Anti-falling early warning method and device based on intelligent pull resistance sensor

Abstract

The invention provides an anti-falling early warning method and device based on an intelligent pull-resistance sensor, and relates to the technical field of safety protection, and the method comprises the steps: installing the intelligent pull-resistance sensor on a safety rope, and obtaining the real-time resistance data; transmitting the real-time resistance data to an upper computer based on a predetermined mode; the upper computer carries out multi-dimensional processing on the real-time resistance value data to obtain real-time tension data, the number of times of punching and falling and the number of times of effective use; based on the real-time tension data, the punching and falling times and the effective use times, storing and displaying the real-time tension data, the punching and falling times and the effective use times, and judging whether the real-time tension data is greater than a preset tension value or not; and if the real-time tension data is greater than the preset tension value, generating a falling early warning signal. The technical problem that a traditional anti-falling method lacks a real-time monitoring means and is difficult to warn the falling risk in time is solved, and the technical effects that the accuracy and the real-time performance of falling warning are enhanced, and then the safety of high-altitude operation is improved are achieved.

Description

Technical Field

[0001] The present application relates to the field of safety protection technology, and in particular to a fall prevention warning method and device based on an intelligent pull-resistance sensor. Background Art

[0002] In high-altitude operations and other high-risk environments, the reliability of the fall protection system is directly related to the life safety of the workers. Existing traditional fall protection methods use mechanical structures, simple circuits and limit devices for protection, such as static limit devices or mechanical buffers to reduce the impact force caused by falling. Although these devices can prevent people from accidentally falling to a certain extent, their design is mainly based on passive protection and it is difficult to respond to emergencies in a timely manner. In actual use, these methods cannot monitor the status of personnel and the stress of safety ropes in real time, making it difficult to detect the risk of falling in advance, resulting in delayed response. In addition, these methods lack intelligent judgment methods and cannot accurately evaluate the service life and current status of the safety rope, posing great safety hazards. Summary of the invention

[0003] The present application provides a fall prevention warning method and device based on an intelligent pull-resistance sensor, which solves the technical problem that traditional fall prevention methods lack real-time monitoring means and are difficult to timely warn of fall risks, thereby achieving the technical effect of enhancing the accuracy and real-time nature of fall warnings and thereby improving the safety of high-altitude operations.

[0004] In view of the above problems, on the one hand, the present application provides an anti-fall warning method based on an intelligent pull-resistance sensor, the method comprising: installing the intelligent pull-resistance sensor on a safety rope to obtain real-time resistance data; transmitting the real-time resistance data to a host computer based on a predetermined method, wherein the predetermined method includes wired transmission or wireless transmission; the host computer performs multi-dimensional processing on the real-time resistance data to obtain real-time tension data, number of falls and number of effective uses; based on the real-time tension data, the number of falls and the number of effective uses are stored and displayed, and it is determined whether the real-time tension data is greater than the predetermined tension value; if the real-time tension data is greater than the predetermined tension value, a fall warning signal is generated.

[0005] On the other hand, the present application also provides an anti-fall warning device based on an intelligent pull resistance sensor, the device comprising: a real-time resistance value acquisition module, the real-time resistance value acquisition module obtains real-time resistance value data through the intelligent pull resistance sensor, wherein the intelligent pull resistance sensor is installed on a safety rope; a data transmission module, the data transmission module transmits the real-time resistance value data to a host computer based on a predetermined method, wherein the predetermined method includes wired transmission or wireless transmission; a data processing module, the data processing module performs multi-dimensional processing on the real-time resistance value data through the host computer to obtain real-time tension data, the number of falls and the number of effective uses; a judgment module, the judgment module stores and displays the real-time tension data, the number of falls and the number of effective uses, and judges whether the real-time tension data is greater than the predetermined tension value; a fall warning module, the fall warning module is used to generate a fall warning signal when the real-time tension data is greater than the predetermined tension value.

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

[0007] An intelligent pull resistance sensor is installed on a safety rope to obtain real-time resistance data; the real-time resistance data is transmitted to a host computer based on wired or wireless transmission; the host computer performs multi-dimensional processing on the real-time resistance data to obtain real-time tension data, the number of falls and the number of effective uses; the real-time tension data, the number of falls and the number of effective uses are stored and displayed, and it is determined whether the real-time tension data is greater than a predetermined tension value; if the real-time tension data is greater than the predetermined tension value, a fall warning signal is generated.

[0008] In summary, this application uses the collaborative work of intelligent pull resistance sensors, real-time data transmission, multi-dimensional data processing and early warning mechanisms to accurately sense the force changes on the safety rope, calculate the number of falls and service life, and issue timely warnings when the tension exceeds the standard, thereby achieving real-time monitoring and dynamic early warning of safety ropes in high-altitude working environments, effectively avoiding accidental falls. The overall solution improves the accuracy and reliability of fall prevention warnings, enhances the ability to adapt to complex working environments, provides more reliable safety guarantees for high-altitude operations, and thus comprehensively improves the safety of high-altitude operations.

[0009] 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

[0010] Figure 1A schematic flow chart of a fall prevention warning method based on an intelligent pull resistance sensor provided in an embodiment of the present application.

[0011] Figure 2 A schematic diagram of the structure of a fall prevention warning device based on an intelligent pull resistance sensor provided in an embodiment of the present application.

[0012] Description of the accompanying drawings: real-time resistance value acquisition module 10, data transmission module 20, data processing module 30, judgment module 40, fall warning module 50. DETAILED DESCRIPTION

[0013] The embodiments of the present application provide a fall prevention warning method and device based on an intelligent pull-resistance sensor. By integrating the intelligent pull-resistance sensor and a multi-dimensional data processing algorithm, an accurate warning of fall risks is achieved, which solves the technical problem that traditional fall prevention methods lack real-time monitoring means and are difficult to warn of fall risks in a timely manner. This achieves the technical effect of enhancing the accuracy and real-time nature of fall warnings, thereby improving the safety of high-altitude operations.

[0014] Embodiment 1, as Figure 1 As shown, the embodiment of the present application provides an anti-fall warning method based on an intelligent pull resistance sensor, the method comprising:

[0015] Step S1: Install the intelligent pull resistance sensor on the safety rope to obtain real-time resistance data.

[0016] Specifically, the intelligent pull resistance sensor is a sensor device that can sense tension and convert it into resistance changes. Install the intelligent pull resistance sensor on the safety rope or protective device to ensure that the sensor fits tightly to the rope surface. During high-altitude operations, when the safety rope is subjected to tension, the physical structure inside the sensor will change, causing a change in resistance, and obtaining real-time resistance data. The change in real-time resistance of the intelligent pull resistance sensor can accurately reflect the increase or decrease in real-time tension during the operation.

[0017] Step S2: Based on a predetermined method, the real-time resistance value data is transmitted to a host computer, wherein the predetermined method includes wired transmission or wireless transmission.

[0018] Specifically, according to a predetermined transmission method, the real-time resistance data obtained from the intelligent pull-resistance sensor is sent to the host computer. The host computer is a computer device that processes, analyzes, and stores the data transmitted by the intelligent pull-resistance sensor. The transmission method can be wired transmission or wireless transmission. If it is wired transmission, a data cable may be used to connect the sensor and the host computer; if it is wireless transmission, wireless communication technologies such as Bluetooth, Wi-Fi, or ZigBee may be used to transmit data. The choice of transmission method can be flexibly selected according to the actual situation on site to ensure accurate transmission and processing of data.

[0019] Step S3: the host computer performs multi-dimensional processing on the real-time resistance data to obtain real-time tension data, number of falls and number of effective uses.

[0020] Specifically, after receiving the real-time resistance data, the host computer uses the built-in algorithm to process the data, calculates and analyzes the data from multiple aspects, and obtains real-time tension data, number of falls, and effective use times. Among them, the real-time tension data refers to the actual tension value of the current safety rope. The number of falls refers to the number of monitored tension mutations that may indicate a fall. The effective use times refers to the number of times the safety rope is actually used.

[0021] Step S4: storing and displaying the real-time tension data, the number of falls and the number of effective uses, and determining whether the real-time tension data is greater than a predetermined tension value.

[0022] Specifically, the host computer stores the calculated real-time tension data, number of falls, and number of effective uses in a local database or other storage device, and displays these data in real time on the display screen. At the same time, the real-time tension data is continuously monitored, compared with the preset tension threshold, and a comparison result is generated as the basis for fall prevention warning.

[0023] Step S5: If the real-time tension data is greater than the predetermined tension value, a fall warning signal is generated.

[0024] Specifically, when the host computer determines that the real-time tension data is greater than the predetermined tension value, the warning mechanism is immediately triggered to generate a fall warning signal. This signal can be sent through an audible and visual alarm, a display warning, or a text message. For example, if the tension of the operator's safety rope suddenly increases and exceeds the safety threshold, the alarm module in the host computer controls the speaker to sound an alarm to remind the operator and management personnel to take emergency measures.

[0025] Furthermore, the intelligent pull-resistance sensor described in the embodiment of the present application includes a pull-resistance sensor, and a resistance value collection and data transmission circuit board, the resistance value collection and data transmission circuit board includes a power supply circuit, an acquisition circuit, a signal amplification circuit, a single-chip microcomputer and a data transmission circuit, and the data transmission circuit includes a wired transmission circuit and a wireless transmission circuit.

[0026] Specifically, the pull resistance sensor is the core component of the intelligent pull resistance sensor, and its main function is to convert the change of pulling force into the change of resistance value. The resistance value acquisition and data transmission circuit board is the circuit board inside the intelligent pull resistance sensor, which is mainly responsible for collecting the resistance value data of the pull resistance sensor and transmitting the data. The circuit board integrates multiple circuit modules such as power supply circuit, acquisition circuit, signal amplification circuit, single-chip microcomputer and data transmission circuit, and is the key part to realize the sensor function. Among them, the power supply circuit provides stable power support for other circuits on the entire resistance value acquisition and data transmission circuit board. The acquisition circuit is used to collect the resistance value change signal generated by the pull resistance sensor. The signal amplification circuit amplifies the collected weak signal for subsequent processing. The single-chip microcomputer is used to control data acquisition, processing and transmission operations. The data transmission circuit is responsible for transmitting the processed data to the host computer, which includes wired transmission circuit and wireless transmission circuit, and can select the appropriate transmission method according to actual needs.

[0027] The working process of the entire intelligent pull resistance sensor is as follows: First, when the pull resistance sensor is subjected to tension, its resistance changes. Then, the acquisition circuit in the resistance acquisition and data transmission circuit board starts to work and collects the resistance change signal of the pull resistance sensor. Since the signal output by the pull resistance sensor may be weak, the signal amplification circuit will amplify the collected signal. Next, the microcontroller processes the amplified signal, such as converting the analog signal into a digital signal. After that, the wired transmission circuit or wireless transmission circuit in the data transmission circuit will transmit the processed data to the host computer or other receiving devices according to the actual application scenario and requirements. In this process, the power supply circuit always provides stable power for the entire resistance acquisition and data transmission circuit board to ensure the normal operation of each circuit module.

[0028] Furthermore, step S3 of the embodiment of the present application also includes:

[0029] Step S31: converting the tension value according to the real-time resistance data to obtain the real-time tension data.

[0030] Step S32: Based on the real-time tension data, the number of falls is calculated according to a fall number algorithm.

[0031] Step S33: Based on the real-time resistance data, the effective usage times are calculated according to an effective usage times algorithm.

[0032] Specifically, the host computer first processes the real-time resistance data collected by the intelligent pull resistance sensor. Based on the collected real-time resistance data, it converts the resistance data into actual pulling force values ​​through specific algorithms and conversion formulas to obtain real-time pulling force data.

[0033] After obtaining the real-time tension data, the host computer uses the fall count algorithm to analyze the real-time tension data and calculate the fall count. The algorithm can identify specific patterns or mutations in the tension data to determine whether a fall event has occurred. For example, monitor whether the tension data suddenly increases and reaches a certain threshold, and whether the subsequent tension change trend meets the characteristics of a fall. If it meets the characteristic pattern of a fall, the fall count will be increased by one.

[0034] Based on the real-time resistance data, the host computer calculates the effective use times according to the effective use times algorithm. This algorithm can distinguish whether the safety rope is in normal use or in an idle state. As the safety rope is used, the resistance of the pull resistance sensor may have a trend of gradually increasing or increasing fluctuation range. The effective use times algorithm will determine the effective use times based on this trend and the pre-set safety standards. If the change in resistance data exceeds a certain set limit, the effective use times will be adjusted accordingly.

[0035] Furthermore, step S31 of the embodiment of the present application further includes:

[0036] Step S311: Based on the intelligent tensile resistance sensor, a resistance value sample set and a tensile force sample set are loaded.

[0037] Step S312: performing data cleaning according to the resistance sample set and the tension sample set to obtain a resistance-tension conversion set sample.

[0038] Step S313: training and testing are performed based on the resistance-pull-force conversion set samples to establish a resistance-pull-force conversion model.

[0039] Step S314: inputting the real-time resistance data into the resistance-tension conversion model to generate the real-time tension data.

[0040] Specifically, based on the intelligent pull resistance sensor, resistance value sample sets and pull force sample sets are loaded from a pre-stored database or data file. These sample sets may be obtained through multiple experimental tests during the sensor development process, and contain a large number of resistance value and corresponding pull force data points for subsequent training and testing.

[0041] Data cleaning is performed on the resistance sample set and the tension sample set to remove data that exceeds the normal resistance value or tension range and data that deviates too much from other data. For example, if it is found that a certain resistance value data deviates from other resistance value data under similar tension by more than a set threshold (such as 10%), this data is regarded as abnormal data and removed. After data cleaning, the resistance tension conversion set samples are obtained.

[0042] The resistance-pull conversion set samples are used for training and testing, and machine learning algorithms such as linear regression algorithm and neural network algorithm are used to establish the resistance-pull conversion model. Taking the linear regression algorithm as an example, during the training process, the slope and intercept in the linear regression of the model parameters are continuously adjusted to minimize the error between the predicted value and the actual value, and a straight line that can best fit the data points in the resistance-pull conversion set samples is found. Then, the model is tested using a portion of sample data that is not involved in the training to verify the accuracy of the model. If the performance of the model on the test data meets the expected accuracy requirements, the model training is considered complete and the resistance-pull conversion model is output.

[0043] The real-time resistance data is input into the established resistance-tension conversion model. The model calculates the real-time resistance data according to its internal algorithms and parameters, thereby generating the corresponding real-time tension data.

[0044] By constructing a resistance-tension conversion model and converting real-time resistance data into real-time tension data, real-time monitoring of the tension of the safety rope during high-altitude operations is achieved, providing a direct data source for subsequent fall prevention warnings and calculations of effective usage times and falls, thereby improving the real-time and response speed of fall prevention warnings.

[0045] Furthermore, step S32 of the embodiment of the present application further includes:

[0046] Step S321: constructing a tension change curve according to the real-time tension data.

[0047] Step S322: constructing a fall response curve.

[0048] Step S323: Compare the tension change curve with the impact response curve to determine whether the tension change curve satisfies the impact response curve.

[0049] Step S324: If the tension variation curve satisfies the impact response curve, the impact times are counted.

[0050] Specifically, the real-time tension data is sorted in chronological order. Then, the data analysis tool is used to perform curve fitting on these data and draw a tension change curve. For example, the intelligent pull resistance sensor collects real-time resistance data once every second, and after the tension value conversion, the real-time tension data once per second is obtained. By connecting these data points arranged in chronological order, the tension change curve can be obtained. This tension change curve can reflect the fluctuation of tension during the entire monitoring process.

[0051] The fall response curve is a preset curve that describes the typical pattern of tension change when a fall event occurs. The fall response curve is constructed by collecting the safety rope tension change data during a fall. The tension change curve is compared with the fall response curve, and the curve shape, key feature points (such as peak value, valley value, slope change point, etc.) are compared. For example, compare whether the peak values ​​of the two curves are similar, whether the peak values ​​appear at the same time, whether the slopes of the rising and falling curves are similar, etc. If both are relatively consistent in these key features, it can be determined that the tension change curve meets the fall response curve. When the tension change curve meets the fall response curve, the number of falls is increased by 1. This number of falls can be stored in a counter variable, and this variable is increased by 1 each time the condition is met.

[0052] By constructing a tension change curve and a fall response curve and comparing the two, it is possible to accurately determine whether a fall event has occurred, thereby improving the accuracy of fall prevention warnings.

[0053] Furthermore, step S322 of the embodiment of the present application further includes:

[0054] Step S322-1: Loading a fall response tension sample set according to the intelligent tension resistance sensor.

[0055] Step S322-2: performing data cleaning according to the fall response tension sample set to generate a fall response tension data set.

[0056] Step S322-3: performing curve fitting according to the fall response tension data set to generate the fall response curve.

[0057] Specifically, based on the intelligent pull resistance sensor, a fall response tension sample set is loaded from a pre-stored database or data file. This sample set contains the tension change data recorded when the fall event occurs. For example, data collected in a fall simulation experiment using an intelligent pull resistance sensor in a laboratory or from an actual fall accident can be collected.

[0058] Similar to step S312, the loaded fall response tension sample set is cleaned to remove data that is obviously beyond the reasonable range. These data may be caused by sensor failure or acquisition errors. For example, if it is found that the tension data in a fall simulation experiment is several times higher than the maximum tension value in a normal fall, and the data points around the data are very different from it, the data is judged as an outlier and deleted. At the same time, check the consistency of the data. For example, for time series data, ensure that the order of timestamps is correct and there are no missing timestamps. Through data cleaning, a fall response tension data set is generated.

[0059] Polynomial fitting, spline curve fitting and other methods are used to fit the fall response tension data set, and finally the fall response curve is obtained. For example, if polynomial fitting is selected, the degree of the polynomial is first determined, and then the values ​​of each parameter are calculated by the least squares method so that the curve corresponding to the equation can best fit the data points of the fall response tension data set, and the fall response curve is obtained as a reference standard for subsequent fall number calculations.

[0060] Furthermore, step S33 of the embodiment of the present application further includes:

[0061] Step S331: According to the intelligent pull resistance sensor, the real-time weight data corresponding to the real-time resistance data is retrieved.

[0062] Step S332: traverse the real-time weight data and extract the first real-time weight value.

[0063] Step S333: Setting a minimum weight reference value.

[0064] Step S334: determining whether the first real-time weight value is greater than the minimum weight reference value.

[0065] Step S335: If the first real-time weight value is greater than the minimum weight reference value, it is counted into the effective usage times.

[0066] Specifically, the resistance data obtained by the intelligent pull resistance sensor corresponds to the weight of the operator. This correspondence is retrieved and the corresponding real-time weight data is determined based on the real-time resistance data. During the operation, the real-time weight data obtained may fluctuate within a certain range due to factors such as personnel movement.

[0067] Traverse the acquired real-time weight data, extract a random weight data from it, and record it as the first real-time weight value. Set the minimum weight reference value according to the design parameters and usage specifications of the safety rope, compare the real-time weight data and the minimum weight reference value, and when the first real-time weight value extracted is greater than the minimum weight reference value, it means that the safety rope is working within the normal load range. Compare all real-time weight data with the minimum weight reference value respectively. As long as there is real-time weight data greater than the minimum weight reference value, count the number of valid uses and add one.

[0068] By counting the number of effective uses, a quantitative reference can be provided for evaluating the service life and safety performance of safety equipment. For example, if the number of effective uses is close to the design upper limit of the safety rope, the safety rope needs to be inspected or replaced to ensure the safety of high-altitude operations.

[0069] In summary, the anti-fall warning method based on the intelligent pull resistance sensor provided in the embodiment of the present application has the following technical effects:

[0070] The intelligent pull resistance sensor can monitor the tension changes on the safety rope in real time, obtain real-time resistance data, and provide original monitoring data for the subsequent early warning process, thereby improving the real-time and accuracy of fall risk monitoring in dangerous scenarios such as high-altitude operations. The real-time resistance data is transmitted to the host computer by wired or wireless transmission. The design of multiple transmission methods makes data transmission flexible and can be selected according to the actual application scenario. The host computer performs multi-dimensional processing on the real-time resistance data to obtain real-time tension data, the number of falls, and the number of effective uses. When obtaining real-time tension data, the real-time resistance data is converted into tension data by training the resistance-tension conversion model, thereby ensuring that under different operator weights and different working environments, the tension value can be accurately obtained based on the resistance data of the intelligent pull resistance sensor, providing an accurate basis for subsequent safety judgments. When calculating the number of falls, a tension change curve is constructed according to the real-time tension data, and a fall response curve is constructed at the same time. The tension change curve can intuitively reflect the real-time tension change trend during the operation process. By comparing it with the fall response curve, it can accurately determine whether the tension change characteristics meet the fall characteristics, thereby obtaining an accurate number of falls, which provides an important basis for evaluating safety risks and equipment losses. When calculating the effective use times, the real-time weight data corresponding to the real-time resistance data is retrieved according to the intelligent pull resistance sensor, and the first real-time weight value is extracted by traversing these data, and the minimum weight reference value is set and compared. If it is greater than this value, it is counted into the effective use times. In different high-altitude operation scenes, the weight range of the operator is relatively wide. Factors related to the weight of the operator are introduced in the calculation process of the effective use times, making the calculation more in line with the actual situation, and at the same time expanding the scope of application of the embodiment of the present application. By comparing with the minimum weight reference value, the effective use times are accurately counted, which is helpful to evaluate the service life and safety performance of the safety equipment. Based on the obtained real-time tension data, the number of falls and the effective use times are stored and displayed, and it is determined whether the real-time tension data is greater than the predetermined tension value. If it is greater, a fall warning signal is generated. By comparing data, accurate judgment results can be obtained, avoiding false alarms caused by various factors such as environmental and human experience errors, thereby improving the accuracy and reliability of early warnings.

[0071] Overall, the embodiment of the present application realizes real-time monitoring and dynamic warning of safety ropes in high-altitude working environments through the coordinated work of intelligent pull-resistance sensors, real-time data transmission, multi-dimensional data processing and early warning mechanisms, improves the accuracy and reliability of anti-fall warnings, enhances the adaptability to complex working environments, and comprehensively improves the safety of high-altitude operations. At the same time, the number of falls and the number of effective uses also provide reference data for subsequent safety management work. The number of falls can reflect the dangerous situations experienced by the equipment during the operation process, and the number of effective uses helps to evaluate the service life of the equipment and the current safety risk status, thereby providing more reliable safety guarantees for high-altitude operations.

[0072] Embodiment 2, as Figure 2 As shown, the embodiment of the present application provides an anti-fall warning device based on an intelligent pull resistance sensor, the device comprising:

[0073] The real-time resistance value acquisition module 10 obtains real-time resistance value data through an intelligent pull resistance sensor, wherein the intelligent pull resistance sensor is installed on a safety rope.

[0074] The data transmission module 20 transmits the real-time resistance value data to a host computer based on a predetermined method, wherein the predetermined method includes wired transmission or wireless transmission.

[0075] The data processing module 30 performs multi-dimensional processing on the real-time resistance data through the host computer to obtain real-time tension data, the number of falls and the number of effective uses.

[0076] The judging module 40 stores and displays the real-time tension data, the number of falls and the number of effective uses, and judges whether the real-time tension data is greater than a predetermined tension value.

[0077] The fall warning module 50 is used to generate a fall warning signal when the real-time tension data is greater than the predetermined tension value.

[0078] Furthermore, the intelligent pull-resistance sensor described in the embodiment of the present application includes a pull-resistance sensor, and a resistance value collection and data transmission circuit board, the resistance value collection and data transmission circuit board includes a power supply circuit, an acquisition circuit, a signal amplification circuit, a single-chip microcomputer and a data transmission circuit, and the data transmission circuit includes a wired transmission circuit and a wireless transmission circuit.

[0079] Furthermore, the data processing module 30 in the embodiment of the present application is also used to perform the following steps:

[0080] The real-time resistance data is converted into a pulling force value to obtain the real-time pulling force data; the number of falls is calculated based on the real-time pulling force data according to a falling number algorithm; the effective number of uses is calculated based on the real-time resistance data according to an effective number of uses algorithm.

[0081] Furthermore, the data processing module 30 in the embodiment of the present application is also used to perform the following steps:

[0082] Based on the intelligent pull resistance sensor, a resistance sample set and a tension sample set are loaded; data cleaning is performed according to the resistance sample set and the tension sample set to obtain resistance-tension conversion set samples; training and testing are performed according to the resistance-tension conversion set samples to establish a resistance-tension conversion model; the real-time resistance data is input into the resistance-tension conversion model to generate the real-time tension data.

[0083] Furthermore, the data processing module 30 in the embodiment of the present application is also used to perform the following steps:

[0084] According to the real-time tension data, a tension change curve is constructed; a fall response curve is constructed; the tension change curve is compared with the fall response curve to determine whether the tension change curve satisfies the fall response curve; if the tension change curve satisfies the fall response curve, the fall times are counted.

[0085] Furthermore, the data processing module 30 in the embodiment of the present application is also used to perform the following steps:

[0086] According to the intelligent pull resistance sensor, a fall response tension sample set is loaded; according to the fall response tension sample set, data cleaning is performed to generate a fall response tension data set; according to the fall response tension data set, curve fitting is performed to generate the fall response curve.

[0087] Furthermore, the data processing module 30 in the embodiment of the present application is also used to perform the following steps:

[0088] According to the intelligent pull resistance sensor, the real-time weight data corresponding to the real-time resistance data is retrieved; the real-time weight data is traversed to extract the first real-time weight value; a minimum weight reference value is set; it is determined whether the first real-time weight value is greater than the minimum weight reference value; if the first real-time weight value is greater than the minimum weight reference value, it is counted into the effective number of uses.

[0089] Through the above-mentioned detailed description of the anti-fall warning method based on the intelligent pull-resistance sensor in this specification, those skilled in the art can clearly understand the anti-fall warning device based on the intelligent pull-resistance sensor in this embodiment. For the device disclosed in Example 2, since it corresponds to the method disclosed in Example 1, it has corresponding functional modules and beneficial effects. For relevant matters, please refer to the description of the method part.

[0090] The above description of the disclosed embodiments enables those skilled in the art to implement or use the present application. Various modifications to these embodiments will be apparent to those skilled in the art, and the general principles defined herein may be implemented in other embodiments without departing from the spirit or scope of the present application. Therefore, the present application will not be limited to the embodiments shown herein, but will conform to the widest scope consistent with the principles and novel features disclosed herein.

Claims

1. The anti-fall warning method based on the intelligent pull resistance sensor is characterized in that: The method comprises: Install the intelligent pull resistance sensor on the safety rope to obtain real-time resistance data; Based on a predetermined method, transmitting the real-time resistance value data to a host computer, wherein the predetermined method includes wired transmission or wireless transmission; The host computer performs multi-dimensional processing on the real-time resistance data to obtain real-time tension data, number of falls and number of effective uses; storing and displaying the real-time tension data, the number of falls and the number of effective uses, and determining whether the real-time tension data is greater than a predetermined tension value; If the real-time tension data is greater than the predetermined tension value, a fall warning signal is generated.

2. The method according to claim 1, characterized in that The intelligent pull-resistance sensor includes a pull-resistance sensor and a resistance value collection and data transmission circuit board. The resistance value collection and data transmission circuit board includes a power supply circuit, a collection circuit, a signal amplification circuit, a single-chip microcomputer and a data transmission circuit. The data transmission circuit includes a wired transmission circuit and a wireless transmission circuit.

3. The method according to claim 1, characterized in that The host computer performs multi-dimensional processing on the real-time resistance data to obtain real-time pulling force data, the number of falls and the number of effective uses, including: Convert the tension value according to the real-time resistance data to obtain the real-time tension data; Based on the real-time tension data, the number of falls is calculated according to a fall number algorithm; Based on the real-time resistance data, the effective usage times are calculated according to an effective usage times algorithm.

4. The method according to claim 3, characterized in that Converting the tension value according to the real-time resistance data to obtain the real-time tension data includes: Based on the intelligent pull resistance sensor, a resistance value sample set and a pull force sample set are loaded; Perform data cleaning according to the resistance value sample set and the tension force sample set to obtain a resistance value tension force conversion set sample; Training and testing are performed based on the resistance-pull-force conversion set samples to establish a resistance-pull-force conversion model; The real-time resistance data is input into the resistance-tension conversion model to generate the real-time tension data.

5. The method according to claim 3, characterized in that The algorithm for the number of impacts and falls includes: Constructing a tension change curve according to the real-time tension data; Constructing the fall response curve; Comparing the tension change curve with the impact response curve to determine whether the tension change curve satisfies the impact response curve; If the tension variation curve satisfies the impact response curve, the impact times are counted.

6. The method according to claim 5, characterized in that Construct a fall response curve, including: According to the intelligent tensile resistance sensor, a sample set of tensile force in response to a fall is loaded; Performing data cleaning according to the fall response tension sample set to generate a fall response tension data set; Curve fitting is performed according to the fall response tension data set to generate the fall response curve.

7. The method according to claim 3, characterized in that The effective usage count algorithm includes: According to the intelligent pull resistance sensor, the real-time weight data corresponding to the real-time resistance value data is retrieved; Traversing the real-time weight data to extract a first real-time weight value; Set a minimum weight reference value; Determining whether the first real-time weight value is greater than the minimum weight reference value; If the first real-time weight value is greater than the minimum weight reference value, it is counted into the effective usage times. 8.Anti-fall warning device based on intelligent pull resistance sensor, characterized in that: The device is used to execute the method according to any one of claims 1 to 7, comprising: A real-time resistance value acquisition module, wherein the real-time resistance value acquisition module obtains real-time resistance value data through an intelligent pull resistance sensor, wherein the intelligent pull resistance sensor is installed on a safety rope; A data transmission module, wherein the data transmission module transmits the real-time resistance value data to a host computer based on a predetermined method, wherein the predetermined method includes wired transmission or wireless transmission; A data processing module, wherein the data processing module performs multi-dimensional processing on the real-time resistance data through the host computer to obtain real-time tension data, the number of falls and the number of effective uses; A judgment module, which stores and displays the real-time tension data, the number of falls and the number of effective uses, and judges whether the real-time tension data is greater than a predetermined tension value; A fall warning module is used to generate a fall warning signal when the real-time tension data is greater than the predetermined tension value.

Images