Bulk material vibration conveying configuration control method

Abstract

This invention discloses a configuration control method for vibratory conveying of bulk materials, belonging to the field of industrial automation configuration control technology. The method includes: constructing a three-dimensional configuration monitoring interface based on the physical characteristics of the bulk material, comprising a virtual vibratory feeder, a virtual conveyor belt, and virtual material level detection points, and embedding an adaptive adjustment module for the excitation frequency; collecting real-time changes in the stacking height through the virtual material level detection points and mapping them to a feeding rate correction coefficient; calling a fuzzy rule inference engine to calculate the target excitation frequency and target amplitude; encapsulating the target parameters into configuration control instructions, and sending them to the programmable logic controller of the physical vibratory feeder for execution through the configuration software data communication interface. This method utilizes virtual material level sensing and fuzzy inference to achieve dynamic tuning of vibration parameters, and improves the real-time performance and reliability of feeding control through a structured instruction encapsulation and verification mechanism.

Description

Technical Field

[0001] This invention relates to the field of industrial automation configuration control technology, specifically a configuration control method for vibratory conveying of bulk materials. Background Technology

[0002] Vibratory conveyors for bulk materials are widely used in industries such as mining, metallurgy, and building materials. Their feeding accuracy directly affects the stable operation of subsequent processes. Existing configuration control methods typically set fixed operating parameters for the vibratory feeder in the configuration interface, relying on operators to manually adjust the excitation frequency or amplitude based on experience and observation of material accumulation. This control method is an open-loop or semi-open-loop mode. When the physical properties of the bulk material change, such as fluctuations in moisture content, shifts in particle size distribution, or inconsistent bulk density, the fixed vibration parameters cannot match the real-time changes in material flow characteristics. This easily leads to uneven material accumulation on the conveyor belt, resulting in insufficient or overflowing material, and poor feeding stability. Some existing solutions attempt to install physical level sensors on the conveyor belt, introducing the detection signals into the control system for feedback adjustment. While this method can obtain material accumulation information, the sensor installation location is limited by the site structure, the number of detection points is limited, and it is difficult to comprehensively reflect the overall distribution of materials on the conveyor belt. Furthermore, the sensors themselves are susceptible to dust and vibration, resulting in insufficient reliability and consistency of the measured values. At the control strategy level, conventional proportional-integral-derivative (PID) control methods rely solely on linear correction based on a single deviation, which cannot adapt to the prevalent nonlinear and large-inertia characteristics in the feeding process. Parameter tuning effectiveness is limited, and it is difficult to maintain convergence over a wide operating range. Furthermore, data interaction between the configuration software and the field controller typically employs a simple read-write command mode, lacking command integrity verification and communication status confirmation mechanisms. In complex industrial electromagnetic environments, control commands are frequently lost or fail to execute correctly, and the configuration system cannot detect and automatically recover in a timely manner, posing risks to the continuity and safety of the feeding process.

[0003] To address the aforementioned issues, a control method is needed that can sense the material flow status in real time and dynamically adjust the excitation parameters accordingly, while also improving the reliability of the configuration command issuance process and the multi-device collaboration capability. Summary of the Invention

[0004] This paper provides a configuration control method for vibratory conveying of bulk materials to solve the problems of low feeding accuracy caused by the inability of existing configuration control methods to adaptively adjust vibration parameters according to the material flow state, and insufficient execution reliability caused by the lack of confirmation and fault tolerance mechanisms in the issuance of configuration commands.

[0005] The objective of this invention can be achieved through the following technical solutions: This invention proposes a configuration control method for vibratory conveying of bulk materials. By constructing a three-dimensional configuration monitoring interface integrating a virtual vibratory feeder, a virtual conveyor belt, and virtual material level detection points, and embedding an adaptive vibration frequency adjustment module, dynamic optimization control of the conveying process is achieved. This method uses physical characteristics of the bulk material, such as the angle of repose, bulk density, moisture content, and particle size distribution range, as the basis for configuration construction. In the three-dimensional configuration monitoring interface, the side baffle inclination angle of the virtual conveyor belt is set according to the angle of repose; the initial feeding range of the virtual vibratory feeder is set according to the bulk density; the shape parameters of the fuzzy rule inferencer's input membership function are determined according to the moisture content; and the simulated parameters of the screen aperture of the virtual vibratory feeder are set according to the particle size distribution range. This ensures that the configuration model closely matches the actual characteristics of the material, laying the foundation for subsequent precise control.

[0006] In the material flow state sensing stage, at least three virtual material level detection points are deployed at equal intervals along the length of the virtual conveyor belt. Each detection point outputs the material accumulation height value at a fixed sampling period, and the accumulation height values ​​of all detection points at the same time are weighted and averaged to obtain the average accumulation height. This fixed sampling period can be automatically adjusted according to the maximum particle size in the particle size distribution range; the larger the maximum particle size, the longer the sampling period, thereby reducing system resource consumption while ensuring detection accuracy. The difference between the average accumulation height and the preset standard accumulation height is calculated, and this difference is input into a proportional-integral-derivative controller, which outputs a real-time feeding rate correction coefficient. This accurately maps the material accumulation state on the conveyor belt to the feeding rate adjustment signal of the vibrating feeder, enabling the feeding action to respond promptly to real-time changes in material flow.

[0007] As a key aspect of this invention, the dynamic tuning process of vibration parameters utilizes a fuzzy rule inference engine within the adaptive adjustment module for excitation frequency. The fuzzy rule inference engine uses the real-time feed rate correction coefficient as the first input variable and the rate of change of the difference between the average stacking height and the standard stacking height as the second input variable. It defines three fuzzy sets for the first input variable: slow correction, medium correction, and fast correction; and three fuzzy sets for the second input variable: negative change, zero change, and positive change. The output variables include the excitation frequency adjustment increment and the amplitude adjustment increment, correspondingly defined as small adjustment, medium adjustment, and large adjustment fuzzy sets. The Madani inference method is used to retrieve the output fuzzy values ​​from a preset fuzzy rule table based on the fuzzy set combination of the input variables. Then, the centroid method is used to defuzzify the values ​​to obtain the excitation frequency adjustment increment and the amplitude adjustment increment. The fuzzy inference engine integrates the real-time feed rate correction coefficient and the rate of change of stacking height to achieve refined tuning of the vibration parameters, avoiding the control lag and overshoot caused by traditional single threshold judgments.

[0008] After obtaining the adjustment increment output by the fuzzy inference engine, the adaptive excitation frequency adjustment module reads the current operating frequency and amplitude of the physical vibrating feeder. It adds the current operating frequency to the excitation frequency adjustment increment to obtain the target excitation frequency, and adds the current amplitude to the amplitude adjustment increment to obtain the target amplitude. When the target excitation frequency exceeds the lower or upper limit of the physical vibrating feeder's rated frequency, the target excitation frequency is clamped to the corresponding limit, and the amplitude adjustment increment is simultaneously set to zero to prevent abnormal vibration or structural damage under extreme operating conditions. Preferably, when the difference between the target excitation frequency or target amplitude and the current value exceeds a preset safe change step size, the adaptive excitation frequency adjustment module breaks down this difference into multiple step increments and outputs them sequentially at fixed time intervals, allowing the vibrating feeder to smoothly transition to the target operating state, further ensuring equipment operating safety.

[0009] After obtaining the target excitation frequency and target amplitude, they are encapsulated into configuration control instructions in the configuration software. A predefined control data structure for the vibration feeder includes a frequency register address field, an amplitude register address field, a frequency value field, an amplitude value field, and a cyclic redundancy check (CRC) field. The target excitation frequency is written into the frequency value field, and the target amplitude is written into the amplitude value field. The corresponding physical programmable logic controller (PLC) register address is read from the device address mapping table based on the frequency register address and amplitude register address. A CRC algorithm is used to calculate the contents of the frequency value field and the amplitude value field, and the calculation result is filled into the CRC field to complete the instruction encapsulation, thereby ensuring the integrity and reliability of the control data during transmission.

[0010] The configuration software's data communication interface establishes a connection with the programmable logic controller (PLC) of the physical vibrating feeder using an open platform communication unified architecture protocol. Before sending each configuration control command, a connection keep-alive message is sent to the PLC to confirm its online status and prevent invalid commands. The encapsulated configuration control command is encoded according to the data frame format of the open platform communication unified architecture protocol and then sent, waiting for the controller to return a command execution confirmation frame. If no confirmation frame is received within a preset timeout period, the same command is automatically resent. After the maximum number of resentments is reached, a communication fault alarm signal is generated and displayed in the 3D configuration monitoring interface. In this way, the closed-loop confirmation and resentment mechanism significantly improves the success rate of command issuance and the visibility of system operation. Furthermore, the configuration software's data communication interface can maintain a connection with the PLCs of multiple physical vibrating feeders simultaneously and issue configuration control commands sequentially using a polling scheduling method, realizing collaborative control of multiple devices and significantly enhancing the integration and automation level of the bulk material conveying system.

[0011] The beneficial effects of this invention are: By constructing a 3D configuration monitoring interface that includes a virtual vibrating feeder, a virtual conveyor belt, and virtual material level detection points, and embedding an adaptive excitation frequency adjustment module in the virtual vibrating feeder, the physical characteristics of bulk materials, such as the angle of repose, bulk density, moisture content, and particle size distribution range, are directly mapped to the geometric dimensions, initial range, fuzzy rule membership function shape, and screen aperture simulation parameters of the virtual model. This method reconstructs a complete virtual image of material conveying within the configuration space, allowing subsequent perception and decision-making to be independent of the single reliance on on-site physical sensors. Multiple virtual material level detection points are deployed at equal intervals along the length of the virtual conveyor belt, and the material accumulation height values ​​are collected in real time and weighted averaged to obtain the average accumulation height. The average accumulation height is then output as a real-time feed rate correction coefficient through a proportional-integral-derivative controller. The number and location of virtual material level detection points are not limited by on-site installation conditions, and can capture changes in the accumulation morphology of the entire conveyor belt with high spatial resolution. This avoids information loss and dust interference problems caused by local detection by physical sensors, providing a continuous and stable feedback input signal for vibration parameter tuning. The fuzzy rule inferencer in the adaptive vibration frequency adjustment module is invoked, using the real-time feed rate correction coefficient and the rate of change of the difference between the average stacking height and the standard stacking height as dual inputs. A fuzzy set covering slow correction, medium correction, fast correction, and negative, zero, and positive changes is defined. The Madani inference method is used to query and output the vibration frequency adjustment increment and amplitude adjustment increment from a preset rule table. The target vibration frequency and target amplitude are then obtained through defuzzification using the centroid method. During the inference process, the current operating frequency and current amplitude of the physical vibration feeder are read in real time. The inferred increment is superimposed on the current value to generate the target value. If the target vibration frequency touches the rated frequency boundary, it is automatically clamped, and the amplitude adjustment increment is simultaneously set to zero. Furthermore, when the adjustment exceeds the preset safe change step size, it is split into multiple step increments and output sequentially. This fuzzy logic control mechanism effectively addresses the nonlinear dynamics of material flow. When the stacking height deviates, it can coordinately adjust the frequency and amplitude according to the degree of deviation and the trend of change, avoiding response lag or oscillation problems caused by single parameter adjustment. The clamping and step size splitting mechanism prevents over-adjustment and impact of the equipment under extreme working conditions, ensuring the safe and stable operation of the actuator.

[0012] During the issuance of configuration control commands, a predefined control data structure for the vibratory feeder, including the frequency register address, amplitude register address, frequency value, amplitude value, and cyclic redundancy check (CRC) field, is used. The target value is written into the corresponding field, and the physical programmable logic controller (PLC) register address is read according to the address mapping table. A CRC algorithm is used to calculate and fill the checksum in the numerical field. The configuration software data communication interface establishes a connection using an open platform communication unified architecture protocol. Before transmission, the controller's online status is confirmed via a keep-alive message. The configuration control command is encoded and sent according to the unified architecture protocol data frame format. The controller returns a command execution confirmation frame. If no confirmation frame is received within the timeout period, the same command is automatically retransmitted. After the maximum number of retransmissions, a communication fault alarm signal is generated and displayed in the 3D configuration monitoring interface. When multiple physical vibratory feeders are online simultaneously, the data communication interface uses a polling scheduling method to issue commands sequentially. This instruction encapsulation and communication scheme forms a complete closed loop for transmission reliability, from data structure design, frame verification, channel keep-alive, timeout retransmission to multi-machine polling. It effectively overcomes the problem of instruction loss and erroneous execution caused by electromagnetic interference in industrial sites. Multi-machine polling allocates communication resources in an orderly manner, ensuring the configuration system's ability to synchronously control multiple conveyor lines. Attached Figure Description

[0013] The invention will now be further described with reference to the accompanying drawings.

[0014] Figure 1 This is a flowchart of the configuration control method for vibratory conveying of bulk materials; Figure 2 This is a flowchart of PID control based on the average stacking height of virtual material level detection points; Figure 3 This is a flowchart of the fuzzy rule inference engine construction process; Figure 4 This is a flowchart of the excitation frequency adaptive adjustment module. Figure 5 This is the configuration control flowchart for the vibrating feeder. Detailed Implementation

[0015] 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, and 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.

[0016] See Figure 1This invention provides a configuration control method for vibratory conveying of bulk materials. This method constructs a three-dimensional configuration monitoring interface including a virtual vibratory feeder, a virtual conveyor belt, and virtual material level detection points. An adaptive excitation frequency adjustment module is embedded in the virtual vibratory feeder, forming a closed-loop control link from sensing and tuning to command issuance. Specifically, the configuration control is constructed based on the physical characteristic parameters of the bulk material; the material flow state is sensed in real time through the virtual material level detection points, and the change in stacking height is mapped to a real-time feeding rate correction coefficient; based on this correction coefficient, the fuzzy rule inferencer in the adaptive excitation frequency adjustment module is invoked to dynamically tune the vibration parameters, obtaining the target excitation frequency and target amplitude; the target excitation frequency and target amplitude are encapsulated into configuration control commands and sent to the programmable logic controller of the physical vibratory feeder for execution through the data communication interface of the configuration software.

[0017] In practice, the physical properties of bulk materials include the angle of repose, bulk density, moisture content, and particle size distribution range.

[0018] The angle of repose is obtained by slowly injecting bulk material onto a horizontally placed circular platform using the injection method. The bulk material naturally accumulates to form a pile, and the angle between the surface of the pile and the horizontal plane is measured as the angle of repose. The value of the angle of repose is used to characterize the flowability of the bulk material.

[0019] The bulk density is obtained as follows: Take a container with a volume of 0.01 cubic meters, let the bulk material fall into the container naturally without vibration, and after it is full, scrape the surface of the container opening level. Weigh the mass of the bulk material in the container. The ratio of the mass of the bulk material to the volume of the container is the bulk density.

[0020] The moisture content is obtained as follows: Weigh a 100-gram sample of bulk material and dry it in a drying oven at 105 degrees Celsius until constant weight. After drying, weigh the dried mass of the bulk material sample. Divide the difference between the mass of the bulk material sample before drying and the mass after drying by the dried mass. The resulting percentage is the moisture content.

[0021] The particle size distribution range is obtained as follows: A set of standard square hole sieves is used, with the sieve hole size stacked in descending order. A 500-gram sample of bulk material is placed in the top sieve and vibrated for 15 minutes using a vibrating sieve machine. The mass of the material on each sieve and the material under the bottom sieve are weighed separately, and the cumulative percentage of residue is calculated to obtain the particle size distribution range of the bulk material. The particle size distribution range includes the minimum particle size and the maximum particle size.

[0022] The side baffle inclination angle of the virtual conveyor belt is set as follows: The value of the angle of repose is read from the configuration software, and the side baffle inclination angle parameter of the virtual conveyor belt is directly assigned to the value of the angle of repose. When the angle of repose is 32 degrees, the side baffle inclination angle of the virtual conveyor belt is set to 32 degrees.

[0023] The initial feed range setting method for the virtual vibrating feeder is as follows: The initial feed range represents the mass of bulk material passing through the outlet of the virtual vibrating feeder per unit time when the virtual vibrating feeder is operating at its rated amplitude and rated frequency. Bulk density is represented by the symbol... The initial feed range is indicated by the symbol. express, and The relationship between them is determined by the following formula:

[0024] in: This is the range proportionality coefficient. The value of is determined by the flow cross-sectional area of ​​the outlet of the virtual vibrating feeder and the linear velocity of the virtual conveyor belt. The calculation method is as follows: , This represents the projected cross-sectional area of ​​the virtual vibrating feeder outlet in the flow direction. This is the set linear speed of the virtual conveyor belt. When the width of the virtual vibrating feeder outlet is 0.2 meters, the height is 0.15 meters, and the set linear speed of the virtual conveyor belt is 0.4 meters per second, The value is 0.03 square meters. The value is 0.4 m / s, calculated as follows: The value is 0.012. Substituting the numerical value into the above formula, we get The value.

[0025] The shape parameter of the input membership function of the fuzzy rule inferencer in the excitation frequency adaptive adjustment module is determined as follows: the input membership function adopts a Gaussian membership function, and the shape parameter of the Gaussian membership function is the standard deviation of the Gaussian function, denoted by the symbol... Moisture content is indicated by the symbol. express, The value varies The rule of change is: when When less than 3%, Take 0.05; when When the percentage is greater than or equal to 3% and less than 7%, Take 0.1; when When greater than or equal to 7%, Take 0.15. The larger the value of , the flatter the shape of the Gaussian membership function; The smaller the value of , the sharper the shape of the Gaussian membership function.

[0026] The method for setting the screen aperture simulation parameters of the virtual vibrating feeder is as follows: Read the maximum particle size value within the particle size distribution range of the bulk material in the configuration software, and directly assign this maximum particle size value to the screen aperture simulation parameters of the virtual vibrating feeder. When the maximum particle size is 25 mm, the screen aperture simulation parameter is set to 25 mm. The screen aperture simulation parameters are used to control the mesh size display and screening logic of the virtual vibrating feeder screen model in the 3D configuration monitoring interface.

[0027] In specific implementation, please refer to Figure 2 Three virtual material level detection points are deployed at equal intervals along the length of the virtual conveyor belt within the 3D configuration monitoring interface. These three virtual material level detection points are designated as the first virtual material level detection point, the second virtual material level detection point, and the third virtual material level detection point, respectively. The spacing between any two adjacent virtual material level detection points is equal, and the spacing value is one-third of the total length of the virtual conveyor belt. Each virtual material level detection point outputs the material accumulation height value at the current moment according to a fixed sampling period. The material accumulation height value output by the first virtual material level detection point is... This indicates that the material accumulation height value output by the second virtual material level detection point is used... This indicates that the material accumulation height value output by the third virtual material level detection point is used... express.

[0028] The average material accumulation height on the virtual conveyor belt is obtained by weighted averaging the material accumulation height values ​​output from three virtual material level detection points at the same time. The formula for calculating the weighted average is:

[0029] in: The average stacking height, This represents the total number of virtual material level detection points. The value of is 3. This refers to the serial number of the virtual material level detection point. Take a positive integer. For the first The material accumulation height values ​​output by each virtual material level detection point. The weighting of each of the three virtual material level detection points is set to 1; therefore, the weighted average is simplified to an equal-weighted arithmetic average. , and Add them together and divide by 3 to get The value.

[0030] Calculate the average stacking height The difference between the standard stacking height and the preset standard stacking height is used as Indicates the difference. , The preset standard stacking height, The value is calculated from the target conveying capacity of the bulk material based on the linear velocity and bulk density of the virtual conveyor belt. The difference is then... The input is fed into the proportional-integral-derivative (PID) controller, and the output of the PID controller is the real-time feed rate correction coefficient.

[0031] The proportional-integral-derivative (PID) controller is expressed as follows:

[0032] However, in the digital implementation, an incremental algorithm is used, and the proportional coefficient of the proportional-integral-derivative controller is... Take 0.8, integration time constant Take 2.0 seconds, differential time constant The above parameters, taken as 0.1 seconds, were obtained through step response tests on an actual bulk material conveying line and tuned using the critical proportionality method. When... When the value is positive, it indicates that the average stacking height on the virtual conveyor belt is higher than the standard stacking height, and the real-time feed rate correction coefficient output by the proportional-integral-derivative controller is negative, ranging from -1.0 to 0; when... When the value is negative, the real-time feed rate correction coefficient output by the proportional-integral-derivative controller is positive, with a value range of 0 to 1.0.

[0033] The fixed sampling period is automatically adjusted based on the maximum particle size within the particle size distribution range of the bulk material. A mapping table between the maximum particle size and the fixed sampling period is pre-stored in the configuration software. This table records three sets of correspondences: when the maximum particle size in the bulk material's particle size distribution range is less than or equal to 10 mm, the fixed sampling period is 100 milliseconds; when the maximum particle size in the bulk material's particle size distribution range is greater than 10 mm but less than or equal to 25 mm, the fixed sampling period is 200 milliseconds; and when the maximum particle size in the bulk material's particle size distribution range is greater than 25 mm, the fixed sampling period is 300 milliseconds. Each time the configuration software starts the material flow status sensing task, it reads the maximum particle size value within the current bulk material's particle size distribution range, retrieves the corresponding fixed sampling period from the mapping table using a lookup method, and loads the obtained fixed sampling period value into the sampling timers of the three virtual material level detection points.

[0034] In specific implementation, please refer to Figure 3 The construction of a fuzzy rule inference engine consists of six parts: setting input variables, defining fuzzy sets, setting output variables, constructing fuzzy rule tables, determining inference methods, and determining defuzzification methods.

[0035] The input variable is set as follows: the real-time feeding rate correction coefficient is set as the first input variable of the fuzzy rule inference engine, using the symbol... Indicates the first input variable. The theoretical domain of physics is The rate of change of the difference between the average stacking height and the standard stacking height is set as the second input variable of the fuzzy rule inference engine, denoted by the symbol... Indicates the second input variable. The physical domain is determined based on the maximum and minimum values ​​of the historical data on the rate of change of stacking height collected on the bulk material conveying line. The theoretical domain of physics is .

[0036] The fuzzy set is defined as follows: [The first input variable is...] Three fuzzy sets are defined: a slow-correction fuzzy set, a medium-correction fuzzy set, and a fast-correction fuzzy set. The slow-correction fuzzy set uses a triangular membership function, with the three vertex parameters set to -1.0, -0.6, and -0.2 respectively. When the value of is -0.6, the membership degree of the slow-corrected fuzzy set is equal to 1.0. The medium-corrected fuzzy set uses a triangular membership function, with the three vertex parameters set to -0.4, 0, and 0.4 respectively. When the value of is 0, the membership degree of the medium-speed corrected fuzzy set is equal to 1.0. The fast-speed corrected fuzzy set uses a triangular membership function, with the three vertex parameters of the triangular membership function set to 0.2, 0.6, and 1.0 respectively. When the value of is 0.6, the membership degree of the fast-corrected fuzzy set is equal to 1.0.

[0037] For the second input variable Three fuzzy sets are defined: a negative change fuzzy set, a zero change fuzzy set, and a positive change fuzzy set. The negative change fuzzy set uses a triangular membership function, with the three vertex parameters set to -15.0, -15.0, and -5.0 respectively. When the value of is less than or equal to -15.0, the membership degree of the negatively changing fuzzy set is equal to 1.0. The zero-change fuzzy set uses a triangular membership function, with the three vertex parameters set to -5.0, 0, and 5.0 respectively. When the value of is 0, the membership degree of the zero-change fuzzy set is equal to 1.0. The positive-change fuzzy set uses a triangular membership function, with the three vertex parameters set to 5.0, 15.0, and 15.0 respectively. When the value of is greater than or equal to 15.0, the membership degree of the positive change fuzzy set is equal to 1.0.

[0038] The output variables are set as follows: the output variables of the fuzzy rule inference engine are set to the excitation frequency adjustment increment and the amplitude adjustment increment. The excitation frequency adjustment increment is represented by the symbol... express, The theoretical domain of physics is The amplitude adjustment increment is indicated by the symbol. express, The theoretical domain of physics is For output variables and We define three common fuzzy sets: the small-adjustment fuzzy set, the medium-adjustment fuzzy set, and the large-adjustment fuzzy set.

[0039] Slightly adjust the fuzzy set in A triangular membership function is used on the universe of discourse, with the three vertex parameters of the triangular membership function set to -2.0, -2.0, and -0.8. When the value is less than or equal to -2.0, the membership degree of the fuzzy set is slightly adjusted to 1.0; For minor adjustments to the fuzzy set over its universe of discourse, a triangular membership function is used, with the three vertices set to -0.3, -0.3, and -0.12. For medium adjustments to the fuzzy set... A triangular membership function is used on the universe of discourse, with the three vertex parameters of the triangular membership function set to -1.2, 0, and 1.2; The moderately adjusted fuzzy set on the universe of discourse adopts a triangular membership function, with the three vertex parameters of the triangular membership function set to -0.18, 0, and 0.18. The largely adjusted fuzzy set... A triangular membership function is used on the universe of discourse, with the three vertex parameters of the triangular membership function set to 0.8, 2.0, and 2.0; The fuzzy set is significantly adjusted on the domain of discourse to adopt a triangular membership function, with the three vertex parameters of the triangular membership function set to 0.12, 0.3, and 0.3.

[0040] The preset fuzzy rule table contains nine fuzzy rules, each corresponding to a first input variable. The three fuzzy sets and the second input variable The Cartesian product of three fuzzy sets. The first fuzzy rule is: if It is a slow correction and If it is a negative change, then It is a minor adjustment and It's a minor adjustment. The second fuzzy rule is: if It is a slow correction and If it is zero change, then It is a minor adjustment and It's a minor adjustment. The third fuzzy rule is: if It is a slow correction and If it is a positive change, then It is a moderate adjustment and It's a medium-amplitude adjustment. The fourth fuzzy rule is: if It is a medium-speed correction and If it is a negative change, then It is a moderate adjustment and It's a moderate adjustment. The fifth fuzzy rule is: if It is a medium-speed correction and If it is zero change, then It is a moderate adjustment and It's a medium-amplitude adjustment. The sixth fuzzy rule is: if... It is a medium-speed correction and If it is a positive change, then It is a major adjustment and It's a moderate adjustment. The seventh fuzzy rule is: if... It is a quick fix and If it is a negative change, then It is a major adjustment and It's a moderate adjustment. The eighth fuzzy rule is: if... It is a quick fix and If it is zero change, then It is a major adjustment and It's a significant adjustment. The ninth fuzzy rule is: if... It is a quick fix and If it is a positive change, then It is a major adjustment and It's a significant adjustment.

[0041] The Madani inference method is used to perform inference based on the fuzzy set combination of the first and second input variables. In the Madani inference method, the first input variable... The real-time values ​​are fuzzified to calculate the first input variable. The real-time values ​​of the membership degrees of the slow-corrected fuzzy set, the medium-corrected fuzzy set, and the fast-corrected fuzzy set; for the second input variable The real-time values ​​are fuzzified to calculate the second input variable. The real-time values ​​are the membership degrees of the negatively changing fuzzy sets, the zero-changing fuzzy sets, and the positively changing fuzzy sets. For each fuzzy rule in the preset fuzzy rule table, the antecedent truth value of the fuzzy rule is calculated using the minimum operation, i.e., the first input variable is taken. The membership degree of the corresponding fuzzy set and the second input variable The smaller of the membership degrees of the corresponding fuzzy sets is taken as the antecedent truth value. The antecedent truth value is then used to determine the consequent of the fuzzy rule. and The corresponding fuzzy sets are truncated by taking the smaller of the truth value of the antecedent and the membership function value of the consequent. This truncating process yields nine fuzzy rules. Truncate the fuzzy set and perform a union operation to obtain... The comprehensive output fuzzy set; the nine fuzzy rules are truncated to obtain nine sets. Truncate the fuzzy set and perform a union operation to obtain... The comprehensive output fuzzy set.

[0042] The target excitation frequency and target amplitude are obtained by deblurring using the center-of-gravity method. The centroid of the area enclosed by the membership function curve and the horizontal axis of the fuzzy set is calculated from the comprehensive output. The horizontal coordinate value of the centroid is... The precise value. The center of gravity method for... The centroid of the area enclosed by the membership function curve and the horizontal axis of the fuzzy set is calculated from the comprehensive output. The horizontal coordinate value of the centroid is... The precise value. The calculation process using the center of gravity method is expressed by the following formula:

[0043] in: This represents the precise output value after deblurring. The solution, correspond The exact value; for The solution, correspond The precise value. This represents the total number of sampling points used to discretize the output universe of discourse. The value is 200, which means... Theoretical domain Divided into 200 discrete sampling intervals, or Theoretical domain It is divided into 200 discrete sampling intervals. The index of the discrete sampling point. Take an integer between 1 and 200. Indicates the first The x-coordinate value of each discrete sampling point Indicates the fuzzy set of the comprehensive output at the th... The membership degree values ​​at each discrete sampling point.

[0044] In specific implementation, please refer to Figure 4The excitation frequency adaptive adjustment module sends a read request to the programmable logic controller (PLC) of the physical vibrating feeder via the data communication interface of the configuration software to read the current operating frequency and current amplitude of the physical vibrating feeder. The current operating frequency of the physical vibrating feeder is represented by the symbol... The current amplitude of the physical vibrating feeder is indicated by the symbol. This indicates that the programmable logic controller (PLC) returns [the following information]. numerical sum The numerical values ​​are stored in the internal cache by the excitation frequency adaptive adjustment module.

[0045] The excitation frequency adaptive adjustment module simultaneously inputs the real-time feed rate correction coefficient and the rate of change of the difference between the average stacking height and the standard stacking height into the fuzzy rule inference engine. After fuzzification, fuzzy inference, and defuzzification processing, the fuzzy rule inference engine outputs the excitation frequency adjustment increment using a symbol. Symbols are used to represent amplitude adjustment increments. This indicates that the excitation frequency adaptive adjustment module will adjust the current operating frequency of the physical vibration feeder. Adjusting the excitation frequency increment Add them together to calculate the target excitation frequency, denoted by the symbol. The calculation method is as follows: The vibration frequency adaptive adjustment module adjusts the current amplitude of the physical vibration feeder. With amplitude adjustment increment Add them together, and calculate the target amplitude using the symbol. The calculation method is as follows: .

[0046] The vibration frequency adaptive adjustment module reads the lower limit of the rated frequency of the physical vibration feeder using the symbol. The symbol indicates the upper limit of the rated frequency. express, The value is 25 Hz. The value is 50 Hz, and this value is determined by the nameplate parameters of the drive motor of the physical vibrating feeder. When The value is less than When the value is specified, the excitation frequency adaptive adjustment module will... The value is forced to be set to The value, and simultaneously adjust the amplitude increment. The value is set to zero. When The value is greater than When the value is specified, the excitation frequency adaptive adjustment module will... The value is forced to be set to The value, and simultaneously adjust the amplitude increment. The value is set to zero. Amplitude adjustment increment. After being set to zero, the target amplitude The value and the current amplitude of the physical vibratory feeder The values ​​are equal.

[0047] The excitation frequency adaptive adjustment module presets the safety change step size of the excitation frequency using symbols. express, The value is 0.5 Hz, obtained from the mechanical resonance frequency response test of the physical vibrating feeder. When the frequency change step exceeds 0.5 Hz, the amplitude response of the physical vibrating feeder exhibits an overshoot exceeding 5%. The safe change step size of the preset amplitude in the excitation frequency adaptive adjustment module is indicated by the symbol... express, The value is 0.1 mm, which is determined based on the response time constant of the amplitude control servo mechanism of the physical vibrating feeder. The response time constant of the servo mechanism is 20 milliseconds. To ensure that the amplitude tracking is free of overshoot, the amplitude change corresponding to a single step increment does not exceed 0.1 mm. The excitation frequency adaptive adjustment module presets a fixed time interval using the symbol... express, The value is 50 milliseconds, which is twice the scan cycle of the programmable logic controller (PLC) of the physical vibrating feeder, which is 25 milliseconds.

[0048] Obtaining the target excitation frequency Then, the excitation frequency adaptive adjustment module calculates... Compared with the current operating frequency of the physical vibrating feeder The difference between them is represented by the sign. express, .when The value is greater than When the value is specified, the excitation frequency adaptive adjustment module will adjust the difference. The increment is divided into multiple step sizes, and the total number of step sizes after the division is calculated according to the following formula:

[0049] in: This represents the total number of step sizes after the target excitation frequency is decomposed. This indicates the rounding up operation. Indicates the difference The absolute value, This represents the safe step size for changing the excitation frequency. The value of each step increment is... , symbols and The signs are the same. The excitation frequency adaptive adjustment module will... Each step increment is at a fixed time interval. The data is sequentially output to the programmable logic controller of the physical vibrating feeder via the data communication interface of the configuration software. The time interval between two adjacent step increment outputs is strictly equal to The value. When The value is less than or equal to When the value is specified, the excitation frequency adaptive adjustment module does not perform a split operation, but directly sets the target excitation frequency. Send as a single output value.

[0050] In obtaining the target amplitude Then, the excitation frequency adaptive adjustment module calculates... Current amplitude of the physical vibrating feeder The difference between them is represented by the sign. express, .when The value is greater than When the value is specified, the excitation frequency adaptive adjustment module will adjust the difference. The increment is broken down into multiple step increments, and the total number of step increments after the breakdown is indicated by the symbol. express, The value of each step increment is... The excitation frequency adaptive adjustment module will... Each step increment is at a fixed time interval. The programmable logic controller outputs sequentially to the physical vibrating feeder. When The value is less than or equal to When the value is specified, the excitation frequency adaptive adjustment module does not perform a split operation and directly sets the target amplitude. Send as a single output value.

[0051] In specific implementation, please refer to Figure 5 The control data structure for the vibrating feeder is predefined in the configuration software. This structure includes a frequency register address field, an amplitude register address field, a frequency value field, an amplitude value field, and a cyclic redundancy check (CRC) field. The frequency register address field stores the Modbus address of the holding register controlling the excitation frequency in the programmable logic controller (PLC) of the physical vibrating feeder. The amplitude register address field stores the Modbus address of the holding register controlling the amplitude in the PLC of the physical vibrating feeder. The frequency value field stores the 16-bit unsigned integer value corresponding to the target excitation frequency. The amplitude value field stores the 16-bit unsigned integer value corresponding to the target amplitude. The CRC field stores the checksum obtained after performing CRC calculations on the frequency and amplitude value fields.

[0052] Write the target excitation frequency to the frequency value field. Before writing, multiply the floating-point value of the target excitation frequency by 10, round it down, and convert it to a 16-bit unsigned integer. Store the converted value in the frequency value field. When the target excitation frequency is 42.5 Hz, the stored value of the frequency value field is 425. Write the target amplitude to the amplitude value field. Before writing, multiply the floating-point value of the target amplitude by 100, round it down, and convert it to a 16-bit unsigned integer. Store the converted value in the amplitude value field. When the target amplitude is 1.8 mm, the stored value of the amplitude value field is 180.

[0053] The corresponding physical programmable logic controller (PLC) register address is read from the device address mapping table in the configuration software based on the frequency register address. This device address mapping table, stored in the configuration software's configuration file, records the correspondence between frequency register addresses and PLC register addresses in key-value pairs; for example, frequency register address 3030 corresponds to PLC register address 40001. Similarly, the corresponding PLC register address is read from the device address mapping table based on the amplitude register address; for example, amplitude register address 3031 corresponds to PLC register address 40002.

[0054] The Cyclic Redundancy Check (CRC) algorithm is used to calculate the values ​​of the frequency and amplitude fields. The CRC-16-IBM standard is adopted, and the generator polynomial is... The initial value is 0xFFFF, and the output XOR value is 0x0000. During calculation, the 16-bit unsigned integer of the frequency value field and the 16-bit unsigned integer of the amplitude value field are concatenated into a 32-bit data sequence. The lower 8 bits of the frequency value field are transmitted, followed by the higher 8 bits, then the lower 8 bits and the higher 8 bits of the amplitude value field. The concatenated 32-bit data sequence is input byte-by-byte into the cyclic redundancy check (CRCD) calculation module. The CRCD calculation is performed bit-by-bit using a shift-XOR method, resulting in a 16-bit CRCD code. This 16-bit CRCD code is then filled into the CRCD field, with the lower 8 bits padded first and the higher 8 bits padded last, completing the encapsulation of the configuration control instructions.

[0055] The configuration software's data communication interface establishes a connection with the physical vibrating feeder's programmable logic controller (PLC) using the Open Platform Communication Unified Architecture Protocol (OPCIP), version 1.04. The configuration software's data communication interface acts as the client of the OPCIP, while the PLC of the physical vibrating feeder acts as the server. During connection establishment, the configuration software's data communication interface initiates a Transmission Control Protocol (TCP) connection request to port 4840 of the PLC. After a three-way handshake to establish the TCP connection, the configuration software's data communication interface sends a Hello message using the OPCIP, and the PLC returns an Acknowledge message, completing the secure channel establishment. Subsequently, the configuration software's data communication interface sends a CreateSession request using the OPCIP, and the PLC returns a CreateSession response, completing session creation.

[0056] Before sending configuration control commands, the configuration software's data communication interface first sends a connection keep-alive message to the programmable logic controller (PLC) of the physical vibrating feeder. This keep-alive message is a ReadRequest message using the Open Platform Communication Unified Architecture Protocol (OPCIP). It reads the server status variables of the PLC, and the node identifier corresponding to these server status variables is... If the programmable logic controller (PLC) of the physical vibrating feeder returns a ReadResponse message within 100 milliseconds with a server status field value of 0, the PLC is confirmed to be online, allowing the configuration control command sending process to continue. If the PLC does not return a ReadResponse message within 100 milliseconds, or the returned server status field value is not 0, the PLC is confirmed to be offline or faulty, the current configuration control command sending process is terminated, and an offline fault indicator is displayed next to the corresponding virtual vibrating feeder model in the 3D configuration monitoring interface.

[0057] The encapsulated configuration control instructions are encoded according to the data frame format of the Open Platform Communication Unified Architecture Protocol. The encoding method for the configuration control instructions is as follows: the physical programmable logic controller (PLC) register addresses corresponding to the frequency register address and the physical programmable logic controller (PLC) register addresses corresponding to the amplitude register address in the vibration feeder control data structure are mapped to the target node identifiers of the WriteRequest message in the Open Platform Communication Unified Architecture Protocol. The content of the frequency value field and the amplitude value field are used as the write values ​​for the corresponding target node identifiers. The cyclic redundancy check (CRC) field is used as the message payload along with the write values. A timestamp field, a request handle field, a timeout field, and an authentication token field are set in the header of the WriteRequest message. The value of the request handle field increments by 1 each time, and the value of the timeout field is set to 3000 milliseconds.

[0058] The encoded WriteRequest message is sent to the programmable logic controller (PLC) of the physical vibrating feeder via the data communication interface of the configuration software. Upon receiving the WriteRequest message, the PLC of the physical vibrating feeder parses the target node identifier and the write value in the message, writes the write value to the corresponding PLC register address, and returns a WriteResponse message to the data communication interface of the configuration software. The WriteResponse message contains an execution status code field; a value of 0x00000000 indicates successful instruction execution.

[0059] After sending a WriteRequest message, the configuration software's data communication interface starts a preset timeout timer with a preset timeout value of 500 milliseconds. The preset timeout is determined based on the following: the maximum instruction processing time of the physical vibrating feeder's programmable logic controller (PLC) is 200 milliseconds; the round-trip latency of the open platform's unified communication architecture protocol network does not exceed 100 milliseconds within the local area network; considering this margin, 500 milliseconds is chosen as the timeout threshold. Within the preset timeout, the configuration software's data communication interface continuously listens for instruction execution confirmation frames returned by the PLC of the physical vibrating feeder. These confirmation frames are WriteResponse messages. If an instruction execution confirmation frame is received within the preset timeout of 500 milliseconds, the configuration software's data communication interface checks the execution status code field. If the execution status code field value is 0x00000000, the instruction execution is confirmed as successful, and the process ends. If no instruction execution confirmation frame is received within the preset timeout of 500 milliseconds, the configuration software's data communication interface initiates a retransmission process. During the retransmission process, the configuration software's data communication interface reconnects and retransmits the same WriteRequest message via the Transmission Control Protocol (TCP). After transmission, a 500-millisecond timeout timer is restarted. The maximum number of retransmissions is three. This limit is based on the following: when network jitter occurs, three retransmissions can cover 99.9% of the instantaneous fault recovery window, while avoiding excessive resource consumption of the configuration software's data communication interface during continuous network interruptions. The retransmission count starts from 1, incrementing by 1 with each retransmission. If no instruction execution confirmation frame is received after three retransmissions, the configuration software's data communication interface generates a communication fault alarm signal. This alarm signal includes the physical vibrating feeder's device identifier, the target excitation frequency failure value, and a timestamp. The device identifier, target excitation frequency failure value, and timestamp from the communication fault alarm signal are concatenated into an alarm text string, which is displayed in the alarm information prompt box above the corresponding virtual vibrating feeder model in the 3D configuration monitoring interface.

[0060] The configuration software's data communication interface maintains simultaneous connections with the programmable logic controllers (PLCs) of multiple physical vibrating feeders. The number of PLCs for these physical vibrating feeders is [number missing]. tower, The value is an integer greater than or equal to 2. The configuration software's data communication interface maintains an independent Open Platform Communication Unified Architecture Protocol (OPCIP) session for each physical vibrating feeder's programmable logic controller (PLC). Each independent OPCIP session has a unique session identifier, which is obtained from the PLC of the physical vibrating feeder by the configuration software's data communication interface during the CreateSession phase. A polling scheduling method is used to sequentially issue configuration control commands to each PLC. The execution logic of the polling scheduling method is as follows: the configuration software's data communication interface maintains a polling queue, which is arranged in ascending order of device identifier. A physical vibrating feeder is used. The configuration software's data communication interface retrieves the device identifier of the first physical vibrating feeder from the head of the polling queue and executes a complete process to the programmable logic controller (PLC) of the first physical vibrating feeder, including sending a connection keep-alive message, encapsulating configuration control commands, sending configuration control commands, and receiving a command execution confirmation frame. After the configuration control command issuance process for the first physical vibrating feeder is completed, the configuration software's data communication interface retrieves the device identifier of the second physical vibrating feeder from the polling queue and executes the same process until the [number missing]th physical vibrating feeder is reached. The configuration control command issuance process for the physical vibrating feeder ends, completing one polling cycle. After one polling cycle ends, the configuration software's data communication interface immediately restarts the next polling cycle from the head of the polling queue. The polling scheduling reference time interval between two adjacent polling schedules is denoted by the symbol... express, The calculation method is as follows:

[0061] in: This indicates the total number of physical vibrating feeders. This indicates the polling sequence number of the physical vibrating feeder. The value ranges from 1 to positive integers, Indicates the first The connection keep-alive message sending and confirmation time of the physical vibrating feeder. The value is fixed at 100 milliseconds. Indicates the first Configuration control command encoding time for a physical vibrating feeder. The value is fixed at 5 milliseconds. Indicates the first The configuration control command sending time of the physical vibrating feeder. The value is fixed at 15 milliseconds. Indicates the first Waiting time for the instruction execution confirmation frame of the physical vibrating feeder. The maximum value is 500 milliseconds. When When it equals 4, The value is approximately 2480 milliseconds.

[0062] The foregoing has provided a detailed description of one embodiment of the present invention, but this description is merely a preferred embodiment and should not be construed as limiting the scope of the invention. All equivalent variations and modifications made within the scope of the claims of this invention should still fall within the patent coverage of this invention.

Claims

1. A configuration control method for a vibratory conveyor of bulk materials, characterized in that, include: Step 1: Configuration control construction. Based on the physical property parameters of the bulk material, a three-dimensional configuration monitoring interface is constructed, which includes a virtual vibrating feeder, a virtual conveyor belt, and virtual material level detection points. An adaptive adjustment module for excitation frequency is embedded in the virtual vibrating feeder. Step 2: Material flow status perception. The change in the stacking height of bulk materials on the virtual conveyor belt is collected in real time through virtual material level detection points, and the change in stacking height is mapped to the real-time feeding rate correction coefficient of the vibrating feeder. Step 3: Dynamic tuning of vibration parameters. Based on the real-time feeding rate correction coefficient, the fuzzy rule inferencer in the excitation frequency adaptive adjustment module is called to calculate the target excitation frequency and target amplitude of the vibrating feeder. Step 4: Send out the configuration command. Encapsulate the target excitation frequency and target amplitude into a configuration control command, and send it to the programmable logic controller of the physical vibration feeder for execution through the data communication interface of the configuration software.

2. The configuration control method for a vibratory conveying of bulk materials according to claim 1, characterized in that, The physical property parameters of the bulk material in step one include the angle of repose, bulk density, moisture content, and particle size distribution range of the bulk material. The specific method for constructing the configuration control is as follows: The inclination angle of the side baffles of the virtual conveyor belt is set according to the angle of repose; Set the initial feeding range of the virtual vibrating feeder according to the bulk density; The shape parameters of the input membership function of the fuzzy rule inferencer in the excitation frequency adaptive adjustment module are determined based on the moisture content. The simulated parameters for the screen aperture of the virtual vibrating feeder are set according to the particle size distribution range.

3. The configuration control method for a vibratory conveying of bulk materials according to claim 2, characterized in that, The specific implementation method of material flow state sensing in step two is as follows: In the 3D configuration monitoring interface, at least three virtual material level detection points are deployed at equal intervals along the length of the virtual conveyor belt. Each virtual material level detection point outputs the material accumulation height value at the current moment according to a fixed sampling period. The average accumulation height on the virtual conveyor belt is obtained by weighting and averaging the material accumulation height values ​​output by all virtual material level detection points at the same time. The difference between the average stacking height and the preset standard stacking height is calculated, and the difference is input to the proportional-integral-derivative (PID) controller, which then outputs a real-time feed rate correction coefficient.

4. The configuration control method for vibratory conveying of bulk materials according to claim 3, characterized in that, The fixed sampling period in step two is automatically adjusted based on the maximum particle size in the particle size distribution range of the bulk material. The larger the maximum particle size, the longer the fixed sampling period.

5. The configuration control method for a vibratory conveyor of bulk materials according to claim 3, characterized in that, The fuzzy rule inference engine in step three is constructed as follows: Set the real-time feeding rate correction coefficient as the first input variable of the fuzzy rule inference engine, and set the rate of change of the difference between the average stacking height and the standard stacking height as the second input variable; Define three fuzzy sets for the first input variable: slow correction, medium correction, and fast correction; define three fuzzy sets for the second input variable: negative change, zero change, and positive change. The output variables of the fuzzy rule inference engine are set as the excitation frequency adjustment increment and the amplitude adjustment increment, and three fuzzy sets are defined for the output variables: small adjustment, medium adjustment and large adjustment. The Madani inference method is used to obtain the fuzzy value of the output variable from the preset fuzzy rule table based on the fuzzy set combination of the first and second input variables. Then, the target excitation frequency and target amplitude are obtained by defuzzification using the centroid method.

6. The configuration control method for a vibratory conveyor of bulk materials according to claim 5, characterized in that, The specific method for obtaining the target excitation frequency and target amplitude by calling the fuzzy rule inference engine in the excitation frequency adaptive adjustment module in step three is as follows: The vibration frequency adaptive adjustment module reads the current operating frequency and current amplitude of the physical vibration feeder; The real-time feed rate correction coefficient and the rate of change of the difference between the average stacking height and the standard stacking height are simultaneously input into the fuzzy rule inference engine to obtain the excitation frequency adjustment increment and amplitude adjustment increment. The target excitation frequency is obtained by adding the current operating frequency to the excitation frequency adjustment increment, and the target amplitude is obtained by adding the current amplitude to the amplitude adjustment increment. When the target excitation frequency exceeds the lower or upper limit of the rated frequency of the physical vibrating feeder, the target excitation frequency is clamped to the lower or upper limit of the rated frequency, and the amplitude adjustment increment is set to zero at the same time.

7. The configuration control method for vibratory conveying of bulk materials according to claim 6, characterized in that, When the difference between the target excitation frequency or target amplitude and the current operating frequency or current amplitude exceeds the preset safe change step size, the excitation frequency adaptive adjustment module breaks down the difference into multiple step increments and outputs them one by one at fixed time intervals.

8. The configuration control method for a vibratory conveyor of bulk materials according to claim 6, characterized in that, The specific method for encapsulating the target excitation frequency and target amplitude into configuration control commands in step four is as follows: The control data structure of the vibratory feeder is predefined in the configuration software. This data structure includes a frequency register address field, an amplitude register address field, a frequency value field, an amplitude value field, and a cyclic redundancy check field. Write the target excitation frequency into the frequency value field, write the target amplitude into the amplitude value field, and read the corresponding physical programmable logic controller register address from the device address mapping table of the configuration software according to the frequency register address and the amplitude register address. The cyclic redundancy check algorithm is used to calculate the contents of the frequency and amplitude numerical fields, and the calculation results are filled into the cyclic redundancy check field to complete the encapsulation of the configuration control command.

9. The configuration control method for a vibratory conveyor of bulk materials according to claim 8, characterized in that, The specific method by which the data is sent to the programmable logic controller of the physical vibrating feeder via the data communication interface of the configuration software in step four is as follows: The configuration software's data communication interface uses an open platform communication unified architecture protocol to establish a connection with the programmable logic controller of the physical vibrating feeder; Before sending configuration control commands, the data communication interface first sends a connection keep-alive message to the programmable logic controller to confirm that the programmable logic controller is online. The encapsulated configuration control commands are encoded according to the data frame format of the open platform communication unified architecture protocol and sent to the programmable logic controller through the data communication interface. The system receives instruction execution confirmation frames returned by the programmable logic controller. If no instruction execution confirmation frame is received within the preset timeout period, the same configuration control instruction is repeatedly sent. After the number of repeated sending reaches the upper limit, a communication fault alarm signal is generated and displayed in the three-dimensional configuration monitoring interface.

10. The configuration control method for vibratory conveying of bulk materials according to claim 9, characterized in that, The configuration software's data communication interface is simultaneously connected to the programmable logic controllers of multiple physical vibrating feeders, and uses a polling scheduling method to sequentially issue configuration control commands to each programmable logic controller.

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