Intelligent lifting screen control method based on POE technology

Abstract

The invention discloses an intelligent lifting screen control method based on a POE technology, and the method comprises the steps: obtaining an input signal of a lifting screen, and judging whether the signal is an effective control signal or not based on the high and low levels of the input signal; constructing a power demand prediction model, predicting the power demand required by the module for executing the control instruction, and generating a power distribution instruction; transmitting the power supply signal and the control signal at the same time through the same network cable, judging the stability and effectiveness of the power supply signal and the control signal by adopting a voltage test tool, and optimizing the power supply signal and the control signal; judging whether a module which operates in an overload mode or is insufficient in power exists, and adjusting electric energy distribution of the module; and monitoring feedback data of the lifting screen are obtained through a sensor arranged in the lifting screen, and a power distribution scheme and a power demand prediction model are optimized. The intelligent degree of the lifting screen control system is improved, and meanwhile the safety, stability and practicability of the lifting screen control system in a complex application scene are remarkably enhanced.

Description

Technical Field

[0001] This invention relates to the field of next-generation information technology, and in particular to an intelligent lifting screen control method based on PoE technology. Background Technology

[0002] With the widespread adoption of information-based and intelligent office environments, intelligent lifting screens are increasingly being used as crucial terminal display devices in multimedia teaching, conference systems, smart classrooms, and government command centers. Existing lifting screen control systems primarily rely on independent power supplies and single communication signal lines for control operations, resulting in complex wiring, high installation costs, severe signal interference, and low energy utilization. These problems are amplified, especially in multi-module interconnected system architectures, significantly impacting equipment response efficiency and operational stability. Traditional lifting screens often employ a separate power supply and control system architecture, with power lines and control signal lines laid out separately. This leads to cumbersome wiring, long construction periods, and high maintenance difficulty. In complex environments, such as embedded structures in conference rooms or multi-screen collaboration scenarios, signals are prone to crosstalk, distortion, and delays, affecting the accuracy of control command transmission. Furthermore, most current lifting screen systems lack the ability to perceive and predict power consumption status. When system load fluctuates drastically or there is a momentary power shortage, it can easily lead to module malfunctions or even hardware damage, posing risks to equipment safety and user experience. Against this backdrop, the development of Power over Ethernet (PoE) technology has provided new solutions for the intelligent and integrated operation of lifting screen systems. PoE technology can transmit power and control signals simultaneously through a single standard network cable, greatly simplifying the system's cabling architecture and reducing deployment and maintenance costs. Simultaneously, by utilizing internal and external sensors to collect parameter data such as voltage, current, power signals, and data transmission status of each module during the lifting screen's operation, and by constructing a power demand prediction model, the system can predict the power requirements for different control operations in advance. This assists the system in achieving precise dynamic allocation of power resources, effectively avoiding problems such as module overload or power waste. Furthermore, in complex multi-module collaborative lifting screen systems, there may be certain functional dependencies and resource sharing relationships between modules. Without the identification and management of these dependencies, insufficient power allocation to critical modules, signal interference, or task execution failures can easily occur. Therefore, there is an urgent need for an intelligent lifting screen control method based on PoE technology that can simplify the cabling structure while avoiding control signal interference through an efficient signal separation mechanism. Summary of the Invention

[0003] To address the problems existing in the prior art, this invention provides a smart lifting screen control method based on PoE technology, mainly comprising:

[0004] By connecting external sensors to the lifting screen control device, the input signal of the lifting screen is obtained, and the high or low level of the input signal is used to determine whether it is a valid control signal.

[0005] By employing signal separation technology, control signals from different modules are isolated, a power demand prediction model is constructed to predict the power demand required by the modules to execute control commands, and power distribution commands are generated.

[0006] Power and control signals are transmitted simultaneously through the same network cable. Voltage testing tools are used to determine the stability and effectiveness of the power and control signals, and the power and control signals are optimized.

[0007] Based on the voltage and current data of each module, the current power consumption of each module is calculated, it is determined whether each module is operating under overload or underpowered, and the power distribution of the modules is adjusted accordingly.

[0008] By acquiring monitoring feedback data from the built-in sensors of the lifting screen, the power distribution scheme and power demand prediction model can be optimized.

[0009] Furthermore, the step of acquiring the input signal of the lifting screen through an external sensor connected to the lifting screen control device, and determining whether the signal is a valid control signal based on the high or low level of the input signal, includes:

[0010] The system acquires input signals from the lifting screen control device by connecting external sensors, including changes in the screen's operating status, lifting requirements, or voltage variations. Signal decoding technology is used to determine the high or low level of the input signal and whether it is a valid control signal. If the input signal is a valid control signal, the corresponding lifting screen control command is output through the decoding circuit.

[0011] Furthermore, the signal separation technology is used to isolate the control signals of different modules, construct a power demand prediction model, predict the power demand required for the module to execute control commands, and generate power distribution commands, including:

[0012] Based on the control commands received by the lifting screen, signal separation technology is used to isolate the control signals of different modules and send them to different output control modules. Based on the module type, operating status, and current power consumption of each output control module, a recurrent neural network is used for model training to build a power demand prediction model. This model predicts the power requirements for each module to execute control commands. Module types include core modules and auxiliary modules. Based on the power requirements of each module, a power allocation scheme is formulated. Corresponding power allocation commands are generated based on the scheme and sent to the PoE switch. The power allocation scheme includes the allocated power value and maximum usable power for each module.

[0013] Furthermore, the simultaneous transmission of power and control signals via the same network cable, the use of voltage testing tools to determine the stability and effectiveness of the power and control signals, and the optimization of the power and control signals include:

[0014] The system acquires control signals based on power distribution instructions, obtains 12V power signals from an external power source, integrates control and power signals onto a single network cable, and transmits the power signal to the control device via the network cable. A voltage testing tool measures the voltage and current of the power signal to determine its integrity and stability. The control device's network protocol decoding module converts the control signal into a serial port protocol format. According to the PoE protocol, the power signal is converted to a 5V output power signal, and the serial port protocol-converted control signal is then converted to the protocol format required by the screen. A voltage testing tool and a network protocol analyzer are used to check the converted power and control signals to determine their stability and effectiveness. If the power or control signal is unstable or lost, the test results are fed back to the controller for optimization. The optimized power and control signals are then transmitted to the lifting screen via the controller. Based on the control signals, the lifting controller controls the extension and retraction of the rising and lowering telescopic supports, adjusting the lifting height, speed, and tilt angle of the lifting screen, and monitoring voltage stability.

[0015] Furthermore, the step of calculating the current power consumption of each module based on the voltage and current data of each module, determining whether each module is operating under overload or underpowered, and adjusting the power distribution of the modules includes:

[0016] If multiple modules operate simultaneously, the voltage and current data of each module are acquired in real time through current and voltage sensors inside the lifting screen. Based on the voltage and current data of each module, the current power consumption of each module is calculated, and a power demand prediction model is used to predict the current power demand of the module. Based on the current power consumption and power demand of each module, it is determined whether there are modules operating under overload or with insufficient power. Based on historical power distribution instruction data, the Apriori algorithm is used to train the model, determine the calling patterns and correlations between modules, and obtain a candidate set of module dependencies. The candidate set of module dependencies contains a list of module pairs with potential dependencies. The system tracks the call frequency, time series, and shared power resources between modules. If dependencies exist between modules, the power demand growth coefficient of each module is determined based on its power demand and dependencies to identify high-risk modules. The fixed-point detection module of the lifting controller is used to locate the running trajectory of the lifting motor in real time. If a high-risk module is in a state of insufficient power or voltage fluctuations are detected, the screen is locked at the nearest fixed point through the preset uniform screen locking point on the lifting controller, and power is preferentially allocated to the high-risk module, while power allocation to modules with lower priority is temporarily stopped or delayed. If the voltage fluctuations and the power shortage state of the high-risk module are eliminated, power allocation to low-risk modules is gradually restored.

[0017] This also includes determining the power demand growth coefficient of each module based on its power demand and dependencies, and identifying high-risk modules, specifically including:

[0018] Based on historical power allocation instruction data, the Apriori algorithm is used for model training to determine the calling patterns and correlations between modules, resulting in a candidate set of module dependencies. This candidate set contains a list of module pairs with potential dependencies, along with the calling frequency, time series, and power resource sharing data for each pair. By fixing the operating state of module i, the power demand of module j is gradually adjusted, and the response of module i's power demand to changes in module j's load is recorded. The formula is then used to... Calculate the impact coefficient α of module j on the electricity demand of module i. ij , representing the transmission effect of electricity demand from module j to electricity demand from module i, where P j This represents the power demand of module j, ΔP. i and ΔP j Let D represent the changes in electricity demand for modules i and j, respectively; by fixing the initial electricity demand of module i and gradually increasing or decreasing the demand for modules j ∈ D. i The load is recorded, and the response curve of module i's power demand as a function of dependent modules is analyzed. Based on the trend of power demand changes, a polynomial fitting curve is used. Obtain the power demand amplification factor β of module ii , representing the nonlinear growth effect of the electricity demand of module i as it changes with the dependent modules, where P i0 This refers to the basic power demand of module i without the influence of other dependent modules, obtained through historical data and hardware standard documents. i It is the set of dependent modules of module i, obtained through the candidate set of module dependencies; based on the impact coefficient and amplification coefficient of electricity demand, the electricity demand growth coefficient formula is used. Determine the electricity demand growth factor PGF for module i. i This indicates that the electricity demand of module i is relative to its base electricity demand P. i0 The degree of growth; if PGF i If the power demand of module i exceeds the preset coefficient threshold, then module i is judged to be a high-risk module because its power demand is highly sensitive to changes in the power consumption of other modules; otherwise, the module is judged to be a low-risk module.

[0019] Furthermore, the step of acquiring monitoring feedback data from the lifting screen via its built-in sensors to optimize the power distribution scheme and power demand prediction model includes:

[0020] The system uses sensors built into the lifting screen to acquire monitoring feedback data, evaluate the effectiveness of the current power distribution scheme and the accuracy of the power demand prediction model. The monitoring feedback data includes power signal, voltage stability, and data transmission quality. If voltage fluctuations, current exceeding limits, or data transmission quality degradation are detected, the power distribution scheme and power demand prediction model are optimized and then executed.

[0021] The technical solutions provided by the embodiments of the present invention may include the following beneficial effects:

[0022] This invention provides an intelligent lifting screen control method based on PoE technology. This invention transmits power and control signals simultaneously via the same network cable, significantly simplifying system wiring and reducing installation and maintenance costs. It introduces external sensors and a high / low level signal recognition mechanism to achieve accurate identification of input control signals, improving system response accuracy. Signal separation technology isolates control signals from multiple modules, effectively avoiding crosstalk and ensuring the independent and stable operation of each module. Simultaneously, a power demand prediction model is constructed based on real-time collected voltage and current data to dynamically adjust the power distribution scheme, achieving efficient power scheduling and risk control, preventing module overload or insufficient power supply. In multi-module collaborative operation scenarios, this method can also identify dependencies between modules and analyze power demand growth trends, thereby prioritizing the operation of critical modules. This invention enhances the intelligence level of the lifting screen control system while significantly improving its safety, stability, and practicality in complex application scenarios. Attached Figure Description

[0023] Figure 1 This is a flowchart of an intelligent lifting screen control method based on POE technology according to the present invention;

[0024] Figure 2 This is a schematic diagram of an intelligent lifting screen control method based on POE technology according to the present invention;

[0025] Figure 3 This is another schematic diagram of an intelligent lifting screen control method based on POE technology according to the present invention. Detailed Implementation

[0026] To make the objectives, technical solutions, and advantages of this invention clearer, the invention will be described in detail below with reference to the accompanying drawings and specific embodiments.

[0027] like Figure 1-3 This embodiment of a smart lifting screen control method based on PoE technology may specifically include:

[0028] Step S101: Obtain the input signal of the lifting screen by connecting an external sensor to the lifting screen control device, and determine whether the signal is a valid control signal based on the high or low level of the input signal.

[0029] The system acquires input signals from the lifting screen control device via external sensors, including changes in the screen's operating status, lifting requirements, or voltage variations. Signal decoding technology is used to determine the high or low level of the input signal and whether it is a valid control signal. If the input signal is a valid control signal, the corresponding lifting screen control command is output through the decoding circuit.

[0030] For example, in a control system for a lifting screen, an external sensor acquires the input signal of the lifting screen. When the external sensor detects a change in the operating state of the lifting screen, such as a lifting motion or a voltage change, it outputs a voltage signal, which is transmitted to the control device. If the set input signal range is 0 to 5 volts, and a preset voltage threshold of 2 volts is used, the system determines whether the voltage is low or high. A voltage signal of 0 volts represents a low level, while a voltage signal of 5 volts represents a high level. The receiving control device uses signal decoding technology to determine the state of this signal. If the signal is a low level of 0.1 volts, it indicates that the system is in standby mode; if the signal is a high level of 5 volts, it indicates that the lifting screen needs to perform a lifting operation. If the sensor detects a 5-volt voltage signal (a high-level signal), the control device recognizes it as a valid control signal and outputs a lifting screen control command through the decoding circuit. This command includes starting the motor of the lifting bracket to raise or lower the screen, or adjusting the screen angle. The decoded command is then sent to the lifting screen's motor system via appropriate circuitry to control the screen's raising and lowering. For example, when the sensor's voltage signal reaches 5 volts, it indicates that the lifting screen needs to be activated. The control device recognizes this valid signal and issues the corresponding operation command, initiating the lifting action. Conversely, if the signal is low (e.g., 0.1 volts), the system remains in standby mode and performs no operation. In this way, the lifting screen can automatically adjust its state or position based on the input control signal.

[0031] Step S102: Using signal separation technology, control signals of different modules are isolated, a power demand prediction model is constructed to predict the power demand required for the module to execute control commands, and power distribution commands are generated.

[0032] Based on the control commands received by the lifting screen, signal separation technology is used to isolate the control signals of different modules and send them to different output control modules. According to the module type, operating status, and current power consumption of each output control module, a recurrent neural network is used for model training to build a power demand prediction model. This model predicts the power requirements needed for each module to execute control commands. Module types include core modules and auxiliary modules. Based on the power requirements of each module, a power allocation scheme is formulated. Corresponding power allocation commands are generated based on the scheme and sent to the PoE switch. The power allocation scheme includes the allocated power value and maximum usable power for each module.

[0033] For example, a lifting screen system is being controlled with multiple modules, including a core module and auxiliary modules. The core module includes a lifting motor and a screen display module, while the auxiliary modules include a temperature control module and an audio module. In this lifting screen system, after receiving control commands from the lifting screen, the control device uses input signal separation technology to distinguish the control signals and send them to different output control modules. For example, the lifting motor module receives the lifting command, the screen display module receives the display adjustment command, and the temperature control module receives the temperature adjustment command. Afterward, the lifting screen system analyzes the power requirements of each module. Assuming the lifting motor in the core module typically requires 30 watts of power to perform the lifting task, the display module requires 15 watts for display, and the temperature control module in the auxiliary modules requires 5 watts to maintain a stable system temperature. A recurrent neural network is used for model training, and a power demand prediction model is built based on the type, current operating state, and power consumption data of each module. This power demand prediction model can predict the power requirements of each module when executing control commands. At the current moment, the lifting motor is performing a lifting operation, the display module is running, and the temperature control module is in standby mode. Based on previous power demand predictions, the lifting motor requires 30 watts, the display module requires 15 watts, and the temperature control module requires 0 watts because it is in standby mode. A power distribution plan is developed based on these power requirements to ensure that each module receives sufficient power. In this plan, the lifting motor module will receive 30 watts, the display module will receive 15 watts, and the temperature control module will receive 0 watts because it is in standby mode. The maximum power value for each module is calculated: if the maximum power of the lifting motor module is 50 watts, the maximum power of the display module is 20 watts, and the maximum power of the temperature control module is 10 watts. This power distribution plan will be sent to the PoE switch via a power distribution command to ensure that each module operates according to its required power, avoiding power shortages or waste.

[0034] Step S103: Power signals and control signals are transmitted simultaneously through the same network cable. Voltage testing tools are used to determine the stability and effectiveness of the power signals and control signals, and the power signals and control signals are optimized.

[0035] The system acquires control signals based on power distribution instructions, obtains 12V power signals from an external power source, integrates control and power signals onto a single network cable, and transmits the power signal to the control device via the network cable. Voltage and current of the power signal are measured using a voltage testing tool to determine its integrity and stability. The control device's network protocol decoding module converts the control signal into a serial port protocol format. According to the PoE protocol, the power signal is converted to a 5V output power signal, and the serial port protocol-converted control signal is then converted to the protocol format required by the screen. Voltage testing tools and a network protocol analyzer are used to check the converted power and control signals to determine their stability and validity. If instability or loss of power or control signals is detected, the test results are fed back to the controller for optimization. The optimized power and control signals are then transmitted to the lifting screen via the controller. Based on the control signals, the lifting controller controls the extension and retraction of the rising and lowering telescopic supports, adjusts the lifting height, speed, and tilt angle of the lifting screen, and monitors voltage stability.

[0036] For example, in a control system for a lifting screen, power and control signals need to be transmitted simultaneously via a single network cable to control the lifting and lowering action of the screen. An external power supply provides a 12V voltage signal. A PoE switch integrates the power and control signals onto a single network cable, which is then transmitted to the control device. Upon receiving these signals, the control device first uses a voltage testing tool to measure the voltage and current of the power signal transmitted via the network cable to determine if the power signal is stable and complete. If the testing tool displays a 12V voltage and 1.5A current in the network cable, it indicates that the power signal is normal and stable. If the power signal is normal, the control device continues to operate, converting the control signal from the network into a serial protocol format using a network protocol decoding module. For instance, the control signal might be a command for the lifting and lowering action of the screen, indicating that the screen needs to be raised 3 meters. The control device converts this control signal into a serial protocol format suitable for the screen to receive. The control device converts the 12V power signal to a 5V power signal according to the PoE protocol. For example, the lifting motor and display module require 5V power to ensure normal operation, and the temperature control module may also only require 5V. Simultaneously, the control signal is converted to a protocol format suitable for the screen driver. At this point, a voltage testing tool is used to measure the converted power signal again to confirm its stability, and a network protocol analyzer is used to check the correctness of the control signal. The test results show that the converted power signal is 5V and the current is 2A, meaning the power supply is stable and can provide sufficient power to the lifting screen. The control signal is also displayed as correctly formatted by the protocol analyzer, without any loss or errors, indicating that the integrity of the power and control signals is good. If voltage fluctuations are found in the power signal during measurement, or if the control signal format is unstable or missing, the control device will optimize the power and control signals based on these test results. If the voltage fluctuation is within ±0.5V, the control device may adjust the output of the power regulator or resend the control signal to ensure correct transmission. The optimized signal will then be retransmitted to the lifting screen through the control device. Based on the received optimized control signals, the control device instructs the lifting controller to activate the lifting support. For example, if the control signal instructs the lifting screen to rise 0.5 meters, the lifting controller will adjust the extension and retraction of the telescopic support, adjusting the lifting height, speed, and tilt angle of the screen to ensure a smooth lifting process. Simultaneously, the system continues to monitor voltage stability to ensure a stable and reliable power supply throughout the entire lifting process.

[0037] Step S104: Calculate the current power consumption of each module based on the voltage and current data of each module, determine whether each module is operating under overload or underpowered, and adjust the power distribution of the modules.

[0038] If multiple modules operate simultaneously, the voltage and current data of each module are acquired in real time using current and voltage sensors inside the lifting screen. Based on the voltage and current data of each module, the current power consumption of each module is calculated, and a power demand prediction model is used to predict the current power demand of the modules. Based on the current power consumption and power demand of each module, it is determined whether any modules are operating under overload or have insufficient power. Using historical power allocation instruction data, the Apriori algorithm is used to train the model, determining the calling patterns and correlations between modules, and obtaining a candidate set of module dependencies. This candidate set contains a list of module pairs with potential dependencies, along with the calling frequency, time series, and power resource sharing data between each pair of modules. If dependencies exist between modules, the power demand growth coefficient of each module is determined based on its power demand and dependencies, identifying high-risk modules. The lifting controller's fixed-point detection module performs real-time positioning of the lifting motor's running trajectory. If a high-risk module experiences insufficient power or voltage fluctuations, the screen is locked at the nearest fixed point using a preset uniform screen locking mechanism on the lifting controller. Power is then prioritized for the high-risk module, temporarily halting or delaying power allocation to lower-priority modules. Once the voltage fluctuations and the high-risk module's insufficient power condition are resolved, power allocation to low-risk modules is gradually restored.

[0039] For example, in a lifting screen control system, multiple modules operate simultaneously, including a lifting motor module, a display module, and a temperature control module. The power requirements and energy consumption of each module are dynamically changing, thus requiring real-time monitoring and adjustment of power distribution. During operation, current and voltage sensors inside the lifting screen monitor the voltage and current data of each module in real time. Monitoring reveals that the lifting motor module currently has a voltage of 5V and a current of 2A, the display module has a voltage of 5V and a current of 1A, and the temperature control module has a voltage of 5V and a current of 0.5A. Based on this data, the power consumption of each module can be calculated: the lifting motor module consumes 10 watts, the display module consumes 5 watts, and the temperature control module consumes 2.5 watts. Using a power demand prediction model, the current power demand of each module is predicted. If the prediction is accurate, the lifting motor module's power demand is 12 watts, the display module's is 6 watts, and the temperature control module's is 3 watts. After comparing the current power consumption with the predicted power demand, it was found that the power consumption of the lifting motor module was lower than the demand, as were the power consumption of the display module and the temperature control module. All modules were within the normal range, with no overload operation. The Apriori algorithm was used to analyze historical power distribution data to determine the calling patterns and correlations between modules. After training, the Apriori algorithm found a strong dependency between the lifting motor module and the display module, and that they frequently used a large amount of power resources simultaneously within the same time period. Based on this data, a power demand growth coefficient was derived, and high-risk modules were identified. If the power demand growth coefficients of the lifting motor module and the display module were high, it meant that their power demand was highly sensitive to changes in the power consumption of other modules, thus classifying them as high-risk modules. The fixed-point detection module of the lifting controller monitored the operating trajectory of the lifting motor in real time and detected voltage fluctuations in the lifting motor at a certain moment, indicating a possible power shortage. The screen lock function was activated, temporarily fixing the lifting screen at the nearest fixed position to prevent loss of control due to power shortage. Simultaneously, power was prioritized for high-risk modules, including the lifting motor module and the display module, to ensure their operation was unaffected. Power allocation to lower-priority temperature control modules was temporarily suspended or delayed. As system monitoring continued, voltage fluctuations gradually subsided, and the power needs of the lifting motor and display modules were met. Power allocation to the low-risk temperature control modules was gradually restored, returning to normal, and the entire system resumed stable operation.

[0040] Based on the power demand and dependencies of each module, determine the power demand growth coefficient of the module and identify high-risk modules.

[0041] Based on historical power allocation command data, the Apriori algorithm is used for model training to determine the calling patterns and correlations between modules, resulting in a candidate set of module dependencies. This candidate set contains a list of module pairs with potential dependencies, along with the calling frequency, time series, and power resource sharing data for each pair. By fixing the operating state of module i, the power demand of module j is gradually adjusted, and the response of module i's power demand to changes in module j's load is recorded. The formula is then used to... Calculate the impact coefficient α of module j on the electricity demand of module i. ij , representing the transmission effect of electricity demand from module j to electricity demand from module i, where P j This represents the power demand of module j, ΔP. i and ΔP j Let be the changes in power demand for module i and module j, respectively. This is achieved by fixing the initial power demand of module i and gradually increasing or decreasing the demand for module j ∈ D. i The load is recorded, and the response curve of module i's power demand as a function of dependent modules is analyzed. Based on the trend of power demand changes, a polynomial fitting curve is used. Obtain the power demand amplification factor β of module i i , representing the nonlinear growth effect of the electricity demand of module i as it changes with the dependent modules, where P i0 This refers to the basic power demand of module i without the influence of other dependent modules, obtained through historical data and hardware standard documents. i This is the set of dependent modules of module i, obtained through the candidate set of module dependencies. Based on the impact coefficient and amplification coefficient of electricity demand, the electricity demand growth coefficient formula is used. Determine the electricity demand growth factor PGF for module i. i This indicates that the electricity demand of module i is relative to its base electricity demand P. i0 The degree of growth. If PGF i If the power demand of module i exceeds the preset coefficient threshold, then module i is judged to be a high-risk module because its power demand is highly sensitive to changes in the power consumption of other modules; otherwise, the module is judged to be a low-risk module.

[0042] For example, in a lifting screen system, there are three modules: module i is the lifting motor module, module j is the display module, and module k is the temperature control system. Using historical power distribution data, the Apriori algorithm is used to analyze the dependencies between the modules. The algorithm identifies a strong dependency between module i and module j; module j typically needs to operate in conjunction with motor module i, and their power consumption is correlated. By fixing the operating state of module i and gradually adjusting the power demand of module j, it is found that when module i is operating, the power demand of module j changes with the load change of module i. When the power demand of module i increases by 10 watts, the power demand of module j increases by 5 watts. This means that the power demand of module j responds to changes in the load of module i to a certain extent. Using the formula... Calculate the impact coefficient α of module j on the electricity demand of module i. ij , where P j This represents the power demand of module j, ΔP. i and ΔP j Let be the changes in power demand for module i and module j, respectively. If the change in power demand for module i is 10 watts and the change in power demand for module j is 5 watts, then the influence coefficient α of module j on the power demand of module i is... ij The value is 2, indicating that the change in power demand of module B is twice that of module i, demonstrating that the power demand of module B has a certain impact on the power demand of module i. With the initial power demand of module i fixed, the load of module j is gradually increased or decreased, and the response of module i's power demand to changes in module j is recorded. Assuming that the load of module j increases by 5 watts, the power demand of module i increases by 3 watts. A polynomial fitting curve is then used to determine the response. Fitting these changing data yields a power demand amplification factor of 1.5. This means that the power demand of module i increases non-linearly with the increase of the load on module B, with an increase of 1.5 times, where P i0 This refers to the basic power demand of module i without the influence of other dependent modules, obtained through historical data and hardware standard documents. i This is the set of dependent modules of module i, obtained through the candidate set of module dependencies. The electricity demand growth coefficient of module i is calculated based on the influence coefficient and amplification coefficient. Assuming the base electricity demand of module i is 20 watts, the electricity demand growth coefficient given by the dependency relationship of module j is 0.3. The electricity demand growth coefficient formula is then used. The power demand growth coefficient for module i is 0.9. Since this coefficient exceeds the preset threshold of 0.7, module i is classified as a high-risk module because its power demand is highly sensitive to changes in the power consumption of other modules. Therefore, ensuring its power supply is prioritized, and appropriate adjustments to the power allocation to other modules are necessary to prevent system instability due to insufficient power. Conversely, if the power demand growth coefficient for module i is below the preset threshold of 0.7, module i is classified as a low-risk module.

[0043] Step S105: Obtain monitoring feedback data of the lifting screen through the built-in sensors to optimize the power distribution scheme and power demand prediction model.

[0044] The system uses sensors built into the power distribution panel to acquire monitoring feedback data, assessing the effectiveness of the current power distribution scheme and the accuracy of the power demand prediction model. This monitoring feedback data includes power signal strength, voltage stability, and data transmission quality. If voltage fluctuations, excessive current, or degraded data transmission quality are detected, the power distribution scheme and power demand prediction model are optimized and implemented.

[0045] For example, in a multi-module lifting screen system, built-in current, voltage, and data transmission sensors are collecting critical operational data in real time. For instance, at a certain moment, the monitoring shows the lifting motor module's supply voltage is 5 volts and current is 2.5 amps, the display module's supply voltage is 5 volts and current is 1.2 amps, the data transmission module's transmission rate is 50 Mbps, and the bit error rate is 0.01%. Based on this data, the current power distribution scheme is deemed reasonable, and the power demand prediction model largely matches the actual consumption, thus the entire system is operating stably. However, approximately two minutes later, a sudden voltage fluctuation is detected in the lifting motor module, dropping from 5 volts to 4.3 volts, while the current increases to 3.1 amps. This indicates that under insufficient voltage, the module is attempting to obtain more current to maintain operation, potentially indicating an uneven load. Simultaneously, the data transmission quality of the module also begins to deteriorate, with the transmission rate decreasing to 30 Mbps and the bit error rate increasing to 0.1%. These changes are captured in real time by the built-in sensors and flagged by the system as abnormal power operation. Faced with this situation, a feedback evaluation mechanism was immediately triggered. First, it was determined that the current power distribution scheme might be insufficiently supplying power to the lifting motor module, especially due to power instability under high load. Second, the power demand prediction model failed to anticipate the module current surge to 3.1 amps during peak periods, indicating a prediction bias. Therefore, the system began optimizing two aspects. Firstly, the power demand prediction model was retrained, incorporating more historical data samples similar to load fluctuations and adjusting model parameters to make it more sensitive to power demand changes in the lifting module during the initial stages of movement. Secondly, at the power distribution level, the system decided to prioritize allocating more power to the lifting motor module, especially during startup or descent, briefly reducing the power supply ratio of the display module and temperature control module to 80% and 60% respectively to ensure the lifting module's voltage remained above 5 volts. After optimization, the system monitored the system again and found that the lifting motor module's voltage stabilized at 5.1 volts, the current dropped to 2.6 amps, the data transmission rate recovered to 48 Mbps, and the bit error rate decreased to 0.02%. This indicates that the optimized power distribution scheme and power demand prediction model are more in line with actual load demand, successfully alleviating the operational problems previously caused by inaccurate predictions and insufficient power allocation.

[0046] The above description is merely a preferred embodiment of this application and an explanation of the technical principles employed. Those skilled in the art should understand that the scope of the invention involved in this application is not limited to technical solutions formed by specific combinations of the above-described technical features, but should also cover other technical solutions formed by arbitrary combinations of the above-described technical features or their equivalents without departing from the concept of this application. For example, technical solutions formed by substituting the above features with (but not limited to) technical features with similar functions disclosed in this application.

Claims

1. A smart lifting screen control method based on PoE technology, characterized in that, The method includes: By connecting external sensors to the lifting screen control device, the input signal of the lifting screen is obtained, and the signal is determined to be a valid control signal based on the high or low level of the input signal. By employing signal separation technology, control signals from different modules are isolated, a power demand prediction model is constructed to predict the power demand required by the modules to execute control commands, and power distribution commands are generated. Power and control signals are transmitted simultaneously through the same network cable. Voltage testing tools are used to determine the stability and effectiveness of the power and control signals, and the power and control signals are optimized. Based on the voltage and current data of each module, the current power consumption of each module is calculated, it is determined whether each module is operating under overload or underpowered, and the power distribution of the modules is adjusted accordingly. By acquiring monitoring feedback data from the built-in sensors of the lifting screen, the power distribution scheme and power demand prediction model can be optimized.

2. The method according to claim 1, wherein, The process of acquiring input signals from the lifting screen via external sensors connected to the lifting screen control device, and determining whether the signal is a valid control signal based on its high or low level, includes: The system acquires input signals from the lifting screen control device by connecting external sensors, including changes in the screen's operating status, lifting requirements, or voltage variations. Signal decoding technology is used to determine the high or low level of the input signal and whether it is a valid control signal. If the input signal is a valid control signal, the corresponding lifting screen control command is output through the decoding circuit.

3. The method according to claim 1, wherein, The aforementioned signal separation technology isolates the control signals of different modules, constructs a power demand prediction model, predicts the power demand required for the modules to execute control commands, and generates power distribution commands, including: Based on the control commands received by the lifting screen, signal separation technology is used to isolate the control signals of different modules and send them to different output control modules. Based on the module type, operating status, and current power consumption of each output control module, a recurrent neural network is used for model training to build a power demand prediction model. This model predicts the power requirements for each module to execute control commands. Module types include core modules and auxiliary modules. Based on the power requirements of each module, a power allocation scheme is formulated. Corresponding power allocation commands are generated based on the scheme and sent to the PoE switch. The power allocation scheme includes the allocated power value and maximum usable power for each module.

4. The method according to claim 1, wherein, The method of simultaneously transmitting power and control signals through the same network cable, using voltage testing tools to determine the stability and effectiveness of the power and control signals, and optimizing the power and control signals includes: The system acquires control signals based on power distribution instructions, obtains 12V power signals from an external power source, integrates control and power signals onto a single network cable, and transmits the power signal to the control device via the network cable. A voltage testing tool measures the voltage and current of the power signal to determine its integrity and stability. The control device's network protocol decoding module converts the control signal into a serial port protocol format. According to the PoE protocol, the power signal is converted to a 5V output power signal, and the serial port protocol-converted control signal is then converted to the protocol format required by the screen. A voltage testing tool and a network protocol analyzer are used to check the converted power and control signals to determine their stability and effectiveness. If the power or control signal is unstable or lost, the test results are fed back to the controller for optimization. The optimized power and control signals are then transmitted to the lifting screen via the controller. Based on the control signals, the lifting controller controls the extension and retraction of the rising and lowering telescopic supports, adjusting the lifting height, speed, and tilt angle of the lifting screen, and monitoring voltage stability.

5. The method according to claim 1, wherein, The process of calculating the current power consumption of each module based on its voltage and current data, determining whether each module is operating under overload or experiencing insufficient power, and adjusting the power distribution of the modules includes: If multiple modules operate simultaneously, the voltage and current data of each module are acquired in real time through current and voltage sensors inside the lifting screen. Based on the voltage and current data of each module, the current power consumption of each module is calculated, and a power demand prediction model is used to predict the current power demand of the module. Based on the current power consumption and power demand of each module, it is determined whether there are modules operating under overload or with insufficient power. Based on historical power distribution instruction data, the Apriori algorithm is used to train the model, determine the calling patterns and correlations between modules, and obtain a candidate set of module dependencies. The candidate set of module dependencies contains a list of module pairs with potential dependencies. The system tracks the call frequency, time series, and shared power resources between modules. If dependencies exist between modules, the power demand growth coefficient of each module is determined based on its power demand and dependencies to identify high-risk modules. The fixed-point detection module of the lifting controller is used to locate the running trajectory of the lifting motor in real time. If a high-risk module is in a state of insufficient power or voltage fluctuations are detected, the screen is locked at the nearest fixed point through the preset uniform screen locking point on the lifting controller, and power is preferentially allocated to the high-risk module, while power allocation to modules with lower priority is temporarily stopped or delayed. If the voltage fluctuations and the power shortage state of the high-risk module are eliminated, power allocation to low-risk modules is gradually restored.

6. The method according to claim 5, wherein, The process of determining the power demand growth coefficient of each module based on its power demand and dependencies, and identifying high-risk modules, includes: Based on historical power allocation instruction data, the Apriori algorithm is used for model training to determine the calling patterns and correlations between modules, resulting in a candidate set of module dependencies. This candidate set contains a list of module pairs with potential dependencies, along with the calling frequency, time series, and power resource sharing data for each pair. By fixing the operating state of module i, the power demand of module j is gradually adjusted, and the response of module i's power demand to changes in module j's load is recorded. The formula is then used to... Calculate the impact coefficient α of module j on the electricity demand of module i. ij , representing the transmission effect of electricity demand from module j to electricity demand from module i, where P j This represents the power demand of module j, ΔP. i and ΔP j Let D represent the changes in electricity demand for modules i and j, respectively; by fixing the initial electricity demand of module i and gradually increasing or decreasing the demand for modules j ∈ D. i The load is recorded, and the response curve of module i's power demand as a function of dependent modules is analyzed. Based on the trend of power demand changes, a polynomial fitting curve is used. Obtain the power demand amplification factor β of module i i , representing the nonlinear growth effect of the electricity demand of module i as it changes with the dependent modules, where P i0 This refers to the basic power demand of module i without the influence of other dependent modules, obtained through historical data and hardware standard documents. i It is the set of dependent modules of module i, obtained through the candidate set of module dependencies; based on the impact coefficient and amplification coefficient of electricity demand, the electricity demand growth coefficient formula is used. Determine the electricity demand growth factor PGF for module i. i This indicates that the electricity demand of module i is relative to its base electricity demand P. i0 The degree of growth; if PGF i If the power demand of module i exceeds the preset coefficient threshold, then module i is judged to be a high-risk module because its power demand is highly sensitive to changes in the power consumption of other modules; otherwise, the module is judged to be a low-risk module.

7. The method according to claim 1, wherein, The process of acquiring monitoring feedback data from the lifting screen via its built-in sensors to optimize power distribution schemes and power demand prediction models includes: The system uses sensors built into the lifting screen to acquire monitoring feedback data, evaluate the effectiveness of the current power distribution scheme and the accuracy of the power demand prediction model. The monitoring feedback data includes power signal, voltage stability, and data transmission quality. If voltage fluctuations, current exceeding limits, or data transmission quality degradation are detected, the power distribution scheme and power demand prediction model are optimized and then executed.

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