Preparation method and system of whole aramid fiber core flame-retardant conveying belt based on intelligent monitoring

Abstract

The invention relates to the technical field of data processing, and discloses a preparation method and system of an aramid fiber core whole-core flame-retardant conveying belt based on intelligent monitoring, and the method comprises the steps: constructing a snakelike grid layout of the conveying belt based on pre-obtained conveying belt data; the method comprises the following steps: constructing a resistance-capacitance hybrid sensing network of the conveyor belt based on a snakelike grid layout, monitoring the preparation process of the whole aramid fiber core flame-retardant conveyor belt in real time based on the resistance-capacitance hybrid sensing network, obtaining real-time electrical data, and carrying out conveyor belt damage calculation to obtain a real-time damage condition; carrying out edge intelligent node packaging on the signal conditioning chip and the resistance-capacitance hybrid sensing network to obtain an intelligent monitoring network, carrying out real-time monitoring on the preparation process based on the intelligent monitoring network to obtain real-time thermodynamic data, and carrying out vulcanization calculation on the conveyor belt to obtain a real-time vulcanization condition; therefore, the production parameters in the preparation process are dynamically adjusted. The preparation method can improve the preparation efficiency of the whole aramid fiber core flame-retardant conveying belt.

Description

Technical Field

[0001] The present invention relates to the technical field of data processing, and particularly to a preparation method and system for an aramid core fully flame-retardant conveyor belt based on intelligent monitoring. Background Art

[0002] In the traditional preparation process of aramid core fully flame-retardant conveyor belts, the prior art mainly relies on manual experience and off-line detection means, resulting in difficulties in synchronously improving production efficiency and product quality. Specifically, first, key parameters such as temperature and pressure during the vulcanization process cannot be dynamically adjusted in real time, easily causing local over-vulcanization or under-vulcanization phenomena, resulting in unstable material properties; second, the fiber tension distribution and cover rubber thickness during the forming stage of the conveyor belt lack accurate monitoring, leading to structural defects such as longitudinal tearing or transverse wear in the finished product; third, the traditional single-point resistance sensing network is difficult to comprehensively capture the damage evolution process inside the material, and there is a lag in damage detection, requiring frequent shutdowns for quality inspection, significantly reducing the production speed.

[0003] In the existing preparation process, there is a lack of effective real-time monitoring means, and it is impossible to timely and accurately grasp the damage situation and vulcanization state of the conveyor belt during the preparation process. This leads to the inability to quickly adjust production parameters when problems occur, resulting in a high defective rate and seriously affecting product quality; at the same time, due to the inability to dynamically optimize the production process according to the actual production situation, the entire preparation process takes a long time and the production speed is difficult to increase; ultimately, these factors combined will result in low preparation efficiency of aramid core fully flame-retardant conveyor belts. Summary of the Invention

[0004] The present invention provides a preparation method and system for an aramid core fully flame-retardant conveyor belt based on intelligent monitoring, and its main purpose is to solve the problem of low preparation efficiency of aramid core fully flame-retardant conveyor belts.

[0005] To achieve the above object, the preparation method for an aramid core fully flame-retardant conveyor belt based on intelligent monitoring provided by the present invention includes:

[0006] S1. Construct a serpentine grid layout of the conveyor belt based on the pre-acquired conveyor belt data;

[0007] S2. Construct a resistance-capacitance hybrid sensing network of the conveyor belt based on the serpentine grid layout, and perform real-time monitoring on the preparation process of the aramid core fully flame-retardant conveyor belt based on the resistance-capacitance hybrid sensing network to obtain the real-time electrical data of the conveyor belt, and calculate the real-time damage situation of the conveyor belt by performing damage calculation on the real-time electrical data.

[0008] S3. Package the signal conditioning chip and the resistor-capacitor hybrid sensing network into an edge intelligent node to obtain the intelligent monitoring network of the conveyor belt, and based on the intelligent monitoring network, monitor the preparation process in real time to obtain the real-time thermodynamic data of the conveyor belt, and perform conveyor belt vulcanization calculation on the real-time thermodynamic data to obtain the real-time vulcanization condition of the conveyor belt;

[0009] S4. Dynamically adjust the production parameters in the preparation process based on the real-time damage condition and the real-time vulcanization condition.

[0010] In a preferred embodiment, the construction of the serpentine grid layout of the conveyor belt based on the pre-acquired conveyor belt data includes:

[0011] Design the grid layout of the conveyor belt based on the pre-acquired conveyor belt data to obtain the basic grid layout of the conveyor belt;

[0012] Analyze the stress distribution of the conveyor belt to obtain the high-strain area of the conveyor belt;

[0013] Add wire branches to the basic grid layout based on the high-strain area to obtain the serpentine grid layout of the conveyor belt.

[0014] In a preferred embodiment, the construction of the resistor-capacitor hybrid sensing network of the conveyor belt based on the serpentine grid layout includes:

[0015] Print a longitudinal conductive layer on the conveyor belt based on the serpentine grid layout to obtain the resistance sensing grid of the conveyor belt;

[0016] Spin-coat a photocurable polyurethane dielectric layer on the resistance sensing grid to obtain the insulating layer of the resistance sensing grid;

[0017] Print a transverse capacitor plate on the insulating layer based on the serpentine grid layout to obtain the capacitance sensing array of the conveyor belt;

[0018] Fill the resistance sensing grid and the capacitance sensing array with conductive silver paste to obtain the resistor-capacitor hybrid sensing network of the conveyor belt.

[0019] In a preferred embodiment, the real-time monitoring of the preparation process of the aramid core fully flame-retardant conveyor belt based on the resistor-capacitor hybrid sensing network to obtain the real-time electrical data of the conveyor belt includes:

[0020] Activate the resistor-capacitor hybrid sensing network to obtain the sensing network of the conveyor belt;

[0021] Monitor the preparation process of the aramid core fully flame-retardant conveyor belt in real time based on the sensing network to obtain the electrical fingerprint spectrum of the conveyor belt;

[0022] Perform frequency-domain decoupling on the extracted pure resistance change and capacitance change components in the electrical fingerprint spectrum to obtain the real-time electrical data of the conveyor belt.

[0023] In a preferred embodiment, the calculation of the damage of the conveyor belt from the real-time electrical data to obtain the real-time damage condition of the conveyor belt includes:

[0024] Perform moving average filtering on the real-time electrical data to obtain the electrical standard data of the conveyor belt;

[0025] Extract the damage time-frequency domain characteristics from the electrical standard data to obtain the resistance change characteristics and capacitance change characteristics of the conveyor belt;

[0026] Calculate the damage of the conveyor belt based on the resistance change characteristics and the capacitance change characteristics to obtain the real-time damage condition of the conveyor belt.

[0027] In a preferred embodiment, the calculation of the damage of the conveyor belt based on the resistance change characteristics and the capacitance change characteristics to obtain the real-time damage condition of the conveyor belt includes:

[0028] Calculate the longitudinal tear degree of the conveyor belt based on the resistance change characteristics to obtain the longitudinal damage of the conveyor belt, where the formula for calculating the longitudinal tear degree is:

[0029]

[0030] In the formula, is the longitudinal damage, is the resistance tear coefficient, is the real-time resistance, is the reference resistance, is the value of the resistance change characteristics, is the grid cell area of the serpentine grid layout, is the grid length of the serpentine grid layout;

[0031] Calculate the lateral wear degree of the conveyor belt based on the capacitance change characteristics to obtain the lateral damage of the conveyor belt, where the formula for calculating the lateral wear degree is:

[0032]

[0033] In the formula, is the lateral damage, is the capacitance wear coefficient, is the natural logarithm, is the real-time capacitance, is the reference capacitance, is the value of the capacitance change characteristic, is the standard thickness of the cover layer of the conveyor belt;

[0034] The lateral damage and the longitudinal damage are aggregated into the real-time damage condition of the conveyor belt.

[0035] In a preferred embodiment, the edge intelligent node encapsulation of the signal conditioning chip and the resistor-capacitor hybrid sensing network to obtain the intelligent monitoring network of the conveyor belt includes:

[0036] Wire connection is made between the signal conditioning chip and the resistor-capacitor hybrid sensing network to obtain the initial monitoring network of the conveyor belt;

[0037] Zero-point calibration is performed on the initial monitoring network based on near-field communication to obtain the intelligent monitoring network of the conveyor belt, wherein the intelligent monitoring network is configured with an adaptive filtering algorithm and a Kalman prediction model.

[0038] In a preferred embodiment, the calculation of the conveyor belt vulcanization for the real-time thermodynamic data to obtain the real-time vulcanization condition of the conveyor belt includes:

[0039] Sliding average filtering is performed on the real-time thermodynamic data to obtain the thermodynamic standard data of the conveyor belt, and zero-point calibration is performed on the initial monitoring network based on near-field communication to obtain the intelligent monitoring network of the conveyor belt, wherein the intelligent monitoring network is configured with an adaptive filtering algorithm and a Kalman prediction model;

[0040] Based on the thermodynamic standard data, vulcanization calculation is performed on the conveyor belt to obtain the real-time vulcanization condition of the conveyor belt, wherein the formula for the vulcanization calculation is:

[0041]

[0042] In the formula, is the real-time vulcanization condition, is the natural exponential function, is the time variable, is the pre-exponential factor, is the natural constant, is the minimum energy threshold required for the vulcanization reaction, is the th grid at the absolute temperature at the moment, is the moment factor, is the ideal gas constant, For integrating where is the reaction order, is the grid number of the serpentine grid layout, is the vulcanization reaction rate.

[0043] In a preferred embodiment, the dynamic adjustment of the production parameters in the preparation process based on the real-time damage condition and the real-time vulcanization condition includes:

[0044] Performing data spatio-temporal alignment and fusion on the real-time damage condition and the real-time vulcanization condition to obtain a comprehensive working condition evaluation matrix of the conveyor belt;

[0045] Based on the comprehensive working condition evaluation matrix, dynamically regulating the vulcanization temperature of the conveyor belt to obtain the adjusted vulcanization temperature of the conveyor belt;

[0046] Based on the comprehensive working condition evaluation matrix, compensating the pressure of the conveyor belt to obtain the adjusted cover rubber thickness of the conveyor belt;

[0047] Based on the comprehensive working condition evaluation matrix, optimizing the production line speed of the conveyor belt to obtain the adjusted production speed of the conveyor belt;

[0048] Based on the comprehensive working condition evaluation matrix, rebalancing the fiber tension of the conveyor belt to obtain the adjusted distribution tension of the conveyor belt;

[0049] Based on the comprehensive working condition evaluation matrix, adjusting the injection of flame retardant into the conveyor belt to obtain the adjusted flame retardant content of the conveyor belt;

[0050] Dynamically adjusting the production parameters in the preparation process based on the adjusted vulcanization temperature, the adjusted cover rubber thickness, the adjusted production speed, the adjusted distribution tension, and the adjusted flame retardant content.

[0051] To solve the above problems, the present invention also provides a preparation system for an aramid core integral flame-retardant conveyor belt based on intelligent monitoring, the system includes:

[0052] A serpentine network layout generation module for constructing a serpentine grid layout of the conveyor belt based on pre-acquired conveyor belt data;

[0053] A real-time damage calculation module for constructing a resistance-capacitance hybrid sensing network of the conveyor belt based on the serpentine grid layout, performing real-time monitoring on the preparation process of the aramid core integral flame-retardant conveyor belt based on the resistance-capacitance hybrid sensing network to obtain real-time electrical data of the conveyor belt, and calculating the real-time damage condition of the conveyor belt from the real-time electrical data;

[0054] A real-time vulcanization status module is used to package the signal conditioning chip and the resistor-capacitor hybrid sensor network into edge intelligent nodes to obtain an intelligent monitoring network for the conveyor belt, to monitor the preparation process in real time based on the intelligent monitoring network to obtain real-time thermodynamic data of the conveyor belt, to perform conveyor belt vulcanization calculation on the real-time thermodynamic data to obtain real-time vulcanization status of the conveyor belt;

[0055] A preparation process parameter adjustment module is used to dynamically adjust the production parameters in the preparation process based on the real-time damage situation and the real-time vulcanization situation.

[0056] Compared with the prior art, the present invention has the following beneficial effects:

[0057] 1. By building a resistor-capacitor hybrid sensor network to monitor the preparation process in real time, the real-time electrical data of the conveyor belt can be accurately obtained. The calculated real-time damage situation can reflect the degree of longitudinal tearing and transverse wear in detail, which is convenient for timely detection of potential defects; at the same time, the intelligent monitoring network monitors the real-time thermodynamic data and calculates the vulcanization, which can accurately grasp the real-time vulcanization of the conveyor belt, avoid local over-vulcanization or under-vulcanization, ensure the stability of material performance, and effectively improve product quality.

[0058] 2. The intelligent monitoring network provides real-time feedback on the status of the conveyor belt, and optimizes parameters such as vulcanization temperature, pressure, and production line speed based on the comprehensive working condition evaluation matrix, thus avoiding frequent shutdowns for quality inspections due to unreasonable parameters in the traditional preparation process and reducing downtime. At the same time, the production process is optimized to make the entire preparation process more efficient and smooth, thereby greatly improving the preparation speed and comprehensively improving the preparation efficiency of the aramid core flame-retardant conveyor belt. BRIEF DESCRIPTION OF THE DRAWINGS

[0059] Figure 1 A schematic flow chart of a method for preparing an aramid core flame-retardant conveyor belt based on intelligent monitoring provided by one embodiment of the present invention;

[0060] Figure 2 A functional module diagram of a system for preparing an aramid core flame-retardant conveyor belt based on intelligent monitoring provided by one embodiment of the present invention;

[0061] The realization of the purpose, functional features and advantages of the present invention will be further explained in conjunction with embodiments and with reference to the accompanying drawings. DETAILED DESCRIPTION

[0062] It should be understood that the specific embodiments described herein are only used to explain the present invention, and are not used to limit the present invention.

[0063] An embodiment of the present application provides a preparation method for an aramid core fully flame-retardant conveyor belt based on intelligent monitoring. The execution subject of the preparation method for the aramid core fully flame-retardant conveyor belt based on intelligent monitoring includes, but is not limited to, at least one of electronic devices such as a server, a terminal, etc. that can be configured to execute the method provided by the embodiment of the present application. In other words, the preparation method for the aramid core fully flame-retardant conveyor belt based on intelligent monitoring can be executed by software or hardware installed on a terminal device or a server device. The server includes, but is not limited to: a single server, a server cluster, a cloud server, or a cloud server cluster, etc. The server can be an independent server or a cloud server that provides basic cloud computing services such as cloud services, cloud databases, cloud computing, cloud functions, cloud storage, network services, cloud communications, middleware services, domain name services, security services, Content Delivery Network (CDN), and big data and artificial intelligence platforms.

[0064] Referring to Figure 1 As shown, it is a schematic flowchart of a preparation method for an aramid core fully flame-retardant conveyor belt based on intelligent monitoring provided by an embodiment of the present invention. In this embodiment, the preparation method for the aramid core fully flame-retardant conveyor belt based on intelligent monitoring includes:

[0065] S1. Construct a serpentine grid layout of the conveyor belt based on pre-acquired conveyor belt data;

[0066] In the embodiment of the present invention, constructing the serpentine grid layout of the conveyor belt based on the pre-acquired conveyor belt data includes:

[0067] Design a grid layout for the conveyor belt based on the pre-acquired conveyor belt data to obtain a basic grid layout of the conveyor belt;

[0068] Perform a stress distribution analysis on the conveyor belt to obtain a high-strain area of the conveyor belt;

[0069] Add wire branches to the basic grid layout based on the high-strain area to obtain the serpentine grid layout of the conveyor belt.

[0070] Specifically, the serpentine grid layout is a special circuit topology structure designed specifically for a flexible sensing network, and its wires are arranged in a continuous wavy or spiral path (shaped like a snake). The high-strain area is an area where, during the operation of the conveyor belt, due to factors such as mechanical loads, bending, or joints, the local stress is significantly higher than the average level, and these areas are more likely to suffer fatigue damage or structural failure.

[0071] Specifically, systematically collect relevant data such as the conveyor belt material, size, usage scenario, and stress characteristics, clean the data, remove outliers and duplicate data, fill in missing values, and make the data more standardized and accurate, thus obtaining conveyor belt data.

[0072] Furthermore, in combination with the expected performance, production process, and cost requirements of the conveyor belt, clarify the goals of the grid layout, such as enhancing the strength in a specific direction, and at the same time clarify the relevant constraints, such as the upper and lower limits of the grid size.

[0073] Furthermore, conduct a preliminary grid division of the conveyor belt to generate the basic grid cloth of the conveyor belt.

[0074] Specifically, according to the structural shape of the conveyor belt, use finite element analysis software to establish a mechanical model of the conveyor belt and simulate the stress conditions of the conveyor belt under actual working conditions.

[0075] Furthermore, set reasonable boundary conditions in the model, such as fixed-end constraints, the magnitude and direction of the applied force, etc., to ensure that the simulation is close to the actual working conditions.

[0076] Furthermore, run the mechanical model for solution, calculate the stress value distribution of each point inside the conveyor belt, and generate a stress distribution nephogram.

[0077] Furthermore, set a strain threshold, and by analyzing the stress distribution nephogram, screen out the areas where the stress value is higher than the strain threshold. These areas are the high-strain areas of the conveyor belt.

[0078] Specifically, based on the results of the high-strain areas obtained from the analysis of the conveyor belt stress distribution, accurately mark these areas on the basic grid layout diagram.

[0079] Furthermore, in combination with the shape and size of the high-strain area and the structure of the basic grid layout, design the routing path of the wire branch inside and around the high-strain area to ensure that it can effectively enhance the monitoring ability of this area.

[0080] Furthermore, among the nodes of the basic grid layout, select suitable positions as the connection points of the wire branch to ensure the stable and reliable electrical connection between the wire branch and the original grid layout.

[0081] Furthermore, draw a serpentine wire branch on the basic grid layout according to the planned routing and connection points, so that it is closely combined with the high-strain area.

[0082] Furthermore, carefully check the drawn serpentine wire branch to see if there are any connection errors or unreasonable routing. If there are problems, adjust and optimize them in a timely manner.

[0083] Further, after checking and ensuring there are no errors, the basic grid layout with added wire branches is arranged into the final serpentine grid layout.

[0084] Generally speaking, the serpentine grid layout is the infrastructure for subsequent construction of the resistance-capacitance hybrid sensing network and realization of intelligent monitoring.

[0085] Generally speaking, by analyzing the stress distribution of the conveyor belt to determine the high-strain area, and adding wire branches on this basis to obtain the serpentine grid layout, the sensing network can be more accurately arranged in the areas of the conveyor belt prone to problems.

[0086] Generally speaking, a reasonable grid layout provides physical support for accurately collecting the electrical and thermodynamic data of the conveyor belt during the preparation process, ensuring the comprehensiveness and effectiveness of data collection.

[0087] S2. Based on the serpentine grid layout, construct the resistance-capacitance hybrid sensing network of the conveyor belt, and based on the resistance-capacitance hybrid sensing network, conduct real-time monitoring on the preparation process of the aramid core fully-integrated flame-retardant conveyor belt to obtain the real-time electrical data of the conveyor belt, and perform conveyor belt damage calculation on the real-time electrical data to obtain the real-time damage condition of the conveyor belt;

[0088] In the embodiment of the present invention, the constructing the resistance-capacitance hybrid sensing network of the conveyor belt based on the serpentine grid layout includes:

[0089] Based on the serpentine grid layout, conduct longitudinal conductive layer printing on the conveyor belt to obtain the resistance sensing grid of the conveyor belt;

[0090] Spin-coat a photocurable polyurethane dielectric layer on the resistance sensing grid to obtain the insulating layer of the resistance sensing grid;

[0091] Based on the serpentine grid layout, conduct transverse capacitor plate printing above the insulating layer to obtain the capacitance sensing array of the conveyor belt;

[0092] Fill the resistance sensing grid and the capacitance sensing array with conductive silver glue to obtain the resistance-capacitance hybrid sensing network of the conveyor belt.

[0093] The conducting real-time monitoring on the preparation process of the aramid core fully-integrated flame-retardant conveyor belt based on the resistance-capacitance hybrid sensing network to obtain the real-time electrical data of the conveyor belt includes:

[0094] Power on and activate the resistance-capacitance hybrid sensing network to obtain the sensing network of the conveyor belt;

[0095] Based on the sensing network, conduct real-time monitoring on the preparation process of the aramid core fully-integrated flame-retardant conveyor belt to obtain the electrical fingerprint of the conveyor belt;

[0096] Decouple the extracted pure resistance change and capacitance change components in the electrical fingerprint map in the frequency domain to obtain the real-time electrical data of the conveyor belt.

[0097] Perform conveyor belt damage calculation on the real-time electrical data to obtain the real-time damage condition of the conveyor belt, including:

[0098] Perform moving average filtering on the real-time electrical data to obtain the electrical standard data of the conveyor belt;

[0099] Extract damage time-frequency domain features from the electrical standard data to obtain the resistance change feature and capacitance change feature of the conveyor belt;

[0100] Perform damage calculation on the conveyor belt based on the resistance change feature and the capacitance change feature to obtain the real-time damage condition of the conveyor belt.

[0101] The performing damage calculation on the conveyor belt based on the resistance change feature and the capacitance change feature to obtain the real-time damage condition of the conveyor belt includes:

[0102] Perform longitudinal tear degree calculation on the conveyor belt based on the resistance change feature to obtain the longitudinal damage of the conveyor belt. Among them, the formula for the longitudinal tear degree calculation is:

[0103]

[0104] In the formula, is the longitudinal damage, is the resistance tear coefficient, is the real-time resistance, is the reference resistance, is the value of the resistance change feature, is the grid cell area of the serpentine grid layout, is the grid length of the serpentine grid layout;

[0105] Perform transverse wear degree calculation on the conveyor belt based on the capacitance change feature to obtain the transverse damage of the conveyor belt. Among them, the formula for the transverse wear degree calculation is:

[0106]

[0107] In the formula, is the transverse damage, is the capacitance wear coefficient, is the natural logarithm, is the real-time capacitance, is the reference capacitance, is the value of the capacitance change characteristic, is the standard thickness of the cover layer of the conveyor belt;

[0108] Collect the transverse damage and the longitudinal damage to obtain the real-time damage condition of the conveyor belt.

[0109] Specifically, the real-time electrical data includes resistance change data and capacitance change data.

[0110] Specifically, the electrical fingerprint is obtained by continuously collecting signals related to the electrical characteristics of the conveyor belt during the preparation process of the conveyor belt by the sensing network. These signals contain information on the electrical characteristic changes caused by the changes in the internal structure and performance of the conveyor belt during preparation. Finally, these signals are sorted and recorded in chronological order and according to certain rules, and finally an electrical fingerprint is formed.

[0111] Specifically, a first layer of silver nanowire-MXene composite conductive ink is deposited on the pretreated flame-retardant rubber substrate by using a high-precision inkjet printing process to form a longitudinal continuous serpentine resistive sensing grid (line width ≤ 100 μm).

[0112] Further, a photo-curable polyurethane dielectric layer (thickness 50 μm, dielectric strength > 200 kV / mm) is spin-coated, and a uniform insulating isolation layer is formed by ultraviolet curing.

[0113] Further, a second layer of wavy transverse wires is printed on the insulating layer, which is orthogonally arranged with the lower longitudinal grid to form a distributed parallel plate capacitive sensing array (plate spacing 2 mm).

[0114] Further, micro-vias with a diameter of 30 μm are etched using femtosecond laser, and conductive silver paste is filled to realize the electrical interconnection of the key nodes of the resistive-capacitive network.

[0115] Further, a flexible silicone protective layer (thickness 200 μm) is sprayed to ensure the long-term stable operation of the sensing network in a friction and humid-heat environment.

[0116] Further, a micro signal conditioning module (size 5 × 5 mm²) is welded every 2 m to realize data acquisition, filtering and wireless transmission (BLE5.0).

[0117] Further, a standard tensile / wear load is applied to establish a mapping database of the resistance-capacitance change amount and the damage degree (ΔR - tear length, ΔC - wear depth).

[0118] Further, an LSTM model is embedded in the edge node to compensate for the baseline drift caused by temperature / humidity in real time (error < ±0.5%).

[0119] Furthermore, the resistance (low frequency) and capacitance (medium frequency) signals are separated by Kalman filtering to eliminate cross-interference (crosstalk < 0.5%). After noise cleaning, the resistance-capacitance hybrid sensing network of the conveyor belt is finally obtained.

[0120] Specifically, power is supplied to the sensing network to activate its working state, enabling the sensing network to continuously perceive the physical changes during the preparation process of the conveyor belt.

[0121] Furthermore, during the preparation process of the conveyor belt, the activated sensing network is used to continuously collect signals related to the electrical properties of the conveyor belt, and these signals are sorted and recorded in chronological order and according to certain rules to form an electrical fingerprint spectrum.

[0122] Furthermore, specific signal processing algorithms and tools are used to separate the signal components caused by resistance changes and the signal components caused by capacitance changes from the electrical fingerprint spectrum.

[0123] Furthermore, frequency-domain decoupling technology is adopted to analyze and process the separated resistance change components and capacitance change components in the frequency domain respectively, remove interference signals, extract pure resistance change and capacitance change information, and finally obtain the real-time electrical data of the conveyor belt.

[0124] Furthermore, the calibration method of the resistance tear coefficient is as follows:

[0125]

[0126] where is the resistance tear coefficient, is the resistance change, is the reference resistance, is the tear width, is the tear length, is the unit grid area after the conveyor belt is divided into grids.

[0127] Furthermore, wear grooves with different depths are preset on the surface of the cover layer (simulating lateral wear), and a precision capacitance measuring instrument is used to record the capacitance change and the wear depth , and the capacitance wear coefficient is deduced based on the parallel plate capacitance formula. Among them, the calculation formula of the capacitance wear coefficient is:

[0128]

[0129] where is the capacitance wear coefficient, is the real-time capacitance, is the reference capacitance, is the standard thickness of the cover layer of the conveyor belt, is the wear depth.

[0130] Furthermore, capacitance sensing depends on the relative change in the cover layer thickness, which has been obtained through and during calibration, and its value essentially embeds the dependency. Therefore, also needs to be used in the calculation of the capacitance wear coefficient.

[0131] Furthermore, the resistance change of the serpentine resistance sensing grid is caused by the geometric deformation of the conductor. According to Ohm's law:

[0132]

[0133] where is the cross-sectional area of the conductor, is the grid length of the serpentine grid layout, is the conveyor belt density, is the resistance of the serpentine resistance sensing grid.

[0134] Furthermore, during longitudinal tearing, increases, and

[0135] decreases due to the Poisson effect of the material (assuming isotropy).

[0136] Furthermore, to reflect the damage energy density per unit area, the damage amount needs to be correlated with the normalized geometric parameters of the grid cells. / has a dimension of 1, and the unit area has a dimension of ²].

[0137] Furthermore, introducing balances the overall dimension of the formula and meets the requirements of a dimensionless damage index.

[0138] Furthermore, physically, affects the effective length of the longitudinal tear propagation path, corresponding to the square root term of the energy release rate in fracture mechanics.

[0139] Generally speaking, by using a resistance-capacitance hybrid sensing network to monitor the manufacturing process in real time and obtain real-time electrical data, the dynamic changes in the electrical properties of the conveyor belt during the manufacturing process can be captured in a timely manner. These data reflect the real-time conditions of the internal structure and performance of the conveyor belt, providing a key basis for judging whether there is damage to the conveyor belt and the degree of damage.

[0140] Generally speaking, damage calculation is performed on real-time electrical data, and the real-time damage situation is obtained by using a specific calculation formula.

[0141] Generally speaking, the longitudinal tear degree calculation formula calculates longitudinal damage based on the resistance change characteristics, and the transverse wear degree calculation formula calculates transverse damage based on the capacitance change characteristics.

[0142] Generally speaking, through these two formulas, the abstract electrical data is converted into specific damage indicators, accurately quantifying the damage degree of the conveyor belt in different directions, and providing an accurate reference for taking targeted measures.

[0143] S3. Edge intelligent node packaging is performed on the signal conditioning chip and the resistance-capacitance hybrid sensing network to obtain the intelligent monitoring network of the conveyor belt. Based on the intelligent monitoring network, real-time monitoring of the preparation process is carried out to obtain the real-time thermodynamic data of the conveyor belt, and conveyor belt vulcanization calculation is performed on the real-time thermodynamic data to obtain the real-time vulcanization situation of the conveyor belt;

[0144] In the embodiment of the present invention, the edge intelligent node packaging of the signal conditioning chip and the resistance-capacitance hybrid sensing network to obtain the intelligent monitoring network of the conveyor belt includes:

[0145] Wire connection is performed on the signal conditioning chip and the resistance-capacitance hybrid sensing network to obtain the initial monitoring network of the conveyor belt;

[0146] Zero calibration is performed on the initial monitoring network based on near-field communication to obtain the intelligent monitoring network of the conveyor belt, wherein the intelligent monitoring network is configured with an adaptive filtering algorithm and a Kalman prediction model.

[0147] The conveyor belt vulcanization calculation of the real-time thermodynamic data to obtain the real-time vulcanization situation of the conveyor belt includes:

[0148] Moving average filtering is performed on the real-time thermodynamic data to obtain the thermodynamic standard data of the conveyor belt. Zero calibration is performed on the initial monitoring network based on near-field communication to obtain the intelligent monitoring network of the conveyor belt, wherein the monitoring network is configured with an adaptive filtering algorithm and a Kalman prediction model;

[0149] Based on the thermodynamic standard data, vulcanization calculation is performed on the conveyor belt to obtain the real-time vulcanization situation of the conveyor belt, wherein the formula for the vulcanization calculation is:

[0150]

[0151] In the formula, is the real-time vulcanization situation, is the natural exponential function, is the time variable, is the pre-exponential factor, is the natural constant, is the minimum energy threshold required for the vulcanization reaction, is the th grid at the absolute temperature at time is the time factor, is the ideal gas constant, is the integral of with respect to is the reaction order, is the grid number of the serpentine grid layout, is the vulcanization reaction rate

[0152] Specifically, the real-time thermodynamic data includes local real-time temperature, production line speed-related temperature / stress data, thermal expansion strain, temperature gradient, and vulcanization reaction rate.

[0153] Specifically, a low-power signal conditioning chip (such as TI ADS1299) and a BLE5.0 wireless module are selected to ensure impedance matching with the resistor-capacitor sensing network (input impedance > 1 GΩ).

[0154] Furthermore, a flexible PCB is made using a polyimide substrate, integrating a signal conditioning circuit, an MCU, and a radio frequency module, with the thickness controlled within 0.3 mm to adapt to the bending of the conveyor belt.

[0155] Furthermore, anisotropic conductive adhesive (ACA) is used to connect the chip pins to the printed serpentine wires, with a contact resistance < 0.1 Ω and a bending resistance > 100,000 times.

[0156] Furthermore, the chipset is encapsulated in a silicone protective layer (size 10×10×2 mm³) through a transfer molding process (Transfer Molding), with an IP67 waterproof rating.

[0157] Furthermore, a piezoelectric energy harvesting module (PZT fiber array) and a micro-supercapacitor (capacity 10 mF) are printed at the bottom of the node to achieve energy autonomy (power consumption < 100 μW).

[0158] Furthermore, an intelligent node is arranged every 2 meters along the conveyor belt, adopting a Mesh networking protocol (such as Zigbee3.0) to ensure full coverage without blind spots.

[0159] Furthermore, an adaptive filtering algorithm and a Kalman prediction model are pre-installed, and zero calibration is completed through near-field communication (NFC) (error compensation < ±0.5%).

[0160] Furthermore, a wear-resistant polyurethane coating (Shore hardness 80A) is added to the outer layer of the encapsulation to resist the friction and impact of the conveyor belt (wear rate < 0.01 mm / year).

[0161] Furthermore, simulate the operating conditions of the conveyor belt (vibration 5 - 200 Hz, tensile strain 10%) to verify the stability of node signal transmission (packet loss rate < 0.1%).

[0162] Specifically, multi-source data synchronous acquisition: Real-time collect the data of the resistance-capacitance sensing network (1 kHz), the data of the embedded temperature sensor (accuracy of ±0.5 °C), and the strain gauge signal (±0.1% FS) through the edge intelligent node to form a multi-dimensional data set with time stamp alignment.

[0163] Furthermore, construct a thermodynamic model of the conveyor belt cross-section based on the finite volume method (FVM), and map the electrical signals (ΔR / ΔC) to the temperature gradient (0 - 150 °C) and stress distribution (0 - 20 MPa).

[0164] Furthermore, track the temperature-pressure change curve of the cover layer during the vulcanization stage, calculate the actual vulcanization degree of the core layer through the heat transfer inversion algorithm (error < 3%), and dynamically adjust the vulcanization parameters.

[0165] Furthermore, use the resistance network to monitor the microcracks at the aramid fiber / rubber interface (sensitivity 0.01 mm²), and combine with the Arrhenius equation to predict the interface aging rate.

[0166] Furthermore, invert the frictional heat generation power during operation (Q = μFv) through the change of the capacitance plate spacing, and calculate the belt body temperature rise in real time (ΔT resolution 0.1 K).

[0167] Furthermore, adopt a temperature-strain decoupling model to eliminate the interference of thermal expansion on the resistance strain measurement (the strain measurement error after compensation < 0.05%).

[0168] Furthermore, when the local temperature > 180 °C is monitored, trigger the secondary calibration of the infrared thermal imager to confirm whether the decomposition temperature of the flame retardant meets the MT668 standard.

[0169] Furthermore, input the thermodynamic data stream into the digital twin in real time to generate a three-dimensional temperature-stress cloud map (update frequency 10 Hz).

[0170] Furthermore, establish an LSTM time series prediction model, and trigger a hierarchical alarm when the temperature / stress deviates from the process reference curve > 5σ (response delay < 200 ms).

[0171] Generally speaking, the signal conditioning chip and the resistor-capacitor hybrid sensing network are encapsulated into edge intelligent nodes to construct an intelligent monitoring network. The signal conditioning chip can process the signals collected by the sensing network, such as amplification, filtering, analog-to-digital conversion, etc., to improve the signal quality and make it more suitable for subsequent analysis and calculation. The edge intelligent node encapsulation endows the network with local data processing and analysis capabilities, without the need to transmit a large amount of raw data to a remote server, reducing data transmission delay and network burden, and realizing real-time monitoring and rapid response to the preparation process.

[0172] Generally speaking, based on the intelligent monitoring network, the preparation process is monitored in real time to obtain the real-time thermodynamic data of the conveyor belt, including time variables, local real-time temperature, reaction order, etc. These data reflect the thermal state changes of the conveyor belt during the vulcanization process and are key information for understanding the vulcanization process and judging the vulcanization quality.

[0173] Generally speaking, the conveyor belt vulcanization calculation is carried out on the real-time thermodynamic data, and the real-time vulcanization situation is obtained by using the vulcanization calculation formula. This formula comprehensively considers factors such as time, temperature, reaction order, etc., can quantify the vulcanization degree of the conveyor belt at different times and positions, accurately describe the process of the vulcanization reaction, and provide a scientific basis for controlling the vulcanization process.

[0174] Generally speaking, through real-time monitoring and accurate calculation of the vulcanization situation, the phenomena of local over-vulcanization or under-vulcanization can be effectively avoided. Over-vulcanization will cause the conveyor belt material to age and its performance to decline, while under-vulcanization cannot make the material reach the best performance. By using the intelligent monitoring network and vulcanization calculation, the vulcanization process can be accurately controlled, ensuring uniform vulcanization of the whole conveyor belt, improving the physical properties and stability of the product, reducing product defects caused by improper vulcanization, and improving product quality.

[0175] S4. Dynamically adjust the production parameters in the preparation process based on the real-time damage situation and the real-time vulcanization situation.

[0176] In the embodiment of the present invention, the dynamically adjusting the production parameters in the preparation process based on the real-time damage situation and the real-time vulcanization situation includes:

[0177] Perform data spatio-temporal alignment and fusion on the real-time damage situation and the real-time vulcanization situation to obtain the comprehensive working condition evaluation matrix of the conveyor belt;

[0178] Based on the comprehensive working condition evaluation matrix, dynamically regulate the vulcanization temperature of the conveyor belt to obtain the adjusted vulcanization temperature of the conveyor belt;

[0179] Based on the comprehensive working condition evaluation matrix, perform pressure compensation on the conveyor belt to obtain the adjusted thickness of the cover rubber of the conveyor belt;

[0180] Optimize the production line speed of the conveyor belt based on the comprehensive working condition evaluation matrix to obtain the generated adjustment speed of the conveyor belt;

[0181] Rebalance the fiber tension of the conveyor belt based on the comprehensive working condition evaluation matrix to obtain the distributed adjustment tension of the conveyor belt;

[0182] Adjust the injection of flame retardant for the conveyor belt based on the comprehensive working condition evaluation matrix to obtain the adjusted content of flame retardant for the conveyor belt;

[0183] Dynamically adjust the production parameters in the preparation process based on the vulcanization adjustment temperature, the adjusted thickness of the cover rubber, the generated adjustment speed, the distributed adjustment tension, and the adjusted content of flame retardant.

[0184] Furthermore, for the two types of data, namely the real-time damage situation and the real-time vulcanization situation of the conveyor belt, synchronize the data collected at different times with a unified time standard, and correspond the monitoring data of each sensor to the specific physical position of the conveyor belt through position identification. Integrate information such as the damage degree, damage location, and vulcanization degree to form a comprehensive working condition evaluation matrix that can fully present the working conditions of each position and each time point of the conveyor belt, providing comprehensive data support for subsequent adjustments.

[0185] Furthermore, based on the working condition information of each part of the conveyor belt presented in the comprehensive working condition evaluation matrix, clarify the vulcanization temperature required for different regions. Through the temperature adjustment equipment, make targeted adjustments to the heating unit of the vulcanization equipment so that each part of the conveyor belt is at an appropriate temperature during the vulcanization process, ensuring that the vulcanization process is uniform and sufficient, and avoiding problems such as insufficient vulcanization or over-vulcanization.

[0186] Furthermore, based on the deformation and stress distribution of each part of the conveyor belt in the comprehensive working condition evaluation matrix, combined with the pressure-bearing characteristics of the material, determine the magnitude of the pressure that needs to be compensated for different regions. Use the pressure control system to adjust the pressure application device during the vulcanization process in real time, apply differential pressure to the cover rubber of the conveyor belt, ensure that the cover rubber is uniformly pressed under different working conditions, make the thickness of the cover rubber meet the standards, and enhance the bonding strength between the cover rubber and the belt core.

[0187] Furthermore, analyze the data in the comprehensive working condition evaluation matrix, judge the limiting factors in the production process of the conveyor belt under the current working conditions, and determine the appropriate operating speed range. By adjusting the production line drive device, make the production line speed match the real-time working conditions of the conveyor belt, while ensuring production efficiency, prevent quality problems caused by inappropriate speed, and maintain the stable operation of the production process.

[0188] Furthermore, based on the stress conditions of the fibers in each part of the conveyor belt in the comprehensive working condition evaluation matrix and combined with the principles of material mechanics, the numerical values of the tension that the fibers in each area should be adjusted are calculated. With the help of the tension adjustment mechanism, real-time tension control is implemented on the fiber layer of the conveyor belt, and the fiber tensions at different positions are adjusted respectively to ensure that the fiber tensions are evenly distributed within the overall range of the conveyor belt, eliminate local stress concentration phenomena, and improve the structural stability of the conveyor belt.

[0189] Furthermore, referring to the comprehensive working condition evaluation matrix and combining the requirements for flame retardant performance in the conveyor belt usage scenario, the injection amounts of flame retardants required for each part are determined. Using metering equipment, the flow rate and pressure of the flame retardant injection equipment are precisely controlled to enable the flame retardant to be quantitatively and evenly injected into each area of the conveyor belt, ensuring that the flame retardant performance of the conveyor belt meets the standards while avoiding waste of the flame retardant.

[0190] Furthermore, the adjustment results such as vulcanization temperature, pressure compensation value, production line speed, fiber tension, and flame retardant injection amount obtained through the comprehensive working condition evaluation matrix are uniformly transmitted to the production control system. The production control system, according to the established rules, collaboratively controls the vulcanization equipment, pressure device, drive system, tension adjustment mechanism, and flame retardant injection equipment to achieve dynamic optimization of all production parameters during the preparation process of the conveyor belt, ensuring that the production process adapts to the changes in the working conditions of the conveyor belt and stabilizing the product quality.

[0191] Specifically, sort out the time information: respectively extract the corresponding timestamp information in the real-time damage situation data and the real-time vulcanization situation data, and clarify the time sequence of data generation.

[0192] Furthermore, based on the structural characteristics of the conveyor belt and the layout of the sensor network, the damage situation and vulcanization situation data are correspondingly matched according to the spatial positions on the conveyor belt, such as the data corresponding to different grid areas.

[0193] Furthermore, standardize the data of the real-time damage situation and the real-time vulcanization situation to unify the dimension and value range of the data for subsequent fusion calculation.

[0194] Furthermore, according to the division of the monitoring area of the conveyor belt, determine the row-column structure of the comprehensive working condition evaluation matrix. The rows can represent different monitoring areas, and the columns represent different indicators related to damage and vulcanization.

[0195] Furthermore, according to the corresponding relationship between time and space, fill the standardized data into the corresponding positions in the comprehensive working condition evaluation matrix to complete the spatio-temporal alignment and fusion of the data, and obtain the comprehensive working condition evaluation matrix of the conveyor belt.

[0196] Specifically, analyze the comprehensive working condition evaluation matrix: deeply study the data in the matrix, and determine the indicators related to the vulcanization state, such as the real-time vulcanization situation and the differences in vulcanization degrees in different areas.

[0197] Furthermore, based on the material properties, process requirements, and historical production data of the conveyor belt, a mathematical model reflecting the relationship between vulcanization temperature and vulcanization effect is constructed.

[0198] Furthermore, the data related to vulcanization extracted from the comprehensive working condition evaluation matrix are input into the established temperature control model.

[0199] Furthermore, through model operation, the preliminary adjustment value of the vulcanization temperature required to achieve the ideal vulcanization effect under the current working condition is obtained.

[0200] Furthermore, check whether the calculated initial adjustment value meets the boundary conditions such as the temperature adjustable range of the production equipment and the requirements of safe production.

[0201] Furthermore, if the initial adjustment value meets the boundary conditions, it is determined as the vulcanization adjustment temperature; if not, the adjustment value is corrected until the required vulcanization adjustment temperature is obtained.

[0202] Specifically, carefully analyze each item of data in the matrix to identify information related to the pressure distribution, damage condition, and vulcanization state of the conveyor belt, etc.

[0203] Furthermore, based on the material properties, mechanical principles, and actual production experience of the conveyor belt, a mathematical model reflecting the relationship between pressure and the thickness of the cover rubber is constructed.

[0204] Furthermore, extract data related to pressure compensation and cover rubber thickness adjustment from the comprehensive working condition evaluation matrix, such as pressure differences in different regions and the severity of damage, etc.

[0205] Furthermore, substitute the extracted data into the pressure - cover rubber thickness correlation model to calculate the preliminary adjustment value of the cover rubber thickness required to compensate for the pressure.

[0206] Furthermore, considering the production process limitations of the conveyor belt, such as the minimum and maximum cover rubber thickness ranges and the processing accuracy of the production equipment, etc., correct the preliminary adjustment value.

[0207] Furthermore, through the calculation and adjustment of the above steps, finally determine the adjusted thickness of the cover rubber of the conveyor belt that meets the pressure compensation requirements and conforms to the production process requirements.

[0208] Specifically, screen out the key data affecting the production line speed, including the damage condition of the conveyor belt, vulcanization progress, and the state of each region, etc.

[0209] Furthermore, based on the material properties, production process standards, and past production experience of the conveyor belt, construct a mathematical model of the relationship between the production line speed and the comprehensive working conditions.

[0210] Further, substitute the key data extracted from the comprehensive working condition evaluation matrix into the constructed mathematical model.

[0211] Further, through model operation, obtain the initial optimized value of the production line speed that is theoretically more appropriate under the current comprehensive working condition.

[0212] Further, combine the performance limits of production equipment, such as the maximum and minimum operating speeds, acceleration and deceleration capabilities, etc., to judge the rationality of the initial optimized value.

[0213] Further, if the initial optimized value meets the equipment performance requirements, determine it as the generated adjustment speed; if not, adjust the initial optimized value according to the equipment performance until the generated adjustment speed that meets the requirements is obtained.

[0214] Specifically, deeply study the comprehensive working condition evaluation matrix, and accurately extract the data related to fiber tension therein, such as the stress distribution in each area, the influence of damage on stress, etc.

[0215] Further, based on the structural characteristics of the conveyor belt, the properties of fiber materials, and mechanical principles, construct a relationship model between fiber tension and conveyor belt performance.

[0216] Further, substitute the extracted data related to fiber tension into the constructed relationship model.

[0217] Further, through model operation, preliminarily calculate the tension value that needs to be adjusted to achieve fiber tension rebalancing, and obtain the initial adjustment tension.

[0218] Further, combine the constraints such as the tension range that the fiber can withstand in actual production and the control accuracy of the equipment for tension to conduct a feasibility analysis of the initial adjustment tension.

[0219] Further, according to the results of the feasibility analysis, make necessary corrections to the initial adjustment tension, and finally determine the conveyor belt distribution adjustment tension that meets the requirements.

[0220] Specifically, carefully review the comprehensive working condition evaluation matrix, and screen out the key information related to the damage condition, vulcanization degree, characteristics of different areas, etc. of the conveyor belt and affecting the demand for flame retardants.

[0221] Further, based on the flame retardant performance standards of the conveyor belt, material composition, production process, as well as the experimental data and production experience of previous flame retardant additions, establish an association model between the flame retardant injection amount and the comprehensive working condition.

[0222] Further, substitute the key information extracted from the comprehensive working condition evaluation matrix into the established association model.

[0223] Furthermore, through the calculation of the model, the preliminary value of the flame retardant adjustment content required to achieve the ideal flame retardant effect of the conveyor belt under the current comprehensive working conditions was preliminarily obtained.

[0224] Furthermore, the rationality of the preliminary values ​​is judged based on actual conditions such as the flame retardant supply capacity, cost constraints, and the maximum loadable flame retardant amount of the conveyor belt during the production process.

[0225] Furthermore, according to the rationality judgment result, the preliminary value is adjusted to finally determine the adjusted content of flame retardant in the conveyor belt that meets the actual production and flame retardant requirements.

[0226] Specifically, according to the vulcanization adjustment temperature, the temperature setting value of the vulcanization equipment is modified accordingly to ensure that the conveyor belt undergoes vulcanization reaction at an appropriate temperature.

[0227] Furthermore, according to the adjusted thickness of the covering rubber, the coating equipment parameters of the covering rubber, such as the spacing between the rubber coating rollers, the rubber coating speed, etc., are adjusted to control the conveyor belt covering rubber to reach the adjusted thickness.

[0228] Furthermore, according to the generated adjustment speed, the rotation speed of the motor of the production line or the transmission device parameters are changed so that the moving speed of the conveyor belt on the production line reaches the optimized generated adjustment speed.

[0229] Furthermore, for the distributed adjustment tension, the tension control components on the fiber conveying device, such as the tension sensor, the tensioning wheel, etc., are adjusted so that the tension of the fiber in the conveyor belt reaches the distributed adjustment tension after rebalancing.

[0230] Furthermore, according to the flame retardant content, the flow rate, pressure and other parameters of the flame retardant injection equipment are adjusted so that the amount of flame retardant injected into the conveyor belt meets the adjusted requirements.

[0231] Furthermore, by comprehensively considering the above-mentioned adjusted parameters, the production rhythm, equipment coordination and other aspects of the entire preparation process are coordinated as a whole to ensure orderly coordination of all links and realize dynamic adjustment of production parameters.

[0232] In general, timely and targeted adjustments should be made to production parameters based on the actual status of the conveyor belt during the preparation process. Real-time damage conditions reflect whether there are problems such as longitudinal tearing and transverse wear on the conveyor belt. Adjusting production parameters based on this information can prevent further damage.

[0233] For example, if it is monitored that the longitudinal damage in a certain area has an increasing trend, the stress condition of the internal structure of the conveyor belt can be improved by adjusting the fiber tension and rebalancing, thus preventing the longitudinal tearing from worsening.

[0234] Generally speaking, the real-time vulcanization situation shows whether the vulcanization process is normal. Based on this, adjusting parameters such as vulcanization temperature and time can ensure that the vulcanization reaction is sufficient and uniform, thus guaranteeing the product quality.

[0235] Compared with the prior art, the present invention has the following beneficial effects:

[0236] 1. By constructing a resistance-capacitance hybrid sensing network to monitor the preparation process in real time, the real-time electrical data of the conveyor belt can be accurately obtained. The real-time damage situation calculated can reflect in detail the longitudinal tear and transverse wear degrees, facilitating the timely discovery of potential defects. At the same time, the intelligent monitoring network's monitoring of real-time thermodynamic data and vulcanization calculation can accurately grasp the real-time vulcanization situation of the conveyor belt, avoid local over-vulcanization or under-vulcanization phenomena, ensure the stability of material properties, and thus effectively improve the product quality.

[0237] 2. By the intelligent monitoring network's real-time feedback of the conveyor belt's status information, optimizing parameters such as vulcanization temperature, pressure, and production line speed according to the comprehensive working condition evaluation matrix, it avoids the frequent shutdown for quality inspection caused by unreasonable parameters in the traditional preparation process, reduces the shutdown time. At the same time, it optimizes the production process, makes the whole preparation process more efficient and smooth, and then greatly improves the preparation speed, comprehensively improving the preparation efficiency of the aramid core solid-core flame-retardant conveyor belt.

[0238] As Figure 2 shown, it is a functional module diagram of a preparation system for an aramid core solid-core flame-retardant conveyor belt based on intelligent monitoring provided by an embodiment of the present invention.

[0239] The preparation system 100 for an aramid core solid-core flame-retardant conveyor belt based on intelligent monitoring according to the present invention can be installed in an electronic device. According to the functions achieved, the preparation system 100 for an aramid core solid-core flame-retardant conveyor belt based on intelligent monitoring can include a serpentine network layout generation module 101, a real-time damage calculation module 102, a real-time vulcanization situation module 103, and a preparation process parameter adjustment module 104. The modules in the present invention can also be referred to as units, which refer to a series of computer program segments that can be executed by an electronic device processor and can complete fixed functions, and are stored in the memory of the electronic device.

[0240] In this embodiment, the functions of each module / unit are as follows:

[0241] The serpentine network layout generation module 101 is used to construct a serpentine grid layout of the conveyor belt based on the pre-acquired conveyor belt data;

[0242] The real-time damage calculation module 102 is configured to construct a resistance-capacitance hybrid sensing network of the conveyor belt based on the serpentine grid layout, monitor the preparation process of the aramid core fully flame-retardant conveyor belt in real time based on the resistance-capacitance hybrid sensing network, obtain real-time electrical data of the conveyor belt, and perform conveyor belt damage calculation on the real-time electrical data to obtain the real-time damage condition of the conveyor belt;

[0243] The real-time vulcanization condition module 103 is configured to perform edge intelligent node packaging on the signal conditioning chip and the resistance-capacitance hybrid sensing network to obtain an intelligent monitoring network of the conveyor belt, monitor the preparation process in real time based on the intelligent monitoring network, obtain real-time thermodynamic data of the conveyor belt, and perform conveyor belt vulcanization calculation on the real-time thermodynamic data to obtain the real-time vulcanization condition of the conveyor belt;

[0244] The preparation process parameter adjustment module 104 is configured to dynamically adjust the production parameters in the preparation process based on the real-time damage condition and the real-time vulcanization condition.

[0245] In several embodiments provided by the present invention, it should be understood that the disclosed methods and systems can be implemented in other ways. For example, the system embodiments described above are merely illustrative. For example, the division of the modules is only a logical function division, and there may be other division methods in actual implementation.

[0246] The modules described as separate components may or may not be physically separated, and the components shown as modules may or may not be physical units, that is, they may be located in one place, or may be distributed to multiple network units. Some or all of the modules can be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0247] In addition, the functional modules in each embodiment of the present invention can be integrated in a processing unit, or each unit can exist physically alone, or two or more units can be integrated in one unit. The above integrated unit can be implemented in the form of hardware, or in the form of a hardware plus software functional module.

[0248] For those skilled in the art, it is obvious that the present invention is not limited to the details of the above exemplary embodiments, and can be implemented in other specific forms without departing from the spirit or basic characteristics of the present invention.

[0249] The embodiments of the present application can acquire and process relevant data based on artificial intelligence technology. Among them, artificial intelligence is a theory, method, technology, and application system that uses digital computers or machines controlled by digital computers to simulate, extend, and expand human intelligence, perceive the environment, acquire knowledge, and use knowledge to obtain the best results.

[0250] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and not to limit them. Although the present invention has been described in detail with reference to the preferred embodiments, those of ordinary skill in the art should understand that the technical solutions of the present invention can be modified or equivalently replaced without departing from the spirit and scope of the technical solutions of the present invention.

Claims

1. A preparation method of an aramid core fully flame-retardant conveyor belt based on intelligent monitoring, characterized in that, The method includes: S1. Construct a serpentine grid layout of the conveyor belt based on the pre-acquired conveyor belt data; S2. Construct a resistance-capacitance hybrid sensing network of the conveyor belt based on the serpentine grid layout, and perform real-time monitoring on the preparation process of the aramid core fully flame-retardant conveyor belt based on the resistance-capacitance hybrid sensing network to obtain the real-time electrical data of the conveyor belt, and calculate the damage of the conveyor belt from the real-time electrical data to obtain the real-time damage condition of the conveyor belt; S3. Package the signal conditioning chip and the resistance-capacitance hybrid sensing network into an edge intelligent node to obtain the intelligent monitoring network of the conveyor belt, and perform real-time monitoring on the preparation process based on the intelligent monitoring network to obtain the real-time thermodynamic data of the conveyor belt, and calculate the vulcanization of the conveyor belt from the real-time thermodynamic data to obtain the real-time vulcanization condition of the conveyor belt; S4. Dynamically adjust the production parameters in the preparation process based on the real-time damage condition and the real-time vulcanization condition.

2. The preparation method of the aramid core fully flame-retardant conveyor belt based on intelligent monitoring according to claim 1, characterized in that The construction of the serpentine grid layout of the conveyor belt based on the pre-acquired conveyor belt data includes: Design the grid layout of the conveyor belt based on the pre-acquired conveyor belt data to obtain the basic grid layout of the conveyor belt; Analyze the stress distribution of the conveyor belt to obtain the high-strain area of the conveyor belt; Add wire branches to the basic grid layout based on the high-strain area to obtain the serpentine grid layout of the conveyor belt.

3. The preparation method of the aramid core fully flame-retardant conveyor belt based on intelligent monitoring according to claim 1, wherein, The construction of the resistance-capacitance hybrid sensing network of the conveyor belt based on the serpentine grid layout includes: Print a longitudinal conductive layer on the conveyor belt based on the serpentine grid layout to obtain the resistance sensing grid of the conveyor belt; Spin-coat a photocurable polyurethane dielectric layer on the resistance sensing grid to obtain the insulating layer of the resistance sensing grid; Print a transverse capacitor plate above the insulating layer based on the serpentine grid layout to obtain the capacitance sensing array of the conveyor belt; Fill the resistance sensing grid and the capacitance sensing array with conductive silver glue to obtain the resistance-capacitance hybrid sensing network of the conveyor belt.

4. The preparation method of the aramid core fully flame-retardant conveyor belt based on intelligent monitoring according to claim 1, characterized in that, The real-time monitoring of the preparation process of the aramid core fully flame-retardant conveyor belt based on the resistance-capacitance hybrid sensing network to obtain the real-time electrical data of the conveyor belt includes: Activate the resistance-capacitance hybrid sensing network by energization to obtain the sensing network of the conveyor belt; Perform real-time monitoring on the preparation process of the aramid core fully flame-retardant conveyor belt based on the sensing network to obtain the electrical fingerprint spectrum of the conveyor belt; Perform frequency-domain decoupling on the extracted pure resistance change and capacitance change components in the electrical fingerprint spectrum to obtain the real-time electrical data of the conveyor belt.

5. The preparation method of the aramid core fully flame-retardant conveyor belt based on intelligent monitoring according to claim 1, characterized in that, The calculation of the conveyor belt damage from the real-time electrical data to obtain the real-time damage condition of the conveyor belt includes: Perform moving average filtering on the real-time electrical data to obtain the electrical standard data of the conveyor belt; Extract the damage time-frequency domain characteristics from the electrical standard data to obtain the resistance change characteristics and capacitance change characteristics of the conveyor belt; Based on the resistance change characteristics and the capacitance change characteristics, damage calculation is performed on the conveyor belt to obtain the real-time damage condition of the conveyor belt.

6. The preparation method of the aramid core fully flame-retardant conveyor belt based on intelligent monitoring according to claim 5, characterized in that, The performing damage calculation on the conveyor belt based on the resistance change characteristics and the capacitance change characteristics to obtain the real-time damage condition of the conveyor belt includes: Performing longitudinal tear degree calculation on the conveyor belt based on the resistance change characteristics to obtain the longitudinal damage of the conveyor belt, where the formula for the longitudinal tear degree calculation is: ； In the formula, is the longitudinal damage, is the resistance tearing coefficient, is the real-time resistance, is the reference resistance, is the value of the resistance change characteristic, is the grid cell area of the serpentine grid layout, is the grid length of the serpentine grid layout; Performing transverse wear degree calculation on the conveyor belt based on the capacitance change characteristics to obtain the transverse damage of the conveyor belt, where the formula for the transverse wear degree calculation is: ； Wherein, is the transverse damage, is the capacitance wear coefficient, is the natural logarithm, is the real-time capacitance, is the reference capacitance, is the value of the capacitance change characteristic, is the standard thickness of the covering layer of the conveyor belt; Aggregating the transverse damage and the longitudinal damage as the real-time damage condition of the conveyor belt.

7. The preparation method of the aramid core fully flame-retardant conveyor belt based on intelligent monitoring according to claim 1, characterized in that, The encapsulating the signal conditioning chip and the resistance-capacitance hybrid sensing network into an edge intelligent node to obtain the intelligent monitoring network of the conveyor belt includes: Connecting the signal conditioning chip and the resistance-capacitance hybrid sensing network with wires to obtain the initial monitoring network of the conveyor belt; Performing zero-point calibration on the initial monitoring network based on near-field communication to obtain the intelligent monitoring network of the conveyor belt, where the intelligent monitoring network is configured with an adaptive filtering algorithm and a Kalman prediction model.

8. The preparation method of the aramid core fully flame-retardant conveyor belt based on intelligent monitoring according to claim 1, characterized in that The performing conveyor belt vulcanization calculation on the real-time thermodynamic data to obtain the real-time vulcanization condition of the conveyor belt includes: Performing moving average filtering on the real-time thermodynamic data to obtain the thermodynamic standard data of the conveyor belt, where the thermodynamic standard data includes: time variable, local real-time temperature, and reaction order; Performing vulcanization calculation on the conveyor belt based on the thermodynamic standard data to obtain the real-time vulcanization condition of the conveyor belt, where the formula for the vulcanization calculation is: ； In the formula, represents the real-time vulcanization situation, is the natural exponential function, is the time variable, is the pre-exponential factor, is the natural constant, is the minimum energy threshold required for the vulcanization reaction, is the th grid in the absolute temperature at the moment, is the time factor, is the ideal gas constant, is the integral of with respect to is the reaction order, is the grid number of the serpentine grid layout, is the vulcanization reaction rate.

9. The preparation method of the aramid core fully flame-retardant conveyor belt based on intelligent monitoring according to claim 1, characterized in that, The dynamically adjusting the production parameters in the preparation process based on the real-time damage condition and the real-time vulcanization condition includes: Performing data spatio-temporal alignment and fusion on the real-time damage condition and the real-time vulcanization condition to obtain the comprehensive working condition evaluation matrix of the conveyor belt; Performing dynamic regulation of the vulcanization temperature on the conveyor belt based on the comprehensive working condition evaluation matrix to obtain the adjusted vulcanization temperature of the conveyor belt; Performing pressure compensation on the conveyor belt based on the comprehensive working condition evaluation matrix to obtain the adjusted thickness of the cover rubber of the conveyor belt; Performing production line speed optimization on the conveyor belt based on the comprehensive working condition evaluation matrix to obtain the adjusted production speed of the conveyor belt; Performing fiber tension rebalancing on the conveyor belt based on the comprehensive working condition evaluation matrix to obtain the adjusted distribution tension of the conveyor belt; Performing flame retardant injection adjustment on the conveyor belt based on the comprehensive working condition evaluation matrix to obtain the adjusted flame retardant content of the conveyor belt; Dynamically adjusting the production parameters in the preparation process based on the adjusted vulcanization temperature, the adjusted thickness of the cover rubber, the adjusted production speed, the adjusted distribution tension, and the adjusted flame retardant content.

10. The preparation system of an aramid core fully flame-retardant conveyor belt based on intelligent monitoring, characterized in that, The system includes: A serpentine network layout generation module for constructing a serpentine grid layout of the conveyor belt based on pre-acquired conveyor belt data; A real-time damage calculation module, which is used to construct a resistance-capacitance hybrid sensing network of the conveyor belt based on the serpentine grid layout, monitor the preparation process of the aramid core fully flame-retardant conveyor belt in real time based on the resistance-capacitance hybrid sensing network, obtain the real-time electrical data of the conveyor belt, and calculate the damage of the conveyor belt from the real-time electrical data to obtain the real-time damage condition of the conveyor belt; A real-time vulcanization condition module, which is used to perform edge intelligent node packaging on the signal conditioning chip and the resistance-capacitance hybrid sensing network to obtain the intelligent monitoring network of the conveyor belt, monitor the preparation process in real time based on the intelligent monitoring network, obtain the real-time thermodynamic data of the conveyor belt, and calculate the vulcanization of the conveyor belt from the real-time thermodynamic data to obtain the real-time vulcanization condition of the conveyor belt; A preparation process parameter adjustment module, which is used to dynamically adjust the production parameters in the preparation process based on the real-time damage condition and the real-time vulcanization condition.

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