A sandstone factory short process conveying system optimization design method

Abstract

The present application relates to the technical field of conveying optimization, and particularly relates to a sandstone factory short process conveying system optimization design method, and the present application determines whether to perform path heterogeneity analysis on each conveying process node on a sandstone conveying path based on a conveying state, marks a risk conveying process node according to an operation vector of each reference conveying process node in a process node set and the rest conveying process nodes, performs material analysis on each risk conveying process node, screens a risk monitoring sub-period according to a material response curve segment in each monitoring sub-period, and performs conveying optimization on sandstone conveying conveyors of each risk conveying process node in the risk monitoring sub-period. The present application realizes identification of a conveying state of a region where a sandstone factory is located, adaptively optimizes sandstone conveying conveyors according to actual conditions of a sandstone conveying path and coupling conditions of material states, and improves reliability and conveying efficiency of a sandstone factory short process conveying system.

Description

Technical Field

[0001] This invention relates to the field of conveying optimization technology, and in particular to an optimization design method for a short-process conveying system in a sand and gravel plant. Background Technology

[0002] As an indispensable basic material for infrastructure construction, the design level of sand and gravel aggregate production and processing systems directly affects the efficiency and quality of engineering construction. With the continuous advancement of infrastructure construction in my country, the demand for sand and gravel aggregate continues to grow, placing higher demands on the design efficiency, operational reliability, and intelligent level of sand and gravel plants. In recent years, Building Information Modeling (BIM) technology has been initially applied in the design of sand and gravel plants. It can establish a 3D equipment model family library for sand and gravel processing systems, create 3D models of various workshops and 3D parametric models of conveyor belts, and realize the 3D assembly and visualization of sand and gravel processing systems, assisting in rapid construction in complex scenarios. However, for… For sand and gravel plants located in mountainous areas with complex terrain, undulating topography, and significant elevation differences, existing technologies lack scientific methods for quantifying regional terrain risks. This makes it impossible to objectively determine the impact of complex terrain on the conveying system. Furthermore, current conveying path optimization primarily focuses on static parameters such as equipment selection and path planning, neglecting the impact of spatial geometrical changes in the conveying path on the lateral stability of materials. It also lacks tracking and analysis of the temporal changes of materials during the conveying process, resulting in a significant disconnect between the design conditions and actual operating conditions of the conveying system. Therefore, improving the reliability and conveying efficiency of short-process conveying systems in sand and gravel plants is an urgent technical problem to be solved.

[0003] For example, Chinese Patent Application Publication No. CN111954632A discloses a transport vehicle system and a transport vehicle control method, including multiple transport vehicles traveling along a track and a controller. The controller performs allocation processing by accepting multiple loading requests. The allocation processing involves selecting transport vehicles that are expected to arrive at the loading destination earlier from the multiple transport vehicles according to the priority of the multiple loading requests from high to low, and allocating them to the multiple transport vehicles. When the controller recognizes a change in the expected arrival order of the multiple transport vehicles traveling towards the loading destination due to the loading instructions or a change in the priority of the loading requests, it cancels the previously allocated loading instructions to the multiple transport vehicles and re-allocates the loading instructions through the allocation processing.

[0004] The existing technology still has the following problems: the existing technology does not consider the impact of changes in the spatial geometry of the sand and gravel conveying path on the stability of material conveying; the existing technology cannot quickly identify the conveying status of the area where the sand and gravel plant is located; and it cannot adaptively optimize the sand and gravel conveying belt according to the actual situation of the sand and gravel conveying path and the coupling of material status, which affects the reliability and conveying efficiency of the short-process conveying system of the sand and gravel plant. Summary of the Invention

[0005] To address this, the present invention provides an optimized design method for a short-process conveying system in a sand and gravel plant, which overcomes the problems of existing technologies being unable to quickly identify the conveying status of the area where the sand and gravel plant is located, and being unable to adaptively optimize the sand and gravel conveyor belt according to the actual situation of the sand and gravel conveying path and the coupling of material status, thus affecting the reliability and conveying efficiency of the short-process conveying system in the sand and gravel plant.

[0006] To achieve the above objectives, the present invention provides an optimized design method for a short-process conveying system in a sand and gravel plant, comprising: The three-dimensional point cloud data of several monitoring points in the area where the sand and gravel plant is located are obtained by drone to analyze the transportation status of the area where the sand and gravel plant is located. Based on the conveying status, determine whether to perform path heterogeneity analysis on each conveying process node on the sand and gravel conveying path. The path heterogeneity analysis includes obtaining the running vector of each conveying process node to construct a set of several process nodes. Risk transport process nodes are marked based on the running vectors of each benchmark transport process node and the remaining transport process nodes within the set of process nodes. Material analysis is performed on each of the aforementioned risk transport process nodes. The material analysis includes acquiring several material characteristics of each risk transport process node during the monitoring period and constructing a material response curve. The material characteristics include material particle size distribution parameters and material moisture content. The monitoring period is divided into several monitoring sub-periods, and risk monitoring sub-periods are selected based on the material response curve segments within each monitoring sub-period. During the risk monitoring sub-period, the conveyor belts of sand and gravel conveying processes at each risk conveying process node are optimized, and node joint analysis is performed on each risk conveying process node to determine whether to perform collaborative optimization on the upstream conveying process node. The conveying optimization includes reducing the belt speed of the sand and gravel conveyor belt and increasing the belt tension of the sand and gravel conveyor belt.

[0007] Furthermore, the process of analyzing the conveying status of the area where the sand and gravel plant is located includes, The elevation heterogeneous parameters are determined based on the characteristic three-dimensional point cloud data of each monitoring point, wherein the characteristic three-dimensional point cloud data is point cloud data perpendicular to the horizontal plane. If the elevation heterogeneity parameter of the area where the sand and gravel plant is located exceeds the elevation heterogeneity benchmark value, then the transportation status of the area where the sand and gravel plant is located is determined to be a risky transportation status.

[0008] Furthermore, the process of determining whether to perform path heterogeneity analysis on each conveying process node along the sand and gravel conveying route includes, If the transportation status of the area where the sand and gravel plant is located is the risky transportation status, then path heterogeneity analysis is performed on each transportation process node on the sand and gravel transportation path.

[0009] Furthermore, the process of constructing a set of process nodes and determining path tortuosity parameters includes, The running vectors of each conveying process node are obtained according to the short process conveying sequence of sand and gravel. The three adjacent conveying process nodes and the running vectors corresponding to each conveying process node are stored in a single data set, and the data set is determined as the process node set. The benchmark conveying process node is determined based on the short-process conveying sequence of sand and gravel in the conveying process node; The vector angles between the running vectors of the baseline transport process node and the running vectors of the other transport process nodes are determined to determine the path tortuosity parameters. The running vector is determined based on the sand and gravel conveying direction of the conveying process node.

[0010] Furthermore, the process of marking risk delivery process nodes includes, If the path tortuosity parameter of the process node set exceeds the path tortuosity baseline value, then the transport process node in the process node set is marked as a risk transport process node.

[0011] Furthermore, the process of constructing the material response curve includes, During the monitoring period, the particle size distribution parameters and moisture content of the materials at several monitoring times are obtained for each of the aforementioned risk transport process nodes. The adhesion trend parameters are determined based on the particle size distribution parameters and moisture content of the material at the same monitoring time. The material response curve is constructed with the adhesion trend parameter as the vertical axis and time as the horizontal axis.

[0012] Furthermore, the process of selecting risk monitoring sub-periods includes, If the response change parameter of the monitored sub-period exceeds the response change benchmark value, the monitored sub-period will be marked as a risk monitoring sub-period.

[0013] Furthermore, the process of optimizing the conveyor belts of sand and gravel conveying processes at each risk-prone process node during the risk monitoring sub-period includes, Obtain the response change parameters of each risk transmission process node in each risk monitoring sub-period and the path tortuosity parameters of each risk transmission process node; The decrease in belt speed is positively correlated with the response change parameter, and the increase in belt tension is positively correlated with the path tortuosity parameter.

[0014] Furthermore, the process of conducting joint node analysis on each risk transmission process node includes, Obtain the current allowable throughput and upstream material supply rate for each of the aforementioned risk transport process nodes; If the upstream material supply rate exceeds the current allowable throughput, then the upstream conveying process nodes are optimized collaboratively, and the upstream collaborative parameters are determined based on the path tortuosity parameters of the risk conveying process nodes and the response change parameters of the risk monitoring sub-period.

[0015] Furthermore, the process of determining the response time window for collaborative analysis of the risk transmission process nodes includes, If a risk transmission process node meets the immediate response condition, then a collaborative analysis is performed on the risk transmission process node at the current moment. If a risk transmission process node does not meet the immediate response conditions, a collaborative analysis of the risk transmission process node will be performed after a first time period. The first time period is determined based on the difference between the upstream collaborative parameters and the upstream collaborative benchmark value.

[0016] Compared with the prior art, the beneficial effects of the present invention are as follows: The present invention uses UAVs to acquire three-dimensional point cloud data of several monitoring points in the area where the sand and gravel plant is located to analyze the conveying status of the area. Based on the conveying status, it determines whether to perform path heterogeneity analysis on each conveying process node on the sand and gravel conveying path. It marks risky conveying process nodes according to the running vectors of each benchmark conveying process node and other conveying process nodes in the process node set. It performs material analysis on each risky conveying process node, selects risky monitoring sub-periods based on the material response curve segments in each monitoring sub-period, optimizes the sand and gravel conveying conveyor belts of each risky conveying process node in the risky monitoring sub-period, and performs node joint analysis on each risky conveying process node to determine whether to perform collaborative optimization on the upstream conveying process nodes. Thus, it realizes the identification of the conveying status in the area where the sand and gravel plant is located, and adaptively optimizes the sand and gravel conveying conveyor belts according to the actual situation of the sand and gravel conveying path and the coupling of material status, thereby improving the reliability and conveying efficiency of the short-process conveying system of the sand and gravel plant.

[0017] In particular, this invention determines the heterogeneous elevation parameters based on various three-dimensional point cloud data, analyzes the conveying status of the area where the sand and gravel plant is located, and realizes the automated determination of the terrain complexity of the area where the sand and gravel plant is located. This makes the regional terrain risk assessment repeatable and comparable. At the same time, a single UAV aerial survey can cover the entire plant area, which can quickly complete the initial screening of terrain risks in a large area. This makes the overall optimization design process more efficient and provides an efficient and accurate pre-regional risk assessment method for the optimization design of short-process conveying systems in sand and gravel plants in complex terrain scenarios. In turn, it improves the reliability and conveying efficiency of short-process conveying systems in sand and gravel plants.

[0018] In particular, this invention determines path tortuosity parameters based on the running vectors of each benchmark conveying process node and the remaining conveying process nodes within the process node set, thereby marking risky conveying process nodes. This enables quantitative characterization of the severity of changes in the sand and gravel conveying path and automatic marking of risky nodes. By quantifying the degree of path tortuosity through path tortuosity parameters, it is possible to identify the path segments that have the greatest impact on the lateral stability of materials, thereby improving the reliability and conveying efficiency of the short-process conveying system in sand and gravel plants.

[0019] In particular, this invention determines several response change parameters based on the material response curve segments within each monitoring sub-period to screen risk monitoring sub-periods, thereby enabling visualization of the evolution of material risk over time. Simultaneously, subsequent analysis and optimization are performed only within these marked sub-periods, avoiding the adverse effects of continuous adjustment throughout the entire time period on equipment lifespan and energy consumption. Consequently, the reliability and conveying efficiency of the short-process conveying system in sand and gravel plants are improved.

[0020] In particular, this invention optimizes the conveying of sand and gravel at each risk-prone process node during the risk monitoring sub-period. By intervening in speed reduction in advance based on the trend reflected by the response change parameters, it reserves conveying margin for upcoming severe working conditions before the material reaches its worst state, avoiding material blockage, spillage, and belt damage after material deterioration. By increasing the belt tension according to the path tortuosity parameters, the adjustment force is matched with the risk level, avoiding the problem of over-adjustment in low-risk or under-adjustment in high-risk situations in the fixed parameter mode. Thus, it improves the reliability and conveying efficiency of the short-process conveying system in the sand and gravel plant.

[0021] In particular, this invention performs joint node analysis on each risky conveying process node to determine whether to perform collaborative optimization on the upstream conveying process node. Using the upstream feeding rate and the current allowable throughput as pre-filtering conditions, it identifies conveying scenarios with the risk of material accumulation. Based on the path space geometric risk and the material time evolution risk, it constructs upstream collaborative parameters to characterize the urgency of collaborative optimization, and determines a graded strategy of immediate or delayed response, so that the timing of collaborative analysis matches the urgency of the risk. At the same time, during the collaborative analysis stage, based on the relative position of the real-time material level of the intermediate buffer silo and the material level benchmark interval, it executes differentiated strategies to achieve a dynamic balance between the upstream feeding rate and the downstream throughput capacity, thereby improving the operational stability and intelligence level of the short-process conveying system in the sand and gravel plant. Attached Figure Description

[0022] Figure 1 This is a step diagram illustrating the optimized design method for a short-process conveying system in a sand and gravel plant according to an embodiment of the present invention. Figure 2 A flowchart illustrating the logic of determining whether to perform path heterogeneity analysis on each transport process node along the sand and gravel transport path in an embodiment of the present invention. Figure 3 A logical flowchart for marking risk transmission process nodes in an embodiment of the present invention; Figure 4 This is a flowchart illustrating the logic for determining whether to perform collaborative optimization on upstream transport process nodes in an embodiment of the present invention. Detailed Implementation

[0023] To make the objectives and advantages of the present invention clearer, the present invention will be further described below with reference to embodiments; it should be understood that the specific embodiments described herein are merely for explaining the present invention and are not intended to limit the present invention.

[0024] Preferred embodiments of the present invention will now be described with reference to the accompanying drawings. Those skilled in the art should understand that these embodiments are merely illustrative of the technical principles of the present invention and are not intended to limit the scope of protection of the present invention.

[0025] It should be noted that in the description of this invention, the terms "upper," "lower," "inner," "outer," etc., which indicate the direction or positional relationship, are based on the direction or positional relationship shown in the drawings. This is only for the convenience of description and is not intended to indicate or imply that the device or element must have a specific orientation, or be constructed and operated in a specific orientation. Therefore, it should not be construed as a limitation of this invention.

[0026] Furthermore, it should be noted that, in the description of this invention, unless otherwise explicitly specified and limited, the terms "installation" and "connection" should be interpreted broadly. For example, they can refer to a fixed connection, a detachable connection, or an integral connection; they can refer to a mechanical connection or an electrical connection; they can refer to a direct connection or an indirect connection through an intermediate medium; and they can refer to the internal connection of two components. Those skilled in the art can understand the specific meaning of the above terms in this invention according to the specific circumstances.

[0027] Please see Figure 1 The diagram illustrates the steps of an optimization design method for a short-process conveying system in a sand and gravel plant according to an embodiment of the present invention. The method includes: Step S100: Based on the UAV, three-dimensional point cloud data of several monitoring points in the area where the sand and gravel plant is located are obtained, and the elevation heterogeneous parameters are determined according to each of the three-dimensional point cloud data to analyze the transportation status of the area where the sand and gravel plant is located. In this embodiment of the invention, the method of setting up several monitoring points in the area where the sand and gravel plant is located is not specifically limited. It can be determined by those skilled in the art based on the terrain features, area range and required terrain expression accuracy of the area where the sand and gravel plant is located. Preferably, the monitoring points can be evenly distributed in the area where the sand and gravel plant is located, or they can be densely set up in characteristic terrain locations such as places with drastic slope changes, steep slopes or edges with abrupt elevation changes, and transition areas between valleys and ridges.

[0028] Step S200: Based on the conveying status, determine whether to perform path heterogeneity analysis on each conveying process node on the sand and gravel conveying path. The path heterogeneity analysis includes obtaining the running vector of each conveying process node to construct a set of several process nodes. Step S300: Determine the path tortuosity parameters based on the running vectors of each benchmark transport process node and the other transport process nodes in the process node set, so as to mark the risk transport process nodes. Step S400: Perform material analysis on each of the risk transport process nodes. The material analysis includes acquiring several material characteristics of each risk transport process node during the monitoring period to determine adhesion trend parameters and constructing material response curves. The material characteristics include material particle size distribution parameters and material moisture content. In this embodiment of the invention, the particle size distribution parameter can be the proportion of fine particles. The definition of the particle size range of the fine particles is not specifically limited. Preferably, the fine particles refer to particle components with a particle size smaller than the lower limit of the finished sand and gravel aggregate particle size. The lower limit of the finished sand and gravel aggregate particle size is not limited and can be determined by those skilled in the art based on the actual production process requirements.

[0029] In this embodiment of the invention, the method of obtaining material characteristics is not specifically limited. Preferably, an online laser particle size analyzer can be used to sample and detect the sand and gravel material in real time during the conveying process, obtain the particle size distribution data of the material at each monitoring time, and then determine the proportion of fine particles to obtain the particle size distribution parameters of the material. An online microwave moisture meter can be used to detect the moisture content of the sand and gravel material in real time during the conveying process to obtain the moisture content of the material. Of course, other forms can also be used, which will not be elaborated here.

[0030] Step S500: Divide the monitoring period into several monitoring sub-periods, and determine several response change parameters based on the material response curve segments within each monitoring sub-period to screen risk monitoring sub-periods; Step S600: During the risk monitoring sub-period, the conveyor belts of sand and gravel conveying processes at each risk conveying process node are optimized, and node joint analysis is performed on each risk conveying process node to determine whether to perform collaborative optimization on the upstream conveying process node. The conveying optimization includes reducing the belt speed of the sand and gravel conveyor belt and increasing the belt tension of the sand and gravel conveyor belt.

[0031] Specifically, the process of determining the elevation heterogeneous parameters based on the three-dimensional point cloud data includes: acquiring three-dimensional point cloud data of several monitoring points within the area where the sand and gravel plant is located; and determining the elevation heterogeneous parameters based on the characteristic three-dimensional point cloud data of each monitoring point, wherein the characteristic three-dimensional point cloud data is point cloud data perpendicular to the horizontal plane.

[0032] In this embodiment of the invention, by acquiring characteristic three-dimensional point cloud data of several monitoring points within the area where the sand and gravel plant is located, the standard deviation of the characteristic three-dimensional point cloud data is determined as the elevation heterogeneity parameter. The larger the elevation heterogeneity parameter, the greater the dispersion of the characteristic three-dimensional point cloud data of each monitoring point in the area relative to the average characteristic three-dimensional point cloud data, the more dramatic the terrain undulations, and the more varied the elevation. Conversely, the smaller the elevation heterogeneity parameter, the closer the characteristic three-dimensional point cloud data of each monitoring point in the area is to the average characteristic three-dimensional point cloud data, the smaller the difference in altitude among all monitoring points, and the flat terrain or gentle, uniform slope.

[0033] Specifically, the process of analyzing the transport status of the area where the sand and gravel plant is located includes: if the elevation heterogeneity parameter of the area where the sand and gravel plant is located exceeds the elevation heterogeneity benchmark value, then the transport status of the area where the sand and gravel plant is located is determined to be a risky transport status; if the elevation heterogeneity parameter of the area where the sand and gravel plant is located does not exceed the elevation heterogeneity benchmark value, then the transport status of the area where the sand and gravel plant is located is determined to be a non-risky transport status.

[0034] In this embodiment of the invention, an elevation heterogeneous benchmark value is determined based on the topographic relief characteristics of the area where the sand and gravel plant is located. This ensures that complex terrain areas can be effectively identified while avoiding misclassification of normal undulating terrain as risky terrain. Three-dimensional point cloud data of sample areas with several known terrain types are acquired, and elevation heterogeneous parameters for each sample area are calculated. The distribution range of elevation heterogeneous parameters for flat terrain sample areas and complex terrain sample areas is statistically analyzed. The elevation heterogeneous benchmark value is determined based on the distribution characteristics of elevation heterogeneous parameters corresponding to normal and complex terrain samples to ensure that complex terrain areas can be effectively distinguished from normal terrain areas. Setting the elevation heterogeneous benchmark value too small can easily lead to misclassification of normal undulating terrain as risky terrain, increasing computational resource waste and extending the design cycle. Setting the elevation heterogeneous benchmark value too large prevents timely detection and intervention of terrain risks, reducing the effectiveness of the optimization design method. It is understandable that setting the elevation heterogeneity benchmark value within the transitional range between the elevation heterogeneity parameter distribution in normal terrain and that in complex terrain allows subsequent path heterogeneity analysis and material analysis to be based on the effective identification of risky terrain areas, while also ensuring the rational use of computational resources and the comprehensiveness of risk identification. Preferably, it can be adaptively adjusted based on feedback from actual applications.

[0035] Understandably, when a short-process conveyor system in a sand and gravel plant is operating in a mountainous area with complex terrain, the large elevation difference will cause the belt conveyor to tilt more or even exceed the limit. The material is prone to slipping on the inclined belt, and the path is forced to meander with the terrain, resulting in changes in the horizontal direction. The material is prone to spillage due to the influence of lateral inertial force.

[0036] Please see Figure 2 As shown, it is a logical flowchart of an embodiment of the present invention for determining whether to perform path heterogeneity analysis on each conveying process node on the sand and gravel conveying path. If the conveying state of the area where the sand and gravel plant is located is the risky conveying state, then path heterogeneity analysis is performed on each conveying process node on the sand and gravel conveying path; if the conveying state of the area where the sand and gravel plant is located is the non-risky conveying state, then path heterogeneity analysis is not performed on each conveying process node on the sand and gravel conveying path.

[0037] Specifically, the process of constructing a set of process nodes includes obtaining the running vector of each conveying process node according to the short process conveying order of sand and gravel; storing three adjacent conveying process nodes and the running vectors corresponding to each conveying process node into a single data set, and determining the data set as a set of process nodes; wherein, the running vector is determined according to the sand and gravel conveying direction of the conveying process node.

[0038] In this embodiment of the invention, "adjacent" refers to nodes on the conveying path being adjacent, and the running vector takes the conveying process node as the starting point and the conveying direction of the sand and gravel at the conveying process node as the direction of the vector.

[0039] Specifically, the process of determining the path tortuosity parameters based on the running vectors of each reference transport process node and the other transport process nodes within the process node set includes: obtaining the transport process nodes within the process node set and the running vectors corresponding to each transport process node; determining the reference transport process nodes based on the short-process transport sequence of the transport process nodes; and determining the vector angles between the running vectors of the reference transport process nodes and the running vectors of the other transport process nodes to determine the path tortuosity parameters.

[0040] In this embodiment of the invention, the angle between the running vector of the benchmark conveying process node in the same set of process nodes and the running vectors of the other conveying process nodes is obtained, and the absolute value of the difference between the two vector angles is determined as the path tortuosity parameter. The path tortuosity parameter determines the change in conveying direction of the sand and gravel conveying path in this section. The larger the path tortuosity parameter, the more significant the change in the direction of the conveying path, the greater the turning angle the material undergoes when passing through this section, the stronger the lateral inertial force, and the higher the risk of belt misalignment and material spillage. The smaller the path tortuosity parameter, the gentler the change in the direction of the conveying path, the more consistent the material's running direction, the smaller the lateral inertial force, and the better the conveying stability.

[0041] Please see Figure 3 As shown, it is a logical flowchart of marking risk transportation process nodes in an embodiment of the present invention. If the path tortuosity parameter of the process node set exceeds the path tortuosity benchmark value, then the transportation process node in the process node set is marked as a risk transportation process node; if the path tortuosity parameter of the process node set does not exceed the path tortuosity benchmark value, then the transportation process node in the process node set is not marked.

[0042] In this embodiment of the invention, a path tortuosity benchmark value is determined based on the directional change characteristics of the sand and gravel conveying path under normal operating conditions. This ensures that path segments with drastic directional changes can be effectively identified, while avoiding misclassifying normal path bends as risk nodes. Specifically, the operating vector data of several sample segments of the conveying path with known operating states are obtained, the path tortuosity parameters of each sample segment are calculated, and the distribution range of path tortuosity parameters for normal path segments and high-risk path segments is statistically analyzed. The path tortuosity benchmark value is determined based on the distribution characteristics of path tortuosity parameters corresponding to normal and high-risk path segments. This ensures that path segments with drastic directional changes can be effectively distinguished from normal path bends. If the path tortuosity benchmark value is set too small, normal path bends are easily misclassified as risk nodes, leading to an expansion of the material analysis scope and an increased computational burden; if the path tortuosity benchmark value is set too large, risk nodes may be missed. The path tortuosity benchmark value is set in a transitional range between the distribution of normal and high-risk path bend parameters, balancing identification accuracy and computational efficiency.

[0043] Specifically, the process of determining the adhesion trend parameter and constructing the material response curve includes: acquiring the material particle size distribution parameter and material moisture content of each of the risk transport process nodes at several monitoring times during the monitoring period; determining the adhesion trend parameter based on the material particle size distribution parameter and the material moisture content at the same monitoring time; wherein the material response curve is constructed with the adhesion trend parameter as the vertical axis and time as the horizontal axis.

[0044] In this embodiment of the invention, the particle size distribution parameter and moisture content of the material are obtained at the same monitoring time, and the product of the particle size distribution parameter and the moisture content is determined as the adhesion trend parameter. The larger the adhesion trend parameter, the higher the adhesion risk of the material at that time; the smaller the adhesion trend parameter, the lower the adhesion risk.

[0045] Understandably, particle size distribution parameters characterize the proportion of particles in different size ranges within sand and gravel materials. Preferably, the particle size distribution parameter can be the proportion of fine particles. A higher proportion of fine particles indicates a higher content of fine powder in the material. Fine particles typically have a smaller angle of repose than coarse particles, making them more prone to relative sliding during inclined or turning conveying. Simultaneously, fine particles have a larger total specific surface area, making them more susceptible to adhesion when in contact with moisture. The moisture content parameter characterizes the mass percentage of water contained in the sand and gravel material. Higher moisture content causes fine particles to adhere to the belt surface, chute walls, and transfer points, leading to problems such as return material carryover, blockage, and dust suppression failure. Lower moisture content indicates a drier material and a lower risk of adhesion. A higher adhesion tendency parameter indicates more fine particles and a more abundant supply of raw materials that can combine with water to form a slurry-like substance. Higher moisture content also results in stronger liquid bridging forces between fine particles, increasing the risk of adhesion.

[0046] In this embodiment of the invention, the method of constructing the material response curve is not limited. For example, the material response curve can be fitted by MATLAB correlation fitting software, which will not be elaborated here.

[0047] Specifically, the process of screening risk monitoring sub-periods includes: if the response change parameter of the monitoring sub-period exceeds the response change benchmark value, then the monitoring sub-period is marked as a risk monitoring sub-period; if the response change parameter of the monitoring sub-period does not exceed the response change benchmark value, then the monitoring sub-period is not marked.

[0048] In this embodiment of the invention, material response curve segments within a monitoring sub-period are obtained, and the average slope of the material response curve segments is determined as the response change parameter. A larger response change parameter indicates a faster rate of material deterioration during that sub-period, while a smaller response change parameter indicates an improvement in the material condition during that sub-period.

[0049] In this embodiment of the invention, a response change benchmark value is determined based on the response change characteristics of the material under normal conveying conditions. This ensures that periods of rapid material deterioration can be effectively identified, while avoiding misjudging normal fluctuations as risky sub-periods. Specifically, material response curve data for sample sub-periods with known conveying conditions are acquired, response change parameters for each sample sub-period are calculated, and the distribution range of response change parameters for normal and risky sub-periods is statistically analyzed. The response change benchmark value is determined based on the distribution characteristics of response change parameters corresponding to the normal and risky sub-periods. Setting the response change benchmark value too small can easily trigger unnecessary parameter adjustments, increasing equipment operation frequency and energy consumption; setting it too large prevents the conveying system from proactively intervening in the early stages of material deterioration, and the problem of control lag cannot be effectively solved. Setting the response change benchmark value in the transition range between normal fluctuation distribution and rapid deterioration distribution ensures that the screening of risky sub-periods is based on an accurate assessment of the material change trend, while also considering the timeliness and stability of control.

[0050] Specifically, the process of optimizing the conveyor belts of sand and gravel conveying processes at each risk transport process node during the risk monitoring sub-period includes obtaining the response change parameters and path tortuosity parameters of each risk transport process node during each risk monitoring sub-period; the decrease in belt speed is positively correlated with the response change parameters, and the increase in belt tension is positively correlated with the path tortuosity parameters.

[0051] Understandably, the larger the response change parameter, the faster the material adhesion trend deteriorates, the shorter the remaining time for the system to evolve from the current state to the extreme condition, the earlier the deceleration start time needs to be, and the deceleration magnitude must be increased accordingly, so as to start intervention before the material reaches the worst state and provide sufficient conveying margin; the smaller the response change parameter, the slower the material deteriorates, the deceleration timing can be appropriately delayed, and the deceleration magnitude can be reduced accordingly.

[0052] It is understandable that when materials pass through the risky conveying process nodes, they are subjected to a relatively violent lateral inertial force that reverses direction, pushing the materials to the outside of the belt, causing material to roll, spill, and the belt to deviate. The lateral restraint capability of the belt on the materials depends on the friction between the materials and the belt, and this friction is related to the normal pressure of the belt on the materials. The greater the normal pressure, the greater the friction, and the better the lateral stability of the materials. Increasing the belt tension can increase the normal pressure of the belt on the materials, thereby enhancing the lateral restraint capability of the belt on the materials. The larger the path tortuosity parameter, the more violent the path reversal, the greater the lateral inertial force on the materials, and the greater the normal pressure required to provide sufficient friction to restrain the materials. Therefore, a greater increase in tension is required. The smaller the path tortuosity parameter, the smoother the path direction change, the less lateral force the materials experience, the less additional normal pressure is required, and the increase in tension is correspondingly smaller.

[0053] Please see Figure 4 The diagram illustrates the logic flowchart for determining whether to perform collaborative optimization on upstream conveying process nodes according to an embodiment of the present invention. The process of joint node analysis for each risky conveying process node includes: obtaining the current allowable throughput and upstream material supply rate for each risky conveying process node; if the upstream material supply rate exceeds the current allowable throughput, then collaborative optimization is performed on the upstream conveying process node, and upstream collaborative parameters are determined based on the path tortuosity parameters of the risky conveying process node and the response change parameters of the risk monitoring sub-period. A larger upstream collaborative parameter indicates that the node simultaneously bears a higher risk of path direction deflection and material adhesion deterioration, resulting in severe and continuously decreasing throughput loss after speed reduction, and a higher overall risk of upstream material accumulation, thus requiring immediate initiation of collaborative analysis. Conversely, a smaller upstream collaborative parameter indicates a relatively straight path and stable material condition at the node, with limited throughput loss after speed reduction, and a lower overall risk of upstream material accumulation, thus avoiding unnecessary premature intervention.

[0054] If the upstream material supply rate does not exceed the current allowable throughput, then no collaborative optimization will be performed on the upstream conveying process nodes.

[0055] Specifically, the current allowable throughput can be determined by those skilled in the art based on the belt speed and belt tension at the risk conveying process node during the conveying optimization, combined with the material characteristics, where material characteristics include material density, angle of repose, etc., which will not be elaborated here.

[0056] In this embodiment of the invention, the path tortuosity parameter and the response change parameter are normalized to eliminate dimensional differences, and then weighted and summed according to preset weight coefficients to obtain the upstream coordination parameter. The allocation of the weight coefficients is determined according to the actual operating conditions of the sand and gravel plant. When the change in path geometry is the dominant risk factor, the path tortuosity parameter is given a larger weight. When the change in material state is the dominant risk factor, the response change parameter is given a larger weight. The upstream coordination parameter is used to determine the degree of upstream material accumulation risk caused by path direction deflection and material adhesion deterioration at the current risk transport process node during the risk monitoring sub-period, thereby providing a quantitative basis for the response timing of the coordination analysis.

[0057] Specifically, the process of determining the response time window for collaborative analysis of the risk transmission process nodes includes: if the risk transmission process node meets the immediate response condition, then collaborative analysis is performed on the risk transmission process node at the current time; if the risk transmission process node does not meet the immediate response condition, then collaborative analysis is performed on the risk transmission process node after a first time period, wherein the first time period is determined based on the difference between the upstream collaborative parameter and the upstream collaborative benchmark value, and the first time period is positively correlated with the difference; the immediate response condition is that the upstream collaborative parameter of the risk transmission process node exceeds the upstream collaborative benchmark value.

[0058] Understandably, the larger the difference between the upstream coordination parameter and the upstream coordination benchmark, the lower the overall urgency of the current risk transport process node's actual path direction deviation risk and material adhesion deterioration risk relative to the benchmark. The buffer silo is expected to safely accept excess material for a longer period without overflowing, allowing for a longer delay. Conversely, the smaller the difference between the upstream coordination parameter and the upstream coordination benchmark, the closer the current overall risk is to the trigger threshold for immediate response. The buffer silo's remaining capacity is insufficient, requiring intervention in a shorter time to avoid overflow risk. This achieves dynamic matching between the timing of collaborative analysis intervention and the system's remaining safe time window, reducing unnecessary premature intervention while ensuring timely response in high-risk scenarios, thus balancing system safety and operational stability.

[0059] In this embodiment of the invention, an upstream coordination benchmark value is determined based on the distribution characteristics of upstream coordination parameters corresponding to delayed coordination scenarios and scenarios requiring immediate response during the historical operation of a sand and gravel plant. This ensures that high-risk scenarios can be identified in a timely manner and trigger immediate coordination analysis, while avoiding excessive intervention in low-risk scenarios. Setting the upstream coordination benchmark value too small will cause upstream conveying process nodes to frequently execute unnecessary linkage speed reductions, increasing the number of equipment actions and energy consumption, and reducing the operational stability of the conveying system. Setting the upstream coordination benchmark value too large will cause the buffer silo to overflow rapidly in high-risk scenarios due to failure to receive timely upstream linkage speed reductions, increasing the risk of overflow and blockage. It is understandable that setting the upstream collaborative benchmark value within the transitional range between the distribution of scenarios that can be delayed and the distribution of scenarios that require immediate response ensures that the timing of collaborative analysis is based on an accurate assessment of the risk level, while also taking into account both the timeliness of intervention and the stability of system operation.

[0060] In this embodiment of the invention, the collaborative analysis includes obtaining the real-time material level of the intermediate buffer silo between the risk transport process node and the upstream transport process node; If the real-time material level of the intermediate buffer silo is lower than the lower limit of the material level reference range, then no collaborative optimization will be performed on the upstream conveying process node. Instead, the intermediate buffer silo will be used to buffer the incoming material from the upstream, and the risk conveying process node will be controlled to operate at the belt speed and belt tension optimized by the conveying process. If the real-time material level of the intermediate buffer silo is higher than the upper limit of the material level reference range, then collaborative optimization is performed on the upstream conveying process node. Based on the ratio of the current allowable throughput to the upstream feeding rate, the belt speed of the upstream conveying process node of the risky conveying process node is reduced. The reduction in the belt speed of the upstream conveying process node is positively correlated with the ratio. If the real-time material level of the intermediate buffer silo is within the material level reference range, no collaborative optimization is performed on the upstream conveying process node. The incoming material from the upstream is stored in the intermediate buffer silo, and the real-time material level is continuously monitored.

[0061] Understandably, the ratio of the current allowable throughput to the upstream feed rate reflects the degree of matching between the current throughput capacity of the risky conveying process node after conveying optimization and the upstream input load. The smaller the ratio, the lower the current allowable throughput relative to the upstream feed rate, the more severe the excess of upstream material, and the higher the risk of overflow. Conversely, the larger the ratio, the more the current allowable throughput is close to the upstream feed rate, supply and demand are approaching equilibrium, and only a slight reduction in the upstream belt speed is needed to achieve flow matching, thus achieving a precise match between the upstream feed rate and the downstream throughput capacity.

[0062] The embodiments of the present invention do not specifically limit the method of obtaining the real-time material level of the intermediate buffer silo. Preferably, it can be obtained by a material level detection device installed on the intermediate buffer silo. The material level detection device can be a radar level gauge, an ultrasonic level gauge, etc., which will not be described in detail here.

[0063] In this embodiment, the lower and upper limits of the material level reference range are determined based on the material level distribution characteristics of the buffer silo during the normal buffering phase and the phase requiring coordinated deceleration during the historical operation of the sand and gravel plant. This ensures that the material level reference range can accurately divide the three operating conditions: buffering, continuous monitoring, and coordinated deceleration. If the lower limit is set too low, the buffering strategy will be activated too late, reducing the continuity of conveying; if the lower limit is set too high, unnecessary upstream coordinated deceleration will be triggered prematurely, increasing the number of equipment actions. If the upper limit is set too low, it will lead to reduced conveying efficiency and frequent equipment speed adjustments; if the upper limit is set too high, overflow or blockage accidents are likely to occur. It is understandable that setting the lower and upper limits of the material level reference range to the transition range between the normal buffer material level distribution and the material level distribution that requires linkage speed reduction allows the three strategies of buffering, monitoring and linkage speed reduction in the intermediate buffer silo to each have a reasonable range of action. Material level management is based on the premise that the buffer capacity has been fully utilized and the risk has been effectively controlled, while taking into account both conveying efficiency and operational safety.

[0064] All technologies not mentioned in the above embodiments are applicable to existing technologies. It is understood that no specific limitation is made to any preset parameter or critical parameter in the embodiments of the present invention, and the above values ​​are not limited thereto. Those skilled in the art can adjust the preset parameters or critical parameters accordingly based on actual needs, analysis of historical data, or equipment usage.

[0065] The technical solution of the present invention has been described above with reference to the preferred embodiments shown in the accompanying drawings. However, it will be readily understood by those skilled in the art that the scope of protection of the present invention is obviously not limited to these specific embodiments. Without departing from the principles of the present invention, those skilled in the art can make equivalent changes or substitutions to the relevant technical features, and the technical solutions after these changes or substitutions will all fall within the scope of protection of the present invention.

[0066] The above description is merely a preferred embodiment of the present invention and is not intended to limit the invention. Various modifications and variations can be made to the present invention by those skilled in the art. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the scope of protection of the present invention.

Claims

1. A method for optimizing the design of a short-process conveying system in a sand and gravel plant, characterized in that, include: The three-dimensional point cloud data of several monitoring points in the area where the sand and gravel plant is located are obtained by drone to analyze the transportation status of the area where the sand and gravel plant is located. Based on the conveying status, determine whether to perform path heterogeneity analysis on each conveying process node on the sand and gravel conveying path. The path heterogeneity analysis includes obtaining the running vector of each conveying process node to construct a set of several process nodes. Risk transport process nodes are marked based on the running vectors of each benchmark transport process node and the remaining transport process nodes within the set of process nodes. Material analysis is performed on each of the aforementioned risk transport process nodes. The material analysis includes acquiring several material characteristics of each risk transport process node during the monitoring period and constructing a material response curve. The material characteristics include material particle size distribution parameters and material moisture content. The monitoring period is divided into several monitoring sub-periods, and risk monitoring sub-periods are selected based on the material response curve segments within each monitoring sub-period. During the risk monitoring sub-period, the conveyor belts of sand and gravel conveying processes at each risk conveying process node are optimized, and node joint analysis is performed on each risk conveying process node to determine whether to perform collaborative optimization on the upstream conveying process node. The conveying optimization includes reducing the belt speed of the sand and gravel conveyor belt and increasing the belt tension of the sand and gravel conveyor belt.

2. The optimized design method for a short-process conveying system in a sand and gravel plant according to claim 1, characterized in that, The process of analyzing the conveying status of the area where the sand and gravel plant is located includes, The elevation heterogeneous parameters are determined based on the characteristic three-dimensional point cloud data of each monitoring point, wherein the characteristic three-dimensional point cloud data is point cloud data perpendicular to the horizontal plane. If the elevation heterogeneity parameter of the area where the sand and gravel plant is located exceeds the elevation heterogeneity benchmark value, then the transportation status of the area where the sand and gravel plant is located is determined to be a risky transportation status.

3. The optimized design method for a short-process conveying system in a sand and gravel plant according to claim 2, characterized in that, The process of determining whether to perform path heterogeneity analysis on each transport node along the sand and gravel transport route includes: If the transportation status of the area where the sand and gravel plant is located is the risky transportation status, then path heterogeneity analysis is performed on each transportation process node on the sand and gravel transportation path.

4. The optimized design method for a short-process conveying system in a sand and gravel plant according to claim 3, characterized in that, The process of constructing a set of process nodes and determining path tortuosity parameters includes, The running vectors of each conveying process node are obtained according to the short process conveying sequence of sand and gravel. The three adjacent conveying process nodes and the running vectors corresponding to each conveying process node are stored in a single data set, and the data set is determined as the process node set. The benchmark conveying process node is determined based on the short-process conveying sequence of sand and gravel in the conveying process node; The vector angles between the running vectors of the baseline transport process node and the running vectors of the other transport process nodes are determined to determine the path tortuosity parameters. The running vector is determined based on the sand and gravel conveying direction of the conveying process node.

5. The optimized design method for a short-process conveying system in a sand and gravel plant according to claim 4, characterized in that, The process of marking risk delivery process nodes includes, If the path tortuosity parameter of the process node set exceeds the path tortuosity baseline value, then the transport process node in the process node set is marked as a risk transport process node.

6. The optimized design method for a short-process conveying system in a sand and gravel plant according to claim 5, characterized in that, The process of constructing a material response curve includes, During the monitoring period, the particle size distribution parameters and moisture content of the materials at several monitoring times are obtained for each of the aforementioned risk transport process nodes. The adhesion trend parameters are determined based on the particle size distribution parameters and moisture content of the material at the same monitoring time. The material response curve is constructed with the adhesion trend parameter as the vertical axis and time as the horizontal axis.

7. The optimized design method for a short-process conveying system in a sand and gravel plant according to claim 6, characterized in that, The process of selecting risk monitoring sub-periods includes, If the response change parameter of the monitored sub-period exceeds the response change benchmark value, the monitored sub-period will be marked as a risk monitoring sub-period.

8. The optimized design method for a short-process conveying system in a sand and gravel plant according to claim 7, characterized in that, The process of optimizing the conveyor belts of sand and gravel conveying processes at each risk-prone process node during the risk monitoring sub-period includes: Obtain the response change parameters of each risk transmission process node in each risk monitoring sub-period and the path tortuosity parameters of each risk transmission process node; The decrease in belt speed is positively correlated with the response change parameter, and the increase in belt tension is positively correlated with the path tortuosity parameter.

9. The optimized design method for a short-process conveying system in a sand and gravel plant according to claim 8, characterized in that, The process of conducting joint node analysis on each risk transmission process node includes, Obtain the current allowable throughput and upstream material supply rate for each of the aforementioned risk transport process nodes; If the upstream material supply rate exceeds the current allowable throughput, then the upstream conveying process nodes are optimized collaboratively, and the upstream collaborative parameters are determined based on the path tortuosity parameters of the risk conveying process nodes and the response change parameters of the risk monitoring sub-period.

10. The optimized design method for a short-process conveying system in a sand and gravel plant according to claim 9, characterized in that, The process of determining the response time window for collaborative analysis of the aforementioned risk transmission process nodes includes, If a risk transmission process node meets the immediate response condition, then a collaborative analysis is performed on the risk transmission process node at the current moment. If a risk transmission process node does not meet the immediate response conditions, a collaborative analysis of the risk transmission process node will be performed after a first time period. The first time period is determined based on the difference between the upstream collaborative parameters and the upstream collaborative benchmark value.

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