Self-adaptive intelligent loading system and method under variable material characteristic condition

Abstract

The self-adaptive intelligent loading system comprises a belt conveyor, a surge bin, a quantitative bin and a loading chute which are connected in sequence, four sets of gates are arranged at the bottom of the surge bin, the gates are connected with a PID controller in an electric control mode, the PID controller controls opening and closing of the gates, and the quantitative bin is connected with the loading chute in an electric control mode. The opening degree of the gate can be adjusted; the quantitative bin is provided with a weighing sensor, a material level height sensor and a three-dimensional measuring instrument. The volume of materials in a quantitative bin is calculated in real time based on a point cloud slicing algorithm, density data is obtained by combining the weight, different attributes of different materials or the same material are judged, the opening degree of a gate is dynamically adjusted based on an electro-hydraulic servo system of fuzzy control, and self-adaptive loading under the condition that the characteristics of the materials are changeable is achieved; the problem that self-matching of loading parameters is difficult due to change of loaded materials is solved, the loading precision is effectively improved, and the intelligent degree and the working efficiency of a loading station are remarkably improved.

Description

TECHNICAL FIELD

[0001] The present application relates to a kind of self-adapting intelligent car loading system and method under the condition of material characteristics variable, it is a kind of self-adapting intelligent car loading system and method of automatic transport machinery loading bulk material. BACKGROUND

[0002] With the increasingly mature new energy technology, the position of coal in energy structure will be reduced, but in a long period of time in the future, the energy pattern mainly with coal will not change. China is rich in coal resources, but the distribution is extremely unbalanced, and coal transportation plays an important role in the distribution of coal resources among different regions. Rapid quantitative loading is a crucial link to improve the efficiency of coal transportation, which mainly uses batching and weighing equipment to weigh the material quantitatively, and then quickly pours the material into the loading compartment. The batching system control is the key to the control of the rapid quantitative loading system.

[0003] The existing loading method has many shortcomings in practical application: in the control of loading accuracy, the parameters are set depending on manual experience, and in the process of loading and batching, the diversity of coal types and the variability of loading compartment models often lead to the need to continuously adjust the batching parameters to adapt to the batching requirements in different environments. Different coals have significant differences in density, granularity, humidity and other properties, which directly affect the setting of batching parameters. It is difficult for the operator to adjust the gate opening in time and accurately, and there is a lack of real-time density monitoring and linkage mechanism of the actuator, resulting in large loading deviation and affecting the accuracy of subsequent transportation measurement. The collaborative control of efficiency and safety has short boards, and the rough flow control is often used, which cannot dynamically match the conveying rate according to the material level, and due to the lag of safety threshold, the underloading may affect the efficiency or the overloading may trigger the rework. The degree of process automation cannot meet the needs of large-scale production, and the key links such as gate opening and closing and material quantity correction rely on manual operation, which not only increases the loading time due to delayed response, but also inevitably produces large deviation in the batching process. The existence of this deviation greatly affects the material loading accuracy and speed, and reduces the loading efficiency and the efficiency of coal transportation. SUMMARY

[0004] In view of the defects in the prior art, the present application provides a self-adapting intelligent car loading system and method under the condition of material characteristics variable, which adjusts the deviation of car loading and batching parameters by monitoring the material density in real time, improves the material loading accuracy and speed, and improves the car loading efficiency.

[0005] The purpose of the present application is achieved as follows: a self-adapting intelligent car loading system and method under the condition of material characteristics variable includes two aspects: First aspect: An adaptive intelligent loading system under the condition of material property change, comprising a belt conveyor, a buffer bin, a quantitative bin and a loading chute connected in turn, the bottom of the buffer bin is provided with four groups of gates, the gates are connected with a PID controller through electric control, the opening and closing of the gates are controlled by the PID controller, and the opening degree of the gates can be adjusted; the quantitative bin is provided with a weighing sensor, a material level height sensor and a three-dimensional measuring instrument; the material level height sensor is provided with four-stage material level monitoring points, the first-stage monitoring point is used for initial deceleration triggering, the second-stage monitoring point is used for flow grading regulation, the third-stage monitoring point is used for end precise flow limiting, and the fourth-stage monitoring point is used for extreme volume limiting.

[0006] Further, the material level height sensor is a laser radar or a video camera or a combination of a laser radar and a video camera.

[0007] Further, the first-stage material level monitoring point for monitoring the lowest material level in the material level height sensor comprises a weight-type material level meter.

[0008] Further, the gate is provided with a hydraulic oil cylinder for controlling the action of the gate and a displacement sensor for monitoring the displacement of the piston rod of the hydraulic oil cylinder.

[0009] Further, the PID controller is electrically connected with a signal selector, the signal selector can directly output a switching signal to close the gate, or can select a PID controller signal to output, so as to regulate and control the opening degree of the gate for precise control.

[0010] Further, the three-dimensional measuring instrument can be a three-dimensional laser scanner or a ToF time-of-flight camera or a laser ranging sensor array.

[0011] Further, the quantitative bin is connected with the loading chute through a distributing machine, a guide rail is arranged at the lower end of the discharge port of the quantitative bin, and the distributing machine is arranged at the discharge port of the quantitative bin at the lower end and can move horizontally forward and backward through the guide rail, so as to realize uniform distribution of the loaded vehicle.

[0012] Further, the distributing machine comprises a storage box connected with the discharge port of the quantitative bin, the longitudinal lower end of the storage box is connected with the loading chute on one side, the loading chute is a telescopic chute, a scraper conveyor belt is arranged in the storage box, the scraper conveyor belt is used for conveying the material discharged from the quantitative bin into the storage box to the loading chute, and then the material is discharged from the chute into the loaded vehicle.

[0013] Second aspect: An adaptive intelligent loading method under the condition of material property change, comprising a fuzzy control method of gate opening degree and a gate regulation method.

[0014] I. The fuzzy control method of gate opening degree comprises: 1) Based on the loading data experience, combined with the professional knowledge of the operation experts, the basic control method is constructed, which focuses on the relationship between the gate opening and the material density, and considers different materials or different properties of the same material as the basis for judgment; 2) Using the material density and gate opening data in the historical loading data, the trend and law are analyzed, and the control method is optimized and improved; the loading data includes: material density, gate opening.

[0015] II. The gate control method is divided into two stages of coarse matching and fine matching, including the following steps: Let the four gates of the buffer bin be gate A, gate B, gate C and gate D; First coarse matching stage: Step S1, preparation of ingredients: Obtain the loading parameters, including the common density of the loaded material, the standard loading capacity and the standard loading volume of the vehicle to be loaded; Step S2, full-speed matching: The four gates of the buffer bin are fully opened, and the material falls rapidly into the quantitative bin; Step S3, initial deceleration trigger: When the material reaches the first monitoring point, the initial deceleration trigger is triggered, and the PID controller adjusts the four gates from full opening to half opening; Step S4, flow classification control: When the material reaches the second monitoring point, the flow classification control is entered, the PID controller closes the gate A and the gate C, two hydraulic gates, and enters the secondary precise control, at this time the gate B and the gate D are in the open state; Step S5, end precise flow limiting: When the material reaches the third monitoring point, the end precise flow limiting is started, and the PID controller controls the gate D to close, at this time only the gate B is left open.

[0016] Second fine matching stage: Step S6, fine matching starts: According to the common density of the material set as the standard density according to the fuzzy control method, the PID controller adjusts the opening of the gate B; Step S7, deviation adjustment according to density: The three-dimensional measuring instrument 8 obtains the material volume in real time, the weighing sensor obtains the material weight, and the real-time material density is calculated by the calculation unit; the real-time material density data is obtained and compared with the standard density to generate a deviation e, and the deviation e and the differential signal ec reflecting the change rate are used as the input of the PID controller, and the control signal for adjusting the opening of the gate is generated by the PID controller, which is used to adjust the opening of the gate B; Step S8, determine the material loading capacity: The control signal generated by the PID controller is input into the signal selector together with the gate closing signal, and the signal selector discriminates according to the following conditions: If the weighing sensor value of the quantitative bin is less than the standard loading capacity of the loaded vehicle, and the real-time material volume obtained by the three-dimensional measuring instrument is less than the standard loading volume of the loaded vehicle, the signal selector selects the signal of the PID controller to adjust the opening degree of the gate B, and continues to execute step S7; If the weighing sensor value of the quantitative bin is greater than or equal to the standard loading capacity of the loaded vehicle, or the real-time material volume obtained by the three-dimensional measuring instrument is greater than or equal to the standard loading volume of the loaded vehicle, or the material reaches the fourth monitoring point, the signal selector selects the closing gate signal to control the gate B to close; Step S9, the batching is completed: The unloading gate of the quantitative bin is opened, and the material is loaded into the loaded vehicle through the loading chute, and the batching is completed.

[0017] The beneficial effects of the present application are: high loading accuracy, the three-dimensional measuring instrument of the quantitative bin calculates the material volume in real time, the density is dynamically obtained in combination with the weighing sensor data, different materials or different properties of the same material are discriminated, the electro-hydraulic servo system based on fuzzy control dynamically adjusts the opening degree of the gate, self-adaptive loading under the condition of changing material characteristics is realized, the problem that the loading parameters are difficult to self-match due to the change of loaded material is solved, the loading accuracy is effectively improved, and the intelligent degree and working efficiency of the loading station are significantly improved; Efficiency and safety are considered, the four monitoring points of the material level height sensor have clear division of labor, and cooperate with the four groups of gates, which can not only speed up the loading speed through flow control, but also avoid overload, and realize the balance of the two; improve the overall operation efficiency, the PID controller automatically controls the gate, the program automatically executes the batching parameter calling, deviation adjustment and other processes, reduces the lag and error caused by manual intervention, and realizes continuous autonomous operation of the loading; Stability and adaptability are enhanced, multiple sensors provide multi-dimensional data, the PID controller and the program logic respond in real time, so that the system flexibly adapts to complex environment, maintains stable loading quality, and reduces equipment wear and failure risk.

[0018] The present application will be further explained in detail in combination with the description and specific embodiments of the drawings. BRIEF DESCRIPTION OF DRAWINGS

[0019] Figure 1 is a structural schematic diagram of the intelligent loading system of the present application; Figure 2 is a buffer bin gate control method flow chart of the intelligent loading method of the present application; Figure 3 is a buffer bin gate control method flow chart of the intelligent loading method of the present application.

[0020] In the figure: 1-belt conveyor, 2-buffer bin, 3-gate, 4-quantity bin, 5-loading chute, 6-weighing sensor, 7-material level sensor, 8-three-dimensional measuring instrument, 9-material placing boom, 10-hydraulic cylinder, 11-displacement sensor, 12-loaded vehicle, 13-guide rail, 91-material storage box, 92-scraper conveyor. DETAILED DESCRIPTION

[0021] Example 1: An adaptive intelligent loading system under conditions of variable material properties, such as Figure 1 As shown, it includes a steel structure frame as a bearing base, on which a belt conveyor 1, a buffer bin 2, and a quantitative bin 4 are sequentially installed from top to bottom. The output end of the belt conveyor 1 corresponds to the feed port of the buffer bin 2.

[0022] Four groups of gates 3 are installed at the bottom of the buffer bin 2, namely gates A, B, C, and D. The bottom of the gates 3 is connected to the quantitative bin 4. The gates 3 are equipped with a hydraulic cylinder 10 to control their movement and a displacement sensor 11 to monitor the displacement of the hydraulic cylinder piston rod.

[0023] Figure 1 It is a simplified structural diagram, in which the steel structure frame is omitted and only the positional relationship of the belt conveyor 1, buffer bin 2, quantitative bin 4, loading chute 5, etc., as well as various sensors are expressed.

[0024] The quantitative bin 4 is equipped with a weighing sensor 6 at the bottom or supporting structure, and is provided with a material level height sensor 7 with four monitoring points, including a weight hammer level meter for minimum material level detection, which can be combined with a radar sensor, a video camera, and an ultrasonic sensor. The quantitative bin 4 is also equipped with a three-dimensional measuring instrument 8, which obtains the three-dimensional coordinates of the material surface by actively emitting a laser beam and receiving the reflected signal, and calculates the real-time volume through a point cloud algorithm.

[0025] The control system includes a central control system, a signal selector, a PID controller, and a hydraulic system. The hydraulic system delivers pressurized hydraulic oil to the hydraulic cylinder 10 via a hydraulic pump. The central control system is electrically connected to the three-dimensional measuring instrument 8, the load cell 6, the material level sensor 7, the signal selector, and the gate at the outlet of the metering bin 4. The signal selector is electrically connected to the PID controller and can select an output signal based on the material status of the metering bin 4. When the load or volume of the metering bin 4 is determined to be less than the standard load or volume of the loaded vehicle 12, a signal is output to the PID controller to adjust the opening of the gate 3. When the load or volume of the metering bin 4 is determined to be greater than or equal to the standard load or volume of the loaded vehicle 12, or when the material reaches the fourth-level monitoring point, a closing signal is output to directly close the gate 3.

[0026] PID controller is electrically connected with the hydraulic system, outputs control instruction to the hydraulic system according to the set signal, adjusts the hydraulic oil flow and pressure output by the hydraulic pump, drives the piston rod of the hydraulic cylinder 10 to stretch and retract; the displacement sensor 11 monitors the displacement of the piston rod of the hydraulic cylinder 10 in real time and feeds back the signal to the PID controller, the PID controller compares the actual displacement value with the preset value, adjusts the control signal output to the hydraulic system by using proportional, integral and differential algorithm, accurately controls the action amplitude of the hydraulic cylinder 10, and accurately controls the opening of the gate 3.

[0027] The running logic of the intelligent loading system is that the belt conveyor 1 transports the material to the buffer bin 2, and the buffer bin 2 supplies the material to the quantitative bin 4 through the gate 3. Because the material density fluctuates due to factors such as humidity and granularity, single weight or volume as a control parameter is easy to cause deviation of loading accuracy. The intelligent loading system uses real-time density as the core control basis, the three-dimensional measuring instrument 8 transmits the three-dimensional coordinates of the material surface to the central control system, the central control system calculates the real-time volume, receives the real-time weight data of the weighing sensor 6, calculates the real-time density of the material, and dynamically compensates the influence of density fluctuation on the loading amount. The central control system combines the four-stage material level monitoring data of the material level height sensor 7 (the first stage is used for initial deceleration triggering, the second stage is used for flow classification control, the third stage is used for end precise flow limiting, and the fourth stage is used for extreme volume limiting), controls the opening and closing of the gates A, B, C and D according to the preset program, and can transmit the related data to the PID controller and the signal selector. The signal selector selects the output signal according to the weighing and volume data of the quantitative bin 4 and the material level state: if the weighing value is less than the standard loading amount and the volume is less than the standard loading volume, the signal is sent to the PID controller, the PID controller controls the hydraulic cylinder 10 through the electro-hydraulic servo proportional valve, dynamically adjusts the opening of the gate B, and continuously supplies the material to the quantitative bin 4; if any of the conditions of reaching the standard weighing value, reaching the standard volume or the material reaching the fourth stage monitoring point is triggered, the closing signal is output to directly control the gate B to close, and the material in the quantitative bin 4 is unloaded to the loaded vehicle 12 through the lower discharge port.

[0028] The material distribution machine 9 is a material transfer device connected with the lower end of the quantitative bin 4, and is driven by a motor to move horizontally along the guide rail 13 on the vehicle route through the bottom roller cooperating with the guide rail 13 fixed below the lower end of the quantitative bin 4. The material distribution machine 9 comprises a storage box 91 connected with the discharge port of the quantitative bin 4, a scraper conveyor 92 in the storage box 91, and a loading chute 5 on one side of the longitudinal lower end of the storage box 91. The loading chute 5 is a telescopic structure with at least two sleeves, and is driven to extend and retract by a pneumatic cylinder. The discharge port of the quantitative bin 4 is communicated with the storage box 91, and the output end of the scraper conveyor 92 corresponds to the inlet of the loading chute 5. The loading process when selected is as follows: after the material in the quantitative bin 4 enters the storage box 91, it is sent to the loading chute 5 by the scraper conveyor 92. First, the outlet of the loading chute 5 is sunk into the bottom of the carriage, the chute gate is opened to unload the material, and the outlet is gradually lifted to the same height as the carriage board after the material is unloaded, then the motor drives the material distribution machine 9 to move horizontally backward along the guide rail 13, and the chute gate is kept open until the loading is completed.

[0029] The three-dimensional measuring instrument 8 can select a three-dimensional laser scanner, a ToF time-of-flight camera or a laser ranging sensor array according to the scene, and the specific working process is as follows: 1. Point cloud data acquisition: The three-dimensional laser scanner obtains the three-dimensional coordinates of the material surface by actively emitting a laser beam and receiving the reflected signal; the ToF time-of-flight camera measures the time difference (ToF principle) from the emission to the reception of an infrared light pulse, and directly calculates the depth value of each pixel point; the laser ranging sensor array generates material surface point cloud data by multi-sensor synchronous ranging and splicing, and all of them can output point cloud data containing three-dimensional coordinates (X, Y, Z), thereby constructing a digital three-dimensional model of the material surface.

[0030] 2. Point cloud preprocessing: noise points are removed by using a filtering algorithm (such as voxel grid filtering and statistical outlier filtering), and the point cloud density is optimized by a smoothing algorithm to ensure data accuracy.

[0031] 3. Point cloud slicing processing: the three-dimensional point cloud data after preprocessing is cut at a set interval by using a point cloud slicing algorithm, and is converted into multiple two-dimensional cross-section point cloud data.

[0032] 4. Contour boundary identification: the point cloud data in each slice is processed, and the contour boundary of the material in the cross-section is identified and determined by using an algorithm, and discrete point interference is removed.

[0033] 5. Cross-section area calculation: the covering area of a single slice is calculated by a polygon area formula or a grid pixel statistical method according to the determined contour boundary.

[0034] 6. Volume integral operation: the total volume of the material is obtained by using trapezoidal integration method to accumulate all slice areas according to the interval of adjacent slices.

[0035] Embodiment 2: The adaptive intelligent loading method of the material under the condition of variable characteristics in this embodiment is a loading method based on the adaptive intelligent loading system of embodiment 1, including a fuzzy control method of gate opening and a gate control method.

[0036] I. The fuzzy control method of the gate opening includes: 1) Based on the long-term accumulated loading data experience, combined with the professional knowledge of the operation experts, a basic control method is constructed, which focuses on the relationship between the gate opening and the material density, and considers different materials or different properties of the same material as the basis for judgment. Different materials have large differences in physical properties: for materials with strong fluidity, the gate may be slightly open to supply too fast under the same density; for materials with poor fluidity, a larger opening is required, and the fluctuation of the same material properties also affects the adaptability. The density is used to determine the humidity increase (viscosity increase) and the particle size to adjust the gate opening. Experts set the initial opening reference for different materials and properties based on experience, determine the basic rules for density and opening adjustment, and provide initial control logic.

[0037] 2) Use the material density and gate opening data in the past and historical loading data to analyze the long-term running trend and rule, and optimize and improve the control method; the loading data includes: material density, gate opening; The system analyzes the data from multiple dimensions: statistics of the opening distribution of different materials under the same density to correct the initial setting; track the opening trajectory when the same material density fluctuates to extract the dynamic rule and convert it into control parameters; Data mining can also find potential rules, such as the non-linear relationship between opening and density after the density reaches a certain value, which needs to be optimized in sections. This iteration can reduce the limitations of experience and improve the loading accuracy.

[0038] II. The gate control method as shown in Figure 2 includes two stages of coarse matching and fine matching, including the following steps: In the coarse matching stage, the impact on the loading accuracy is smaller.

[0039] Let the four gates 3 of the buffer bin 2 be gate A, gate B, gate C, and gate D.

[0040] Step S1, preparation of ingredients: Before coarse matching, the loading ingredient parameters are first obtained, including the common density of the loaded material, the standard loading capacity and the standard loading volume of the loaded vehicle 12.

[0041] Step S2, full-speed matching: At the beginning of the ingredients, the PID controller controls the A, B, C, D four hydraulic gate full open, the material falls to the quantitative warehouse 4, at this time, without considering the volume and mass of the material in the quantitative warehouse 4, full efficiency loading material.

[0042] Step S3, initial deceleration trigger: When the material reaches the first level monitoring point, the PID controller controls the A, B, C, D four hydraulic gate from full open to half open state, controls the loading speed.

[0043] Step S4, flow grading control: When the material reaches the second level monitoring point, the PID controller closes the A, C two hydraulic gate, further controls the loading speed, prevents overloading, enters the secondary precision control, at this time, the gate B and gate D are in the open state.

[0044] Step S5, end precision flow limiting: When the material reaches the third level monitoring point, the end precision flow limiting is opened, the PID controller controls the gate D to close, only the gate B is open.

[0045] Fine matching stage: Step S6, fine matching starts: The opening of the gate B is adaptively adjusted according to the difference of the material density to be loaded, so as to take into account the loading efficiency and prevent overloading control: when the material density is large, the opening of the gate B is reduced, so that the speed of the material flowing from the buffer bin 2 into the quantitative warehouse 4 is slowed down, thereby providing sufficient time for precise control; when the material density is small, the opening of the gate B is increased accordingly, so as to speed up the material flow speed and improve the loading efficiency. This adjustment mechanism matches the material density and the gate opening in real time, ensures the loading precision, and optimizes the loading efficiency; Due to the variability of the material and the non-linear factors of the system, it is difficult to establish an accurate model of the entire system. In order to improve the robustness and precision of the system, a fuzzy control method is established according to expert experience or knowledge base in the fine matching stage. This method does not require an accurate mathematical model. Through the fuzzy control adaptive electro-hydraulic servo control system, the real-time monitoring data is dynamically judged according to the different materials or different properties of the same material, and the opening of the gate B is adjusted according to the judgment basis, so as to realize the adaptive matching of the loading strategy and the material properties. The gate control method process of the buffer bin 2 in this fine matching stage is shown in Figure 3 ; The standard density of the material is set as the commonly used density of the loaded material type in the matching stage, the PID control parameter is dynamically adjusted by using the fuzzy control method, the current signal of the PID controller is output to the electro-hydraulic servo proportional valve, the valve core is driven to move to adjust the flow of hydraulic oil, and the opening degree of the gate B is controlled; the opening degree of the gate B is controlled through the hydraulic oil cylinder 10, and the displacement sensor 11 monitors the extension amount of the oil cylinder piston rod in real time to provide feedback data for the control system.

[0046] Step S7, deviation adjustment according to density: The volume V of the material is obtained in real time through the three-dimensional measuring instrument 8, the real-time mass M of the material is obtained through the weighing sensor 6, and the real-time density data is obtained through the operation of the industrial-grade embedded computing unit based on the density formula p = M / V . The deviation e is generated by comparing the real-time density data of the material with the preset standard density. e The differential operation is performed on the deviation ec to obtain the rate of change of the deviation (i.e., the density fluctuation rate), and the deviation e is taken as the proportional term and the rate of change of the deviation ec is taken as the differential term, which are input into the PID controller to form a complete control signal in combination with the integral term. The control signal is used to adjust the opening degree of the gate B.

[0047] Step S8, determine the material loading capacity: The PID control signal and the gate closing signal are input into the signal selector, and the signal selector is waiting for discrimination. The signal selector discriminates according to the following conditions: If the value of the weighing sensor 6 of the quantitative bin 4 is less than the standard loading capacity of the loaded vehicle, and the real-time material volume obtained by the three-dimensional measuring instrument 8 is less than the standard loading capacity of the loaded vehicle, the signal selector selects the signal output by the PID controller. After the control amount output by the PID controller is converted, the electro-hydraulic servo proportional valve is driven to adjust the hydraulic oil cylinder, the opening degree of the gate B is dynamically adjusted, and step S7 is continued. If the value of the weighing sensor 6 of the quantitative bin 4 is greater than or equal to the standard loading capacity of the loaded vehicle 12, or the real-time material volume obtained by the three-dimensional measuring instrument 8 is greater than or equal to the standard loading capacity of the loaded vehicle, or the material reaches the fourth monitoring point, the signal selector selects the signal to close the gate B.

[0048] Step S9, end of batching: The unloading gate of the quantitative bin 4 is opened, the material is loaded into the loaded vehicle 12 through the loading chute 5, and the material loading is completed.

[0049] It should be pointed out finally that the above merely serves to illustrate the technical solutions of the present application but not to limit the present application. Although the present application has been described in detail with reference to the preferred embodiments, those skilled in the art should understand that the technical solutions of the present application can be modified or equivalently replaced without departing from the spirit and scope of the present application.

Claims

1. An adaptive intelligent loading system under conditions of variable material properties, comprising a belt conveyor (1), a buffer bin (2), a quantitative bin (4) and a loading chute (5) connected in sequence, characterized in that: The bottom of the buffer bin (2) is provided with four groups of gates (3), which are electrically connected to a PID controller. The PID controller controls the opening and closing of the gates (3) and can adjust the opening of the gates (3). The quantitative bin (4) is provided with a weighing sensor (6), a material level sensor (7) and a three-dimensional measuring instrument (8). The material level sensor (7) is provided with four levels of material level monitoring points, wherein the first level monitoring point is used for initial deceleration triggering, the second level monitoring point is used for flow rate classification control, the third level monitoring point is used for terminal precise flow limiting, and the fourth level monitoring point is used for ultimate volume limiting.

2. The adaptive intelligent loading system under conditions of variable material properties according to claim 1 is characterized in that: The material level sensor (7) is a laser radar or a video camera or a combination of a laser radar and a video camera.

3. The adaptive intelligent loading system under conditions of variable material properties according to claim 1, characterized in that: The first-level material level monitoring point for monitoring the lowest material level in the material level height sensor (7) includes a weight-type material level meter.

4. The adaptive intelligent loading system under conditions of variable material properties according to claim 1, characterized in that: The gate (3) is provided with a hydraulic cylinder (10) for controlling the movement of the gate and a displacement sensor (11) for monitoring the displacement of the piston rod of the hydraulic cylinder.

5. The adaptive intelligent loading system under conditions of variable material properties according to claim 1, characterized in that: The PID controller is electrically connected to a signal selector, and the signal selector can directly output a switch signal to close the gate (3), or can selectively output a PID controller signal to adjust the gate (3) opening for precise control.

6. The adaptive intelligent loading system under conditions of variable material properties according to claim 1, characterized in that: The three-dimensional measuring instrument (8) can be a three-dimensional laser scanner, a ToF time-of-flight camera, or a laser ranging sensor array.

7. The adaptive intelligent loading system under conditions of variable material properties according to claim 1, characterized in that: The quantitative bin (4) is connected to the loading chute (5) via a material distribution machine (9). A guide rail (13) is provided at the lower end of the discharge port of the quantitative bin (4). The material distribution machine (9) can be moved horizontally forward and backward via the guide rail (13) to the discharge port at the lower end of the quantitative bin (4), thereby achieving uniform material distribution to the front and rear of the loaded vehicle (12).

8. The adaptive intelligent loading system under conditions of variable material properties according to claim 7, characterized in that: The material distributing machine (9) includes a storage box (91) connected to the discharge port of the quantitative bin (4), and one side of the longitudinal lower end of the storage box (91) is connected to the loading chute (5). The loading chute (5) is a retractable chute. A scraper conveyor (92) is provided in the storage box (91). The scraper conveyor (92) is used to convey the material discharged from the quantitative bin (4) to the storage box (91) to the loading chute (5), and then discharge the material from the chute to the loaded vehicle (12).

9. An adaptive intelligent loading method under conditions of variable material properties, based on the loading system according to any one of claims 1 to 8, characterized in that: The loading method includes: a fuzzy control method of gate opening and a gate regulation method; 1. Establishing and improving the fuzzy control method of gate opening includes: 1) Based on loading data experience and the expertise of practical experts, a basic control method is constructed. This method focuses on the relationship between gate opening and material density, taking into account the different properties of different materials or the same material as the basis for judgment; 2) Utilize historical loading data, including material density and gate opening, to analyze trends and patterns and optimize control methods; 2. The gate control method is divided into two stages: rough adjustment and fine adjustment, including the following steps: Assume that the four gates (3) of the buffer bin (2) are gate (A), gate (B), gate (C) and gate (D); The first rough matching stage: Step S1, ingredient preparation: Obtaining loading parameters, including the common density of the loaded material, the standard loading amount and the standard loading volume of the loaded vehicle (12); Step S2, full speed assembly: The four gates (3) of the buffer bin (2) are fully opened, and the material quickly falls into the quantitative bin (4); Step S3, initial deceleration trigger: When the material reaches the first-level monitoring point, the initial deceleration is triggered, and the PID controller adjusts the four gates (3) from fully open to half-open; Step S4: Traffic level control: When the material reaches the second-level monitoring point, it enters the flow classification control mode. The PID controller closes the two hydraulic gates, gate (A) and gate (C), and enters the secondary precise control mode. At this time, gate (B) and gate (D) are in the open state. Step S5: accurate current limiting at the end: When the material reaches the third-level monitoring point, the terminal precise current limiting is turned on, and the PID controller controls the gate (D) to close, leaving only the gate (B) open; The second stage of fine matching: Step S6, fine matching begins: The common density of the material set according to the material type is used as the standard density. According to the fuzzy control method, the PID controller adjusts the opening of the gate (B); Step S7, adjust the deviation according to the density: The three-dimensional measuring instrument (8) obtains the material volume in real time, the weighing sensor (6) obtains the material weight, and the calculation unit calculates the real-time material density; the material density data is obtained in real time and compared with the standard density to generate a deviation e, and the deviation e and the differential signal ec reflecting the rate of change are used as inputs of the PID controller, and the PID controller generates a control signal for adjusting the gate opening, which is used to adjust the opening of the gate (B); Step S8, determine the material loading capacity: The control signal generated by the PID controller and the gate closing signal are input into the signal selector, which makes a judgment based on the following conditions: It is determined that the value of the weighing sensor (6) of the quantitative bin (4) is less than the standard loading capacity of the loaded vehicle, and the real-time material volume obtained by the three-dimensional measuring instrument (8) is less than the standard loading capacity of the loaded vehicle, the signal selector selects the output signal of the PID controller, adjusts the opening of the gate (B), and continues to execute step S7; When it is determined that the value of the weighing sensor (6) of the quantitative bin (4) is greater than or equal to the standard loading volume of the loaded vehicle (12), or the real-time material volume obtained by the three-dimensional measuring instrument (8) is greater than or equal to the standard loading volume of the loaded vehicle, or the material reaches the fourth-level monitoring point, the signal selector selects the gate closing signal to control the gate (B) to close; Step S9, batching is completed: The unloading gate of the quantitative bin (4) is opened, and the material is loaded into the loaded vehicle (12) through the loading chute (5), and the batching is completed.

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