Intelligent anti-blocking and anti-collapse collaborative control method for bulk storage bin

Abstract

The application discloses a kind of intelligent bulk storage safety protection system anti-blocking, anti-collapse collaborative control method, specifically related to bulk storage safety protection control technical field;By arranging material pressure, strain, vibration and humidity sensor in the storehouse, multi-source state parameter set is constructed, material accumulation form, bridge feature and wet partial load risk are identified, and storehouse blocking risk identification is formed;Combined with the strain change and vibration response of structural support node, it is judged whether there is structural instability trend, and collapse storehouse early warning feature is generated;Through space overlap degree, grade similarity and evolution trend and other factors, build blocking and collapsing collaborative risk judgment model, output joint high-risk state and generate control instruction, and then link to execute top speed limit feeding, bottom rhythm pressure relief and wall body inflatable support response;The method has the advantages of high recognition accuracy, timely response, strong structure protection capability and the like, effectively improves the operation safety of bulk storage.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of bulk storage safety protection control, in particular to a kind of intelligent bulk storage safety protection system anti-blocking, anti-collapse collaborative control method. BACKGROUND

[0002] At present, bulk storage is widely used in port, power plant, mine, grain depot and other bulk storage and transportation scenes, which is mainly used for short-term or long-term storage of coal, ore, grain and other granular or blocky materials. Since most of the bulk storage is gravity self-flowing structure, under the condition of high-frequency loading and unloading operation, problems such as blocking and bridging of the material in the storage may occur due to uneven distribution of the material, humidity change, particle adhesion and other factors, which may even induce instability of the storage wall or collapse of the storage bottom structure, resulting in equipment damage and personal injury accidents.

[0003] The current common solution is mostly local sensing monitoring or single structure reinforcement means, which has problems such as response delay, disposal lag and alarm misjudgment. Especially under the conditions of high humidity and high dust or seasonal alternating climate, the causes of blocking are more complex, and the traditional method is difficult to realize the linkage identification and collaborative protection of blocking and collapse.

[0004] For example, in a large bulk transfer station in a port, the uneven load bridging caused by the accumulation of wet coal powder in the storage may cause abnormal concentration of pressure on the upper part of the storage body, which may cause local deformation of the storage body. Such deformation often develops rapidly in a short time, and the traditional monitoring method cannot accurately identify the "blocking-collapse" collaborative evolution process in the key window period. SUMMARY

[0005] The purpose of the present application is to provide an intelligent bulk storage safety protection system anti-blocking, anti-collapse collaborative control method to solve the problems in the background art.

[0006] In order to achieve the above purpose, the present application provides the following technical scheme: an intelligent bulk storage safety protection system anti-blocking, anti-collapse collaborative control method, comprising:

[0007] Collecting data of material pressure sensors, strain sensors, vibration sensors and humidity sensors arranged at different heights and different wall regions inside the bulk storage to form a multi-source state parameter set;

[0008] According to the distribution gradient of material pressure along the height direction in the parameter set, the material accumulation form is identified, and according to the corresponding change relationship between strain and vibration signals, it is determined whether there is local bridging feature in the current storage, and a preliminary blocking risk identification B1 is generated;

[0009] According to the numerical change of humidity sensor in the bottom and wall corner region of the storage, combined with historical material characteristics and temperature and humidity interaction records, it is judged whether there is a condition that may cause adhesion or wet uneven load, and the blocking risk identification is updated to B2;

[0010] If the blockage risk identifier B2 is in a high-risk state, further analyze the rate of change of the strain sensor at the support node set on the wall of the bin, and compare it with the preset vibration response threshold to identify whether there is a structural instability trend, forming a bin collapse early warning feature;

[0011] Logically associate the blockage risk identifier B2 with the bin collapse early warning feature to construct a blockage and collapse collaborative risk judgment model. If the model output result determines a joint high-risk state, a joint response control instruction is generated;

[0012] According to the control instruction, control the discharge speed of the top hopper of the bulk material bin, and simultaneously control the bottom adjustable discharge mechanism to unload pressure in rhythm, and activate the internal inflation support device of the wall body.

[0013] Preferably, the preliminary blockage risk identifier B1 is generated, comprising:

[0014] Linearly fitting the material pressure values at different height positions in the multi-source state parameter set to obtain a pressure gradient curve, and calculating the pressure change rate ΔP between adjacent measuring points;

[0015] When ΔP appears a sudden change in the set interval and the pressure gradient curve has a local flat section, it is determined that the region may form a bridging structure;

[0016] Retrieve the strain and vibration sensor signals at the corresponding position. If the strain change rate Δε and the vibration amplitude A are negatively correlated within the time window t, it is confirmed that the region is a restricted vibration area;

[0017] Match the position of the restricted vibration area and the pressure abnormal area. If the spatial overlap degree exceeds the preset threshold, generate the preliminary blockage risk identifier B1.

[0018] Preferably, the determination that the region may form a bridging structure comprises:

[0019] First derivative calculation is performed on the pressure gradient curve to extract the pressure change rate ΔP between each measuring point;

[0020] Compare ΔP with the preset sudden change threshold range If ΔP in a continuous region shows a sudden increase or decrease trend, mark it as a suspected abnormal section Z1;

[0021] Identify a local flat section Z2 with slow pressure change and derivative close to zero in the Z1 section, and further calculate the length L and the upstream and downstream pressure difference ΔP';

[0022] If the length L of Z2 exceeds the set bridging critical value L0, and ΔP' exceeds the set threshold value ΔP0, it is determined that the region is a high-risk area that may form a bridging structure.

[0023] Preferably, the judging whether there is a condition causing sticking or wet bias load comprises:

[0024] Collecting humidity sensor data at the center of the bin bottom and the wall corner area, calculating the humidity growth rate ΔH and the spatial distribution gradient in the time sequence ;

[0025] Calling the historical temperature and humidity characteristic parameter table of the corresponding material, including the critical humidity absorption value and the humidity absorption rate curve with temperature change, judging whether the current humidity level exceeds the critical humidity absorption threshold of the material;

[0026] If ΔH continues to rise, and the humidity of the wall corner area is significantly higher than that of the center of the bin bottom, mark the area as a potential wet sticking area Z3;

[0027] If Z3 and the spatial area of the blockage risk identification B1 overlap, update the risk identification to B2.

[0028] Preferably, the identifying whether there is a structural instability trend comprises:

[0029] Extracting strain sensor data at the set support nodes of the bin wall, calculating the strain change rate Δε in unit time;

[0030] Judging whether Δε continuously exceeds the set strain growth threshold in historical working conditions , if Δε is positive and the growth rate is accelerated in continuous multiple sampling periods, mark it as a strain abnormal point;

[0031] Extracting the corresponding position vibration sensor signal, comparing the current vibration amplitude with the set vibration response threshold , if the vibration response is abnormally attenuated, it is considered that the structural response ability is decreased;

[0032] If the strain abnormal point and the vibration attenuation area coincide in spatial position more than a set threshold , generate a structural instability feature identification.

[0033] Preferably, the logically associating and analyzing the blockage risk identification B2 and the collapse warning feature to build a blockage and collapse collaborative risk judgment model comprises:

[0034] Extracting the blockage risk identification B2 and the structural instability feature identification S in the current risk state, and mapping them into a multi-dimensional state vector V for reflecting the position, level and influence range of the dangerous area in the bin;

[0035] According to the preset risk coupling determination rule, calculating the blockage and collapse coupling factor , the risk coupling determination rule includes spatial overlap degree, risk level similarity and evolution trend consistency;

[0036] like Exceeding the set coupling threshold If all of them are at or above the high-risk level, then the current status is judged as a joint high-risk situation.

[0037] Based on the location and level of the risk area, generate joint response control instructions.

[0038] Preferably, controlling the unloading speed of the top hopper of the bulk material silo according to the control command includes: after receiving the joint response control command, adjusting the opening of the unloading valve of the top hopper of the bulk material silo, reducing the falling speed of the material according to the preset rate curve, controlling the bottom discharge mechanism to operate in an intermittent rhythm, and periodically opening and closing the discharge port.

[0039] The technical effects and advantages provided by the present invention in the above technical solution are as follows:

[0040] 1. The present invention provides a method for coordinated control of blockage and collapse prevention in an intelligent bulk material silo safety protection system. Based on the joint sensing of multi-source state parameters such as material pressure, strain, vibration and humidity, it constructs a full-process monitoring model including silo blockage risk identification, moisture load analysis, structural instability early warning and risk collaborative judgment. It breaks through the technical bottleneck of traditional single-parameter early warning lag and lack of coordination, realizes dynamic identification and closed-loop linkage response of silo blockage-collapse coupling risks, and significantly improves the inherent safety level of bulk material silo operation.

[0041] 2. By setting a rhythmic decompression strategy and a structural reverse support mechanism, this invention not only achieves precise unloading of the congested area but also uses an adjustable air pressure support device to regulate the stress on the silo wall in real time, preventing structural deformation and sudden collapse caused by the evolution of congestion. Compared with existing solutions, this invention has advantages such as rapid response, high control precision, and strong adaptability. It is particularly suitable for the safety protection of heavy-duty bulk material silos in high-frequency loading and unloading scenarios such as ports, power plants, and mines, and has good engineering application prospects. Attached Figure Description

[0042] To more clearly illustrate the technical solutions in the embodiments of this application or the prior art, the drawings used in the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments recorded in this invention. For those skilled in the art, other drawings can be obtained based on these drawings.

[0043] Figure 1 This is a flowchart of the method of the present invention. Detailed Implementation

[0044] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, the technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0045] For examples, please refer to Figure 1 As shown in this embodiment, the anti-blocking and anti-collapse collaborative control method of the intelligent bulk material silo safety protection system includes:

[0046] Data from material pressure sensors, strain sensors, vibration sensors, and humidity sensors installed at different heights and in different areas of the bulk silo wall are collected to form a multi-source state parameter set;

[0047] Based on the distribution gradient of material pressure along the height direction in the parameter set, the material accumulation pattern is identified, and based on the corresponding change relationship between strain and vibration signal, it is determined whether there are local bridging features in the current warehouse, and a preliminary warehouse blockage risk indicator B1 is generated.

[0048] Based on the changes in humidity sensor readings at the bottom and corners of the silo, combined with historical material characteristics and temperature and humidity interaction records, it is determined whether there are conditions that could lead to adhesion or uneven moisture loading, and the silo blockage risk label is updated to B2.

[0049] If the blockage risk indicator B2 is in a high-risk state, further analysis is conducted on the rate of change of the parameter-concentrated strain sensor at the set support node of the warehouse wall, and it is compared with the preset vibration response threshold to identify whether there is a structural instability trend and form a warehouse collapse early warning feature.

[0050] Logical correlation analysis is performed between the blockage risk identifier B2 and the collapse warning feature to construct a blockage-collapse collaborative risk judgment model. If the model output result determines that it is a joint high-risk state, a joint response control command is generated.

[0051] According to the control command, the unloading speed of the top hopper of the bulk material silo is controlled, and the adjustable discharge mechanism at the bottom is linked to perform rhythmic depressurization, while activating the air-support device inside the wall.

[0052] In this invention, the first step is to comprehensively assess the internal operating status of the bulk material silo in order to subsequently determine the risk of blockage and collapse. This step specifically includes the following:

[0053] Various types of sensors are deployed at multiple locations along the height of the bulk material silo and in different silo wall areas, including but not limited to the following:

[0054] Material pressure sensors: These sensors are installed at multiple height levels along the vertical direction inside the bulk material silo to collect real-time data on the pressure distribution on the silo walls and bottom during material accumulation, with particular attention to abnormal increases in local pressure or sudden changes in pressure gradient.

[0055] Strain sensors: installed in key stress areas of the bulk silo structure, such as the middle of the silo wall, support nodes and corner connections, to monitor minute deformations of the structure during stress and determine whether there are potential structural anomalies.

[0056] Vibration sensors: installed on the inner wall of the silo and on the support beams, used to detect the impact, vibration intensity and propagation characteristics of materials during unloading, and to help identify abnormal vibration signals caused by "bridging" or local voiding.

[0057] Humidity sensor: Installed in the lower part and corners of the warehouse, or other areas prone to water accumulation and dampness, to monitor local humidity changes and determine whether the material has absorbed moisture and clumped or is under wet load.

[0058] During operation, the aforementioned sensors continuously collect environmental and structural state parameters at preset time frequencies. The system then aggregates and standardizes this data to form the following multi-source state parameter set P: ;in: This indicates the material pressure value at a specific height. This represents the strain value at the corresponding location on the bin wall; This indicates the vibration frequency and acceleration value at that location; The value represents the humidity; ... and so on, where n represents the total number of collection points.

[0059] In this invention, to achieve early identification of bulk material silo blockage risk, the material accumulation pattern within the silo is first identified based on material pressure data from a multi-source state parameter set. Combined with strain and vibration response characteristics, the presence of local bridging structures is determined, thereby generating a preliminary blockage risk indicator B1. The technical process of this step includes the following:

[0060] First, the system extracts real-time data from multiple material pressure sensors deployed along the height of the bulk silo from a multi-source state parameter set. These pressure measurement data points are then subjected to linear fitting to reconstruct the material accumulation curve and construct a continuous pressure gradient distribution curve.

[0061] The fitted pressure curve is processed, and the pressure change rate ΔP between any two adjacent measuring points is calculated. That is, the difference between the pressure measured by the upper and lower sensors is divided by the vertical distance between them to obtain the pressure change value per unit height.

[0062] To determine whether the material forms a bridging structure in a certain area, that is, whether the accumulated material forms a self-stabilizing structure locally, the present invention further performs the following processing:

[0063] The first derivative of the aforementioned pressure gradient curve is calculated to obtain the local pressure change rate ΔP at each point. Then, these rates of change are compared with a set abrupt change threshold range. Compare them. This mutation threshold. It is derived from the statistical analysis of the maximum ΔP fluctuation range under normal stacking conditions in historical operating conditions, and can usually be set to a range of 2.5 times the standard deviation. If ΔP shows a sharp increase or decrease in a certain continuous area, and the duration is not less than the distance between 3 measuring points, then the area is marked as a suspected abnormal section Z1.

[0064] Further analysis of the local pressure change trend within section Z1 is conducted to identify a continuous section Z2 where the rate of pressure change approaches zero (i.e., the absolute value of the derivative is less than the set micro-change threshold ε0, typically set to 0.01 kPa / m). The length L of this smooth section and the pressure difference ΔP′ between its upstream and downstream sides are then calculated.

[0065] If the length L of the gentle section Z2 is greater than the set bridging threshold L0 (e.g., set to 1 / 5 of the silo's diameter), and the upstream and downstream pressure difference ΔP′ is greater than the set threshold ΔP0 (e.g., set to 10 kPa), then the area is determined to be a potential high-risk bridging zone. The aforementioned thresholds L0 and ΔP0 can be set according to the silo's structural dimensions and material characteristics, and have a certain degree of adjustability.

[0066] For the Z2 section, which is identified as a high-risk bridge-building area, data from the synchronous time window of strain sensors and vibration sensors deployed at the corresponding spatial locations are retrieved. The time window t can be set between 10 and 60 seconds to capture dynamic response trends.

[0067] A comparative analysis of the trends of strain rate of change Δε and vibration signal amplitude A within a time window t is performed. If it is found that the strain signal continuously increases while the vibration signal gradually weakens, and there is a significant negative correlation between the two (e.g., Pearson correlation coefficient r < 0.05), then... If the signal is positive, it indicates that the vibration in the area is restricted, the structural stress is concentrated, and it has typical "bridging" restricted characteristics.

[0068] The identified restricted vibration zone and pressure anomaly zone are spatially matched. If the overlap between the two exceeds the set overlap threshold K in space (e.g., the projected overlap length exceeds 70% of the length of the anomaly zone), the current area is determined to be a high-risk area for blockage, and a preliminary blockage risk indicator B1 is generated.

[0069] In this invention, based on the changes in humidity sensor readings at the bottom and corners of the storage chamber, combined with historical material characteristics and temperature-humidity interaction records, it is determined whether conditions leading to adhesion or uneven moisture loading exist, and the storage blockage risk flag is updated to B2. This step includes the following technical contents:

[0070] Humidity sensors are installed at the center of the bottom of the bulk silo and in the contact areas between the silo wall and the corners to collect humidity change data in real time and construct a humidity time series curve H(t). The data collection frequency for each sensor point is set to once per minute.

[0071] The humidity curves of each sensor are differentiated to calculate the humidity growth rate ΔH per unit time, which is the change in humidity over a continuous time interval divided by the time span. The ΔH value reflects the rate of change in local humidity.

[0072] Compare the humidity values ​​at the center of the warehouse floor with those at the corners to calculate the spatial distribution gradient. That is, the difference between the humidity value at the corner and the humidity value at the center of the wall, divided by the distance between the two. If G h The value remains positive and exceeds the set threshold. (For example, 5%RH / m) indicates that there may be a tendency for moisture to accumulate in the corner areas.

[0073] Access the material temperature and humidity characteristic parameter table stored in the database, including but not limited to the following key indicators:

[0074] Critical hygroscopic humidity : This refers to the minimum humidity threshold at which the material will clump or stick together under a set temperature condition, expressed in %RH.

[0075] Moisture absorption sensitivity coefficient α: represents the rate of change in moisture absorption capacity caused by a unit increase in temperature.

[0076] Combined with the currently measured ambient temperature Compare the humidity values ​​with the humidity readings to determine if the humidity at each measuring point exceeds the critical hygroscopic humidity at the corresponding temperature. This can be obtained through a linear model or a lookup table method. If the humidity at any measuring point in the corner area exceeds... If this is the case, it can be preliminarily determined that there is a risk of material absorbing moisture and sticking together.

[0077] If both of the following conditions are met:

[0078] The humidity growth rate ΔH of each corner sensor is consistently greater than (Empirical value such as 0.5%RH / min);

[0079] Spatial distribution gradient Exceeding the set threshold Furthermore, the humidity at the corners of the walls is more than 8% RH higher than the humidity at the center.

[0080] The corner area is then marked as a potential moisture adhesion area Z3. This area may experience moisture accumulation due to poor ventilation, local condensation, or rainwater leakage, which can easily cause material adhesion and uneven accumulation, resulting in static load eccentricity.

[0081] Spatial matching analysis is performed between the spatial range of the generated preliminary blockage risk marker B1 and the current potential moisture adhesion area Z3. If the overlap of their projections exceeds a set threshold (e.g., 60%), the blockage is considered highly likely to be induced by moisture adhesion. In this case, the original blockage risk marker B1 is upgraded to B2, indicating that the blockage risk in this area is enhanced by moisture-induced off-center loading.

[0082] In this invention, if the blockage risk indicator B2 is in a high-risk state, the rate of change of the parameter-concentrated strain sensor at the set support node of the silo wall is further analyzed and compared with a preset vibration response threshold to identify whether there is a structural instability trend, thus forming a silo collapse early warning feature. This step mainly includes the following technical processes:

[0083] Real-time data collected by strain sensors deployed at key support nodes of the silo wall (such as stiffener connection points, arch positions, and silo corner turning areas) is extracted. Sampling is performed at fixed time intervals (e.g., 1 minute), and the strain change rate Δε is calculated by dividing the difference between the current and previous strain values ​​by the time interval, expressed in microstrain per minute. Threshold. The maximum continuous strain change rate under historical stable operating conditions should be used to determine the boundary. It is recommended to use a method based on a statistical model, such as using the mean of the historical strain change rate plus twice the standard deviation as the boundary.

[0084] Set a continuous sampling period window N (e.g., 5 minutes). If the Δε values ​​within this window are all positive, it indicates that the strain is continuously increasing, and the Δε values ​​are gradually increasing, showing an accelerating growth trend. If the above conditions are met, the support node is marked as a "strain anomaly point," meaning that the structural unit it is located in may be experiencing continuous stress concentration or deformation accumulation.

[0085] Retrieve vibration sensor data located spatially adjacent to the strain anomaly point to obtain the vibration amplitude A within the current time period. Threshold Based on the minimum effective amplitude setting under normal vibration conditions, the lower quartile of the vibration amplitude under normal operating conditions can be selected as the reference critical value.

[0086] If the current vibration amplitude A remains below The trend of change is steadily decreasing, indicating that the structural position response capability is weakened, which may be due to internal cracks, loose connections or decreased structural stiffness.

[0087] Spatially match the aforementioned "strain anomaly points" with the "vibration attenuation areas". Using the projected positions of the sensor deployment points as the basis for comparison, calculate their spatial overlap rate, i.e., the proportion of sensor points in the overlapping area.

[0088] If the spatial overlap rate exceeds a preset threshold (If set to 60%), it is considered that abnormal strain and vibration characteristics occur synchronously in the same area, exhibiting typical characteristics of structural instability evolution. Based on this, a structural instability characteristic identifier S is generated, indicating that the area is at potential risk of collapse.

[0089] In this invention, the blockage risk identifier B2 and the collapse early warning feature are logically correlated to construct a blockage-collapse collaborative risk judgment model. If the model output result determines that it is a joint high-risk state, a joint response control command is generated.

[0090] Extract the current congestion risk identifier B2 and structural instability feature identifier S, which respectively contain information such as risk location (spatial coordinates), risk level (e.g., medium, high, extremely high), and influence radius (in meters). Map the information extracted from B2 and S into a unified multi-dimensional state vector V, whose structure includes: location information V1 (e.g., position height, circumferential angle); risk level V2 (numerical encoding, e.g., "high" is 2, "extremely high" is 3); influence range V3 (unit length represents the radius of the influence interval); and time stamp V4 (timestamp sequence used for trend analysis). The state vector facilitates quantitative analysis and coupling calculations of risk attributes in subsequent models.

[0091] To comprehensively assess the inherent correlation between the risks of margin calls and margin calls, this invention constructs a risk coupling judgment rule R1, which includes the following three dimensions:

[0092] To analyze the degree of overlap between the spatial regions corresponding to B2 and S, the ratio of the intersection area to the minimum envelope area is used as the overlap parameter G1, which is usually in the range of 0 to 1.

[0093] Perform a difference calculation on the risk levels of B2 and S, and define the level difference. And calculate similarity parameters. , where Max_Level is the maximum level value set (e.g., 3).

[0094] By comparing the time-varying trends in state vector V4, it is determined whether risk indicators are rising or deteriorating synchronously, thus constructing a trend consistency parameter G3. If the proportion of two types of risks growing in the same direction over the past N time steps exceeds 60%, they are considered to have a consistent trend. Combining the above three indicators, a weighted formula is used to calculate the plugging-up coupling factor. The expression is: The weight It can be set according to the on-site working conditions; for example, the default value can be... =0.4, =0.3, =0.3.

[0095] Set the threshold for determining the plugging coupling factor This is usually derived from statistical analysis of historical failure cases, for example... =0.75. When the calculated coupling factor... If the threshold is exceeded and both B2 and S are at a high or very high risk level, the current state will be judged as a joint high-risk state, indicating that the bulk silo faces a combined risk of both blockage and collapse.

[0096] After identifying a joint high-risk status, a joint response control instruction C is generated based on the location and level of the risk area, specifically including:

[0097] Limiting the feeding rate: By controlling the hopper feed rate, the accumulation speed of materials in the silo is slowed down, reducing the new load; Starting the depressurization procedure: The bottom unloading device is activated to discharge part of the material at an intermittent rhythm, releasing the pressure in the blocked area; Activating local structural support units: The built-in airbag support or silo wall support rod is triggered to provide additional support in the structural strain section; Issuing early warning signals: Real-time risk notifications are pushed to the monitoring platform and maintenance personnel terminals to prompt on-site intervention.

[0098] In this invention, the unloading speed of the top hopper of the bulk silo is controlled according to control commands, and the adjustable discharge mechanism at the bottom is linked to perform rhythmic depressurization, while simultaneously activating the internal air-support device of the wall. This process includes the following steps:

[0099] Upon receiving the joint response control command, the control system immediately enters the rhythmic depressurization execution mode and initiates the control subroutine for the top hopper discharge valve.

[0100] Based on the preset material discharge rate curve (which can be a linear decreasing curve or an exponentially flattening curve) of the control strategy, the material falling speed is reduced by adjusting the electric actuator of the discharge valve. This rate curve is generated by combining historical discharge conditions and material flow characteristics, and its commonly used form is... ,in t represents the initial unloading speed, k is the adjustment coefficient, and t is the time. This slows down the pressure transmission of newly added material at the top to the clogged area, reduces the concentrated load effect above the clogged area, and prevents crown collapse or eccentric load expansion induced by rapid accumulation.

[0101] The control system periodically controls the opening and closing state of the adjustable discharge mechanism at the bottom according to the set pressure relief rhythm T_on / T_off (e.g., working for 30 seconds and pausing for 90 seconds), so as to realize the "pulse" discharge of materials.

[0102] The screw conveyor or pneumatic sliding valve actuator is controlled by a variable frequency motor to start and stop the discharge device in each control cycle, and the rhythm control of releasing the accumulated pressure is achieved by gradually unloading the material.

[0103] Pre-install airbag-type or telescopic inflatable support devices in designated areas of the warehouse wall, and place them in easily deformable locations, such as the middle or corner areas of the warehouse wall. The airbags are precisely inflated and deflated through an air pressure control system.

[0104] Based on the strain amplitude and risk level obtained from the analysis, the inflation pressure range (e.g., 0.2~0.5 MPa) is set, and the pressure change is controlled by a proportional valve to apply a controllable reverse support force.

[0105] In areas where localized strain is concentrated in the structure, appropriate air pressure support is used to offset the localized stress, improve the structural stiffness, and prevent further deformation or instability.

[0106] During the rhythmic decompression and support process, real-time data from strain sensors and vibration sensors on the silo wall were continuously collected to construct time series ε(t) and A(t) curves and assess the structural recovery trend.

[0107] Setting structural strain safety thresholds Vibration amplitude recovery threshold ,For example =100με (micro-strain). =0.3 g (standardized unit of gravitational acceleration). If within multiple consecutive sampling periods... and This indicates that the market is trending towards stability.

[0108] If the above safety judgment conditions are met, the response control process will automatically exit, the normal operation mode will be restored, and the regular material feeding and discharge control and risk monitoring logic will be restarted.

[0109] The above description is merely a specific embodiment of this application, but the scope of protection of this application is not limited thereto. Any changes or substitutions that can be easily conceived by those skilled in the art within the scope of the technology disclosed in this application should be included within the scope of protection of this application.

Claims

1. A method for intelligent anti-blocking and anti-collapse collaborative control of bulk material silos, characterized in that: include: Data from material pressure sensors, strain sensors, vibration sensors, and humidity sensors installed at different heights and in different areas of the bulk silo wall are collected to form a multi-source state parameter set; Based on the distribution gradient of material pressure along the height direction in the parameter set, the material accumulation pattern is identified, and based on the corresponding change relationship between strain and vibration signal, it is determined whether there are local bridging features in the current warehouse, and a preliminary warehouse blockage risk indicator B1 is generated. Based on the changes in humidity sensor readings at the bottom and corners of the silo, combined with historical material characteristics and temperature and humidity interaction records, it is determined whether there are conditions that could lead to adhesion or uneven moisture loading, and the silo blockage risk label is updated to B2. The determination of whether conditions leading to adhesion or moisture-induced uneven loading exist includes: collecting humidity sensor data located at the center of the warehouse floor and multiple corner areas, and calculating the humidity growth rate ΔH and spatial distribution gradient within the time series. ; Retrieve the historical temperature and humidity characteristic parameter table for the corresponding material, including the moisture absorption critical value. And the moisture absorption rate versus temperature curve, to determine whether the current humidity level exceeds the critical moisture absorption threshold of the material; if ΔH continues to rise, and If the humidity in the corner area is significantly higher than that in the center of the warehouse bottom, the area is marked as a potential damp and sticky area Z3; if Z3 overlaps with the spatial area of ​​the warehouse blockage risk marker B1, the risk marker is updated to B2. If the blockage risk indicator B2 is in a high-risk state, further analysis is conducted on the rate of change of the parameter-concentrated strain sensor at the set support node of the warehouse wall, and it is compared with the preset vibration response threshold to identify whether there is a structural instability trend and form a warehouse collapse early warning feature. Logical correlation analysis is performed between the blockage risk identifier B2 and the collapse warning feature to construct a blockage-collapse collaborative risk judgment model. If the model output result determines that it is a joint high-risk state, a joint response control command is generated. According to the control command, the unloading speed of the top hopper of the bulk material silo is controlled, and the adjustable discharge mechanism at the bottom is linked to perform rhythmic depressurization, while activating the air-support device inside the wall.

2. The intelligent anti-blocking and anti-collapse collaborative control method for bulk material silos according to claim 1, characterized in that: The generation of the initial blockage risk indicator B1 includes: Linear fitting was performed on the material pressure values ​​at different height positions in the multi-source state parameter set to obtain the pressure gradient curve, and the pressure change rate ΔP between adjacent measuring points was calculated. When ΔP changes abruptly within the set range and the pressure gradient curve has a local flat section, the judgment area may form a bridging structure. The determination region may form a bridging structure, including: calculating the first derivative of the pressure gradient curve and extracting the pressure change rate ΔP between each measuring point; comparing ΔP with a preset abrupt change threshold range. By comparison, if ΔP shows a sudden increase or decrease trend in a certain continuous area, it is marked as a suspected abnormal section Z1; in section Z1, a local flat section Z2 with slow pressure change and derivative close to zero is identified, and its length L and the pressure difference ΔP′ between its upstream and downstream are further calculated; if the length L of Z2 exceeds the set bridging critical value L0, and ΔP′ exceeds the set threshold ΔP0, then the area is comprehensively judged as a high-risk area where bridging may form. Retrieve strain and vibration sensor signals at the corresponding locations. If the strain rate of change Δε and the vibration amplitude A are negatively correlated within the time window t, then the area is confirmed as a restricted vibration zone. The restricted vibration zone and the abnormal pressure zone are matched in location. If the spatial overlap exceeds a preset threshold, a preliminary blockage risk indicator B1 is generated.

3. The intelligent anti-blocking and anti-collapse collaborative control method for bulk material silos according to claim 1, characterized in that: The identification of whether there is a structural instability trend includes: Extract strain sensor data set at designated support nodes on the silo wall and calculate the strain change rate Δε per unit time; Determine whether Δε continuously exceeds the strain growth threshold set under historical operating conditions. If Δε is positive and the growth rate accelerates in multiple consecutive sampling periods, it is marked as a strain anomaly point. Extract the vibration sensor signal at the corresponding location and compare the current vibration amplitude with the set vibration response threshold. If the vibration response decays abnormally, it is considered that the structural response capability has decreased. If the strain anomaly point and the vibration attenuation region coincide in spatial location for more than a set threshold, Then, structural instability feature identifiers are generated.

4. The intelligent anti-blocking and anti-collapse collaborative control method for bulk material silos according to claim 1, characterized in that: The step of logically associating the blockage risk identifier B2 with the collapse early warning features to construct a blockage-collapse collaborative risk judgment model includes: Extract the blockage risk identifier B2 and structural instability feature identifier S under the current risk status, and map them uniformly into a multi-dimensional state vector V to reflect the location, level and impact range of the dangerous area within the warehouse; Calculate the plugging coupling factor based on the preset risk coupling judgment rules. The risk coupling determination rules include spatial overlap, risk level similarity, and consistency of evolution trend; like Exceeding the set coupling threshold If both B2 and S have risk levels of high or very high, then the current state is judged to be a joint high-risk state. Based on the location and level of the risk area, generate joint response control instructions.

5. The intelligent anti-blocking and anti-collapse collaborative control method for bulk material silos according to claim 4, characterized in that: The control of the unloading speed of the top hopper of the bulk material silo according to the control command includes: after receiving the joint response control command, adjusting the opening of the unloading valve of the top hopper of the bulk material silo, reducing the falling speed of the material according to the preset rate curve, controlling the bottom discharge mechanism to operate in an intermittent rhythm, and periodically opening and closing the discharge port.

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