Feeding device for water conservancy construction engineering

Abstract

The invention is suitable for the technical field of water conservancy construction engineering equipment, and provides a water conservancy construction engineering feeding device which comprises a base, the base is connected with a first belt conveyor, and a second belt conveyor is arranged on the upper side of the first belt conveyor in parallel. The two ends of the second belt conveyor are connected with the base through electric lifting rods, a protection plate is arranged on the upper side of the first belt conveyor, and the protection plate is fixedly connected with the base; the invention further comprises a clamping distance optimization system. According to the invention, the machine operation state, the material physical characteristics and the environmental parameters are monitored in real time through the clamping distance optimization system, and the target spacing is dynamically output based on the multi-dimensional evaluation model, so that the intelligent adjustment of the clamping spacing is realized.

Description

Technical Field

[0001] This invention belongs to the technical field of water conservancy construction equipment, and particularly relates to a material supply device for water conservancy construction projects. Background Technology

[0002] In water conservancy construction projects, material feeding devices play a crucial role in the continuous and stable delivery of building materials such as concrete and aggregates to the work surface. Traditional single-belt conveyors commonly suffer from unstable conveying and material spillage when handling highly viscous, easily scattered, or irregularly shaped materials. To improve this problem, material feeding devices composed of dual-belt conveyors are widely used, which form a clamping zone by arranging the upper and lower belts in parallel to hold the materials. However, the spacing between the upper and lower conveyor belts in existing dual-belt feeding devices is usually fixed or can only be adjusted manually by machinery. This adjustment method is difficult to cope with complex and ever-changing construction conditions.

[0003] Existing equipment lacks a real-time monitoring and response mechanism for equipment health status. The construction environment also experiences dynamic fluctuations; for example, the tilt angle of the feeding device changes with terrain, and the speed matching of the upper and lower conveyors fluctuates due to differences in the drive systems. These factors significantly increase the risk of material slippage, but existing technologies have failed to establish an effective risk assessment model. Operators rely on experience to manually adjust the spacing, resulting in low adjustment accuracy, delayed response, and an inability to comprehensively consider multi-dimensional parameters such as material characteristics, equipment status, and operating conditions, leading to decreased conveying efficiency and increased material loss. Although some equipment uses electric lifting rods for spacing adjustment, their control logic is simple, typically relying only on single-point feedback based on material flow or pressure signals, failing to construct an intelligent collaborative control system that integrates material physical characteristics, conveying equipment health status, and real-time operating conditions.

[0004] Therefore, developing a hydraulic construction material supply device capable of automatically, accurately, and adaptively optimizing the clamping spacing to minimize damage and risk while ensuring stable material transport has become an urgent need for current technological development. Existing technologies urgently require improvement to address these issues. Summary of the Invention

[0005] The purpose of this invention is to provide a material supply device for water conservancy construction projects, which aims to solve the problem that existing devices lack a real-time monitoring and response mechanism for the health status of the equipment.

[0006] The present invention is implemented as follows: a material supply device for water conservancy construction projects includes a base, a first belt conveyor connected to the base, a second belt conveyor arranged parallel to the upper side of the first belt conveyor, both ends of the second belt conveyor being connected to the base via electric lifting rods, and a protective plate provided on the upper side of the first belt conveyor, the protective plate being fixedly connected to the base.

[0007] It also includes a clamping distance optimization system, which includes:

[0008] Machine condition assessment unit: Based on the belt surface temperature, average belt wear, actual belt tension and vibration intensity, a machine condition assessment model is constructed. The above parameters of the first belt conveyor and the second belt conveyor are substituted into the machine condition assessment model, and the output machine condition index of the first belt conveyor and the second belt conveyor are output respectively.

[0009] Material property assessment unit: Based on the static friction coefficient between the material and the belt, the material density, and the material size during material conveying, a material property assessment model is constructed, and the material property index is output;

[0010] Material slippage risk assessment unit: Under the influence of the speed matching degree between the first belt conveyor and the second belt conveyor, based on the material characteristic index, the smaller value of the machine condition index of the first belt conveyor and the second belt conveyor at the same time, and the tilt angle of the second belt conveyor, a slippage risk assessment model is constructed, and the material slippage risk index is output.

[0011] Spacing adjustment unit: Based on the material deformation during material conveying and discharge, the material slippage risk index, and the rated spacing between the first belt conveyor and the second belt conveyor, a spacing optimization adjustment model is constructed, and the target spacing is output;

[0012] The control unit is capable of receiving the target spacing output from the spacing adjustment unit and controlling the spacing between the first belt conveyor and the second belt conveyor by controlling the extension and retraction length of all electric lifting rods.

[0013] A further technical solution involves substituting the belt surface temperature, average belt wear, actual belt tension, and vibration intensity into a maximum-minimum normalization formula for normalization, thereby generating the belt surface temperature index, average belt wear index, belt tension index, and belt vibration intensity index in sequence.

[0014] Further technical solutions, in the machine condition assessment model:

[0015] The machine condition index is obtained by subtracting a weighted sum from 1. This weighted sum includes the belt surface temperature index, the belt average wear index, the absolute value of the difference between the belt tension index and the optimal belt tension index, and the belt vibration intensity index. Each index is multiplied by its corresponding weighting coefficient. The machine condition index is used to reflect the current operational stability of the first belt conveyor and the second belt conveyor.

[0016] A further technical solution involves substituting the static friction coefficient between the material and the belt, the material density, and the material size into the maximum-minimum normalization formula for normalization, and then generating the static friction index, the material density index, and the material size index in sequence.

[0017] Further technical solutions, in the material property evaluation model:

[0018] The material property index is obtained by linearly weighting the static friction index and the material density index, and then adding it to a material size index processed by a nonlinear function; the processing of the material size index includes: inputting it into an exponential decay function with the index as the variable for transformation.

[0019] A further technical solution involves substituting the actual tilt angle of the second belt conveyor into the maximum-minimum normalization formula for normalization processing, and generating a machine tilt angle index.

[0020] In the slip risk assessment model: the speed matching degree is used as a multiplicative attenuation factor and applied to a natural exponential function to calculate the material slip risk index; wherein, the exponential part of the natural exponential function is the negative value of the weighted sum of the following three items: the smaller value of the machine condition index of the first belt conveyor and the second belt conveyor at the same time, the material characteristic index, and the machine tilt angle index.

[0021] A further technical solution involves substituting the material deformation during material conveying and discharge into a maximum-minimum normalization formula for normalization, and generating a material deformation index; in the spacing optimization adjustment model:

[0022] Based on the rated spacing, the target spacing is calculated using an adjustment coefficient. The adjustment coefficient is obtained by subtracting a positive adjustment term composed of the material deformation index from a negative adjustment term composed of the material slip risk index after being triggered by a Sigmoid function threshold. The input value of the Sigmoid function is the comparison result between the material slip risk index and a preset risk threshold.

[0023] Compared with the prior art, the beneficial effects of the present invention are as follows:

[0024] This application uses a clamping distance optimization system to monitor the machine's operating status, material physical properties, and environmental parameters in real time, and dynamically outputs the target distance based on a multi-dimensional evaluation model, thereby realizing intelligent adjustment of the clamping distance. It has the advantages of achieving automatic, precise, and adaptive adjustment of the clamping distance, effectively coping with differences in material properties and changes in equipment operating status, ensuring stable material conveying, reducing material spillage and loss, and improving the material supply efficiency of water conservancy construction.

[0025] This application achieves real-time dynamic optimization of clamping spacing by constructing a collaborative mechanism that integrates machine condition assessment, material characteristic assessment, material slippage risk assessment, and spacing adjustment. Compared to the fixed or coarse adjustment methods in existing technologies, this mechanism comprehensively considers multi-dimensional parameters such as material physical characteristics, real-time equipment status, and operating conditions, effectively solving the problem of unstable conveying caused by poor material adaptability, neglect of equipment status influence, and insufficient dynamic response to operating conditions. As a preferred implementation, the system employs a multi-source data-driven model-based evaluation method to ensure simultaneous optimization of clamping effect and operational safety during material conveying.

[0026] This application can accurately quantify the impact of dynamic changes in the tilt angle of the second belt conveyor 3 on the risk of material slippage, enabling the risk assessment model to respond to changes in working conditions in real time. This ensures that the target spacing output by the spacing adjustment unit effectively adapts to the tilted conveying scenario, significantly reducing the risk of material slippage or falling during tilted conveying, and improving the operational stability and material conveying reliability of the hydraulic construction material supply device.

[0027] This application achieves an objective and quantitative assessment of equipment health status, avoiding the subjectivity and uncertainty of traditional methods that rely on human experience. The machine condition assessment model, through a rigorous mathematical structure, integrates multi-dimensional state parameters into a single dimensionless index, significantly improving the accuracy and adaptability of equipment condition assessment. This scheme ensures that the machine condition index accurately reflects the real-time operating status of the first belt conveyor 2 and the second belt conveyor 3, providing a reliable data foundation for material slippage risk assessment. This supports precise dynamic adjustment of the clamping distance, effectively reducing the risk of material spillage caused by abnormal equipment operation while ensuring stable material conveying. Attached Figure Description

[0028] Figure 1 This is a schematic diagram of the clamping distance optimization system in this invention;

[0029] Figure 2 This is a schematic diagram of the material supply device for water conservancy construction projects in this invention;

[0030] Figure 3 This is a schematic diagram showing the connection between the base and the first belt conveyor;

[0031] Figure 4 This is a schematic diagram showing the connection between the second belt conveyor and the electric lifting rod.

[0032] In the attached diagram: 1. Base; 2. First belt conveyor; 3. Second belt conveyor; 4. Electric lifting rod; 5. Protective plate. Detailed Implementation

[0033] To make the objectives, technical solutions, and advantages of this invention clearer, the invention will be further described in detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative and not intended to limit the invention.

[0034] The specific implementation of the present invention will be described in detail below with reference to specific embodiments.

[0035] like Figures 1-4 As shown, a material supply device for water conservancy construction engineering provided in an embodiment of the present invention includes a base 1, a first belt conveyor 2 connected to the base 1, a second belt conveyor 3 arranged parallel to the upper side of the first belt conveyor 2, both ends of the second belt conveyor 3 being connected to the base 1 through an electric lifting rod 4, and a protective plate 5 provided on the upper side of the first belt conveyor 2, the protective plate 5 being fixedly connected to the base 1;

[0036] It also includes a clamping distance optimization system, which includes:

[0037] Machine condition assessment unit: Based on the belt surface temperature, average belt wear, actual belt tension and vibration intensity, a machine condition assessment model is constructed. The above parameters of the first belt conveyor 2 and the second belt conveyor 3 are substituted into the machine condition assessment model, and the output machine condition index of the first belt conveyor 2 and the second belt conveyor 3 are output respectively.

[0038] Material property assessment unit: Based on the static friction coefficient between the material and the belt, the material density, and the material size during material conveying, a material property assessment model is constructed, and the material property index is output;

[0039] Material slippage risk assessment unit: Under the influence of the speed matching degree between the first belt conveyor 2 and the second belt conveyor 3, a slippage risk assessment model is constructed based on the material characteristic index, the machine condition index of the first belt conveyor 2 or the second belt conveyor 3 (selecting the smaller value of the machine condition index of the first belt conveyor 2 and the second belt conveyor 3 at the same moment), and the tilt angle of the second belt conveyor 3, and the material slippage risk index is output.

[0040] Spacing adjustment unit: Based on the material deformation during material conveying and discharge, the material slippage risk index, and the rated spacing between the first belt conveyor 2 and the second belt conveyor 3, a spacing optimization adjustment model is constructed, and the target spacing is output;

[0041] The control unit is capable of receiving the target spacing output by the spacing adjustment unit and controlling the spacing between the first belt conveyor 2 and the second belt conveyor 3 by controlling the extension and retraction length of all electric lifting rods 4.

[0042] In this embodiment, base 1 provides a basic support structure. A first belt conveyor 2 is fixedly connected to base 1, and a second belt conveyor 3 is arranged parallel to it on its upper side. Both ends of the second belt conveyor 3 are connected to base 1 via electric lifting rods 4, thereby achieving adjustable spacing. A protective plate 5 is provided on the upper side of the first belt conveyor 2, and the protective plate 5 is fixedly connected to base 1 to prevent material spillage. The core of this device lies in the integrated clamping distance optimization system, whose workflow is as follows:

[0043] During material conveying, the machine condition assessment unit collects real-time data on the belt surface temperature, average belt wear, actual belt tension, and vibration intensity of the first belt conveyor 2 and the second belt conveyor 3. Based on a preset machine condition assessment model, it calculates and outputs the machine condition index for each of the two conveyors. Simultaneously, the material characteristic assessment unit acquires the static friction coefficient between the material and the belt, the material density, and the material size parameters. After processing these parameters using a material characteristic assessment model, it outputs the material characteristic index. The material slippage risk assessment unit, considering the speed matching between the first belt conveyor 2 and the second belt conveyor 3, combines the material characteristic index with... The material slip risk index is generated using a slip risk assessment model based on the smaller value of the machine status index of the two conveyors at the same time (to reflect the weakest link in the equipment status) and the tilt angle of the second belt conveyor 3. The spacing adjustment unit further calculates the target spacing based on the material deformation during material conveying and discharge, the material slip risk index, and the rated spacing between the first belt conveyor 2 and the second belt conveyor 3 through a spacing optimization adjustment model. The control unit receives the target spacing command and drives the extension and retraction of all electric lifting rods 4 to precisely adjust the height position of the second belt conveyor 3, thereby dynamically optimizing the clamping spacing between the two conveyors.

[0044] The specific implementation of the clamping distance optimization system can be refined according to actual working conditions. For example, the control unit can be implemented using a programmable logic controller (PLC), such as the Siemens S7-1200 series. It is configured to receive output data from each evaluation unit and perform model calculations, while simultaneously connecting to the drive motor of the electric lifting rod 4 via signal lines to ensure the real-time performance and accuracy of the spacing adjustment through closed-loop control. Furthermore, the input parameters of the machine condition evaluation unit can be obtained through a distributed sensor network. The belt surface temperature is monitored by an infrared thermometer, the average belt wear is estimated by measuring belt thickness changes using a laser displacement sensor, the actual belt tension is directly collected by a tension sensor, and the vibration intensity is recorded and analyzed by an acceleration sensor. These data are continuously transmitted to the control unit for model calculations. The parameters of the material characteristic evaluation unit can be obtained by relying on a material characteristic detection device installed at the feed inlet. For example, the static friction coefficient of the material can be measured using a combination of a pressure sensor and a vision recognition system, the material density can be obtained using a densitometer, and the material size distribution can be analyzed using image processing technology, thus providing reliable input for the model.

[0045] Therefore, the clamping distance optimization system achieves adaptive adjustment of the clamping distance through multi-dimensional data fusion and model-based decision-making. Specifically, when conveying easily deformable materials, the system automatically increases the distance to avoid excessive compression based on the balance between material deformation and slippage risk index; when conveying smooth materials or when the inclination angle of the second belt conveyor 3 increases, the system decreases the distance to enhance clamping force and prevent material slippage. This technical solution effectively solves the problem that the clamping distance in traditional devices cannot be dynamically optimized according to material characteristics, real-time equipment status, and operating conditions, significantly improving the stability of material conveying, reducing the risk of material damage or slippage caused by improper clamping, and thus ensuring the continuity and reliability of the material supply process in water conservancy construction.

[0046] In a preferred embodiment of the present invention, the belt surface temperature, average belt wear, actual belt tension, and vibration intensity are successively substituted into the maximum-minimum normalization formula for normalization processing, and the belt surface temperature index, average belt wear index, belt tension index, and belt vibration intensity index are generated in sequence.

[0047] As a specific implementation method, the solution of this application is implemented as follows: At the water conservancy construction site, infrared temperature sensors installed on the first belt conveyor 2 and the second belt conveyor 3 are used to collect belt surface temperature data in real time. A laser rangefinder is used to monitor belt thickness changes to calculate the average belt wear. A tension sensor is used to obtain the actual belt tension information, and a triaxial accelerometer is used to record the vibration intensity. All collected raw data are transmitted to the control unit. The control unit determines the maximum and minimum value ranges of each parameter based on historical operating data. Then, the maximum-minimum value normalization formula is applied to convert each parameter into a dimensionless exponent in the range [0,1]. Finally, the belt surface temperature index, belt average wear index, belt tension index, and belt vibration intensity index are generated for use by the machine condition assessment model.

[0048] Through the above technical solution, this application effectively solves the problem of model evaluation deviation caused by the difference in dimensions and numerical range of multi-source heterogeneous parameters, ensuring that the machine condition evaluation model can truly and accurately reflect the operating status of the first belt conveyor 2 and the second belt conveyor 3, and providing reliable data support for subsequent clamping distance optimization.

[0049] As a preferred embodiment of the present invention, the machine condition assessment model is as follows:

[0050] ;

[0051] in The influence of surface temperature is weighted. The wear level affects the weight. The weighting is determined by the effect of tension. The weighting is influenced by the intensity of vibration. , , , as well as All greater than ; This refers to the surface temperature index of the belt. The average wear index of the belt. This refers to the belt tension index. The optimal belt tension index, determined based on production experience, is dimensionless. The belt vibration intensity index. This is the machine condition index.

[0052] In this embodiment, the machine condition assessment model refers to a mathematical expression that weights and fuses normalized equipment condition parameters. It can be implemented using a function structure combining linear combination and nonlinear correction, aiming to transform multi-dimensional equipment condition data into a single quantitative indicator; surface temperature influences the weight. This refers to a coefficient characterizing the degree of influence of temperature parameters on equipment condition. It can be determined using regression analysis methods trained on historical fault data, with the aim of dynamically adjusting the contribution ratio of temperature factors in the assessment; tension index. With the optimal tension index The absolute difference term refers to a nonlinear measure that reflects the degree to which the actual tension deviates from the ideal state. It can be characterized by a piecewise linear function or an exponential decay function to represent the influence of tension deviation on equipment stability. The purpose is to accurately capture the nonlinear negative effects on belt operation when the tension deviates from the optimal value.

[0053] Specifically, the solution in this application is to improve the belt surface temperature index. Belt average wear index Belt tension index and belt vibration intensity index As input parameters, based on preset weighting coefficients , , as well as A weighted summation calculation is performed, where the tension index... With the best value of experience The absolute difference term is specifically used to quantify the degree to which the tension deviates from the ideal state. Since the effect of belt tension on operational stability is non-linear, this design avoids the limitations of a simple linear relationship. The machine condition index is then calculated by subtracting the weighted sum from 1. The specification is in a dimensionless range of 0 to 1, with higher values ​​indicating better equipment health. This mathematical structure allows the contribution ratio of each state parameter to be dynamically adjusted according to actual operating conditions, while ensuring the intuitiveness and comparability of the output indicators. This provides an objective and accurate quantitative basis for subsequent material slip risk assessment of equipment status.

[0054] In one specific implementation, the machine condition assessment model of this application can be executed by a control unit in the clamping distance optimization system. This control unit can specifically employ a microcontroller with an ARM Cortex-M7 architecture to implement data processing functions. In actual operation, the microcontroller periodically receives sensor data from the first belt conveyor 2 and the second belt conveyor 3, including belt surface temperature collected by an infrared thermometer, belt wear measured by a laser displacement sensor, belt tension detected by a tension meter, and vibration signals recorded by an acceleration sensor. This raw data, after normalization, is input into a pre-stored machine condition assessment model, where weighting coefficients... , , as well as It can be dynamically loaded via configuration file based on the equipment type. For example, when conveying highly viscous materials, the system automatically calls a weight combination optimized for highly viscous materials, where... The value was set to a relatively high level to highlight the impact of wear on the clamping effect. The calculated machine condition index... It is then transmitted to the material slippage risk assessment unit to participate in the subsequent spacing optimization decision-making process.

[0055] In a preferred embodiment of the present invention, the static friction coefficient between the material and the belt, the material density, and the material size are successively substituted into the maximum-minimum normalization formula for normalization processing, and the static friction index, the material density index, and the material size index are generated in sequence.

[0056] In this embodiment, as a specific implementation, the material property evaluation unit of this application can be specifically implemented by an embedded microcontroller. The microcontroller adopts the ARM Cortex-M7 architecture and is connected to the material property sensor array through a signal conditioning circuit. It collects the original analog signals of the static friction coefficient between the material and the belt, the material density, and the material size in real time, and executes a normalization algorithm in the firmware to generate the corresponding index. The sensor array includes a strain gauge force measurement module for measuring friction characteristics, a gamma-ray transmission sensor for density detection, and a laser profile scanner for size recognition.

[0057] Through the above technical solution, this application effectively solves the model input deviation problem caused by inconsistent dimensions of material parameters, provides a standardized basis for material characteristic evaluation, and thus improves the accuracy of slip risk prediction and the adaptive capability of the spacing adjustment system.

[0058] As a preferred embodiment of the present invention, the material property evaluation model is as follows:

[0059] ;

[0060] in The influence of static friction on the weight, Density affects weights. The size affects the weight. , as well as All greater than ; , The static friction index of the material. The density index of the material. Material size index, To prevent small constants from becoming zero, This is a material characteristic index.

[0061] In this embodiment, the static friction influence weight This refers to the parameter used to adjust the contribution of the static friction coefficient between the material and the belt in the overall evaluation. It can be a fixed constant set based on the equipment manufacturer's recommended value or a value dynamically adjusted through on-site calibration. The purpose is to ensure that materials with high static friction coefficients receive sufficient stability weight during clamping; density affects the weight. This refers to a parameter used to quantify the impact of material density on slip risk. It can be implemented using preset weight values ​​or values ​​matched to a material type database. Its purpose is to accurately reflect the increased slip risk of high-density materials during inclined conveying due to increased inertia; size influence weight. This refers to a parameter used to handle the nonlinear effects of material size. It can be achieved using empirically set values ​​or values ​​optimized based on historical slip data analysis. Its purpose is to distinguish between the low slip risk of small-sized materials (such as fine sand) due to their high fluidity and the high slip risk of large-sized materials (such as stones) due to their tendency to jam or roll. Material size index. Through function Transformation, in which To prevent small constants with zero denominators, the function can be implemented using an embedded algorithm. The purpose is to transform the nonlinear influence of material size on slip risk into a quantifiable index, avoid calculation failure when the size exponent is zero, and ensure that the model runs stably under various material conditions.

[0062] Specifically, the material property evaluation model of this application receives the normalized static friction index of the material. Material density index and material size index According to weight , as well as The parameters are weighted and combined, and a nonlinear function is used. The size effect is addressed, and the final output is a dimensionless material property index. This index, serving as a unified quantitative indicator of material properties, is transmitted in real time to the material slippage risk assessment unit. Together with the machine condition index and tilt angle, it participates in the slippage risk calculation, thereby driving the spacing adjustment unit to dynamically optimize the spacing between the first belt conveyor 2 and the second belt conveyor 3. This process transforms material properties from discrete parameters to a systematic assessment, ensuring the accuracy and adaptability of the clamping spacing adjustment under different material physical properties.

[0063] As a specific embodiment, the solution of this application is implemented as follows: The material property evaluation unit is implemented by an industrial-grade microcontroller. This microcontroller periodically collects sensor signals of the static friction coefficient between the material and the belt, the material density, and the material size through a data interface, and generates the results through a normalization module. , , Then, the material property evaluation model is executed to calculate... For example, when conveying wet-mixed concrete, sensors detect a high static friction coefficient and a low material size, and the microcontroller calculates a higher [value / value] accordingly. The value indicates that the system reduces the clamping gap between the first belt conveyor 2 and the second belt conveyor 3 to prevent excessive material compression; however, when conveying pebbles, the lower static friction coefficient and larger material size result in lower... If the value is too high, the system will increase the clamping distance to prevent material slippage or jamming.

[0064] Through the above scheme, this application realizes the systematic and quantitative evaluation of material characteristics, effectively solves the problem of inaccurate evaluation caused by the dispersion of material characteristics, significantly improves the reliability of material slippage risk prediction, and thus provides a scientific basis for the dynamic optimization and adjustment of the distance between the first belt conveyor 2 and the second belt conveyor 3, reduces the risk of slippage, falling and damage during material transportation, and improves the stability and efficiency of material supply in water conservancy construction.

[0065] In a preferred embodiment of the present invention, the actual tilt angle of the second belt conveyor 3 is substituted into the maximum-minimum normalization formula for normalization processing, and a machine tilt angle index is generated; the slippage risk assessment model is as follows:

[0066] ;

[0067] in The weights are affected by the machine state. The weighting is influenced by material characteristics. The weighting of machine tilt angle , , as well as All greater than , For machine condition index, This is a material characteristic index. This refers to the machine tilt angle index. For speed matching degree, Dimensionless This is the material slippage risk index.

[0068] In this embodiment, the machine tilt index refers to a dimensionless quantitative index generated through normalization. It can be obtained by real-time signal acquisition from the tilt sensor and calculation by the control unit. Its purpose is to dynamically characterize the changing trend of the current tilt state of the second belt conveyor 3 and avoid evaluation deviations caused by fixed parameters. The slip risk assessment model is a mathematical framework used to output risk quantification values ​​by comprehensively considering multiple factors. It can be implemented using an exponential decay function combined with adjustable weight coefficients. Its purpose is to establish a nonlinear relationship between speed matching degree, machine condition index, material characteristic index and machine tilt index, providing a theoretical basis for risk assessment.

[0069] Specifically, the solution in this application generates a machine tilt angle index by normalizing the actual tilt angle data of the second belt conveyor 3. This index, along with the speed matching degree, machine condition index, and material characteristic index, is then input into a slip risk assessment model. The speed matching degree in the model acts as a multiplier factor, directly relating to the speed difference between the upper and lower conveyors. When the speeds of the first belt conveyor 2 and the second belt conveyor 3 are mismatched, the risk index is dynamically amplified to reflect the potential for increased material impact. The index portion uses weighting coefficients... , as well as Adjusting the contribution of each factor, including the machine condition index The lower the value, the worse the equipment's health status. After conversion, it becomes an incremental risk, reflecting the negative impact of equipment wear or vibration on clamping stability; material characteristic index A higher value indicates better material friction characteristics. The risk index is reduced after processing, allowing the model to adapt to the physical properties of different materials; the machine tilt angle index θ is increased directly through... This improves the risk index by precisely quantifying the impact of the tilt angle on the gravitational component of the material. Ultimately, this results in a material slippage risk index. The quantitative results of the integrated four-dimensional factors provide real-time decision-making basis for the spacing adjustment unit, enabling dynamic optimization of the clamping spacing.

[0070] As a preferred embodiment, the solution of this application is specifically implemented as follows: the actual tilt angle of the second belt conveyor 3 is acquired in real time by a tilt angle sensor installed on the conveyor frame. This sensor can be a dual-axis tilt angle measurement module implemented using MEMS technology; normalization processing is performed by the control unit, converting the tilt angle data into a machine tilt angle index in the [0,1] interval; in the slip risk assessment model, the weighting coefficients... , as well as It can be pre-set according to common material types and equipment operating conditions in water conservancy construction; speed matching degree Based on the speed sensor feedback signals of the first belt conveyor 2 and the second belt conveyor 3, when the material slippage risk index R exceeds the preset threshold, the control unit can instruct the electric lifting rod 4 to shorten its extension length to reduce the distance between the first belt conveyor 2 and the second belt conveyor 3, thereby enhancing the material clamping force.

[0071] In a preferred embodiment of the present invention, the material deformation during material conveying and discharging is substituted into a maximum-minimum normalization formula for normalization processing, and a material deformation index is generated; the spacing optimization adjustment model is as follows:

[0072] ;

[0073] ;

[0074] in This is the rated spacing. The weighting of the effect of material deformation Weighting of material slip risk , The range of values ​​is all within between, The material deformation index. Material slip risk index As a risk threshold, Dimensionless For the Sigmoid function, For adjustment coefficients, The target spacing. When hour, The value increases significantly, and the trigger interval is reduced for adjustment.

[0075] In this embodiment, the material deformation index can be understood as a normalized quantified value of the degree of deformation, which can be calculated and generated in real time by the control unit. The purpose is to accurately reflect the material characteristics' adaptability to the clamping distance. The sigmoid function is a nonlinear function with smooth transition characteristics. It can be implemented using mathematical library functions in the embedded system. The purpose is to provide a progressive response mechanism, especially to avoid sudden changes in adjustment when the risk index is close to the threshold.

[0076] Specifically, the solution in this application generates a material deformation index by normalizing the material deformation amount and combines it with a material slippage risk index to construct a nonlinear spacing optimization model. Based on the rated spacing, the model positively adjusts the target spacing to adapt to material deformation requirements. Simultaneously, in high-risk regions, it dynamically generates a nonlinear adjustment factor using the smooth transition characteristics of the Sigmoid function. When the risk index approaches or exceeds a threshold, this factor smoothly transitions to enhance risk suppression. This design allows the system to prioritize material deformation adaptability under low-risk conditions, ensuring the clamping spacing fully accommodates material deformation; under high-risk conditions, it dynamically strengthens anti-slip protection to avoid spacing adjustment instability caused by sudden risk changes, thereby achieving adaptive optimization of the clamping spacing under complex conditions.

[0077] As a specific implementation, the spacing optimization adjustment model of this application can be implemented by a microcontroller in the control unit. The microcontroller receives the material deformation signal from the pressure sensor, generates a material deformation index after normalization, and combines it with the material slip risk index obtained from the material slip risk assessment unit. The target spacing is output by calculating the Sigmoid function, and then the extension and retraction length of all electric lifting rods 4 are controlled to adjust the spacing between the first belt conveyor 2 and the second belt conveyor 3.

[0078] Through the above technical solution, this application can accurately quantify the dynamic impact of material deformation and slippage risk on the spacing, realize a nonlinear adjustment mechanism in high-risk areas, effectively avoid material squeezing damage or slippage and falling caused by the clamping spacing not adapting to real-time working condition changes, and ensure the stability and safety of material transportation during the water conservancy construction material supply process.

[0079] In a preferred embodiment of the present invention, the maximum-minimum value normalization formula is as follows:

[0080] ;

[0081] in This is the actual input value. The minimum allowable value or historical lower limit value is set. The maximum allowed value or historical limit is set. This is the normalized output value.

[0082] In the above embodiments, all "maximum allowable values" and "minimum allowable values" can be determined based on equipment design parameters and experimental data to ensure the accuracy of the evaluation. All initial values ​​of "weights" (such as the weights of material slippage risk and material deformation impact) can be calibrated and set according to the equipment manufacturer's experience or historical data, and can be fine-tuned during operation through online learning algorithms to adapt to specific working conditions.

[0083] Sensor system environmental adaptability description: Considering the characteristics of the water conservancy construction environment, which is dusty, humid and vibrating, the sensors used in this device are equipped with corresponding protection and compensation mechanisms.

[0084] Key material property parameter acquisition strategy: The static friction coefficient between the material and the conveyor belt is obtained by matching a pre-set "material type-friction coefficient" database. Operators can select or fine-tune the parameters according to the actual material through the human-machine interface. Material density and size information can be obtained in real time by the online detection module at the feed inlet, or manually entered as empirically calibrated values ​​during the initial feeding. This system design ensures core optimization functions while also considering the feasibility and economy of engineering implementation.

[0085] The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, and improvements made within the spirit and principles of the present invention should be included within the protection scope of the present invention.

Claims

1. A material supply device for water conservancy construction projects, comprising a base, wherein a first belt conveyor is connected to the base, and a second belt conveyor is arranged parallel to the upper side of the first belt conveyor, wherein both ends of the second belt conveyor are connected to the base via electric lifting rods; Its features are, It also includes a clamping distance optimization system, which includes: Machine condition assessment unit: Based on the belt surface temperature, average belt wear, actual belt tension and vibration intensity, a machine condition assessment model is constructed. The above parameters of the first belt conveyor and the second belt conveyor are substituted into the machine condition assessment model, and the output machine condition index of the first belt conveyor and the second belt conveyor are output respectively. Material property assessment unit: Based on the static friction coefficient between the material and the belt, the material density, and the material size during material conveying, a material property assessment model is constructed, and the material property index is output; Material slippage risk assessment unit: Under the influence of the speed matching degree between the first belt conveyor and the second belt conveyor, based on the material characteristic index, the smaller value of the machine condition index of the first belt conveyor and the second belt conveyor at the same time, and the tilt angle of the second belt conveyor, a slippage risk assessment model is constructed, and the material slippage risk index is output. Spacing adjustment unit: Based on the material deformation during material conveying and discharge, the material slippage risk index, and the rated spacing between the first belt conveyor and the second belt conveyor, a spacing optimization adjustment model is constructed, and the target spacing is output; The control unit is capable of receiving the target spacing output from the spacing adjustment unit and controlling the spacing between the first belt conveyor and the second belt conveyor by controlling the extension and retraction length of all electric lifting rods.

2. The material supply device for water conservancy construction projects according to claim 1, characterized in that, The belt surface temperature, average belt wear, actual belt tension, and vibration intensity are successively substituted into the maximum-minimum normalization formula for normalization, and the belt surface temperature index, average belt wear index, belt tension index, and belt vibration intensity index are generated in sequence.

3. The material supply device for water conservancy construction projects according to claim 2, characterized in that, In the machine condition assessment model: The machine condition index is obtained by subtracting a weighted sum from 1. This weighted sum includes the belt surface temperature index, the belt average wear index, the absolute value of the difference between the belt tension index and the optimal belt tension index, and the belt vibration intensity index. Each index is multiplied by its corresponding weighting coefficient. The machine condition index is used to reflect the current operational stability of the first belt conveyor and the second belt conveyor.

4. The material supply device for water conservancy construction projects according to claim 1, characterized in that, The static friction coefficient between the material and the belt, the material density, and the material size are successively substituted into the maximum-minimum normalization formula for normalization, and the static friction index, material density index, and material size index are generated in sequence.

5. The material supply device for water conservancy construction projects according to claim 4, characterized in that, In the material property evaluation model: The material property index is obtained by linearly weighting the static friction index and the material density index, and then adding it to a material size index processed by a nonlinear function; the processing of the material size index includes: inputting it into an exponential decay function with the index as the variable for transformation.

6. The material supply device for water conservancy construction projects according to claim 1, characterized in that, The actual tilt angle of the second belt conveyor is substituted into the maximum-minimum normalization formula for normalization, and the machine tilt angle index is generated. In the slip risk assessment model: the speed matching degree is used as a multiplicative attenuation factor and applied to a natural exponential function to calculate the material slip risk index; wherein, the exponential part of the natural exponential function is the negative value of the weighted sum of the following three items: the smaller value of the machine condition index of the first belt conveyor and the second belt conveyor at the same time, the material characteristic index, and the machine tilt angle index.

7. The material supply device for water conservancy construction projects according to claim 1, characterized in that, The material deformation during material conveying and discharge is substituted into the maximum-minimum normalization formula for normalization, and a material deformation index is generated. In the spacing optimization adjustment model: Based on the rated spacing, the target spacing is calculated using an adjustment coefficient. The adjustment coefficient is obtained by subtracting a positive adjustment term composed of the material deformation index from a negative adjustment term composed of the material slip risk index after being triggered by a Sigmoid function threshold. The input value of the Sigmoid function is the comparison result between the material slip risk index and a preset risk threshold.

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