and has the most complex material flow; Key structural areas that directly affect RFID communication performance, durability, and subsequent reading stability; S3.2 Processing Quality Assessment After each production station completes processing, the status of the RFID tag is evaluated. The evaluation includes: whether the communication is normal, whether the signal strength meets the standard, and whether the physical position and orientation of the chip meet the design requirements.

[0024] like Figures 1-2 As shown, this embodiment also provides a monitoring system for the processing of embedded RFID tags in military tires, including: Data acquisition and location identification module: The processing flow of military tire embedded RFID tags is divided into several production stations according to function and spatial distribution. Each production station receives processing parameter instructions from the front-end configuration terminal. After collecting multi-dimensional data, based on the stress distribution characteristics of the contact surface between the tire body and the mold, the location that affects the RFID processing quality is identified and marked as a functionally sensitive area. The functionally sensitive area is sent to the management module, the production station is sent to the adaptive adjustment and strategy output module, and the multi-dimensional data is sent to the adaptive adjustment and strategy output module. Adaptive adjustment and strategy output module: continuously compares the actual process parameters of each production station with the preset target value, calculates the real-time deviation between the two, introduces an adaptive dynamic threshold mechanism, and automatically adjusts the allowable fluctuation range of various process parameters according to tire rubber type, ambient temperature and humidity and production cycle variables; monitors the temperature difference between production stations in real time, and activates the heat path control strategy when heat conduction influence between adjacent stations is detected. Management module: Automatically identifies functionally sensitive areas as the highest priority target areas for supervision, centrally allocates system resources, and implements process parameter control and anomaly response strategies.

[0025] In the processing of embedded RFID tags for military tires, due to the variety of tire models (such as different specifications, tire structure, load rating), the different rubber systems used (such as natural rubber, synthetic rubber and their compound formulations), and the strict process requirements (such as high-temperature vulcanization, precise embedding, chip protection, etc.), the entire processing process cannot be controlled by uniform process parameters.

[0026] Therefore, based on the functional module division and spatial layout characteristics of tire processing technology, this application divides the complete RFID tag embedding process into multiple logically independent and functionally complementary production stations. Each station is responsible for specific processing tasks, such as tire body positioning, mold closing, RFID pre-setting, pressure vulcanization, cooling and shaping, etc. Each station usually corresponds to a single tire or multiple tires in the same batch at the same processing stage, ensuring that the processing object is clear and the process is controllable.

[0027] In one embodiment disclosed in this application, each production station is equipped with an independent data interface and control unit, capable of receiving customized processing parameter instructions from a front-end configuration terminal, tailored to the current processing task of that station. These instructions are not general parameters, but rather a differentiated set of process parameters generated comprehensively based on the specific tire model being processed, the type of rubber compound used (e.g., high-wear-resistant rubber, high-temperature-resistant rubber, etc.), the process route (e.g., high-temperature rapid vulcanization or low-temperature long-time vulcanization), and the structural requirements for embedding RFID tags (e.g., chip type, packaging form, embedding depth, etc.). The parameters typically cover multiple key process indicators, such as: The vulcanization temperature profile is the temperature control sequence of the mold during the vulcanization stage, typically including parameters such as heating rate, holding temperature, and holding time. This profile directly affects the degree of cross-linking of the rubber compound and the tolerance of the RFID chip. The embedding pressure threshold refers to the maximum allowable contact pressure of the RFID tag during the pressing process into the matrix. This parameter is directly related to the elastic modulus of the matrix material and the RFID encapsulation strength. Exceeding the threshold may cause chip damage or antenna breakage. The RFID chip tolerance range includes the highest temperature, maximum pressure, and electromagnetic interference limits that the chip can withstand. These parameters are used to constrain the boundary conditions of multiple physical fields such as heat, force, and electricity during processing. The heating time is the total heating time during the vulcanization or curing stage. This parameter is closely related to the reaction kinetics of the rubber compound and the stability after RFID embedding, and needs to be precisely set according to the type of rubber compound.

[0028] In one embodiment disclosed in this application, temperature information is acquired in real time by multiple temperature monitoring units (such as high-response thermocouples or infrared temperature sensors) distributed inside the mold cavity, inside the rubber compound (such as near the RFID implantation location), and around the RFID tag. This acquires the dynamic temperature of various parts during processing, including the mold heating temperature, the internal temperature gradient of the rubber compound, and the local temperature around the RFID chip. This temperature data reflects the thermal field distribution during vulcanization and is an important basis for determining whether the degree of cross-linking of the rubber compound matches the heat resistance of the chip.

[0029] In one embodiment disclosed in this application, pressure information is acquired using a high-sensitivity pressure sensor array (such as a piezoresistive or capacitive sensor matrix) arranged on the contact surface between the tire carcass and the mold. This array collects the stress distribution between the tire carcass and the mold contact surface during processes such as mold closing, pressure vulcanization, and RFID embedding. Particular attention is paid to local pressure changes at the RFID tag embedding location. If this local pressure exceeds the tolerance threshold of the chip or packaging structure, it will directly lead to embedding failure or performance degradation. Through arrayed sensing, two-dimensional or three-dimensional pressure field distributions can be obtained, thereby accurately locating high-pressure areas.

[0030] In one embodiment disclosed in this application, the RFID communication performance is initially evaluated: During the processing (such as after RFID implantation, before or during vulcanization), the built-in low-interference RFID reader / writer module is used to evaluate the communication performance of the tag online, focusing on two core indicators: first, signal readability, i.e. whether the reader / writer can successfully obtain the identification code or data returned by the tag; second, chip response status, including response time, signal strength, bit error rate, etc. These indicators indirectly reflect the chip's working stability and potential damage under the current processing environment (such as high temperature and high pressure).

[0031] In one embodiment disclosed in this application, based on the stress distribution characteristics of the contact surface between the tire carcass and the mold, a regional influence weight analysis processing logic is used to identify functionally sensitive areas. The core idea is as follows: Pressure values ​​from each node in the pressure sensor array are extracted and combined with temperature monitoring data to construct a thermal-mechanical coupling intensity distribution map within the processing area. For each spatial micro-region (e.g., a local area centered on a sensor node), a comprehensive impact index is calculated. This index is determined by the pressure value (reflecting mechanical stress), the temperature value (reflecting thermal load), and the spatial proximity of the location to the RFID tag (reflecting potential electromagnetic or thermal conduction interference). Influence weights are set (e.g., pressure accounts for 40%, temperature accounts for 40%, and spatial distance accounts for 20%), and the comprehensive impact of each micro-region is quantified and ranked using a nonlinear weighted aggregation method. One or more regions with the highest comprehensive impact index are selected and marked as functionally sensitive areas.

[0032] To ensure that each production station strictly adheres to established high-quality process standards, it is essential to implement high-precision real-time monitoring and closed-loop control of key process parameters (such as temperature, pressure, and embedding depth) that affect the RFID tag embedding quality and overall tire performance. This application focuses on the accurate identification of parameter deviations and a dynamic threshold adaptive management mechanism based on multivariable environments. It aims to address the shortcomings of traditional fixed threshold control methods, such as insufficient adaptability and control lag, when facing diverse rubber compound characteristics, complex environmental conditions, and high-paced production rhythms. This will enable both global stability and precise local control of the processing process.

[0033] In one embodiment of this application, continuous real-time parameter acquisition and target value comparison are performed for each production station. That is, the current actual process parameter values ​​of the station are periodically acquired (e.g., the current actual temperature of the mold, the real-time pressure acting on the contact surface between the tire body and the mold, the embedding depth of the RFID tag during the implantation process, etc.), and compared with the preset target values ​​(i.e., the theoretical optimal or the benchmark value required by the process specification) set in advance based on factors such as tire model, rubber system, and process route, and then the real-time deviation of each parameter is calculated. This deviation is usually represented as an absolute difference. For example, for temperature parameters, the deviation is calculated as temperature deviation ΔT = |target temperature - measured temperature|, which means that the absolute difference between the target temperature and the measured temperature reflects the extent to which the current temperature deviates from the ideal state. Similarly, for pressure parameters, the deviation is calculated as pressure deviation ΔP = |set pressure - current pressure|, which measures the degree of deviation between the current mold contact surface pressure and the preset pressure threshold. For other mechanical parameters such as embedding depth, a similar logic can be used to calculate their absolute deviation values ​​to quantitatively describe the difference between the actual processing state and the process requirements.

[0034] After obtaining the real-time deviations of various process parameters, an adaptive dynamic threshold mechanism is introduced to overcome the limitations of traditional fixed tolerance ranges. This allows the permissible fluctuation range of process parameters to be dynamically adjusted and individually adapted according to the actual situation of the current production environment and the processed object. Specifically, the impact of the following three types of key variables on process stability is comprehensively considered: 1) Tire compound type: Different rubber compounds (such as natural rubber, styrene-butadiene rubber, cis-butadiene rubber and their composite systems) have different sensitivities to temperature and pressure during vulcanization. Their crosslinking reaction rate, coefficient of thermal expansion, flow characteristics and other physicochemical properties are significantly different. Therefore, the allowable deviation range of process parameters is also different. 2) Ambient temperature and humidity: The ambient temperature and humidity at the production site will indirectly affect the temperature field distribution of the mold and the reaction process of the rubber through heat exchange, mold heat loss, and moisture absorption of the rubber. For example, a high temperature and high humidity environment may cause the mold to dissipate heat faster or the rubber to soften prematurely, thus requiring the relaxation or tightening of the fluctuation limits of certain parameters. 3) Production cycle time variable: that is, the pace at which a single tire is processed per unit of time (such as the number of tires produced per minute / hour). Under high cycle time production conditions, the adjustment window for process parameters is shorter, and the allowable deviation of certain non-critical parameters needs to be appropriately expanded to avoid frequent intervention. In low cycle time or precision machining mode, the threshold can be tightened to improve control accuracy.

[0035] In one embodiment disclosed in this application, based on the real-time perception of the aforementioned variables (which can be obtained through integrated environmental sensors, a rubber material identification module, and production scheduling), a multi-factor influence weight allocation and hierarchical adjustment processing logic is used to calculate the dynamic threshold range of each process parameter, specifically: For each process parameter (such as temperature and pressure), a basic allowable deviation (i.e., static threshold) is set, which is usually determined based on process specifications and historical experience. Based on the type of rubber compound being processed at the current workstation, a predefined rubber compound-parameter sensitivity mapping table is consulted to obtain the sensitivity level of the rubber compound to the target parameter (such as temperature) (e.g., high sensitivity, medium sensitivity, low sensitivity), and a first-level weight is assigned accordingly. Combining real-time collected environmental temperature and humidity data, a second-level environmental correction factor is calculated using a pre-set environmental condition influence factor model (e.g., increasing temperature threshold tolerance in high-temperature environments and tightening pressure threshold limits in high-humidity environments). Based on the current production cycle time (such as high-speed or regular cycle time), a cycle time adjustment coefficient is introduced to dynamically scale the allowable deviation range in a third-level manner to ensure a balance between control frequency and production efficiency. By combining the three levels of weights and correction factors, a dynamic threshold range (i.e., the maximum allowable positive and negative deviation) is finally generated for the current workstation. This range is automatically adjusted as production conditions change, thereby achieving precise containment and reasonable control of process parameter fluctuations under different working conditions.

[0036] Specifically, the rubber compound-parameter sensitivity mapping table in this application is shown in Table 1, which lists the sensitivity of different rubber compound types to various process parameters (such as temperature and pressure): Table 1: Assuming the current tire manufacturing process uses military-grade high-elasticity rubber (sensitivity level S_T=3), assign weights to different sensitivity levels, for example: Sensitivity level 1 (low) → Weight W1 = 1.0 (basic); Sensitivity level 2 (medium) → Weight W1 = 0.8 (relax the threshold and allow for greater fluctuations); Sensitivity level 3 (high) → Weight W1 = 0.6 (The rubber compound is very sensitive to temperature and must be strictly controlled, so the allowable deviation is reduced). In this example: the rubber compound is military-grade high-elasticity rubber compound → sensitivity level S_T=3 → first-level weight W1=0.6.

[0037] The system collects real-time temperature and humidity data of the current production environment and obtains the environmental correction factor W2 through a rule table using a pre-defined environmental condition influence factor model. The processing logic is as follows: If the ambient temperature is high (e.g., >30℃) or the humidity is high (e.g., >70%RH), the rubber compound dissipates heat slowly and softens prematurely, causing the actual temperature to deviate more easily from the target. Therefore, the allowable deviation needs to be tightened → W2 < 1.0 If the ambient temperature and humidity are moderate, then W2≈1.0. If the environment is cold or dry, the tolerance can be relaxed to W2>1.0. Assuming the current workshop ambient temperature is 35℃ and the humidity is 75%RH, it belongs to high temperature and high humidity, which is not conducive to temperature control. The model calculation shows that the environmental correction factor W2=0.8 (indicating that the allowable deviation needs to be further reduced).

[0038] Production cycle time refers to the number of tires processed per unit of time, such as 1 tire per minute (high speed) or 0.5 tires per minute (normal). Under high cycle time conditions, to avoid frequent production intervention, the system allows for a more relaxed parameter deviation → cycle time adjustment coefficient W3 > 1.0. Under low cycle time or precision machining conditions, to ensure quality, the allowable deviation should be reduced → W3 < 1.0. Assuming the current production cycle is 1.5 pieces per minute (a relatively high cycle), W3 is set to 1.2 when the cycle is high (allowing for slightly looser control to improve efficiency). If the cycle is low (e.g., 0.5 pieces per minute), then W3 is 0.9. In this example: the production cycle is high → the cycle adjustment coefficient W3 = 1.2.

[0039] The upper limit of the dynamic allowable deviation ΔT_dynamic = ΔT_base × W1 × W2 × W3. Here we calculate it in absolute value form, and the final dynamic threshold range is ±ΔT_dynamic; ΔT_base is the basic allowable deviation (static threshold). Let ΔT_base = 5℃, and W1 (resulting in a high-sensitivity rubber compound) = 0.6 (military high-elasticity rubber compound, highly sensitive, requiring strict control), W2 (environmental correction factor) = 0.8 (high temperature and humidity, requiring further suppression of deviation), and W3 (cycle adjustment coefficient) = 1.2 (relatively fast production cycle, allowing for slight relaxation, but with minimal impact). Then, we have: ΔT_dynamic = 5 × 0.6 × 0.8 × 1.2 = 2.4 × 1.2 = 2.88℃. The final dynamic threshold range is ±2.88℃. That is, under the current workstation and processing conditions (military high-elasticity rubber compound + high temperature and humidity environment + relatively high cycle), if the real-time deviation ΔT of the temperature parameter exceeds ±2.88℃, it is considered outside the allowable range, triggering the parameter correction mechanism.

[0040] If the current actual deviation ΔT = |170-173| = 3℃, and the dynamic threshold range is ±2.88℃, since 3℃ > 2.88℃ → the current temperature deviation exceeds the dynamic allowable range, the system will automatically trigger a local parameter correction mechanism (such as adjusting the heating sequence of the heating unit at this workstation, or fine-tuning the vulcanization time).

[0041] In this application, the model obtains the environmental correction factor W2 through a rule table, and the preset rule table is shown in Table 2: Ambient temperature T_env (unit: °C); Ambient relative humidity (RH) (unit: %) Table 2: Note: The lower the score (e.g., 0.7~0.8), the more unfavorable the factor is to process control, and the more strictly the allowable deviation needs to be reduced (i.e., the smaller W2 is).

[0042] When a deviation in actual parameters at a production station exceeds the corresponding dynamic threshold range (e.g., the absolute value ΔT of the difference between the measured temperature and the target temperature is greater than the maximum temperature deviation allowed by the current dynamic threshold), a local processing parameter correction mechanism will be automatically triggered to prevent the accumulation of processing deviations from causing quality problems such as RFID tag embedding failure, tire structure damage, or chip performance degradation. This correction mechanism adopts a strategy of primarily local fine-tuning supplemented by global linkage, and its control methods include, but are not limited to: 1) Adjust the heating sequence of the heating unit at the production station: For example, if the current mold temperature is detected to be lower than the target temperature and the deviation continues to increase, the mold temperature can be quickly brought back to the target range by controlling the power output sequence of the heating module (such as starting auxiliary heating in advance, extending the high temperature holding period, or adjusting the gradient ratio of segmented heating). 2) Delay or advance vulcanization time: For critical work stations in the vulcanization stage, if the RFID chip has not yet entered the optimal tolerance window due to temperature or pressure deviation (such as the temperature not reaching the minimum threshold for stable chip operation, or insufficient pressure causing the chip to not bond firmly with the matrix), the start and end time of the vulcanization process can be automatically fine-tuned (such as appropriately delaying the start of vulcanization to wait for the temperature to stabilize, or ending vulcanization in advance to avoid overheating damage), thereby ensuring that the RFID chip is always within the thermodynamic and mechanical environment range allowed by the design throughout the entire vulcanization process.

[0043] Because production stations are typically closely arranged and share some heat sources or structural supports (such as centralized heating units, continuous mold conveyors, etc.), heat conduction and energy interaction between stations are inevitable. Especially in critical processes such as high-temperature vulcanization and pressurized embedding, temperature fluctuations caused by heating or cooling at a single station can be transmitted to adjacent stations through heat conduction from the mold material, air convection, or structural coupling effects, causing unexpected disturbances in the temperature field of the adjacent area. Such thermal coupling phenomena not only disrupt the precisely set process parameters of each station (such as target vulcanization temperature, pressure holding window, etc.), but may also directly affect the thermal stability of the RFID tag chip, the integrity of the encapsulation material, and the reliability of the bonding between the substrate and the tag, thereby reducing product consistency and functional safety. Therefore, this application focuses on the real-time monitoring and active suppression of thermal interference between adjacent stations, ensuring the thermal stability and process independence of the multi-station processing environment through multi-physics field coupling sensing and structured control strategies.

[0044] In one embodiment disclosed in this application, high-frequency, multi-dimensional real-time monitoring is implemented to detect the actual process parameter differences between adjacent production stations, with a focus on two key thermo-coupling indicators: The temperature difference (ΔT_adj) between production stations is measured in real time using high-precision temperature sensors distributed at key locations in each mold (such as the inner wall of the mold, near the RFID implantation area, and the contact surface of the heating unit). This data is collected from the surface temperature or internal temperature field of adjacent stations, and the instantaneous temperature difference between them is calculated (e.g., if the current temperature of station A is T1 and the current temperature of adjacent station B is T2, then ΔT_adj = |T1 - T2|). This temperature difference is a direct basis for assessing the intensity of heat conduction. When ΔT_adj exceeds a preset temperature difference limit (this limit is pre-calibrated based on the thermosensitive characteristics of the rubber compound, the temperature resistance window of the RFID chip, and the thermal diffusion performance of the mold, for example, set to 10~15℃, the specific value can be adjusted according to process requirements), a significant risk of thermal interference is identified. A comprehensive assessment is conducted on the processing interference risks that may be caused by the coupling of mold structures (such as shared heating plates, heat conduction of mold supports, interconnection of airflow channels, etc.). For example, a sudden high temperature at one station may cause the molds of adjacent stations to soften prematurely, or there may be local overheating in the RFID tag area or misalignment of vulcanization sequence. Although such risks are not directly reflected in temperature differences, they can be identified through historical data correlation analysis, thermal flow simulation experience models, or real-time sensing trend prediction.

[0045] In one embodiment of this application, when a significant heat conduction effect is detected between adjacent workstations and the effect has reached a critical condition that endangers the performance of RFID tags (e.g., ΔT_adj≥ set temperature difference limit), a thermal path control strategy will be automatically activated. This strategy consists of two parts: hardware structure control and process timing control. It aims to suppress thermal interference between workstations in a coordinated manner from two levels: blocking the heat transfer path and dispersing the heat accumulation effect.

[0046] Dynamic expansion control of embedded inflatable thermal insulation structure: An embedded, inflatable thermal insulation structure is pre-embedded during the mold design phase. This structure is typically located inside the mold wall (such as in the interlayer between the outer shell and the inner cavity) and runs through the mold along its overall thickness (i.e., perpendicular to the mold surface and penetrating the mold wall), forming one or more adjustable thermal resistance barriers. Normally, this insulation structure is in a low-pressure or uninflated state, having minimal impact on normal heat conduction within the mold. However, when an excessive temperature difference is detected between adjacent workstations, its automatic expansion control mechanism is triggered. Specifically, an inert gas (such as nitrogen) is injected into the insulation structure via a built-in gas filling control unit (such as a micro-pump or pressure regulating valve), causing it to rapidly expand and fill the predetermined cavities within the mold wall, thus forming a physical thermal barrier layer inside the mold. This expansion process has the following technical effects: After the thermal insulation structure expands, the heat flow that was originally transmitted in a straight line is forced to detour or be transferred through multiple layers of media, significantly increasing the heat conduction path. According to Fourier's law of heat conduction, the longer the path, the lower the heat flux density. The expanded thermal insulation material (such as aerogel composites, multilayer polymer films, etc.) has extremely low thermal conductivity, and its equivalent thermal resistance is much higher than that of the original mold material (such as metal or alloy molds), thus effectively blocking or attenuating direct heat conduction between workstations. The amount of filling gas can be graded and controlled according to the real-time temperature difference (e.g., partial filling with small temperature differences and full expansion with large temperature differences), thereby achieving precise suppression of the degree of thermal coupling and avoiding excessive thermal insulation from affecting the overall thermal balance of the mold.

[0047] The regulation process is handled by a mapping logic based on temperature difference, response threshold, and inflation volume: when ΔT_adj reaches the first-level warning threshold, low-speed inflation is initiated; when ΔT_adj exceeds the second-level danger threshold, rapid inflation is switched to until full expansion, ensuring the optimal balance between regulation response speed and suppression effect.

[0048] In addition to hardware structure control, a time-sharing vulcanization strategy is adopted simultaneously. This involves staggering the vulcanization stages (or other high-temperature and high-pressure key process stages) of adjacent workstations to prevent multiple workstations from entering a high-temperature and high-pressure state at the same time. This prevents the regional heat accumulation effect caused by the superposition of heat sources from multiple workstations in a local area (i.e., multiple heat sources concentrate to cause an abnormal increase in temperature in a certain spatial range, exceeding the tolerance limit of the RFID chip or rubber system).

[0049] Specifically, based on the production cycle plan and real-time workstation status, the vulcanization start time, heat preservation time, or cooling intervention timing of each workstation are dynamically adjusted. For example, if it is detected that workstation A is about to enter the high-temperature vulcanization stage and the current temperature of the adjacent workstation B is close to the critical value, the vulcanization start time of workstation B is automatically delayed by several seconds to tens of seconds, or workstation B is allowed to enter the high-temperature stage after the peak vulcanization period of workstation A has passed, thereby achieving the control target of heat source dispersion and peak staggering.

[0050] Furthermore, this application introduces a targeted control mechanism for functionally sensitive areas. Through priority allocation of resources and precise regulation strategies, key areas that play a decisive role in the performance of RFID tags are prioritized for supervision and control. More stringent process parameter monitoring, deviation tolerance control, and abnormal response mechanisms are implemented than in conventional areas, thereby ensuring that key points of product quality are controllable and risk points are preventable.

[0051] In the preliminary steps (such as S1.2), based on the stress distribution characteristics, temperature field distribution state, and preliminary RFID communication performance evaluation results of the contact surface between the tire body and the mold, the area that has the most significant impact on RFID processing quality, namely the functionally sensitive area, has been identified and marked, and it has been taken as the core object of subsequent targeted control.

[0052] in: The actual embedding location of the RFID tag and its surrounding area are the core areas where the RFID tag is physically implanted into the matrix. They directly determine the integrity of the chip and antenna packaging, the bonding strength with the matrix material, and the stability of the subsequent signal transmission path. This area is extremely sensitive to embedding pressure, temperature gradients, and mechanical shocks. Any slight deviation in parameters (such as excessive pressure, sudden temperature changes, or embedding depth deviation) may lead to chip damage, antenna breakage, or peeling of the packaging layer, thereby affecting the electrical performance and lifespan of the tag.

[0053] During vulcanization or pressurization, certain areas inside the mold (such as the wall near the heating unit, the sealing strip where the tire body contacts the mold, and the intersection of the rubber flow front) often experience large temperature fluctuations, concentrated pressure peaks, and coupling between rubber flow and curing reaction. These areas not only affect the uniformity of tire body molding but also indirectly affect the RFID tag through heat conduction and mechanical contact, causing local overheating, uneven stress, or abnormal embedding posture. These are potentially high-risk areas that may lead to RFID malfunction.

[0054] Key structural areas that directly affect RFID communication performance, durability, and subsequent reading stability typically include the connection between the RFID chip and antenna, the insulation / shielding layer interface between the tag and the tag body, and structural features around the tag that may cause electromagnetic shielding or signal reflection (such as the area near metal inserts or areas covered by thick adhesive layers). If these structural areas change due to improper processing parameters (such as excessively high temperatures causing carbonization of the insulation material, excessive pressure causing antenna deformation, or embedding angle deviation causing signal obstruction), it will directly affect the RFID tag's signal readability, communication distance, anti-interference capability, and long-term durability. These are critical control points for ensuring the stability of tag functionality.

[0055] In one embodiment disclosed in this application, the regulatory priority of the aforementioned functionally sensitive areas is automatically increased, and the specific control strategies include: In allocating control computing resources, sensor monitoring resources, and actuator response resources (such as local heating modules and pressure regulation units), priority is given to ensuring the real-time data acquisition frequency, parameter monitoring accuracy, and control response speed of functionally sensitive areas. For example, a higher sampling frequency is set for the temperature and pressure sensors in this area (such as increasing it from the conventional 1Hz to 10Hz), a smaller allowable fluctuation window is set for key parameters, and a faster adjustment priority is given to the actuators (such as local heating units) corresponding to this area.

[0056] For process parameters involved in functionally sensitive areas (such as embedding pressure threshold, local temperature upper limit, heating sequence, etc.), a stricter tolerance control strategy is adopted than that for conventional areas. This means reducing the allowable real-time deviation range to ensure that these critical areas are always within the optimal process window for RFID tag performance. For example, the temperature deviation threshold for functionally sensitive areas may be set to 50% to 70% of that for conventional areas, and the pressure control accuracy is required to be within ±0.5 Bar to meet high-reliability processing requirements.

[0057] When any parameter within the functionally sensitive area is detected to exceed its customized dynamic threshold range (or when multi-parameter fusion analysis determines that there is an abnormal processing trend in this area), a local rapid response mechanism will be immediately triggered, for example: For temperature exceeding limits, local cooling airflow adjustment or heating power reduction is initiated; for abnormal pressure, the advance speed or pressure holding time of the pressurization mechanism is adjusted immediately; for abnormal initial communication performance assessment, secondary RFID detection is triggered or the batch processed at that station is marked as requiring re-inspection. These response strategies are characterized by fast response speed, fine-grained control, and strong targeting, minimizing the scope and extent of the impact of anomalies on RFID tag performance.

[0058] After each production station completes the processing of the tire body and RFID tags (such as vulcanization, pressurization, embedding, and other key process steps), a comprehensive evaluation of the actual condition of the RFID tags will be conducted to ensure the functional integrity and quality compliance of the final product. This evaluation verifies whether the tags meet design requirements and military standards. This evaluation is not only the final quality control step in the processing flow but also a crucial data source for subsequent product traceability, anomaly analysis, and process optimization.

[0059] The evaluation of this application focuses on the three core functions and structural characteristics of RFID tags, specifically including: 1) Is the communication normal? That is, after the RFID tag is processed, can it still be identified and accessed by standard RFID reading and writing equipment? This is a basic indicator for measuring the integrity of the tag's electrical connection, whether the chip is undamaged, and whether the antenna structure is not interfered with. 2) Does the signal strength meet the standard? Evaluate the strength of the signal returned by the RFID tag (usually represented by RSSI, i.e., received signal strength indication value, or read / write distance) to determine whether it is within the minimum acceptable range specified in the design. This indicator is directly related to the readability and communication reliability of the tag in actual application scenarios (such as complex electromagnetic environment, metal close-to environment, tire high-speed rotation state). 3) Whether the physical position and orientation of the chip meet the design requirements: Verify whether the final embedding position (such as depth, offset relative to the tread or sidewall) and spatial orientation (such as the matching degree of antenna polarity and reading / writing direction) of the RFID chip and antenna in the tire body are consistent with the process design parameters. This indicator affects the signal transmission efficiency, anti-interference ability and stability of the tag during the tire's life cycle.

[0060] In one embodiment disclosed in this application, the specific evaluation process logic is as follows: After processing is completed, the RFID read / write test module integrated at the end of the workstation (or transferred to a dedicated testing area) sends a standard query command (such as the Inventory command in the EPCGen2 protocol) to the RFID tag and listens for the tag's response. If no valid response is received within a specified time (such as 100ms~500ms) (such as no serial number returned or CRC check failure), it is determined to be a communication abnormality. Conversely, if the tag can correctly return a unique identifier (such as an EPC code) and the check passes, it is preliminarily determined that the communication function is normal.

[0061] Based on normal communication, further measure the strength of the tag's returned signal (such as real-time RSSI value or read / write distance test based on fixed power); compare the actual measured value with the preset standard threshold (e.g., RSSI ≥ -XXdBm, or read / write distance ≥ XXcm). If the actual signal strength is lower than the standard limit, it is marked as insufficient signal strength, which may be due to problems such as antenna damage, packaging material interference, or improper embedding position; if it meets or exceeds the standard, the signal strength is judged to be up to standard.

[0062] Using high-resolution visual inspection units (such as industrial cameras + image recognition algorithms) or positioning sensors pre-installed in the mold, the final embedding position coordinates of the RFID tag in the tire carcass (such as X / Y offset relative to the tire tread centerline, embedding depth value) and antenna spatial orientation information (such as angular deviation from the standard direction) are obtained. These measured physical parameters are compared with the allowable ranges specified in the process design documents (e.g., depth tolerance ±0.5mm, offset ≤1mm, orientation deviation ≤5°). If they exceed the limits, the physical position or orientation is determined to be abnormal, which may affect the signal transmission efficiency and reliability of the tag in actual use. If all parameters are within the allowable range, the position and orientation are determined to meet the design requirements.

[0063] In the description of this specification, references to terms such as "an embodiment," "example," "specific example," etc., indicate that a specific feature, structure, material, or characteristic described in connection with that embodiment or example is included in at least one embodiment or example of the invention. In this specification, illustrative expressions of the above terms do not necessarily refer to the same embodiment or example. Furthermore, the specific features, structures, materials, or characteristics described may be combined in any suitable manner in one or more embodiments or examples.

[0064] The preferred embodiments of the present invention disclosed above are merely illustrative of the invention. These preferred embodiments do not exhaustively describe all details, nor do they limit the invention to any specific implementation. Clearly, many modifications and variations can be made based on the content of this specification. This specification selects and specifically describes these embodiments to better explain the principles and practical applications of the invention, thereby enabling those skilled in the art to better understand and utilize the invention. The invention is limited only by the claims and their full scope and equivalents.

Claims

1. A method for monitoring the processing of embedded RFID tags for military tires, characterized by: The supervision method comprises the following steps: S1: Multi-station process parameter configuration and multi-dimensional perception of production state S1.1 Station partition and individualized process configuration The processing flow of the embedded RFID tag of the military tire is divided into several production stations according to functions and spatial distribution, and each production station receives processing parameter instructions issued from a front-end configuration terminal; S1.2 Process data acquisition and regional feature identification After the multi-dimensional data is acquired, the function-sensitive area which has an influence on the RFID processing quality is identified based on the stress distribution characteristics of the contact surface between the tire body and the mold, and is marked as a function-sensitive area; S2: Dynamic regulation of processing parameters based on real-time feedback and thermal interference suppression S2.1 Parameter deviation identification and adaptive threshold management The actual process parameters of each production station are continuously compared with the preset target value, the real-time deviation between the two is calculated, an adaptive dynamic threshold mechanism is introduced, and the allowed fluctuation range of each process parameter is automatically adjusted according to the tire rubber type, environmental temperature and humidity, and production rhythm variables; S2.2 Thermal coupling suppression and structure regulation between adjacent stations The temperature difference between production stations is monitored in real time, and when it is detected that there is a thermal conduction influence between adjacent stations, the thermal path regulation strategy is activated; S3: Function area supervision and processing quality optimization S3.1 Targeted control of function-sensitive areas The function-sensitive area is automatically taken as the highest priority target area for supervision, system resources are concentrated, process parameter control and abnormal response strategies are implemented.

2. The method for military tire embedded RFID tag processing supervision according to claim 1, characterized in that: In step S2.2, the temperature difference between production stations is monitored in real time, and when it is detected that there is a thermal conduction influence between adjacent stations, the thermal path regulation strategy is activated, comprising the following steps: Start the embedded inflatable thermal insulation structure, the inflatable thermal insulation structure is pre-embedded inside the mold wall body, penetrates along the overall thickness direction of the mold, and automatically triggers the inflation regulation mechanism when the temperature difference exceeds the limit, prolongs the thermal diffusion path, reduces the thermal coupling degree between production stations, adopts a time-sharing vulcanization strategy, and avoids the regional heat aggregation effect caused by multiple stations entering an abnormal state at the same time.

3. The method for military tire embedded RFID tag processing supervision according to claim 2, characterized in that: In step S2.1, the parameter deviation identification and adaptive threshold management further comprise the following steps: Once the parameter deviation of a certain production station exceeds the corresponding dynamic threshold, a local processing parameter correction mechanism is automatically triggered, including adjusting the heating timing of the heating unit of the production station, delaying or advancing the vulcanization time.

4. The method for military tire embedded RFID tag processing supervision according to claim 3, characterized in that: In step S2.1, the actual process parameter values of the production station are periodically obtained, and are compared with the preset target values based on the tire model, rubber system and process route elements, and the real-time deviation of each parameter is calculated, and a dynamic threshold range of each process parameter is calculated by using a multi-factor influence weight distribution and hierarchical adjustment processing logic. After obtaining the real-time deviation of each process parameter, a dynamic threshold range of each process parameter is calculated by using a multi-factor influence weight distribution and hierarchical adjustment processing logic. ​ Setting a basic allowable deviation for each process parameter, querying a predefined material-parameter sensitivity mapping table according to the material type of the tire being processed at the current station, obtaining the sensitivity level of the material to the target parameter, and assigning a first-level weight accordingly; Combined with real-time collected environmental temperature and humidity data, a second-level environmental correction factor is calculated through a pre-set environmental condition influence factor model; According to the current production rhythm, a rhythm adjustment coefficient is introduced to dynamically scale the allowable deviation range at a third level; The above-mentioned three-level weights and correction factors are integrated to finally generate a dynamic threshold range for the current production station.

5. The method for military tire embedded RFID tag processing supervision according to claim 3, characterized in that: In step S2.2, the temperature difference between production stations is monitored in real time, and when it is detected that there is a heat conduction effect between adjacent stations, a heat path regulation strategy is activated, including the following steps: Obtain the temperature difference between adjacent production stations and the comprehensive evaluation factor of the processing disturbance risk caused by the coupling of the mold structure; When it is detected that there is a heat conduction effect between adjacent stations and the effect has reached the critical condition of endangering the performance of the RFID tag, the heat path regulation strategy is automatically activated, including the dynamic expansion regulation of the embedded inflatable thermal insulation structure: Synchronously adopt a time-sharing vulcanization strategy to stagger the vulcanization stages of adjacent stations in time.

6. The method for military tire embedded RFID tag processing supervision according to claim 1, characterized in that: In step S1.1, the station partitioning and individualized process configuration further include the following steps: Each production station is used for corresponding tires or corresponding processing links, supporting differentiated parameter configuration for different models, materials and process requirements; The processing parameter instructions include vulcanization temperature curve, embedded pressure threshold, RFID chip tolerance range and heating duration. The functional sensitive area includes:

7. The method for military tire embedded RFID tag processing supervision according to claim 1, characterized in that: The actual embedded position of the RFID and its surrounding area; The area in the mold where the temperature is sensitive, the pressure is concentrated, and the material flow is complex; The structure area that affects the communication performance, durability and later reading stability of the RFID. In step S3: functional area monitoring and processing quality optimization further include the following steps:

8. The method for military tire embedded RFID tag processing supervision according to claim 1, characterized in that: S3.2 Processing quality evaluation After the processing at each production station is completed, the state of the RFID tag is evaluated, and the evaluation content includes whether the communication is normal, whether the signal strength meets the standard, and whether the physical position and direction of the chip meet the design requirements. The multi-dimensional data includes real-time temperature information of the mold cavity, the material inside and the surrounding area of the RFID obtained through the temperature monitoring unit; 9. The method for military tire embedded RFID tag processing supervision according to claim 6, characterized in that: The stress distribution of the contact surface between the tire blank and the mold and the local pressure change during the embedding of the RFID are collected through a pressure sensing array; The communication performance of the RFID is preliminarily evaluated during processing, including signal readability and chip response state. It includes:

10. A system for the processing and control of embedded RFID tags for military tyres, for implementing the control method according to any one of claims 1 to 9, characterised in that it comprises: Data acquisition and area identification module: the processing flow of the embedded RFID tag of the military tire is divided into several production stations according to function and spatial distribution, and each production station receives the processing parameter instructions issued by the front-end configuration terminal; after collecting multi-dimensional data, the areas that have an impact on the processing quality of the RFID are identified based on the stress distribution characteristics of the contact surface between the tire body and the mold, and are marked as functional sensitive areas; ​ Adaptive adjustment and strategy output module: continuously compare the actual process parameters of each production station with the preset target value, calculate the real-time deviation between the two, introduce an adaptive dynamic threshold mechanism, automatically adjust the allowed fluctuation range of each process parameter according to the tire rubber type, environmental temperature and humidity, and production rhythm variables; Real-time monitoring of temperature difference between production stations, when detecting the existence of heat conduction influence between adjacent stations, activate the heat path regulation strategy; Management module: automatically take the function sensitive area as the highest priority target area for supervision, centrally allocate system resources, and implement process parameter control and abnormal response strategy.

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