Fire safety rope tensile stress monitoring equipment and method combined with flexible sensor

Abstract

The invention provides a fire safety rope tensile stress monitoring device and method combined with a flexible sensor, and relates to the technical field of safety rope monitoring, and the device comprises an early warning circuit activation module which is used for activating an early warning circuit; the sensing signal acquisition module is used for acquiring a sensing resistance signal; the tensile stress response value determination module is used for determining the tensile stress response value of the fire safety rope; the stress state verification module is used for determining a tensile stress warning level; and the response early warning module is used for performing response early warning. The technical problem that the stress change of an existing fire safety rope cannot be accurately detected in real time in the tensile stress monitoring process is solved, and the technical effects that through real-time tensile stress monitoring and multi-stage stress early warning, the sensitivity and reliability of emergency response are enhanced, and the safety and stability of the fire safety rope in the using process are ensured are achieved.

Description

Technical Field

[0001] The invention relates to the technical field of safety rope monitoring, and in particular to a fire safety rope tensile stress monitoring device and method combined with a flexible sensor. Background Art

[0002] In modern society, the frequency of fires is gradually increasing, especially in high-rise buildings, chemical plants, warehouses and other places. Once a fire occurs, it will bring great challenges to the evacuation and rescue of personnel. In the process of fire rescue, the fire safety rope, as an indispensable equipment for rescuers, plays a vital role. Although the traditional fire safety rope has sufficient strength and fire resistance, it lacks the means to monitor the actual stress of the rope. It is difficult for rescuers to understand the stress state of the safety rope in real time during use, which may lead to danger due to rope overload and breakage. Summary of the invention

[0003] The present application provides a fire safety rope tensile stress monitoring device and method combined with a flexible sensor, aiming to solve the technical problem that the existing fire safety rope cannot detect stress changes in real time and accurately during the tensile stress monitoring process.

[0004] The first aspect disclosed in the present application provides a fire safety rope tensile stress monitoring device combined with a flexible sensor, the device comprising: an early warning circuit activation module, used to activate the early warning circuit, the early warning circuit is connected to the flexible sensor through a flexible circuit board, the early warning circuit has a built-in stress monitoring model, and the flexible sensor includes two sensing yarns; a sensor signal acquisition module, used to acquire the sensor signal of the fire safety rope through the flexible sensor when the fire safety rope is subjected to tension, and obtain a sensor resistance signal; a tensile stress response value determination module, used to transmit the sensor resistance signal to the early warning circuit through the flexible circuit board, and determine the tensile stress response value of the fire safety rope based on the stress monitoring model in the early warning circuit; a stress state verification module, used to call the tensile stress alarm threshold in the early warning circuit, perform stress state verification on the tensile stress response value, and determine the tensile stress warning level; a response early warning module, used to trigger the sound and light alarm device according to the tensile stress warning level, and perform a response early warning.

[0005] The second aspect disclosed in the present application provides a method for monitoring the tensile stress of a fire safety rope in combination with a flexible sensor. The method is implemented by the above-mentioned fire safety rope tensile stress monitoring device in combination with a flexible sensor, and the method includes: activating an early warning circuit, the early warning circuit is connected to the flexible sensor through a flexible circuit board, the early warning circuit has a built-in stress monitoring model, and the flexible sensor includes two sensing yarns; when the fire safety rope is subjected to tension, the fire safety rope is sensed by the flexible sensor to obtain a sensing resistance signal; the sensing resistance signal is transmitted to the early warning circuit through the flexible circuit board, and the tensile stress response value of the fire safety rope is determined based on the stress monitoring model in the early warning circuit; the tensile stress alarm threshold in the early warning circuit is retrieved, the tensile stress response value is subjected to stress state verification, and the tensile stress warning level is determined; the sound and light alarm device is triggered according to the tensile stress warning level to respond with an early warning.

[0006] One or more technical solutions provided in this application have at least the following technical effects or advantages:

[0007] The early warning circuit is activated, and the early warning circuit is connected to the flexible sensor through a flexible circuit board. The built-in stress monitoring model ensures the accuracy and reliability of real-time tensile stress detection. The flexible sensor contains two sensing yarns, which can quickly respond and collect the sensing resistance signal of the rope when the fire safety rope is subjected to tension. The signal is transmitted to the early warning circuit through the flexible circuit board. The early warning circuit accurately calculates the tensile stress response value of the fire safety rope based on the built-in stress monitoring model. Subsequently, the early warning circuit automatically calls the built-in tensile stress alarm threshold, and performs multi-level stress state verification by comparing it with the tensile stress response value to ensure that the rope state under different stress states is correctly judged. Through this verification mechanism, the current tensile stress warning level of the rope can be quickly determined to ensure the accuracy and timeliness of the early warning information. Finally, according to the determined tensile stress warning level, the sound and light alarm device is triggered to provide intuitive sound and light feedback to ensure that the user can be warned in time at the critical point of danger. Through this real-time and efficient early warning mechanism, the equipment can significantly improve the safety of the use of fire safety ropes in high-stress scenarios and effectively prevent accidents.

[0008] The above description is only an overview of the technical solution of the present application. In order to more clearly understand the technical means of the present application, it can be implemented in accordance with the contents of the specification. In order to make the above and other purposes, features and advantages of the present application more obvious and easy to understand, the specific implementation methods of the present application are listed below. BRIEF DESCRIPTION OF THE DRAWINGS

[0009] Figure 1 A schematic diagram of the structure of a fire safety rope tensile stress monitoring device combined with a flexible sensor provided in an embodiment of the present application;

[0010] Figure 2 A schematic flow chart of a method for monitoring the tensile stress of a fire safety rope in combination with a flexible sensor provided in an embodiment of the present application.

[0011] Explanation of the accompanying drawings: early warning circuit activation module 1, sensor signal acquisition module 2, tensile stress response value determination module 3, stress state verification module 4, response early warning module 5. DETAILED DESCRIPTION

[0012] The embodiments of the present application solve the technical problem that the existing fire safety rope cannot detect stress changes in real time and accurately during the tensile stress monitoring process by providing a fire safety rope tensile stress monitoring device and method combined with a flexible sensor.

[0013] After introducing the basic principles of the present application, various non-limiting implementation methods of the present application will be specifically introduced below in conjunction with the accompanying drawings of the specification. It should be understood that the specific embodiments described herein are only used to explain the present application and are not used to limit the present application.

[0014] Embodiment 1, as Figure 1 As shown, an embodiment of the present application provides a fire safety rope tensile stress monitoring device combined with a flexible sensor, the device comprising:

[0015] The early warning circuit activation module 1 is used to activate the early warning circuit, the early warning circuit is connected to the flexible sensor through a flexible circuit board, the early warning circuit has a built-in stress monitoring model, and the flexible sensor includes two sensing yarns.

[0016] Specifically, in the early warning circuit activation module 1, before using the fire safety rope, the early warning circuit is activated, and the early warning circuit is connected to the flexible sensor through a flexible circuit board. This connection method can maintain the flexibility and reliability of the early warning circuit. A stress monitoring model is integrated inside the early warning circuit to monitor the stress state of the fire safety rope in real time. The flexible sensor is composed of two sensing yarns, and a non-conductive elastic rope skin is woven outside the sensing yarn. A rope bundle as a rope core is embedded in the fire safety rope, which can sense the changes of the fire safety rope when it is subjected to tension. Through this connection method, the tension changes of the fire safety rope can be accurately captured and fed back to the early warning circuit, thereby providing basic data for subsequent stress analysis and early warning.

[0017] Furthermore, the stress monitoring model includes:

[0018] Link the fire safety rope monitoring log, screen the fire safety rope monitoring log for the same model, obtain a sample monitoring data set, and divide the sample monitoring data set to generate a training data set, a supervision data set, and a verification data set; train the stress monitoring model based on the training data set and the supervision data set based on a neural network; verify and evaluate the output result of the stress monitoring model according to the verification data set, iteratively optimize the stress monitoring model according to the evaluation result, and when the preset requirements are met for a preset number of consecutive times, it is considered to be converged, and the stress monitoring model is output.

[0019] Preferably, through a preset interface, a link relationship is established with the fire safety rope monitoring log, and the monitoring log file of the fire safety rope is read. These logs record the tensile stress, sensor resistance signal and other related usage data of each fire safety rope during use. Through these log data, the historical stress state of each fire safety rope can be tracked, providing basic data for the training of the stress monitoring model. In order to ensure the uniformity and accuracy of the model training, the data in the fire safety rope monitoring log is screened, and the log data consistent with the sensor yarn model of the current fire safety rope is selected. Through the same model screening, the fire safety rope data of different models or specifications are excluded to ensure the consistency and validity of the sample data, and obtain the sample monitoring data set. Subsequently, the sample data set is divided into a training data set, a supervision data set and a verification data set, accounting for 70%, 15% and 15% of the total data respectively. The training data set is used for model parameter learning, the supervision data set is used for model tuning, and the verification data set is used to finally evaluate the generalization ability of the model. After the data division is completed, the neural network model is constructed. The input layer receives the training data set, and the input features include sensor resistance signals, etc. In order to capture the complex patterns in the data, the neural network sets two to three hidden layers, each containing 64 or 128 neurons, and the activation function selects ReLU to ensure the expression ability of nonlinear features. The neurons in the output layer are used to generate the tensile stress response value of the flexible sensor, and the output results will be used for subsequent verification and adjustment. After the training process starts, the training data is input into the model through forward propagation, and the predicted tensile stress response value is output after calculation in the hidden layer. After that, the error between the predicted result of the model and the true value is calculated, and the mean square error (MSE) is used as the loss function to measure the prediction error. In order to reduce the error, the weights and biases of the neural network are adjusted by the back propagation algorithm, and the parameters of the model are gradually optimized using algorithms such as the Adam optimizer. Each forward propagation and back propagation constitutes a complete training cycle (epoch), and the model is continuously optimized through multiple iteration cycles. At the end of each training cycle, the model is evaluated using the supervised dataset and the loss value on the supervised dataset is calculated. If the loss value of the supervised dataset no longer decreases for multiple consecutive cycles, the hyperparameters of the model, such as the learning rate or the number of neurons, will be automatically adjusted to avoid overfitting or underfitting. Through such adjustments, the model can perform more stably and accurately on the supervised dataset. When the loss value of the model on the supervised dataset reaches the preset requirements and remains stable in multiple consecutive iterations, it will be judged that the model has converged and the training process is completed. At this time, the validation dataset is used for the final evaluation of the model. Since the data in the validation dataset has never appeared in the training and supervision process, it can truly reflect the generalization ability of the model and ensure that the model can accurately predict the changes in the tensile stress of the fire safety rope in practical applications.The trained, supervised and verified stress monitoring model can be used in actual fire safety rope monitoring scenarios to analyze the tensile stress state of the rope in real time, ensuring the safe use of the fire safety rope in high-stress environments, greatly improving the reliability of the fire safety rope.

[0020] The sensor signal acquisition module 2 is used to acquire the sensor signal of the fire safety rope through the flexible sensor when the fire safety rope is subjected to tension, so as to obtain the sensor resistance signal.

[0021] Specifically, in the sensor signal acquisition module 2, when the fire safety rope is subjected to tension, the flexible sensor starts to work, and the two sensing yarns that make up the flexible sensor will sense the change in the tension of the rope. Under the action of tension, the resistance value of the sensing yarn will change. By capturing these resistance changes in real time and using the standard coefficient for normalization, a sensing resistance signal reflecting the force of the rope is obtained, providing the necessary data basis for subsequent stress analysis.

[0022] Further, obtaining the sensing resistance signal includes:

[0023] The resistance of the sensing yarn is collected to obtain a time series resistance set; a standard coefficient is applied to perform data normalization processing on the time series resistance set to obtain a standard time series resistance set; and a digital signal conversion is performed on the standard time series resistance set based on an analog-to-digital converter to obtain a sensing resistance signal.

[0024] Preferably, when the fire safety rope is subjected to tension, the resistance of the sensing yarn is collected. The sensing yarn will generate different resistance values ​​according to the change of external force, and these resistance values ​​are recorded in time sequence to form a time series resistance set, that is, a resistance data sequence that changes with time. Subsequently, the sensing yarn is analyzed according to the normalization coefficient model to obtain the standard coefficient applicable to the current sensing yarn, and then the standard coefficient is applied to perform data normalization processing on the collected time series resistance set to obtain a standard time series resistance set, that is, a corrected resistance data sequence. The purpose of the normalization processing is to make the resistance data more accurate and stable by eliminating the deviation caused by the problem of the sensing yarn itself. After that, the standard time series resistance set is converted by an analog-to-digital converter (ADC). The analog-to-digital converter converts the continuous resistance signal into a digital signal, so that the digitized sensing resistance signal can be read and analyzed in the subsequent module for further judging the stress state of the rope. Through the above process, the real-time and stable monitoring of the rope tensile stress can be ensured, the accuracy of stress monitoring can be improved, and the reliability of early warning can be ensured.

[0025] Further, a set of standardized timing resistors is obtained, including:

[0026] A normalization coefficient model is set, wherein the normalization coefficient model includes usage time, cumulative pulling times and initial calibration data; the normalization coefficient of the flexible sensor is output according to the usage time, cumulative pulling times and initial calibration data; and a timing resistor set is normalized and corrected based on the normalization coefficient to obtain a normalized timing resistor set.

[0027] Optionally, in order to effectively correct and process the collected resistance signal, a normalization coefficient model for adjusting the performance of the sensing yarn is established. The model can generate a normalization coefficient for correcting the timing resistance data based on key factors such as the use time of the sensing yarn, the cumulative number of tensions, and the initial calibration data. The specific method of constructing this model is the same as the aforementioned construction of the stress monitoring model. After the resistance acquisition is completed, a normalization coefficient is automatically generated according to the current use time of the sensing yarn, the cumulative number of tensions, and the initial calibration data, and the coefficient is applied to the current timing resistance set, that is, the normalization coefficient is multiplied by each resistance value in the timing resistance set to eliminate the error caused by the long-term use of the sensing yarn, thereby obtaining a more accurate and stable normalized timing resistance set. This normalization process ensures the reliability of the resistance data and makes the subsequent data analysis more accurate.

[0028] The tensile stress response value determination module 3 is used to transmit the sensing resistance signal to the early warning circuit through the flexible circuit board, and determine the tensile stress response value of the fire safety rope based on the stress monitoring model in the early warning circuit.

[0029] Specifically, in the tensile stress response value determination module 3, after the sensing resistance signal is transmitted to the early warning circuit through the flexible circuit board, the early warning circuit will process the signal. The stress monitoring model built into the early warning circuit will calculate the tensile stress response value of the fire safety rope in real time based on the sensing resistance signal, helping to timely determine the stress state of the rope and ensure its safety under the action of tension.

[0030] The stress state verification module 4 is used to retrieve the tensile stress alarm threshold in the early warning circuit, perform stress state verification on the tensile stress response value, and determine the tensile stress warning level.

[0031] Specifically, in the stress state verification module 4, the preset tensile stress alarm thresholds are retrieved from the early warning circuit, and the calculated tensile stress response values ​​are compared with these thresholds. Through this stress state verification, it is possible to determine whether the current tensile stress is within a safe range or has reached the early warning standard. According to the comparison result between the tensile stress response value and the threshold, the corresponding tensile stress warning level is determined, thereby triggering alarms of different levels to ensure timely reminders of possible risks to the user.

[0032] Furthermore, determining the tensile stress warning level includes:

[0033] The tensile stress alarm threshold comprises a tensile stress alarm lower limit threshold, a tensile stress alarm middle limit threshold and a tensile stress alarm upper limit threshold; based on the tensile stress alarm threshold and in combination with the tensile stress response value, data classification processing is performed through a multi-level stress state verification mechanism to determine the tensile stress warning level; wherein the multi-level stress state verification mechanism is divided into an interval response determination and a time period retention determination, the interval response determination is to determine the tensile stress warning level based on the tensile stress response value and the tensile stress alarm threshold, and the time period retention determination is to monitor the duration of the tensile stress response value and upgrade the warning level.

[0034] Preferably, the tensile stress alarm threshold retrieved from the early warning circuit has three different levels, namely, the tensile stress alarm lower limit threshold, the tensile stress alarm middle limit threshold, and the tensile stress alarm upper limit threshold. These thresholds are used to define different risk levels of tensile stress, so that corresponding responses can be made in time according to the tensile stress changes of the fire safety rope. When the fire safety rope is subjected to tension, the multi-level stress state verification mechanism is started to judge the current state of the fire safety rope. First, the interval response judgment is performed, that is, the risk level to which it belongs is determined according to the position of the current tensile stress response value in the tensile stress alarm lower limit threshold, middle limit threshold and upper limit threshold. If the tensile stress response value is within the lower limit threshold, it is determined as a low limit alarm; if it is within the middle limit threshold range, it is a middle limit alarm; and if it exceeds the high limit threshold, it is determined as a high limit alarm. This step is to perform graded processing based on the real-time data of tensile stress to determine the preliminary tensile stress warning level. For example, when the tensile stress response value is greater than or equal to 2kN and less than 5kN, a low limit alarm is issued; when the tensile stress response value is greater than or equal to 5kN and less than 10kN, a medium limit alarm is issued; and when the tensile stress response value is greater than or equal to 10kN, a high limit alarm is issued. After determining the threshold interval to which the tensile stress response value belongs, a time period retention judgment is performed to continuously monitor the time that the tensile stress response value stays in the threshold interval. If the duration of the tensile stress response value in a certain alarm interval exceeds the time threshold corresponding to the interval, the current warning level will be upgraded. For example, if the tensile stress value continues to exceed the specified time within the medium limit alarm threshold range, the warning level will be upgraded from the medium limit alarm to the high limit alarm. If it is a high limit alarm, a break signal will be generated. This break signal is used to indicate that the current state of the fire safety rope is about to enter the break state. At this time, the break indication audio and break indication light in the sound and light alarm device will be activated based on this break signal. Through the interval response judgment and the time period retention judgment, the warning level of the tensile stress can be dynamically adjusted according to the real-time situation and duration of the tensile stress response value, ensuring that a higher level of early warning signal is issued in time to prevent accidents.

[0035] The response warning module 5 is used to trigger the sound and light alarm device according to the tensile stress warning level to perform a response warning.

[0036] Specifically, in the response warning module 5, when the tensile stress warning level is obtained, the sound and light alarm device will be triggered immediately to issue a warning. According to different warning levels, the corresponding sound and light signals will be activated to give visual and auditory prompts to the user. For example, when the warning level is a low limit alarm, a green flash will be triggered to remind the user that the rope is under a certain tensile stress, but it is still within a safe range. This early warning mechanism ensures that the user can know the stress of the fire safety rope in real time and respond in time when necessary.

[0037] Furthermore, after the response warning is carried out, it includes:

[0038] A historical response database is established based on the sensor resistance signal and the tensile stress response value; a dynamic reconstruction instruction is obtained, and the dynamic reconstruction instruction is divided into an adaptive iteration instruction and a manual trigger instruction; based on the historical response database, the stress monitoring model is optimized in response in combination with the dynamic reconstruction instruction.

[0039] Preferably, during the monitoring process, the sensing resistance signal and the corresponding tensile stress response value are recorded in real time, and these data are stored in a historical response database. This database contains detailed data during each tensile stress monitoring process, including timestamps, tensile stress change trends, resistance signals, etc. The historical response database not only records the current tensile stress changes, but also includes the accumulated data of long-term monitoring, as well as the data added by the user. Through these data, the stress performance of the fire safety rope under different use conditions can be analyzed to provide a data basis for the adjustment and optimization of the model. Subsequently, the current dynamic reconstruction instructions are obtained, which determine how to optimize the stress monitoring model. Dynamic reconstruction instructions are divided into adaptive iteration instructions and manual triggering instructions. Adaptive iteration instructions are based on the preset interval time, through self-learning and automatic adjustment of model parameters, and adaptively optimize the stress monitoring model according to the latest monitoring data, so that it can better adapt to the current environment and tensile stress changes. Manual triggering instructions are instructions manually issued by users to manually intervene and adjust the stress monitoring model. It is usually triggered when the model needs to be adjusted significantly to quickly reconstruct the model and adapt to new usage scenarios. Afterwards, according to the current dynamic reconstruction instructions and based on the data in the historical response database, the stress monitoring model is optimized using the same training method as mentioned above to ensure that the tensile stress state of the fire safety rope is always accurately monitored.

[0040] Furthermore, the sound and light alarm device comprises:

[0041] The sound and light alarm device includes a sound and light alarm strategy, and the sound and light alarm strategy corresponds to the tensile stress warning level one by one.

[0042] Preferably, the built-in sound and light alarm strategy of the sound and light alarm device corresponds to the tensile stress warning level one by one, and each sound and light alarm strategy will trigger a different sound and light signal to more intuitively prompt the user of the current stress state. When the tensile stress warning level is a low limit alarm, the signal triggered by the sound and light alarm strategy is green light and no sound. This signal indicates that the tensile stress has increased, but it is still within the safe range, and the user does not need to take immediate action. When the tensile stress warning level is a medium limit alarm, the signal triggered by the sound and light alarm strategy is yellow light, accompanied by intermittent beeps. This signal indicates that the tensile stress has increased, but it is still within the safe range, reminding the user to pay close attention to the stress of the rope and be prepared to take preventive measures. When the tensile stress warning level is a high limit alarm, the signal triggered by the sound and light alarm strategy is orange light, accompanied by a continuous high-frequency alarm sound. This signal indicates that the rope stress is close to a dangerous state, and the user needs to take quick action to avoid further increasing the tension, but the rope is still within the tolerance range at this time. In addition, the sound and light alarm device also has a break signal, which triggers a red light and is accompanied by a rapid high-frequency alarm sound, indicating that the rope is in an extremely dangerous state and the user must immediately stop using it and take emergency measures to prevent the rope from breaking. This sound and light alarm strategy ensures that each tensile stress warning level has a unique sound and light signal, helping users quickly distinguish different stress states and respond accordingly.

[0043] In summary, the fire safety rope tensile stress monitoring device combined with a flexible sensor provided in the embodiment of the present application has the following technical effects:

[0044] The early warning circuit activation module 1 is used to activate the early warning circuit, the early warning circuit is connected to the flexible sensor through a flexible circuit board, the early warning circuit has a built-in stress monitoring model, and the flexible sensor includes two sensing yarns; the sensor signal acquisition module 2 is used to collect the sensor signal of the fire safety rope through the flexible sensor when the fire safety rope is subjected to tension, and obtain the sensor resistance signal; the tensile stress response value determination module 3 is used to transmit the sensor resistance signal to the early warning circuit through the flexible circuit board, and determine the tensile stress response value of the fire safety rope based on the stress monitoring model in the early warning circuit; the stress state verification module 4 is used to call the tensile stress alarm threshold in the early warning circuit, perform stress state verification on the tensile stress response value, and determine the tensile stress warning level; the response warning module 5 is used to trigger the sound and light alarm device according to the tensile stress warning level to respond and warn. Through the above steps, the technical problem that the existing fire safety rope cannot detect stress changes in real time and accurately during the tensile stress monitoring process is solved, and the technical effect of enhancing the sensitivity and reliability of emergency response through real-time tensile stress monitoring and multi-level stress warning is achieved, and the safety and stability of the fire safety rope during use are ensured.

[0045] Embodiment 2 is based on the same inventive concept as the fire safety rope tension monitoring device combined with a flexible sensor in the previous embodiment. Figure 2 As shown, an embodiment of the present application provides a method for monitoring the tensile stress of a fire safety rope in combination with a flexible sensor, the method comprising:

[0046] Activate the early warning circuit, the early warning circuit is connected to the flexible sensor through a flexible circuit board, the early warning circuit has a built-in stress monitoring model, and the flexible sensor includes two sensing yarns; when the fire safety rope is subjected to tension, the fire safety rope is sensed by the flexible sensor to obtain a sensing resistance signal; the sensing resistance signal is transmitted to the early warning circuit through the flexible circuit board, and the tensile stress response value of the fire safety rope is determined based on the stress monitoring model in the early warning circuit; the tensile stress alarm threshold in the early warning circuit is retrieved, the stress state of the tensile stress response value is verified, and the tensile stress warning level is determined; the sound and light alarm device is triggered according to the tensile stress warning level to respond to the early warning.

[0047] Further, obtaining a sensing resistance signal includes:

[0048] The flexible sensor is used to collect resistance of the sensing yarn to obtain a timing resistance set; a standard coefficient is applied to perform data normalization processing on the timing resistance set to obtain a standard timing resistance set; and a digital signal conversion is performed on the standard timing resistance set based on an analog-to-digital converter to obtain a sensing resistance signal.

[0049] Further, a standard timing resistor set is obtained, including:

[0050] A normalization coefficient model is set, wherein the normalization coefficient model includes usage time, cumulative pulling times and initial calibration data; the normalization coefficient of the flexible sensor is output according to the usage time, cumulative pulling times and initial calibration data; and a timing resistor set is normalized and corrected based on the normalization coefficient to obtain a normalized timing resistor set.

[0051] Furthermore, the stress monitoring model includes:

[0052] Link the fire safety rope monitoring log, screen the fire safety rope monitoring log for the same model, obtain a sample monitoring data set, and divide the sample monitoring data set to generate a training data set, a supervision data set, and a verification data set; train the stress monitoring model based on the training data set and the supervision data set based on a neural network; verify and evaluate the output result of the stress monitoring model according to the verification data set, iteratively optimize the stress monitoring model according to the evaluation result, and when the preset requirements are met for a preset number of consecutive times, it is considered to be converged, and the stress monitoring model is output.

[0053] Further, determining the tensile stress warning level includes:

[0054] The tensile stress alarm threshold comprises a tensile stress alarm lower limit threshold, a tensile stress alarm middle limit threshold and a tensile stress alarm upper limit threshold; based on the tensile stress alarm threshold and in combination with the tensile stress response value, data classification processing is performed through a multi-level stress state verification mechanism to determine the tensile stress warning level; wherein the multi-level stress state verification mechanism is divided into an interval response determination and a time period retention determination, the interval response determination is to determine the tensile stress warning level based on the tensile stress response value and the tensile stress alarm threshold, and the time period retention determination is to monitor the duration of the tensile stress response value and upgrade the warning level.

[0055] Furthermore, after the response warning is carried out, it includes:

[0056] A historical response database is established based on the sensor resistance signal and the tensile stress response value; a dynamic reconstruction instruction is obtained, and the dynamic reconstruction instruction is divided into an adaptive iteration instruction and a manual trigger instruction; based on the historical response database, the stress monitoring model is optimized in response in combination with the dynamic reconstruction instruction.

[0057] Furthermore, the sound and light alarm device comprises:

[0058] The sound and light alarm device includes a sound and light alarm strategy, and the sound and light alarm strategy corresponds to the tensile stress warning level one by one.

[0059] Any step of the method described above can be stored as a computer instruction or program in an unlimited computer memory, and can be called and recognized by an unlimited computer processor to implement any method in the embodiments of the present application, without any unnecessary restrictions.

[0060] Furthermore, the first or second mentioned above may not only represent an order relationship, but may also represent a specific concept, and / or refer to the selection of multiple elements individually or in whole. Obviously, those skilled in the art may make various changes and modifications to the present application without departing from the scope of the present application. Thus, if these modifications and variations of the present application fall within the scope of the present application and its equivalent technologies, the present application intends to include these modifications and variations.

Claims

1. A fire safety rope tensile stress monitoring device combined with a flexible sensor, characterized in that: The device comprises: An early warning circuit activation module is used to activate an early warning circuit, wherein the early warning circuit is connected to a flexible sensor via a flexible circuit board, wherein the early warning circuit has a built-in stress monitoring model, and wherein the flexible sensor includes two sensing yarns; A sensor signal acquisition module, used for acquiring a sensor signal of the fire safety rope through the flexible sensor to obtain a sensor resistance signal when the fire safety rope is subjected to tension; A tensile stress response value determination module, used to transmit the sensing resistance signal to the early warning circuit through the flexible circuit board, and determine the tensile stress response value of the fire safety rope based on the stress monitoring model in the early warning circuit; A stress state verification module, used to retrieve the tensile stress alarm threshold in the early warning circuit, perform stress state verification on the tensile stress response value, and determine the tensile stress warning level; The response warning module is used to trigger the sound and light alarm device according to the tensile stress warning level to perform a response warning.

2. The fire safety rope tension monitoring device combined with a flexible sensor as claimed in claim 1, characterized in that: Obtain the sensing resistance signal, including: Collecting resistance of the sensing yarn to obtain a time series resistance set; Applying a standard coefficient to perform data standardization processing on the timing resistor set to obtain a standard timing resistor set; The standard timing resistor set is converted into a digital signal based on an analog-to-digital converter to obtain a sensing resistor signal.

3. The fire safety rope tension monitoring device combined with a flexible sensor as claimed in claim 2, characterized in that: Get the specification timing resistor set, including: Setting a normalized coefficient model, wherein the normalized coefficient model includes usage time, cumulative pulling times, and initial calibration data; Outputting the specification coefficient of the flexible sensor according to the usage time, the cumulative number of pulling times and the initial calibration data; The timing resistor set is normalized and corrected based on the normalization coefficient to obtain a normalized timing resistor set.

4. The fire safety rope tension monitoring device combined with a flexible sensor as claimed in claim 1, characterized in that: The stress monitoring model comprises: Linking the fire safety rope monitoring log, screening the fire safety rope monitoring log for the same model, obtaining a sample monitoring data set, and dividing the sample monitoring data set to generate a training data set, a supervision data set, and a verification data set; Training the stress monitoring model based on a neural network using the training data set and the supervision data set; The output result of the stress monitoring model is verified and evaluated according to the verification data set, and the stress monitoring model is iteratively optimized according to the evaluation result. When the preset requirements are met for a preset number of consecutive times, it is considered to be converged, and the stress monitoring model is output.

5. The fire safety rope tension monitoring device combined with a flexible sensor as claimed in claim 1, characterized in that: The tensile stress alarm threshold includes a tensile stress alarm lower limit threshold, a tensile stress alarm middle limit threshold and a tensile stress alarm upper limit threshold; Based on the tensile stress alarm threshold value and in combination with the tensile stress response value, data classification processing is performed through a multi-level stress state verification mechanism to determine the tensile stress warning level; Among them, the multi-level stress state verification mechanism is divided into interval response judgment and time period retention judgment. The interval response judgment is to judge the tensile stress warning level based on the tensile stress response value and the tensile stress alarm threshold, and the time period retention judgment is to monitor the duration of the tensile stress response value and upgrade the warning level.

6. The fire safety rope tension monitoring device combined with a flexible sensor as claimed in claim 1, characterized in that: After the response warning, including: Establishing a historical response database based on the sensor resistance signal and the tensile stress response value; Acquire dynamic reconstruction instructions, where the dynamic reconstruction instructions are divided into adaptive iteration instructions and manual triggering instructions; Based on the historical response database, the stress monitoring model is optimized in response in combination with the dynamic reconstruction instruction.

7. The fire safety rope tension monitoring device combined with a flexible sensor as claimed in claim 1, characterized in that: The sound and light alarm device includes a sound and light alarm strategy, and the sound and light alarm strategy corresponds to the tensile stress warning level one by one.

8. A method for monitoring the tensile stress of a fire safety rope in combination with a flexible sensor, characterized in that: Based on the fire safety rope tensile stress monitoring device combined with a flexible sensor according to any one of claims 1 to 7, the method comprises: activating an early warning circuit, wherein the early warning circuit is connected to a flexible sensor via a flexible circuit board, the early warning circuit has a built-in stress monitoring model, and the flexible sensor includes two sensing yarns; When the fire safety rope is subjected to tension, the fire safety rope is subjected to sensing signal collection by the flexible sensor to obtain a sensing resistance signal; The sensing resistance signal is transmitted to the early warning circuit through the flexible circuit board, and the tensile stress response value of the fire safety rope is determined based on the stress monitoring model in the early warning circuit; Retrieving the tensile stress alarm threshold in the early warning circuit, performing stress state verification on the tensile stress response value, and determining the tensile stress warning level; The sound and light alarm device is triggered according to the tensile stress warning level to respond and warn.

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