Near-infrared online intelligent detection system and method for moisture of materials on conveying belt

Abstract

The invention discloses a near-infrared on-line intelligent detection system and method for material moisture of a conveying belt, and relates to the technical field of material moisture detection.The system comprises a material and environment information acquisition module, a data processing module and a data processing module, meanwhile, temperature, humidity and air pressure sensors are used for monitoring changes of environmental factors in real time, equipment is calibrated and maintained regularly, and parameters are initialized before detection; according to the invention, material characteristic pre-identification and environmental factor real-time sensing functions are integrated, and parameters of the near-infrared detection module are dynamically adjusted by using an intelligent algorithm so as to adapt to changes of different material characteristics and environmental factors, so that the detection system can accurately detect various material characteristics in a complex and changeable industrial field environment. And high-precision and high-stability moisture detection can still be maintained.

Description

Technical Field

[0001] The present invention relates to the technical field of material moisture detection, and specifically to an on-line intelligent near-infrared detection system and method for conveyor belt material moisture. Background Art

[0002] In the industrial production process, the moisture content of materials is a key factor affecting product quality, production efficiency and equipment safety. Precise control of the moisture content is crucial for many industries such as food processing, pharmaceuticals, chemicals, mining, etc. The moisture content of different materials directly affects their physical properties, chemical stability and the performance of the final product. Therefore, realizing real-time monitoring and control of the moisture content of materials is of great significance for improving the automation level and product quality of the production process.

[0003] Traditional moisture detection methods mostly adopt off-line sampling detection, that is, samples of materials are taken regularly and sent to the laboratory for analysis. This method not only takes a long time and has low efficiency, but also has a lag in the detection results and cannot meet the real-time production requirements of modern industry. In addition, the industrial site environment is complex and changeable, and the material characteristics vary widely. These factors will significantly affect the accuracy and reliability of moisture detection. The off-line detection method cannot dynamically adapt to the changes in material characteristics and environmental factors, resulting in large deviations in the detection results. Although the existing near-infrared on-line detection systems have improved the detection efficiency to a certain extent, most of them lack comprehensive consideration of material characteristics and environmental factors and are difficult to ensure the detection accuracy and stability under complex working conditions.

[0004] In view of the above problems, it is necessary to optimize the existing on-line intelligent near-infrared detection system and method for conveyor belt material moisture, and dynamically adjust the detection parameters through intelligent algorithms to reduce the interference of material characteristics and environmental changes on the detection results. Therefore, it is of great significance to develop an on-line intelligent near-infrared detection system and method for conveyor belt material moisture that can comprehensively achieve the above characteristics. Summary of the Invention

[0005] The object of the present invention is to make up for the deficiencies of the prior art and provide a near-infrared on-line intelligent detection system and method for the moisture content of conveyor belt materials. By integrating a material and environment information acquisition module, a near-infrared detection module, a data processing and decision-making module, an intelligent control module, and a communication and remote monitoring module, it realizes real-time on-line detection of the moisture content of materials. The system can automatically identify the physical characteristics of materials, monitor the changes of environmental factors in real time, and dynamically adjust the parameters of the near-infrared detection module according to this information, so as to ensure high-precision and high-stability moisture detection under different working conditions. In addition, the system also has an intelligent control function, which can automatically process materials according to the detection results, reduce manual intervention, and improve the automation level and stability of the production process. It not only overcomes the lag of traditional off-line detection methods, but also significantly improves the accuracy and reliability of moisture detection, providing strong technical support for the control of the moisture content of materials in the industrial production process.

[0006] To solve the above technical problems, the present invention provides the following technical solutions: On the one hand, a near-infrared on-line intelligent detection system for the moisture content of conveyor belt materials, the system includes the following components: Material and environment information acquisition module: Quickly and accurately identify the color, shape and particle size characteristics of materials before they enter the detection area. At the same time, use temperature, humidity and air pressure sensors to monitor the changes of environmental factors in real time, and regularly calibrate and maintain the equipment and initialize the parameters before detection; Near-infrared detection module: Select a near-infrared LED or laser as the light source, emit near-infrared light in a specific wavelength range and precisely control the intensity. Receive the reflected light of the material through a high-sensitivity near-infrared detector, convert the optical signal into an electrical signal, and at the same time correlate the intensity of the optical signal with the moisture content through an algorithm, and adjust the parameters of relevant influencing factors according to the changes of materials and the environment; Data processing and decision-making module: Preprocess the electrical signals transmitted by the detector, combine the material characteristics and environmental factor data, use machine learning algorithms to comprehensively analyze and calculate the moisture content of materials in multiple near-infrared spectral characteristic bands, adjust the parameters of the near-infrared detection module in real time according to the detection conditions, and store, analyze and display the data; Intelligent control module: Compare the moisture content analysis result with the preset standard value, generate control instructions according to the deviation situation, adjust the equipment operation parameters, monitor the equipment status in real time, alarm and transmit information in case of failure, and at the same time evaluate and optimize the intelligent algorithm model and the preset standard value according to the equipment operation and moisture control situation; Communication and remote monitoring module: Use wired or wireless communication methods to transmit the detection and equipment status data to the remote monitoring center after encryption and verification processing, so that operators can remotely view, set, control and receive alarm information.

[0007] Further, the near-infrared detection module correlates the intensity of the optical signal with the moisture content through an algorithm and adjusts the relevant influencing factor parameters according to the changes in the material and the environment. The algorithm formula is as follows: , where is the intensity of the near-infrared light reflected by the material received by the detector, is the initial light intensity emitted by the near-infrared light source, is the absorption coefficient of the material for near-infrared light, which is related to the type, characteristics, and moisture content of the material, is the concentration of moisture in the material, is the path length of the light propagating in the material, is the actual wavelength of the near-infrared light, is the reference wavelength value, which is determined according to the absorption characteristics of different materials for near-infrared light and experiments, is the coefficient of the influence of temperature on light absorption and propagation, which reflects the degree of influence of temperature changes on the interaction between light and the material, is the current ambient temperature value, is the reference temperature value, is the coefficient of the influence of humidity on light absorption and propagation, which reflects the degree of influence of temperature changes on the interaction between light and the material, is the current ambient humidity value, which is measured by the environmental factor real-time sensing module, is the reference humidity value.

[0008] Furthermore, the data processing and decision-making module uses machine learning algorithms to comprehensively analyze and calculate the moisture content of the material for multiple near-infrared spectral characteristic bands. The algorithm formula is as follows: , where is the finally calculated moisture content of the material, is the number of selected near-infrared spectral characteristic bands, which is determined according to the material characteristics and detection accuracy requirements, is the th band weight coefficient, which reflects the importance of different bands for moisture content detection, is the th band measured light absorption value, is the th band background absorption value, which is used to eliminate the interference of ambient light and other non-moisture factors, is the comprehensive correction function, considering the near-infrared light wavelength , ambient temperature , ambient humidity , material color , material particle size on light absorption, is the th band time correlation coefficient.

[0009] Furthermore, the data processing and decision-making module adjusts the parameters of the near-infrared detection module in real time according to the detection conditions, and its adjustment formula is: , where is the adjusted intensity of the near-infrared light source, is the current intensity of the near-infrared light source, is the adjustment coefficient related to the particle size of the material, which reflects the influence degree of the particle size of the material on the light source intensity requirement, is the diameter of the material particles measured currently, is the average reference value of the diameter of this kind of material particles, is the adjustment coefficient related to the environmental humidity, which reflects the influence degree of the environmental humidity on the light source intensity requirement, is the environmental humidity value measured currently, is the standard environmental humidity reference value, which is determined according to the production process and detection requirements, is the adjustment coefficient related to the color of the material, which reflects the influence degree of the color of the material on the light source intensity requirement, is the value of the material color parameter measured currently, is the average reference value of this kind of material color parameter.

[0010] Furthermore, the is the diameter of the material particles measured currently, and its calculation formula is: , where represents the diameter of the material particles, is the comprehensive constant related to the measurement system, which includes the comprehensive influence coefficient of the optical path system, the detector characteristics and the refraction factor of the laser when propagating in the air on the measurement, is the wavelength of the laser, which is determined by the laser light source emitted by the laser particle size measuring device, is the angle of laser scattering or diffraction, which is obtained by measuring the angular distribution of the scattered or diffracted light by the detector, is the coefficient related to the propagation stability of the laser in the air.

[0011] Furthermore, the data processing and decision-making module adjusts the parameters of the near-infrared detection module in real time according to the detection conditions. When encountering materials with large particles, the light source intensity is increased to ensure that there is sufficient light energy irradiating the surface of the materials. At the same time, according to the environmental humidity and temperature factors, the wavelength distribution of the light source is finely adjusted to make the wavelength of the light source more in line with the water absorption characteristics of the materials in the current environment. When the environmental humidity changes greatly, the baseline of the near-infrared spectrum is automatically corrected to eliminate the influence of humidity changes on the spectrum, and according to the color and particle characteristics of the materials, the spectrum acquisition range is further optimized to ensure that the acquired spectrum data can accurately reflect the moisture content of the materials.

[0012] Further, the intelligent control module compares the moisture content analysis result with a preset standard value, generates a control instruction according to the deviation situation, and its judgment formula is: , according to the currently calculated moisture content of the material and the preset lower limit standard value of the moisture content of the material , the upper limit standard value and the buffer coefficient , generates a corresponding control instruction output value , when , start the equipment for increasing moisture, when , do not perform control operations, when , start the equipment for reducing moisture.

[0013] On the other hand, a method for near-infrared online intelligent detection of the moisture content of conveyor belt materials, the method includes the following specific steps: Information collection and parameter adjustment: Start the conveyor belt to transport materials. The material and environment information collection module quickly and accurately identifies the particle size, color, and shape characteristics of the materials and real-time monitors the changes in environmental temperature, humidity, and air pressure factors, transmits the information to the data processing and decision-making module, and automatically adjusts the parameters of the near-infrared detection module according to the information using the built-in algorithm to meet the detection requirements; Material detection and data processing: The near-infrared detection module after parameter adjustment emits near-infrared light to irradiate the material, and the detector receives the reflected light and converts it into an electrical signal and transmits it to the data processing and decision-making module. After preprocessing the electrical signal, according to the near-infrared spectrum and moisture content relationship model that comprehensively considers material characteristics and environmental factors, calculate the moisture content of the material and analyze and judge whether it is within the standard range; Intelligent control and feedback: If the moisture content obtained by the data processing and decision-making module exceeds the standard range, the intelligent control module generates a control instruction according to the deviation situation. At the same time, the communication and remote monitoring module transmits the detection result, material characteristics, environmental factors, and control information to the remote monitoring center to achieve remote feedback; Continuous monitoring and optimization: Continuously detect the moisture content of the materials on the conveyor belt, continuously update the material characteristics, environmental factors, and moisture content data, and evaluate and optimize the intelligent algorithm model and preset standard value of the data processing and decision-making module according to the actual detection situation.

[0014] Compared with the prior art, the near-infrared online intelligent detection system and method for the moisture content of conveyor belt materials have the following beneficial effects: 1. By integrating the functions of pre-identifying material characteristics and real-time sensing of environmental factors, the present invention uses intelligent algorithms to dynamically adjust the parameters of the near-infrared detection module to adapt to the changes in different material characteristics and environmental factors. This enables the detection system to maintain high-precision and high-stability moisture detection in the complex and changeable industrial field environment, even in the face of various material characteristics. In addition, the system also has an intelligent control function, which can automatically process the materials according to the detection results, thereby reducing manual intervention and labor intensity. This not only helps to improve the automation level of the production process but also ensures the stability and reliability of the production process.

[0015] 2. By integrating the material and environmental information collection module and the near-infrared detection module, the present invention realizes real-time on-line detection of material moisture. The system can quickly respond to the changes in the moisture content of the material, providing timely and accurate moisture data for the production process. This real-time nature not only overcomes the lag of traditional off-line detection methods but also enables production personnel to quickly adjust production parameters according to the real-time data, thereby optimizing the production process, reducing the production of unqualified products, and significantly improving production efficiency and product quality.

[0016] Other advantages, objectives, and features of the present invention will be described to some extent in the subsequent specification, and to some extent, will be obvious to those skilled in the art based on the study of the following text, or can be learned from the practice of the present invention. BRIEF DESCRIPTION OF THE DRAWINGS

[0017] To more clearly illustrate the technical solutions in the embodiments of the present invention or in the prior art, the following will briefly introduce the drawings required for use in the description of the embodiments or the prior art. Obviously, the following-described drawings are only some embodiments of the present invention, and those of ordinary skill in the art can obtain other drawings without creative efforts based on these drawings.

[0018] Figure 1 It is a schematic structural diagram of an on-line intelligent near-infrared detection system for the moisture of conveyor belt materials; Figure 2 It is a flowchart of an on-line intelligent near-infrared detection method for the moisture of conveyor belt materials. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0019] To further elaborate on the technical means and effects adopted by the present invention to achieve the predetermined invention objectives, the following will, in conjunction with the accompanying drawings and preferred embodiments, describe in detail the specific embodiments, structures, features, and their effects of the present invention as follows. Embodiment 1

[0020] In grain processing enterprises, the moisture content of paddy has an important impact on the quality, rice yield rate, and storage stability of rice. If the moisture content is too high, the paddy is prone to mildew and deterioration, affecting the taste and quality of rice, and the rice yield rate decreases. If the moisture content is too low, the breakage rate of paddy increases during the processing. Therefore, accurately detecting the moisture content of paddy and controlling it reasonably is crucial.

[0021] At the entrance of the conveyor belt where paddy enters, a high-resolution image acquisition device is installed. Its frame rate and resolution can clearly capture the characteristics such as the shape and color of paddy grains. At the same time, a high-precision laser particle size measuring device is installed, which can accurately measure the size of paddy grains. High-precision temperature, humidity, and air pressure sensors are evenly arranged around the detection area to monitor the environmental parameters in real time. And a near-infrared light source suitable for paddy moisture detection is selected. Its wavelength range is optimized and can be effectively absorbed by the moisture in paddy. It is paired with a highly sensitive detector with a short response time to ensure accurate reception of the reflected light signal.

[0022] Start the conveyor belt, and the paddy enters the detection area. The material and environmental information acquisition module starts to work. The image acquisition device quickly captures the image information of the paddy. Through image recognition algorithms, the characteristics such as the color and shape of the paddy are analyzed. The laser particle size measuring device emits a laser beam to penetrate the paddy flow, and accurately measures the size of paddy grains according to the principles of laser scattering and diffraction. Its calculation formula is: , where, represents the diameter of the material particles, is a comprehensive constant related to the measurement system, including the comprehensive influence coefficients of factors such as the optical path system, detector characteristics, and refraction of laser when propagating in the air, is the wavelength of the laser, which is determined by the laser light source emitted by the laser particle size measuring device, is the angle of laser scattering or diffraction, which is obtained by measuring the angular distribution of scattered or diffracted light by the detector, is a coefficient related to the stability of laser propagation in the air. At the same time, the temperature, humidity, and air pressure sensors monitor the changes in temperature, humidity, and air pressure of the environment in real time and transmit this information to the data processing and decision-making module in real time. The data processing and decision-making module automatically adjusts the light source intensity of the near-infrared detection module according to the received paddy characteristics information and environmental parameters. Its adjustment formula is: , where, is the adjusted near-infrared light source intensity, is the current near-infrared light source intensity, is an adjustment coefficient related to the size of the material particles, reflecting the influence degree of the size of the material particles on the light source intensity requirement, is the diameter of the material particles measured currently, is the average reference value of the diameter of this kind of material particles, is an adjustment coefficient related to the environmental humidity, reflecting the degree of influence of the environmental humidity on the light source intensity requirement. is the currently measured environmental humidity value. is the standard environmental humidity reference value, which is determined according to the production process and detection requirements. is an adjustment coefficient related to the material color, reflecting the degree of influence of the material color on the light source intensity requirement. is the currently measured material color parameter value. is the average reference value of the material color parameters of this type. For example, if it is detected that the rice grains are larger, the light source intensity is appropriately increased. If the environmental humidity changes greatly, the baseline of the near-infrared spectrum is automatically corrected according to the humidity value, and the spectrum acquisition range is optimized.

[0023] The near-infrared detection module emits near-infrared light to irradiate the rice according to the adjusted parameters. The water in the rice absorbs part of the near-infrared light. The detector receives the near-infrared light reflected by the rice and converts the light signal into an electrical signal. The electrical signal is transmitted to the data processing and decision-making module through a shielded cable. This module first preprocesses the electrical signal to remove noise and interference, combines the characteristic information such as the color and particle size of the rice and factors such as the temperature and humidity of the environment, and uses the built-in relationship model between the near-infrared spectrum and the moisture content of the rice to accurately calculate the moisture content of the rice. Its algorithm formula is: , where is the finally calculated moisture content of the material. is the number of selected near-infrared spectrum characteristic bands, which is determined according to the material characteristics and detection accuracy requirements. is the weight coefficient of the th band, reflecting the importance degree of different bands for the moisture content detection. is the light absorption value measured in the th band. is the background absorption value of the th band, which is used to eliminate the interference of environmental light and other non-moisture factors. is the comprehensive correction function, considering the influence of the near-infrared light wavelength , environmental temperature , environmental humidity , material color , material particle size is the time-related coefficient of the

[0024] If the moisture content of paddy exceeds the standard range, the intelligent control module automatically issues instructions according to the deviation. When the moisture content is lower than , the humidifier is started, and the water spray amount of the humidifier is controlled to add water to the paddy. When the moisture content is higher than , the drying equipment is started, and the temperature and wind speed of the drying equipment are adjusted to dry the paddy. At the same time, the communication and remote monitoring module transmits the detection results (including the moisture content of paddy, whether it exceeds the standard range, etc.), the equipment operation status (the start-stop status, operation parameters, etc. of the humidifier or drying equipment), and other information to the central control room in real time. The operator can view these information in real time through the monitoring software in the central control room. If necessary, the operation of the equipment can also be manually intervened.

[0025] The system continuously detects the moisture content of the paddy on the conveyor belt, continuously updates the paddy characteristic information, environmental factor data, and moisture content data, and evaluates and optimizes the intelligent algorithm model and preset standard values in the data processing and decision-making module according to the long-term detection data and actual production situation. For example, if it is found that there is a deviation between the detection result and the actual paddy moisture situation during a certain period, the model is optimized by increasing the sample quantity, improving the algorithm, etc. to improve the accuracy and stability of the detection. At the same time, the equipment of each module of the system is regularly maintained and calibrated to ensure the normal operation of the system.

[0026] Through the application of the present invention, the grain processing enterprise can accurately detect the moisture content of paddy in real time, adjust the moisture content in time, effectively improve the quality and rice yield of rice, reduce the losses caused by improper moisture content, and at the same time improve the automation level and production efficiency of the production process. Embodiment 2

[0027] During the production process of plastic particles, the moisture content directly affects the quality and performance of plastic particles. Too high moisture content will cause defects such as bubbles and deformation in the plastic particles during the processing, affecting the quality of plastic products. Too low moisture content will increase the brittleness of plastic particles, which is not conducive to processing and forming. Therefore, accurately controlling the moisture content of plastic particles is the key to ensuring product quality.

[0028] Install a high-resolution and high-frame-rate image acquisition device at the starting position of the plastic particle conveyor belt, which can quickly and clearly capture the characteristic information of plastic particles such as shape and color. At the same time, a high-precision laser particle size measuring device is equipped, and its measurement accuracy can meet the detection requirements of the tiny particle size of plastic particles to accurately measure the size of plastic particles. In the production workshop, according to the spatial layout and environmental characteristics, a number of high-precision temperature, humidity and air pressure sensors are reasonably arranged to ensure that the environmental changes can be comprehensively and real-time monitored. Select a near-infrared light source that matches the characteristics of plastic particles. The wavelength range of the emitted near-infrared light is strictly screened and optimized, and can be effectively absorbed by the moisture in the plastic particles. It is paired with a highly sensitive and highly stable detector, which can quickly and accurately convert the received reflected light signal into an electrical signal.

[0029] Start the conveyor belt, and the plastic particles enter the detection area. The material and environmental information acquisition module quickly starts to work. The image acquisition device quickly captures the images of the plastic particles, and accurately analyzes the characteristics such as the color and shape of the plastic particles through advanced image recognition algorithms. The laser particle size measuring device emits a laser beam to penetrate the plastic particle flow. Based on the principles of light scattering and diffraction, the size of the plastic particles is accurately measured. Its calculation formula is: , where, represents the diameter of the material particles, is a comprehensive constant related to the measurement system, including the comprehensive influence coefficients of factors such as the optical path system, detector characteristics, and refraction of the laser during propagation in the air on the measurement, is the wavelength of the laser, which is determined by the laser light source emitted by the laser particle size measuring device, is the angle of laser scattering or diffraction, which is obtained by measuring the angular distribution of the scattered or diffracted light by the detector, is a coefficient related to the stability of the laser propagation in the air. At the same time, the temperature, humidity, and air pressure sensors real-time monitor the changes in temperature, humidity, and air pressure in the production workshop, and transmit these environmental parameters to the data processing and decision-making module in real-time. The data processing and decision-making module automatically adjusts the parameters of the near-infrared detection module according to the received plastic particle characteristic information and environmental parameters by using the built-in intelligent algorithm, , where, is the adjusted near-infrared light source intensity, is the current near-infrared light source intensity, is an adjustment coefficient related to the size of the material particles, which reflects the influence degree of the size of the material particles on the light source intensity requirement, is the diameter of the material particles obtained by the current measurement, is the average reference value of the diameter of this kind of material particles, is an adjustment coefficient related to the environmental humidity, which reflects the influence degree of the environmental humidity on the light source intensity requirement, is the currently measured ambient humidity value, is the standard ambient humidity reference value, which is determined according to the production process and detection requirements, is the adjustment coefficient related to the material color, which reflects the influence degree of the material color on the light source intensity requirement, is the currently measured material color parameter value, is the average reference value of the material color parameters of this kind. For example, the light source intensity is adjusted according to the color depth of plastic particles, the detection frequency is adjusted according to the particle size, and the wavelength distribution and spectral acquisition range of the light source are finely adjusted according to the changes of ambient temperature and humidity, etc., to adapt to different detection conditions.

[0030] The near-infrared detection module emits optimized near-infrared light to irradiate plastic particles according to the adjusted parameters. The moisture in the plastic particles absorbs the near-infrared light of the corresponding wavelength. The detector receives the near-infrared light reflected by the plastic particles and converts it into an electrical signal. The electrical signal is stably transmitted to the data processing and decision-making module through a shielded cable. This module first performs preprocessing operations on the electrical signal to effectively remove noise and interference signals. Then, combining the characteristic information such as the color and particle size of the plastic particles and the factors such as the temperature and humidity of the environment, using the relationship model between the high-precision near-infrared spectrum and the moisture content of the plastic particles, the moisture content of the plastic particles is accurately calculated. At the same time, the calculated moisture content data is deeply analyzed to judge whether it is within the preset standard range.

[0031] If the moisture content of the plastic particles exceeds the standard range, the intelligent control module immediately automatically issues a control instruction according to the deviation situation. When the moisture content is higher than , the drying equipment is quickly started, and the temperature and wind speed of the drying equipment are accurately controlled according to the deviation size to dry the plastic particles. When the moisture content is lower than , the humidifier is started in time, and the spray amount of the humidifier is accurately controlled to add water to the plastic particles. At the same time, the communication and remote monitoring module transmits the detection results (including the moisture content of the plastic particles, whether it exceeds the standard range, etc.), the equipment operation status (the start-stop status and operation parameters of the drying equipment or humidifier, etc.) and other information to the monitoring system of the production management department in real time. Managers can view this information in real time on the monitoring system. If any abnormal situation is found or the control strategy needs to be adjusted, they can also manually intervene in the operation of the equipment.

[0032] The system continuously detects the moisture content of plastic particles on the conveyor belt, and constantly updates the information on the characteristics of plastic particles, environmental factor data, and moisture content data. According to the long-term detection data and the changes in the actual production process, the intelligent algorithm model and preset standard values in the data processing and decision-making module are regularly evaluated and optimized. For example, when the production process changes or the variety of plastic particles is replaced, the model is optimized by collecting more sample data, using more advanced algorithms, etc., to improve the accuracy and adaptability of the detection. At the same time, the equipment of each module of the system is comprehensively maintained and calibrated regularly to ensure the stable operation of the system and the detection accuracy.

[0033] Through the present invention, the chemical raw material production enterprise can monitor the moisture content of plastic particles in real time and accurately, adjust the production process in a timely manner according to the detection results, effectively improve the quality and stability of plastic particles, significantly reduce the waste rate caused by improper moisture content, improve the production efficiency and economic benefits of the enterprise, and enhance the competitiveness of the enterprise in the market.

[0034] The above are only the preferred embodiments of the present invention, and do not impose any form of limitation on the present invention. Although the present invention has been disclosed above with the preferred embodiments, it is not intended to limit the present invention. Any person skilled in the art can make some changes or modifications to equivalent embodiments by using the disclosed technical content within the scope of the technical solution of the present invention. However, as long as it does not depart from the content of the technical solution of the present invention, any brief modifications, equivalent changes and modifications made to the above embodiments based on the technical essence of the present invention still fall within the scope of the technical solution of the present invention.

Claims

1. A near-infrared online intelligent detection system for moisture in conveyor belt materials, characterized in that: The system consists of the following components: Material and environmental information collection module: quickly and accurately identify the color, shape and particle size characteristics of the material before it enters the detection area, and use temperature, humidity and air pressure sensors to monitor changes in environmental factors in real time, and regularly calibrate and maintain the equipment and initialize parameters before detection; Near-infrared detection module: Use near-infrared LED or laser to emit near-infrared light and precisely control the intensity. Receive the reflected light from the material through a highly sensitive near-infrared detector, convert the optical signal into an electrical signal, and use an algorithm to correlate the intensity of the optical signal with the moisture content, and adjust the parameters of related influencing factors according to changes in the material and environment. Data processing and decision-making module: pre-processes the electrical signals transmitted by the detector, combines the material characteristics and environmental factors data, uses machine learning algorithms to comprehensively analyze multiple near-infrared spectral characteristic bands to calculate the moisture content of the material, adjusts the near-infrared detection module parameters in real time according to the detection conditions, and stores, analyzes and displays the data; Intelligent control module: compares the moisture content analysis results with the preset standard values, generates control instructions based on the deviation, adjusts the equipment operating parameters, monitors the equipment status in real time, alarms and transmits information in case of failure, and evaluates and optimizes the intelligent algorithm model and preset standard values ​​based on the equipment operation and moisture control conditions; Communication and remote monitoring module: Use wired or wireless communication to transmit detection and equipment status data to the remote monitoring center, so that operators can remotely control and receive alarm information.

2. According to claim 1, a conveyor belt material moisture near-infrared online intelligent detection system is characterized in that: The near-infrared detection module associates the light signal intensity with the moisture content through an algorithm, and adjusts the parameters of related influencing factors according to changes in materials and environment. The algorithm formula is: ,in, It is the intensity of near-infrared light received by the detector after being reflected by the material. is the initial light intensity emitted by the near-infrared light source, It is the absorption coefficient of the material to near-infrared light, which is related to the type, characteristics and moisture content of the material. is the water concentration in the material, is the distance that light travels in the material. is the actual wavelength of near-infrared light, It is the reference wavelength value, which is determined based on the absorption characteristics of different materials to near-infrared light and experiments. It is the coefficient of the effect of temperature on light absorption and propagation, reflecting the influence of temperature change on the interaction between light and materials. is the current ambient temperature value, is the reference temperature value, It is the coefficient of humidity on light absorption and transmission, reflecting the influence of temperature change on the interaction between light and materials. is the current ambient humidity value, measured by the real-time environmental factor sensing module. is the reference humidity value.

3. A conveyor belt material moisture near-infrared online intelligent detection system according to claim 1, characterized in that: The data processing and decision-making module uses a machine learning algorithm to comprehensively analyze multiple near-infrared spectral characteristic bands to calculate the moisture content of the material. The algorithm formula is: ,in, is the final calculated moisture content of the material, is the number of selected near-infrared spectral characteristic bands, which is determined according to the material characteristics and detection accuracy requirements. It is The weight coefficient of each band reflects the importance of different bands to moisture content detection. It is The light absorption value measured in each band is It is The background absorption value of each band is used to eliminate the interference of ambient light and other non-water factors. is a comprehensive correction function, taking into account the near-infrared wavelength , Ambient temperature , Ambient humidity , Material color , Material particle size The effect on light absorption, It is The time correlation coefficient of each band.

4. A conveyor belt material moisture near-infrared online intelligent detection system according to claim 1, characterized in that: The data processing and decision-making module adjusts the near-infrared detection module parameters in real time according to the detection conditions, and the adjustment formula is: ,in, is the adjusted near-infrared light source intensity, is the current near-infrared light source intensity, It is an adjustment factor related to the material particle size, reflecting the influence of the material particle size on the light source intensity requirement. is the particle diameter of the material currently measured, It is the average reference value of the particle diameter of this material. It is an adjustment coefficient related to ambient humidity, reflecting the influence of ambient humidity on the intensity requirement of light source. is the current measured ambient humidity value, It is the standard ambient humidity reference value, which is determined according to the production process and testing requirements. It is the adjustment coefficient related to the material color, which reflects the influence of the material color on the light source intensity requirement. is the material color parameter value currently measured, It is the average reference value of the color parameters of this material.

5. A conveyor belt material moisture near-infrared online intelligent detection system according to claim 4, characterized in that: Said is the particle diameter of the material currently measured, and its calculation formula is: ,in, Represents the diameter of the material particles, It is a comprehensive constant related to the measurement system, including the comprehensive influence coefficient of the optical path system, detector characteristics and the refraction factor of the laser when it propagates in the air on the measurement. is the wavelength of the laser, which is determined by the laser light source emitted by the laser particle size measuring device. It is the angle of laser scattering or diffraction, which is obtained by measuring the angular distribution of scattered or diffracted light through the detector. It is a coefficient related to the stability of laser propagation in air.

6. A conveyor belt material moisture near-infrared online intelligent detection system according to claim 1, characterized in that: The data processing and decision-making module adjusts the parameters of the near-infrared detection module in real time according to the detection conditions. When encountering materials with large particles, the light source intensity is increased to ensure that sufficient light energy is irradiated to the surface of the material. At the same time, according to environmental humidity and temperature factors, the wavelength distribution of the light source is fine-tuned to make the wavelength of the light source more consistent with the moisture absorption characteristics of the material in the current environment. When the environmental humidity changes significantly, the baseline of the near-infrared spectrum is automatically corrected to eliminate the impact of humidity changes on the spectrum. In addition, according to the color and particle characteristics of the material, the spectrum collection range is optimized to ensure that the collected spectral data can accurately reflect the moisture content of the material.

7. A conveyor belt material moisture near-infrared online intelligent detection system according to claim 1, characterized in that: The intelligent control module compares the moisture content analysis result with the preset standard value and generates a control instruction according to the deviation. The judgment formula is: , based on the current calculated moisture content of the material Compared with the pre-set material moisture content lower limit standard value , upper limit standard value And the buffer factor The comparison result generates the corresponding control instruction output value ,when When the moisture-increasing device is started, When no control operation is performed, When the water content is reduced, start the water reduction equipment.

8. A method for online near-infrared intelligent detection of moisture in conveyor belt materials, the method being applicable to a system for online near-infrared intelligent detection of moisture in conveyor belt materials as claimed in any one of claims 1 to 7, characterized in that: The method comprises the following specific steps: Information collection and parameter adjustment: Start the conveyor belt to transport materials. The material and environmental information collection module quickly and accurately identifies the particle size, color and shape characteristics of the material and monitors the changes in environmental temperature, humidity and air pressure factors in real time. The information is transmitted to the data processing and decision-making module, and the built-in algorithm is used to automatically adjust the parameters of the near-infrared detection module according to the information to meet the detection requirements; Material detection and data processing: The near-infrared detection module after parameter adjustment emits near-infrared light to illuminate the material, and the detector receives the reflected light and converts it into an electrical signal to be transmitted to the data processing and decision-making module. After pre-processing the electrical signal, the moisture content of the material is calculated based on the relationship model between the near-infrared spectrum and the moisture content that comprehensively considers the material characteristics and environmental factors, and analyzes and determines whether it is within the standard range; Intelligent control and feedback: If the moisture content obtained by the data processing and decision-making module exceeds the standard range, the intelligent control module generates control instructions based on the deviation. At the same time, the communication and remote monitoring module transmits the test results, material characteristics, environmental factors and control information to the remote monitoring center to achieve remote feedback; Continuous monitoring and optimization: Continuously test the moisture content of materials on the conveyor belt, constantly update the material characteristics, environmental factors and moisture content data, and evaluate and optimize the intelligent algorithm model and preset standard values ​​of the data processing and decision-making module based on the actual test results.

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