Bluetooth-based film rolling machine real-time control and position calibration system

Abstract

The invention discloses a film rolling machine real-time control and position calibration system based on Bluetooth. A control application program is downloaded from a mobile terminal and paired with a built-in Bluetooth module of a film rolling machine, and a communication link is established based on a low-delay Bluetooth protocol to complete system establishment. In terms of basic control, the mobile terminal can send an instruction to control start and stop of the film rolling machine through Bluetooth, limit parameters are adjusted, and equipment position data can also be fed back to the terminal in real time. The system applies predictive control, collects historical and environmental data to construct a neural network regression model, and outputs optimal operation parameters according to environmental changes; local intelligent decision making is realized by means of a lightweight AI model and edge calculation; and the reinforcement learning module continuously optimizes the control strategy. In addition, the system also has the expansion functions of multi-sensor data assistance, multi-device concentration, Internet of Things linkage and the like, and the Bluetooth performance is guaranteed on the hardware and software level. According to the system, the intelligent level of the film rolling machine is improved, and an efficient, accurate and stable film rolling control scheme is provided for agricultural greenhouses and other scenes.

Description

TECHNICAL FIELD

[0001] The application belongs to the technical field of hardware control, and particularly relates to a real-time control and position calibration system of a film winding machine based on Bluetooth. BACKGROUND

[0002] In the traditional control technology of the film winding machine, a control mode based on a service interface is usually adopted. This mode has obvious disadvantages: firstly, the control instruction needs to be transmitted through a cloud or a manufacturer's service interface, and network delay leads to control lag. For example, in the case of an urgent need to shut down the film winding machine, there may be a delay of several seconds or even longer from the issuance of the instruction to the execution of the action by the device, which seriously affects the work efficiency. Secondly, the device state feedback is not timely, and the operator cannot master the accurate position and running state of the film winding machine in real time, which makes it difficult to adjust the operation in a timely manner according to the actual situation, and easily leads to inaccurate operation of the film winding machine and even causes device failure, thereby reducing the operation accuracy and reliability. In addition, the traditional system is also relatively lacking in environmental adaptability and intelligence, and cannot automatically optimize the running state of the film winding machine according to complex and variable environmental parameters. Therefore, a new technical solution is needed to solve these problems. SUMMARY

[0003] The purpose of the present application is to provide a real-time control and position calibration system of a film winding machine based on Bluetooth, so as to solve the problems of large delay and untimely feedback of the traditional control mode.

[0004] The technical solution of the present application to solve the above technical problems is as follows:

[0005] A real-time control and position calibration system of a film winding machine based on Bluetooth:

[0006] Bluetooth communication and basic control: the mobile terminal and the built-in Bluetooth module of the film winding machine device establish a direct communication link through a low-delay Bluetooth protocol. The mobile terminal is provided with a user instruction input interface, and the user can input "start", "stop" and "pause" control instructions. These instructions are encoded into a digital signal C={c1, c2, c3} (wherein c1 represents the "start" instruction, c2 represents the "stop" instruction, and c3 represents the "pause" instruction), and are sent to the control system of the film winding machine device through the Bluetooth link. After receiving the instruction signal, the control system drives the motor to execute the film winding and unwinding action. At the same time, the film winding machine device is equipped with a position sensor, which collects the motion position data P(t) (t is a time variable) in real time. The data is transmitted back to the mobile terminal through the Bluetooth link, and after being analyzed, the device state information is displayed on the interface in real time. Moreover, the total delay ΔT of the control instruction transmission and the state feedback satisfies ΔT≤T0 (T0 is a preset millisecond-level delay threshold), so as to realize millisecond-level response.

[0007] Limit parameter adjustment: the mobile terminal can receive the user's adjustment operation on the limit parameters. The initial limit parameter set is L0={l01 ,l 02}(wherein l 01 represents the overlap distance parameter, l 02 represents the maximum stroke parameter), the adjusted limit parameter set is L1 = {l 11 ,l 12}, the mobile terminal sends L1 to the film winding machine device through the Bluetooth link, and the device control system adjusts the film winding machine operation according to L1.

[0008] Predictive control module: the system is provided with a predictive control module, based on a historical operation data set H = {h1, h2, …, h s}(wherein h k is the kth historical operation data, k = 1, 2, …, s), and an environmental parameter set E = {e1, e2, e3, e4} (wherein e1 is the wind speed parameter, e2 is the temperature parameter, e3 is the soil moisture parameter, and e4 is the light intensity parameter), the best film winding speed v opt and the limit position L opt are calculated by a prediction algorithm model F(H, E), satisfying (v opt , L opt ) = F(H, E), and the calculation result is transmitted to the film winding machine device control system to adjust the operation parameters. Specifically, the historical operation data is cleaned, the abnormal values and missing values are removed, and the normalized processing is performed to the [0, 1] interval, and the environmental parameters are also cleaned and normalized; the historical operation data and the corresponding environmental parameters are integrated into a new data set Support vector regression, random forest regression or neural network regression algorithm is used to construct the prediction algorithm model F(H, E), and the mean square error:

[0009]

[0010] is the loss function, and the model is trained by stochastic gradient descent or its improved algorithm to learn the nonlinear mapping relationship between the environmental parameters and the film winding machine operation parameters.

[0011] Lightweight AI model and edge computing: the film winding machine device is built-in with a lightweight AI model, the raw data collected by the sensor is D raw , the data processed by the lightweight AI model is D proc , D proc = G(D raw ) (G is the lightweight AI model processing function), and the processed data is used for local decision control of the film winding machine, so that the system response speed improvement ratio R satisfies (wherein T old is the system response time before deployment, and T new is the system response time after deployment), reduces the data transmission amount and supports offline intelligent decision-making.

[0012] The reinforcement learning module: the system also includes a reinforcement learning module, which takes the film winding machine running state, control instruction, environmental parameter as the state space S, takes the adjustment of the film winding speed, the adjustment of the limiting position, the start of the motor, the stop of the motor as the action space A, takes the film material stability, energy consumption, control precision as the reward function R(s, a), optimizes the strategy function pi(s) through iterative training, realizes the automatic adjustment of the control parameters according to the feedback results under different working conditions (wherein s belongs to S, a belongs to A), and improves the self-adaptive ability of the film winding machine in a complex and changeable environment.

[0013] Expansion function: the film winding machine device also carries an angle sensor and a tension sensor, the angle sensor collects the angle data theta (t) of the film material in the film winding process in real time, the tension sensor collects the tension data T (t) of the film material in real time, theta (t) and T (t) are returned to the mobile terminal through Bluetooth link, which is used to assist in adjusting the running parameters of the film winding machine; the mobile terminal supports simultaneous control of multiple film winding machines, integrates multiple film winding machine systems through the network, and can send independent control instructions to different film winding machine devices and receive position data P (t), angle data theta (t), tension data T (t) and other state information of each film winding machine device for unified management; the system accesses the Internet of Things platform, and is linked with the temperature and humidity control equipment and the ventilation equipment to automatically and cooperatively adjust the related equipment according to the film winding machine running state and environmental monitoring data; the Bluetooth module adopts a new generation of Bluetooth technology, the data transmission rate is increased to V, and the data packet loss rate is lower than P loss (wherein V and P loss are preset performance parameter thresholds) to ensure reliable transmission of control instructions and state data.

[0014] The present application has the following beneficial effects:

[0015] Low delay and real-time monitoring: abandoning the traditional mode of relying on service interface, the control instruction millisecond-level transmission and the device state real-time feedback are realized through Bluetooth direct connection, the network delay problem is solved, and the device state can be obtained by the operator in time and accurate control can be performed.

[0016] Multi-dimensional accurate control: not only supports manual adjustment of limiting parameters by the user, but also optimizes the running parameters by combining multi-source environmental data with the predictive control algorithm, realizes adaptive control through the reinforcement learning module, and simultaneously assists in adjusting by using various sensor data, so that the accuracy of the film winding machine control is improved from multiple dimensions.

[0017] Intelligentization and integration: built-in lightweight AI model realizes edge computing, supports offline intelligent decision-making; has the functions of multi-device centralized control and Internet of Things linkage, improves the intelligent level and application range of the system; the new generation of Bluetooth technology ensures reliable data transmission and enhances the stability of the system. BRIEF DESCRIPTION OF DRAWINGS

[0018] Fig. 1 is a structural diagram of the system.

[0019] Fig. 2 is a schematic diagram of the interaction between the control application and the Bluetooth module in the system.

[0020] Fig. 3 is a schematic diagram of the interaction associated with the Internet of Things platform of the system. DETAILED DESCRIPTION

[0021] The principles and characteristics of the present application are described below in conjunction with the accompanying drawings, and the examples are used only to explain the present application and are not intended to limit the scope of the present application.

[0022] (I) System construction

[0023] Figs. 1-3 As shown in the actual application scenario, taking an agricultural greenhouse as an example, the system construction process is as follows:

[0024] First, on a mobile terminal (such as a common Android or Apple smartphone), search and download the control application developed specifically for the system through the application store. After installation, turn on the Bluetooth function of the mobile terminal and make sure it is in a discoverable state.

[0025] For the film rolling machine equipment, after completing the hardware installation and debugging, connect the power supply of the equipment. At this time, the Bluetooth module built-in the film rolling machine is automatically started and enters the connectable mode. In the control application of the mobile terminal, click the search device button, and the program will scan the Bluetooth device list nearby. After finding the Bluetooth device name corresponding to the film rolling machine, click to pair and connect. After successful pairing, a direct communication link is established between the mobile terminal and the film rolling machine equipment based on the low-latency Bluetooth protocol (such as BLE5.0), and the system construction is completed, and subsequent operations can be performed.

[0026] (II) Basic control implementation

[0027] Instruction sending and device response: when the user needs to start the film rolling machine, click the "start" virtual button on the control application interface of the mobile terminal. At this time, the application will encode the "start" instruction into a digital signal c1 according to the preset coding rules. The digital signal is sent to the Bluetooth module built-in the film rolling machine equipment in a low-latency manner through the established Bluetooth link. After receiving the signal, the Bluetooth module transmits it to the control system of the film rolling machine. After the control system analyzes the signal, it drives the motor to start, and the motor drives the film rolling device to perform the film rolling and unfolding action through the transmission device, realizing the demand for greenhouse ventilation or lighting.

[0028] Position data collection and feedback: During the operation of the film winding machine, the position sensor (such as a high-precision displacement sensor) installed on the device collects real-time position data P(t) of the film winding machine. The position sensor detects the position of the film winding machine at a fixed sampling frequency (for example, 10 times per second) and converts the detected analog signal into a digital signal. These digital signals are transmitted back to the mobile terminal through a Bluetooth link. After receiving the data, the control application of the mobile terminal analyzes and processes it, and displays the running state of the film winding machine in a visual manner on the interface, such as "start running, current position: 5 meters" and other information, so that the user can always master the running state of the device.

[0029] Pause and close operation: If the user needs to pause the film winding machine during the film winding process, he only needs to click the "pause" button on the mobile terminal interface, and the application will encode the "pause" instruction into a digital signal c3 and send it to the film winding machine control system. After receiving the signal, the control system immediately controls the motor to stop rotating, and the film winding machine pauses the current action. When the user clicks the "close" button, the "close" instruction is encoded into a digital signal c2 and transmitted to the control system, and the motor reverses to drive the film winding device to retract the film until it reaches the initial position or the preset closing limit position.

[0030] (Three) Limit parameter adjustment implementation

[0031] In the process of agricultural production, according to different planting needs, users often need to adjust the limit parameters of the film winding machine. For example, when planting crops with high requirements for light and ventilation, it may be necessary to increase the maximum stroke of the film winding machine. The user finds the limit parameter setting interface in the control application of the mobile terminal. The interface clearly displays the current limit parameter value, and the initial limit parameter set is L0 = {l 01 ,l 02}, where l 01 represents the edge distance parameter, and l 02 represents the maximum stroke parameter. The user adjusts the maximum stroke parameter l 02 from the initial value (such as 10 meters) to a new value (such as 15 meters) through interactive methods such as sliding bars and digital input boxes, forming the adjusted limit parameter set L1 = {l 11 ,l 12}. After adjustment, click the confirmation button, and the mobile terminal sends L1 to the film winding machine device through the Bluetooth link. After receiving the new limit parameters, the device control system updates the internal parameter settings, and in subsequent film winding operations, the film winding machine is controlled to operate according to the new limit parameters, ensuring that the film winding machine works within a safe and effective range.

[0032] (Four) Predictive control implementation

[0033] Data collection and preprocessing: During the operation of the system, the historical operation data and environmental parameters are continuously collected. The historical operation data set H = {h1,h2,…,h s}, each h k It includes information such as operation time, executed control instructions, film rolling speed at that time, limit position reached, etc.; environmental parameter set E = {e1, e2, e3, e4}, where e1 is the wind speed parameter, e2 is the temperature parameter, e3 is the soil moisture parameter, and e4 is the light intensity parameter. These data are collected in real time by various sensors installed in the greenhouse (such as wind speed sensors, temperature sensors, soil moisture sensors, and light sensors). The collected data is first cleaned to remove outliers (such as obvious erroneous data caused by sensor failure) and missing values ​​(supplemented by interpolation and other methods). Then, the data is normalized and its numerical range is uniformly mapped to the [0,1] interval for subsequent calculations and analysis. After processing, the historical operation data and the corresponding environmental parameters are integrated into a new data set.

[0034] Model construction and training: A neural network regression algorithm is used to construct a prediction algorithm model F(H,E). A multi-layer perceptron (MLP) model with three hidden layers is built. The number of neurons in the input layer is determined according to the number of features in the integrated data set. For example, the input layer receives 8 features of the processed historical operation data and environmental parameters, so the input layer has 8 neurons. The number of neurons in the hidden layer is optimized by cross-validation. After many experiments, it is determined that the first hidden layer has 32 neurons, the second hidden layer has 16 neurons, and the third hidden layer has 8 neurons. The output layer contains 2 neurons, which correspond to the output of the optimal film rolling speed v. opt and limit position L opt .

[0035] The model is trained using the integrated dataset D with a mean square error of The Adam optimization algorithm was used to update the model parameters, with a learning rate of 0.001 and 100 training rounds. During training, the connection weights and biases between neurons in the model were continuously adjusted to gradually reduce the loss function, allowing the model to learn the nonlinear mapping relationship between environmental parameters and film roll machine operating parameters.

[0036] Practical application and control: When the system detects changes in environmental parameters, such as a sudden increase in wind speed detected by a wind speed sensor, the system inputs the current environmental parameters and recent historical operation data into the trained prediction algorithm model F(H,E). The model calculates and outputs the optimal film winding speed v under the current environment. opt and limit position Lopt The calculation results are transmitted to the film roll machine control system via a Bluetooth link. The control system automatically adjusts the motor speed and operating time based on the new parameters, reduces the film roll speed, and stops the film roll when the appropriate limit position is reached. This reduces the film material's vibration and position deviation caused by wind speed and ensures the safety of the greenhouse facility.

[0037] (5) Lightweight AI Model and Edge Computing Implementation

[0038] The film rolling machine has a built-in lightweight AI model developed based on the TensorFlowLite framework. The raw data collected by the sensor is D raw , such as the position data collected by the position sensor, the film material angle data θ(t) collected by the angle sensor, and the film material tension data T(t) collected by the tension sensor, are directly transmitted to the microcontroller built into the device. The microcontroller inputs these raw data into the lightweight AI model, and the convolutional layer, fully connected layer and other structures in the model perform feature extraction and analysis to obtain the processed data D proc , that is, D proc =G(D raw The processed data is used for local decision-making. For example, when the model analysis finds that the film tension is too high, a control instruction is directly generated locally on the device to control the motor to reduce the film winding speed to avoid damage to the film due to excessive tension. In this way, the process of transmitting data to mobile terminals or the cloud is reduced, and the system response speed is improved by a ratio R that satisfies Moreover, even when the network signal is poor or there is no network, it can still support the offline intelligent decision-making and normal operation of the film rolling machine.

[0039] (6) Reinforcement Learning Control Implementation

[0040] Defining the state space, action space, and reward function: The reinforcement learning module uses the film roll machine's operating state (including current film roll speed, position, film material angle, tension, etc.), received control commands, and environmental parameters (wind speed, temperature, etc.) as the state space S. It defines actions such as adjusting the film roll speed, adjusting the limit position, starting the motor, and stopping the motor as the action space A. The reward function R(s, a) is based on film material stability (measured by the degree of fluctuation in film material tension and angle), energy consumption (calculated based on motor operating power and time), and control accuracy (the deviation between the actual film roll position and the target position). For example, a higher reward is given when the film material tension is stable within an appropriate range, energy consumption is low, and the film roll position is accurate. Conversely, if the film material is trembling, energy consumption is excessive, or position deviation is large, a lower reward or even a penalty is given.

[0041] Policy learning and optimization: In the initial stage of system operation, the reinforcement learning module adopts a random policy to select actions, i.e., randomly selecting an action from the action space A. As the system continues to run, new state s, action a, and corresponding reward R(s, a) data are generated after each action is executed. For example, in a film rolling operation, the current film tension is unstable (state s), a random action of reducing the film rolling speed (action a) is selected, and then the film tension tends to be stable and a certain reward value (R(s, a)) is obtained. These data are recorded for updating the policy function π(s). Through continuous iterative training, the policy function π(s) is gradually optimized using reinforcement learning algorithms such as Q-learning, enabling the system to automatically adjust control parameters based on feedback from different working conditions. After multiple training, the system can automatically select the optimal film rolling speed and limit position when facing different light intensity and temperature conditions, achieving more efficient and stable film rolling operation and improving the self-adaptive ability of the film rolling machine in complex and variable environments.

[0042] (VII) Implementation of extended functions

[0043] Multi-sensor data assisted control: The angle sensor and tension sensor mounted on the film rolling machine equipment collect angle data θ(t) and tension data T(t) of the film in real time. These data are transmitted back to the mobile terminal through Bluetooth link at a fixed frequency (such as 5 times per second). In the control application program of the mobile terminal, there is a special data analysis interface for real-time display and analysis of angle and tension data. When the angle data shows that the film is tilted, the application program will issue a warning prompt and automatically adjust the running parameters of the film rolling machine according to the preset rules to restore the normal angle of the film. When the tension data exceeds the safety threshold, the system immediately controls the film rolling machine to pause operation to prevent film rupture and ensure equipment and production safety.

[0044] Centralized control of multiple devices: In large-area agricultural greenhouses or industrial plants, multiple film rolling machines are usually deployed. Each film rolling machine is connected to the corresponding mobile terminal through Bluetooth, and then all mobile terminals are integrated with the central management platform through wireless network (such as Wi-Fi). Users can view the real-time status of all film rolling machine devices on the operation interface of the central management platform, including position data P(t), angle data θ(t), tension data T(t), etc. Users can send independent control instructions to different film rolling machine devices through a unified operation interface, such as simultaneously starting some film rolling machines for ventilation and closing another part of the film rolling machines for heat preservation. The central management platform also supports grouping management function, users can divide the film rolling machine devices into different groups according to the greenhouse area or plant function, and centrally control and manage each group of devices to improve operation efficiency.

[0045] Internet of Things linkage control: After the system accesses the Internet of Things platform, linkage control is realized with temperature and humidity control equipment, ventilation equipment, etc. When the temperature and humidity control equipment detects that the temperature in the greenhouse is too high and the humidity is low, the Internet of Things platform transmits environmental data to the film rolling machine control system. The film rolling machine control system combines predictive control algorithm, considers the current light intensity, wind speed and other factors, calculates the appropriate film rolling speed and limiting position through the prediction algorithm model F(H, E), and automatically controls the film rolling machine to expand the film and increase the ventilation volume. At the same time, the Internet of Things platform will also send instructions to the ventilation equipment to start the ventilation equipment, further strengthen the ventilation effect, and adjust the greenhouse environment. When it is detected that the temperature in the greenhouse is too low, the film rolling machine automatically retracts the film material to reduce heat loss, and cooperates with the temperature and humidity control equipment to create suitable environmental conditions for crops or industrial production.

[0046] Bluetooth performance guarantee: The Bluetooth module adopts the new generation of Bluetooth technology (such as Bluetooth 5.3), and in the hardware design, high-gain antennas and optimized radio frequency circuits are adopted to ensure that the data transmission rate is increased to V (such as 2Mbps), and the data packet loss rate is lower than P loss (0.1%). On the software level, through optimizing the Bluetooth protocol stack, adopting adaptive frequency hopping technology and data retransmission mechanism, it is guaranteed that the control instructions and state data can be reliably transmitted in complex electromagnetic environment. Even in the presence of multiple wireless devices interference, the Bluetooth module can still work stably, ensuring the smooth communication between the film rolling machine and the mobile terminal, providing reliable guarantee for the stable operation of the system.

[0047] The above only describes the preferred embodiments of the present application, and is not intended to limit the present application. Any modification, equivalent replacement, improvement, etc. made within the spirit and principles of the present application shall be included in the protection scope of the present application.

Claims

1. A Bluetooth-based film rolling machine real-time control and position calibration system, characterized in that: Including mobile terminals and film rolling machine equipment; The mobile terminal and the film rolling machine device have built-in Bluetooth modules and establish a direct communication link via a low-latency Bluetooth protocol. The mobile terminal is provided with a user command input interface for receiving "on", "off", and "pause" control commands input by the user, encoding the commands into digital signals C = {c1, c2, c3}, where c1 represents the "on" command, c2 represents the "off" command, and c3 represents the "pause" command, and sending the digital signals to the control system of the film rolling machine device via the Bluetooth link. After receiving the command signal, the control system of the film rolling machine equipment drives the motor to execute the film rolling and unwinding action; the film rolling machine equipment is equipped with a position sensor to collect motion position data P(t) in real time, where t is a time variable. The position data P(t) is transmitted back to the mobile terminal via a Bluetooth link, and after being parsed by the mobile terminal, the device status information is displayed in real time on the interface, and the total delay ΔT between the control command transmission and the status feedback satisfies ΔT≤T0, where T0 is a preset millisecond delay threshold.

2. The Bluetooth-based film rolling machine real-time control and position calibration system according to claim 1 is characterized in that: The mobile terminal receives the user's adjustment operation on the limit parameter, and assumes that the initial limit parameter set is L0={l 01 ,l 02 }, where l 01 Represents the overlap distance parameter, l 02 Represents the maximum stroke parameter; the adjusted limit parameter set is L1={l 11 ,l 12 }, the mobile terminal sends L1 to the film rolling machine through the Bluetooth link, and the equipment control system adjusts the operation of the film rolling machine according to L1.

3. The Bluetooth-based film rolling machine real-time control and position calibration system according to claim 1 is characterized in that: The system is provided with a predictive control module, based on a historical operation data set H = {h1, h2, ..., h s }, where h k is the kth historical operation data, k = 1, 2, ..., s, and the environmental parameter set E = {e1, e2, e3, e4}, where e1 is the wind speed parameter, e2 is the temperature parameter, e3 is the soil moisture parameter, and e4 is the light intensity parameter. The optimal film rolling speed v is calculated by the prediction algorithm model F(H, E) opt and limit position L opt , satisfying (v opt ,L opt )=F(H,E), and transmit the calculation results to the film rolling machine equipment control system to adjust the operating parameters.

4. The Bluetooth-based film rolling machine real-time control and position calibration system according to claim 3 is characterized in that: Clean the historical operation data, remove outliers and missing values, and normalize them to the [0,1] range. Clean and normalize the environmental parameters as well. Integrate the historical operation data and the corresponding environmental parameters into a new data set. Use support vector regression, random forest regression or neural network regression algorithm to build the prediction algorithm model F(H,E), and use the mean square error The loss function is used to train the model through stochastic gradient descent or its improved algorithm to learn the nonlinear mapping relationship between environmental parameters and film rolling machine operating parameters.

5. The Bluetooth-based film rolling machine real-time control and position calibration system according to claim 4 is characterized in that: The film rolling machine has a built-in lightweight AI model, and the raw data collected by the sensor is D raw , the data processed by the lightweight AI model is D proc , D proc =G(D raw ), G is a lightweight AI model processing function, and the processed data is used for local decision-making to control the film rolling machine, so that the system response speed is improved by a ratio R that satisfies Where T old is the system response time before deployment, T new It is the system response time after deployment.

6. The Bluetooth-based film rolling machine real-time control and position calibration system according to claim 5 is characterized in that: The system also includes a reinforcement learning module, which uses the film rolling machine's operating status, control instructions, and environmental parameters as the state space S, adjusts the film rolling speed, adjusts the limit position, starts the motor, and stops the motor as the action space A, and uses film material stability, energy consumption, and control accuracy as the reward function R(s,a). Through iterative training, the strategy function π(s) is optimized to automatically adjust the control parameters according to the feedback results under different working conditions, where s∈S and a∈A.

7. The Bluetooth-based film rolling machine real-time control and position calibration system according to claim 1 is characterized in that: The film rolling machine is also equipped with an angle sensor and a tension sensor. The angle sensor collects the angle data θ(t) of the film material during the film rolling process in real time, and the tension sensor collects the tension data T(t) of the film material in real time. θ(t) and T(t) are transmitted back to the mobile terminal via a Bluetooth link to assist in adjusting the operating parameters of the film rolling machine.

8. The Bluetooth-based film rolling machine real-time control and position calibration system according to claim 1 is characterized in that: The mobile terminal supports simultaneous control of multiple film rolling machines and integrates multiple film rolling machine systems through the network. The mobile terminal can send independent control instructions of "open", "close" and "pause" to different film rolling machine devices respectively, and receive the position data P(t), angle data θ(t), and tension data T(t) status information of each film rolling machine device for unified management.

9. The Bluetooth-based film rolling machine real-time control and position calibration system according to claim 5, characterized in that: The system is connected to the Internet of Things platform and is linked with temperature and humidity control equipment and ventilation equipment for control. It automatically coordinates and adjusts the operation of related equipment according to the operating status of the film rolling machine and environmental monitoring data.

10. The Bluetooth-based film rolling machine real-time control and position calibration system according to claim 1, characterized in that: The data transmission rate of the Bluetooth module is increased to V, and the data packet loss rate is lower than P loss , where V and P loss It is a preset performance parameter threshold to ensure the reliable transmission of control instructions and status data.

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