Garbage pusher control method and device, electronic equipment and computer medium

Abstract

The application discloses a kind of garbage pusher control method and device, electronic equipment and computer medium, and specific scheme is: obtaining control parameter sequence value, current flow value, flow set value, primary air, secondary air real-time flow value, garbage pool real-time air intake, hearth bed pressure difference value and control variable sequence value;Based on control parameter sequence value and current flow value, the flow prediction value of main steam flow is calculated;Based on flow set value and flow prediction value, calculate flow deviation;Based on primary air real-time flow value, secondary air real-time flow value and garbage pool real-time air intake, calculate first pusher speed value;Based on control variable sequence value and hearth bed pressure difference value, extract bed thickness characteristic value;Based on bed thickness characteristic value, calculate second pusher speed value;Based on flow prediction value and the above pusher speed value, calculate pusher speed given value;Based on flow deviation and pusher speed given value, obtain and implement pusher target speed value.

Description

Technical Field

[0001] This disclosure belongs to the field of automatic incineration technology, and in particular relates to a waste pusher control method and apparatus, electronic equipment, and computer-readable storage medium. Background Technology

[0002] ACC (Automatic Combustion Control) is a core technology for the operation of incinerators, biomass boilers, and drying furnaces. It is mainly used to achieve automated and intelligent control of the waste incineration process, ensuring stable combustion, compliance with environmental standards, and economical operation. Its core principle is to optimize combustion efficiency by monitoring and adjusting parameters such as grate speed, air volume, and feed rate in real time.

[0003] Conventional ACC control methods establish a combustion chamber thermodynamic model based on the mass conservation equation and the energy conservation equation. However, due to the large variations in waste composition, the mechanism model established based on the thermodynamic model is not applicable to actual production conditions. Summary of the Invention

[0004] To address the shortcomings of the prior art, this disclosure provides a waste pusher control method and apparatus, electronic equipment, and computer-readable storage medium.

[0005] To achieve the above objectives, this disclosure employs the following technical solution: The first aspect of this disclosure provides a waste pusher control method applied to an automatic waste combustion control system. The method includes: acquiring control parameter sequence values ​​related to the main steam flow rate, the current flow rate of the main steam flow rate, the flow rate setpoint of the main steam flow rate, the real-time primary air flow rate, the real-time secondary air flow rate, the real-time air intake of the waste pit, the furnace bed pressure difference value, and control variable sequence values ​​highly related to the furnace bed pressure difference during historical periods; and calculating a predicted flow rate of the main steam flow rate based on the control parameter sequence values ​​and the current flow rate value. Based on the flow setpoint and flow prediction, the flow deviation is calculated; based on the real-time primary air flow, real-time secondary air flow, and real-time air intake of the waste pit, the speed of the first pusher is calculated; based on the control variable sequence value and the furnace material layer pressure difference value, the material layer thickness characteristic value is extracted; based on the material layer thickness characteristic value, the speed of the second pusher is calculated; based on the flow prediction, the speed of the first pusher, and the speed of the second pusher, the pusher speed setpoint is calculated; based on the flow deviation, the pusher speed setpoint is dynamically corrected, and the target pusher speed value is obtained and used to control the speed of the pusher.

[0006] A second aspect of this disclosure provides a waste pusher control device applied to an automatic waste combustion control system. The device includes: an acquisition unit configured to acquire control parameter sequence values, current flow value of main steam flow, flow setpoint of main steam flow, real-time primary air flow, real-time secondary air flow, real-time air intake of the waste pit, furnace bed pressure difference value, and a sequence of control variables highly correlated with furnace bed pressure difference over historical periods; a prediction calculation unit configured to calculate a predicted flow value of main steam flow based on the control parameter sequence values ​​and the current flow value; and a deviation calculation unit configured to calculate the flow rate based on the flow setpoint and the predicted flow value. Deviation; a first speed calculation unit is configured to calculate the speed value of the first pusher based on the real-time flow rate of the primary air, the real-time flow rate of the secondary air, and the real-time air intake of the waste pit; an extraction unit is configured to extract the characteristic value of the material layer thickness based on the sequence value of the control variable and the pressure difference value of the furnace material layer; a second speed calculation unit is configured to calculate the speed value of the second pusher based on the characteristic value of the material layer thickness; a setpoint calculation unit is configured to calculate the setpoint value of the pusher speed based on the predicted flow rate, the speed value of the first pusher, and the speed value of the second pusher; a control unit is configured to dynamically correct the setpoint value of the pusher speed based on the flow deviation, and obtain and use the target speed value of the pusher to control the speed of the pusher.

[0007] A third aspect of this disclosure provides an electronic device comprising: at least one processor; and a memory communicatively connected to the at least one processor, wherein the memory stores instructions executable by the at least one processor to enable the at least one processor to perform a method as described in any implementation of the first aspect.

[0008] The fourth aspect of this disclosure provides a non-transitory computer-readable storage medium storing computer instructions for causing a computer to perform the method described in any implementation of the first aspect.

[0009] Compared with the prior art, the beneficial effects of this disclosure are as follows: Based on the control parameter sequence value and the current flow value, the flow prediction value is predicted and input into the control loop that controls the speed of the pusher. This can effectively improve the response speed of the automatic waste combustion control system. The flow deviation calculated by the flow prediction value can compensate for unnecessary fluctuations in the output of the automatic waste combustion control system in advance, thereby solving the overshoot problem caused by response lag. This allows the control of the waste pusher to be closer to the actual production situation. Attached Figure Description

[0010] Figure 1 This is a flowchart of an embodiment of the waste pusher control method according to the present disclosure; Figure 2 This is a schematic diagram of a structure of the waste pusher control method disclosed herein; Figure 3 This is a schematic diagram of the piecewise linear function of air volume in this disclosure; Figure 4 This is a schematic diagram of one embodiment of the waste pusher control device disclosed herein; Figure 5 This is a block diagram of an electronic device used to implement the waste pusher control method of the embodiments of this disclosure. Detailed Implementation

[0011] The present disclosure is further illustrated below with reference to specific embodiments. It should be understood that these embodiments are for illustrative purposes only and are not intended to limit the scope of the disclosure. Furthermore, it should be understood that after reading the teachings of this disclosure, those skilled in the art can make various alterations or modifications to this disclosure, and these equivalent forms also fall within the scope defined by this application.

[0012] The automatic waste combustion control system includes: feeding equipment, grate system, blower system, slag removal system, monitoring equipment, and a control system with multiple control modules. The feeding equipment includes a waste pusher, used to uniformly and stably feed waste into the incinerator; its speed can be adjusted according to the combustion situation. The control system includes: evaporation control module, waste layer control module, furnace temperature control module, slag ignition rate control module, and oxygen control module. The evaporation control module ensures stable steam production by adjusting the combustion process, guaranteeing process requirements. The grate system includes: drying grate, combustion grate, and burnout grate located in different sections of the furnace. By controlling the operating speed and air supply of each section, segmented combustion of waste is achieved. The blower system includes primary and secondary blowers. The primary blower supplies combustion air to the area under the grate, and the secondary blower supplies air to the secondary combustion chamber; both are controlled by frequency converters.

[0013] In the automatic waste combustion control system, garbage trucks transport waste to the incineration plant and unload it into the waste pit. The waste in the waste pit enters the drying chamber through the conveying assembly. Heating pipes in the drying chamber pre-dry the waste. The waste layer control module calculates the waste layer thickness by measuring the pressure difference between the waste on the combustion grate and the air below the combustion grate, and then adjusts the speed of the waste pusher, drying grate, and combustion grate to stabilize the waste layer thickness. The waste is dried in the drying grate by the high-temperature combustion air in the furnace, as well as the radiant heat from the furnace side walls and the furnace top, with a residence time of approximately 30 minutes. After drying, the waste enters the combustion section for vigorous combustion, with a residence time of approximately 30 minutes, during which 60%-80% of the total combustion air is supplied. In the burnout section, the waste completely combusts the fixed carbon and unburned parts. The residence time is approximately one hour to ensure that the slag's loss on ignition is reduced to 1%-2%. The primary air fan draws air from above the waste pit, heats it via a steam-air preheater, and then sends it to the drying, combustion, and burnout sections of the grate, using variable frequency control. The secondary air fan draws air from above the waste pit, heats it via a steam-air preheater, and then supplies it to the secondary combustion chamber, also using variable frequency control. During waste combustion, parameters such as primary air volume, secondary air volume, and waste feed rate are adjusted to maintain the furnace temperature within a suitable range, ensuring stable waste combustion. Furthermore, based on the oxygen content in the flue gas measured by the oxygen sensor, PID or fuzzy control is used to adjust the speed of the primary and secondary air fans, changing the primary and secondary air volumes to maintain the oxygen content in the flue gas at a reasonable level.

[0014] Current automatic waste combustion control systems establish a thermodynamic model of the combustion chamber based on the mass conservation equation and the energy conservation equation. They use real-time detected values ​​such as furnace temperature, flue gas oxygen content, and steam flow rate as input variables, and output execution commands such as grate speed and primary / secondary fan frequency through a PID controller. However, the composition of waste varies greatly, and the mechanism model based on thermodynamics is not applicable to actual production conditions. Furthermore, there is a long lag period from sensor detection to actuator response, resulting in severe control overshoot and large system fluctuations.

[0015] To address the shortcomings of traditional technologies, this disclosure proposes a waste pusher control method. Figure 1 A flow chart 100 of an embodiment of a waste pusher control method according to the present disclosure is shown, the waste pusher control method comprising the following steps: Step 101: Obtain the control parameter sequence values ​​of the control parameters related to the main steam flow rate, the current flow rate value of the main steam flow rate, the flow rate setpoint value of the main steam flow rate, the real-time flow rate value of the primary air, the real-time flow rate value of the secondary air, the real-time air intake of the waste pool, the furnace material layer pressure difference value, and the control variable sequence values ​​highly related to the furnace material layer pressure difference in the historical period of the waste automatic combustion control system.

[0016] In this embodiment, the historical period is the historical time period of the waste automatic combustion control system. The duration of the historical period can be set according to development needs, such as a historical period of three months. The control parameters are the parameters that affect the main steam flow in the waste automatic combustion control system. These control parameters can be obtained by analyzing parameters through correlation algorithms (such as Pearson's algorithm and Spearman's algorithm), such as main steam pressure, furnace average temperature, and flue gas temperature at the outlet flue. The control parameter sequence value includes the value of at least one control parameter. The value of the control parameter can be obtained by sensors. The control parameter sequence value is a serialized representation of the control parameter value in the historical period.

[0017] In this embodiment, the setpoint for the main steam flow rate is a target value set by the user based on historical experience. This setpoint is determined according to the combustion process and the actual conditions of the furnace. The goal is to maintain the main steam flow rate at its optimal level to ensure combustion efficiency and stability. The real-time primary air flow rate, secondary air flow rate, waste pit inlet air volume, and furnace bed pressure difference can all be measured by their respective sensors. The furnace bed pressure difference refers to the pressure difference generated between the upper and lower sides of the waste bed when airflow passes through it. It is an important indicator of the waste bed's resistance to airflow. In a waste incinerator, the magnitude of the bed pressure difference reflects the accumulation of the waste bed. As the waste bed thickness increases, the resistance to airflow through the bed increases, leading to an increase in the furnace bed pressure difference. By monitoring changes in the furnace bed pressure difference, the trend of bed thickness changes can be determined. For example, if the furnace bed pressure difference gradually increases from an initial 1000 Pa to 1500 Pa, this may indicate an increase in bed thickness.

[0018] In this embodiment, the control variable related to the furnace bed pressure differential height is a parameter in the automatic waste combustion control system that affects the furnace bed pressure differential height. This control variable can be obtained by analyzing variables through correlation measurement algorithms (such as Pearson's algorithm and Spearman's algorithm), such as primary air fan frequency, secondary air fan frequency, furnace negative pressure, drying grate primary air damper opening, combustion grate primary air damper opening, and burnout grate primary air damper opening. The control variable sequence value includes the value of at least one control variable. The value of the control variable can be obtained by sensors. This control variable sequence value is a serialized representation of the control variable values ​​in history.

[0019] Step 102: Calculate the predicted flow rate of the main steam flow rate based on the control parameter sequence value and the current flow rate value.

[0020] In this embodiment, the control parameter sequence value is a serialized representation of the control parameter values ​​in the historical period. Since the main steam flow rate has the highest correlation with itself, the control parameters include the main steam flow rate. By arranging the control parameter sequence value and the current flow rate value together and putting them into a pre-trained time series prediction model, the time series prediction model can refer to the control parameters other than the main steam flow rate in the control parameter sequence value to predict the future value of the main steam flow rate and obtain the flow prediction value.

[0021] Optionally, step 102 above includes: extracting the main steam flow rate and parameter values ​​of control parameters other than the main steam flow rate from the control parameter sequence values; fitting the main steam flow rate curve based on the parameter values ​​of the main steam flow rate; fitting a development trend curve based on the parameter values ​​of control parameters other than the main steam flow rate; adjusting the main steam flow rate curve based on the development trend curve to obtain an adjusted curve; and obtaining the predicted flow rate value of the main steam flow rate based on the adjusted curve.

[0022] Step 103: Calculate the flow deviation based on the flow setpoint and the flow prediction.

[0023] In this embodiment, the flow rate deviation can be obtained by directly subtracting the flow rate prediction value from the flow rate setting value.

[0024] Optionally, step 103 includes: obtaining the parameter value of the main steam flow rate from the control parameter sequence value; determining the upper limit and lower limit of the parameter value of the main steam flow rate; subtracting the lower limit from the upper limit to obtain the limit difference; multiplying the limit difference by a preset coefficient to obtain the deviation limit; subtracting the flow prediction value from the flow set value to obtain the first difference; detecting whether the first difference is within the deviation limit; if the first difference is within the deviation limit, using the first difference as the flow deviation.

[0025] Step 104: Calculate the speed value of the first pusher based on the real-time flow rate of the primary air, the real-time flow rate of the secondary air, and the real-time air intake of the waste pool.

[0026] In this embodiment, the real-time primary air flow rate, the real-time secondary air flow rate, and the real-time air intake of the waste pit are added together to obtain the total real-time air volume value. This total real-time air volume value is then input into the trained speed prediction model to obtain the first pusher speed value output by the model. The speed prediction model is trained using samples generated from the primary air, secondary air, and real-time air intake of the waste pit from the automatic waste combustion control system, along with the speed of the waste pusher.

[0027] Step 105: Extract the characteristic value of the material layer thickness based on the sequence value of the control variable and the pressure difference value of the furnace material layer.

[0028] In this embodiment, mathematical models or machine learning algorithms can be used to extract the feature value of the bed thickness. Assume there is a machine learning model trained on historical data, which uses the sequence values ​​of control variables (such as the waste feeding rate sequence, secondary air volume sequence, etc.) and the furnace bed pressure difference as input features. The model predicts the bed thickness based on the complex relationships between these input features and outputs the bed thickness feature value. For example, the model may find that when the waste feeding rate increases within a certain range, and the furnace bed pressure difference also increases accordingly, the bed thickness will change according to a certain pattern. In this way, the model can output a feature value of the bed thickness, which can be a specific thickness value or an indicator representing the trend of thickness change.

[0029] Step 106: Calculate the speed value of the second pusher based on the material layer thickness characteristic value.

[0030] In this embodiment, the speed of the second pusher is determined by the thickness of the material layer in the furnace. In the automatic waste combustion control system, if the material layer is thick, in order to maintain combustion efficiency and prevent waste accumulation, the speed of the second pusher needs to be increased to push the waste into the furnace faster; conversely, if the material layer is thin, the speed of the second pusher can be slowed down.

[0031] In this embodiment, the speed value of the second pusher can be calculated based on the deviation between the characteristic value of the material layer thickness and the set target material layer thickness. Specifically, the steps include: comparing the extracted characteristic value of the material layer thickness with the preset target material layer thickness to calculate the material layer deviation value. The material layer deviation value is the difference between the characteristic value of the material layer thickness and the target material layer thickness. The material layer deviation value is input into a PID algorithm, which calculates the speed value of the second pusher based on the magnitude and trend of the deviation value. The preset target material layer thickness is set according to the combustion process and the actual conditions of the furnace. This target material layer thickness represents the optimal thickness of the waste material layer that is desired to be maintained in the furnace, ensuring combustion efficiency and stability.

[0032] Step 107: Calculate the feeder speed setpoint based on the predicted flow rate, the first feeder speed value, and the second feeder speed value.

[0033] In this embodiment, the speed value of the third pusher can be determined by the correspondence between the predicted flow rate and the pre-calibrated flow rate. The speed values ​​of the first pusher, the second pusher, and the third pusher are added together to obtain the given speed value of the pusher.

[0034] Step 108: Based on the flow deviation, dynamically correct the feeder speed setpoint to obtain and use the feeder target speed value to control the feeder speed.

[0035] In this embodiment, a fuzzy logic controller (FLC) can be used to obtain the target speed value of the pusher. A fuzzy logic controller is a control method based on fuzzy logic, suitable for handling processes with high uncertainty and fuzziness. The following are the specific implementation steps for obtaining the target speed value of the pusher using a fuzzy logic controller: The input variables of the fuzzy logic controller are: flow deviation ΔF; flow deviation change rate ΔF′, the rate of change of flow deviation, i.e., the derivative of ΔF. The output variable is: pusher speed correction value ΔV: the pusher speed correction value calculated based on the flow deviation and the flow deviation change rate.

[0036] The fuzzy logic controller divides input and output variables into fuzzy sets. For example, the flow deviation ΔF can be divided into fuzzy sets such as "negative large" (NB), "negative small" (NS), "zero" (Z), "positive small" (PS), and "positive large" (PB). Fuzzy rules are defined based on experience and expert knowledge. For example: if ΔF is "negative large" and ΔF′ is "negative large", then ΔV is "positive large"; if ΔF is "zero" and ΔF′ is "zero", then ΔV is "zero"; if ΔF is "positive large" and ΔF′ is "positive large", then ΔV is "negative large".

[0037] The fuzzy logic controller calculates the membership degree of fuzzy sets. Based on the actual value of the input variable, it calculates the membership degree of the variable in each fuzzy set. For example, if ΔF = 10 tons / hour, it calculates the membership degree of the variable in the fuzzy sets "negative large", "negative small", "zero", "positive small", and "positive large".

[0038] The fuzzy logic controller determines the fuzzy set of output variables based on fuzzy rules and membership degrees. For example, according to the fuzzy rules mentioned above, if the membership degree of ΔF is higher in "positive large" and the membership degree of ΔF′ is higher in "positive large", then the membership degree of ΔV is higher in "negative large".

[0039] Fuzzy logic controllers convert fuzzy outputs into specific numerical values. Common defuzzification methods include the maximum membership method and the centroid method. For example, the centroid method is used to calculate the specific value of ΔV; the defuzzified ΔV is added to the feeder speed setpoint Vinital to obtain the feeder target speed value Vtarget, i.e., Vtarget = Vinital + ΔV; the calculated feeder target speed value Vtarget is used as a control signal and sent to the feeder's drive system, which adjusts the feeder's actual operating speed according to the received target speed value.

[0040] The waste pusher control method provided in the embodiments of this disclosure acquires the control parameter sequence values, current flow value, flow setpoint, real-time primary air flow value, real-time secondary air flow value, real-time air intake of the waste pit, furnace bed pressure difference value, and control variable sequence values ​​related to the furnace bed pressure difference height during historical periods of the waste automatic combustion control system; calculates the predicted flow value of the main steam flow based on the control parameter sequence values ​​and the current flow value; calculates the flow deviation based on the flow setpoint and the predicted flow value; calculates the speed value of the first pusher based on the real-time primary air flow value, the real-time secondary air flow value, and the real-time air intake of the waste pit; and extracts the bed thickness feature value based on the control variable sequence values ​​and the furnace bed pressure difference value. Based on the characteristic value of the material layer thickness, the speed value of the second pusher is calculated; based on the predicted flow rate, the speed values ​​of the first and second pushers, the setpoint speed value of the pusher is calculated; based on the flow rate deviation, the setpoint speed value of the pusher is dynamically corrected to obtain and use the target speed value of the pusher to control the speed of the pusher; thus, based on the control parameter sequence value and the current flow rate value, the predicted flow rate value is predicted and input into the control loop that controls the speed of the pusher, which can effectively improve the response speed of the automatic waste combustion control system. The flow rate deviation calculated by the predicted flow rate value can compensate for unnecessary fluctuations in the output of the automatic waste combustion control system in advance, thereby solving the overshoot problem caused by response lag, so that the control of the waste pusher can be closer to the actual production situation.

[0041] Optionally, the above-mentioned waste pusher control method further includes: acquiring images of the waste layer on the combustion grate using a camera (such as an infrared thermal imager); preprocessing, filling holes, and detecting edges in the acquired waste layer images; extracting the edge contour of the waste pile area; then, using the furnace pushing platform size, camera resolution, and field of view information, calculating the actual size of a single pixel; finally, processing the obtained waste pile edge contour image based on the pixel size to obtain the actual thickness of the waste layer on the combustion grate; calculating the additional pusher speed value based on the actual thickness of the waste layer; the above-mentioned calculation of the pusher speed setpoint based on the flow prediction value, the first pusher speed value, and the second pusher speed value includes: obtaining a flow piecewise linear function representing the correspondence between the main steam flow value and the waste pusher speed; obtaining the third pusher speed value based on the flow prediction value and the flow piecewise linear function; and weighted summing the first pusher speed value, the second pusher speed value, the third pusher speed value, and the additional pusher speed value to obtain the pusher speed setpoint. In this embodiment, a computer vision algorithm is used to extract the actual thickness of the waste material layer in the combustion grate as a factor influencing the feeder speed setpoint. This allows for targeted adjustments to the feeder target speed value, improving the accuracy of the feeder target speed value.

[0042] In some optional implementations of this disclosure, the above-mentioned control parameter sequence values ​​are obtained through the following steps: obtaining the historical parameter set of the waste automatic combustion control system; calculating the correlation coefficient between the historical parameter set and the main steam flow rate under different delays; and determining the control parameter sequence values ​​based on the correlation coefficient.

[0043] In this optional implementation, the calculation of the correlation coefficient between the historical parameter set and the main steam flow rate under different delays includes: determining multiple delay times; specifically, these multiple delay times are different delay times, such as specific times like 0s, 5s, 10s, etc., or they can be identified by the number of sampling periods, where one sampling period can be 5s or 10s, etc. Determining the delay time means determining the delay interval and the number of delays. Multiple delay times constitute a continuous time; for example, if every 5s is a delay time and there are 20 delays, the entire delay process is 100s. The delay time characterizes the time when changes in the parameters of the automatic waste combustion control system cause significant changes in the parameters to be predicted in the automatic waste combustion control system (such as the predicted value of the main steam flow rate). Under each delay time, the correlation coefficient between each historical parameter in the historical parameter set and the main steam flow rate is calculated. Specifically, the Pearson algorithm or the Spearman algorithm can be used to calculate the correlation coefficient.

[0044] In this optional implementation, determining the control parameter sequence values ​​based on the correlation coefficient includes: selecting at least one parameter with the highest correlation coefficient in the automatic waste combustion control system as the control parameter, and extracting the parameter value of this control parameter from historical periods to obtain the control parameter sequence values. In actual calculations, the control parameters related to the main steam flow include: steam drum pressure, evaporator inlet temperature (left), main feedwater pressure, evaporator inlet temperature (right), main feedwater flow rate, main steam pressure, first-stage desuperheating water flow rate, furnace average temperature, second-stage desuperheating water flow rate, outlet flue gas temperature (right), economizer inlet oxygen concentration, furnace flue gas temperature (left), furnace flue gas temperature (middle), furnace flue gas temperature (right), furnace outlet flue gas temperature (right), economizer outlet flue gas pressure (left), economizer outlet flue gas pressure (right), NOx concentration, furnace outlet flue gas temperature (left), economizer outlet oxygen concentration (right), pusher speed setpoint, and main steam flow rate. The control parameter sequence values ​​of these control parameters can be values ​​collected by field sensors.

[0045] In this optional implementation, the target delay (or prediction step size k*) is defined as the delay corresponding to the maximum value of the correlation between the control parameter (actively changing parameter) and the controlled parameter (follower parameter). Specifically, it is the response time required for the controlled parameter to reach a steady state after a step change in the control parameter.

[0046] The target delay can be obtained through the step response method or the correlation delay algorithm (cross-correlation method). The step response method is a mature algorithm and will not be elaborated upon here. The core of the correlation delay algorithm lies in: first, determining the multi-step delay time range of the control parameter x and the controlled parameter y. The multi-step delay time range is the range of the delay step k (k is a non-negative integer; typically, the multi-step delay time range is limited to 0 to 2000. This non-negative limitation is because online prediction can only be based on historical data and cannot utilize future data). For each delay step k within this multi-step delay time range (each delay step corresponds to a fixed time interval, such as 5 seconds), a time-aligned control parameter sequence x(t) and a controlled parameter sequence y(t+k) (or equivalent form) need to be constructed. The construction of the sequence is essentially a sliding window process: a new sequence under each delay is obtained by removing the last value of the control parameter and the first value of the controlled parameter from the previous sequence and realigning the time points. For example, when k=0, x(t) and y(t) are aligned (no delay); when k=1, x(t) and y(t+1) are aligned (representing that the y value lags behind the x value by 1 sampling interval, i.e., 5 seconds); when k=2, x(t) and y(t+2) are aligned (lagging by 10 seconds), and so on. Subsequently, the Pearson correlation coefficient ρ(k) of each delay k sequence x(t) and y(t+k) is calculated, the maximum value among all |ρ(k)| is found, and the delay k corresponding to the maximum value is determined as the target delay k*, the mathematical expression of which is shown in Equation (1).

[0047] (1) The k* calculated by this method comprehensively reflects the transmission delay of the physical process and the response time of the measuring instruments.

[0048] This implementation provides a reliable method for obtaining control parameter sequence values ​​(or prediction step size) by calculating the correlation coefficient under different delays and determining the delay corresponding to the maximum value.

[0049] In some optional implementations of this disclosure, the above-mentioned calculation of the main steam flow prediction value based on the control parameter sequence value and the current flow value includes: constructing feature parameter values ​​based on the control parameter sequence value and the current flow value; inputting the feature parameter values ​​into a pre-trained main steam flow prediction model to obtain the flow prediction value output by the main steam flow prediction model.

[0050] In this optional implementation, the construction of feature parameter values ​​based on the control parameter sequence values ​​and the current flow rate value includes: extracting parameter values ​​other than the main steam flow rate from the control parameter sequence values ​​and using these parameter values ​​as input features; extracting the main steam flow rate parameter value and the current flow rate value from the control parameter sequence values ​​and using them as target variables or output features; and combining the input features and target variables or output features into an input feature matrix. Assuming there are three control parameters (pusher speed, secondary air volume, and waste feeding rate) and a time step of 10, the shape of the input feature matrix for each sample is (10, 4), where 4 represents the three control parameters and one flow rate value.

[0051] In this optional implementation, for each control parameter in the control parameter sequence, the aforementioned control parameters are collected every 5 seconds. Initialization requires 200 collections (1000 seconds) to form a 200*22 two-dimensional input feature matrix, which is then input into the main steam flow prediction model. The main steam flow prediction model outputs a one-dimensional array of length 33. The first element of the array is the predicted value after 5 seconds, and the 33rd element is the flow prediction value after 165 seconds. Afterward, input variables are collected every 5 seconds to fill the last row of the input matrix, while the first row (the oldest row) is deleted, ensuring the input matrix always maintains a size of 200*22, and inference for the next cycle begins, repeating this cycle.

[0052] In this optional implementation, the main steam flow prediction model can employ a Long Short-Term Memory (LSTM) network. LSTM is a special type of recurrent neural network capable of learning long-term dependencies. It controls the flow of information through a gating mechanism, avoiding the gradient vanishing problem of traditional recurrent neural networks. The LSTM model includes an input layer, an LSTM layer, a fully connected layer, and an output layer. Each LSTM layer contains an input gate, a forget gate, and an output gate, with each gate controlling the inflow and outflow of information. LSTM is suitable for time series data that requires learning long-term dependencies.

[0053] The method for calculating the predicted flow rate of the main steam flow provided by this optional implementation improves the reliability and accuracy of the predicted flow rate by predicting the characteristic parameter values ​​formed by the control parameter sequence and the current flow rate value through the main steam flow rate prediction model.

[0054] In some optional implementations of this disclosure, the calculation of the first pusher speed value based on the real-time primary air flow rate, the real-time secondary air flow rate, and the real-time air intake of the waste bin includes: weighting and summing the real-time primary air flow rate, the real-time secondary air flow rate, and the real-time air intake of the waste bin to obtain the real-time total air volume value; obtaining an air volume piecewise linear function characterizing the relationship between the total air volume flow rate value and the waste pusher speed; and obtaining the first pusher speed value based on the real-time total air volume value and the air volume piecewise linear function.

[0055] In this optional implementation, the weights of the real-time primary air flow rate, secondary air flow rate, and garbage bin inlet air volume can be set according to their respective proportions in the automatic waste combustion control system. The total real-time air volume is then obtained by multiplying each of these weights by their respective weights and summing the results. It should be noted that in actual commissioning, the real-time garbage bin inlet air volume is measured at a point in the garbage storage area, and its weight is ultimately adjusted to zero. Therefore, the total real-time air volume is primarily determined by the real-time primary air flow rate and the real-time secondary air flow rate. Figure 2 As shown, the real-time primary air flow rate, the real-time secondary air flow rate, and the real-time air intake of the garbage pit are weighted and summed, and then input into the air volume piecewise linear function F1(x).

[0056] In this optional implementation, the airflow piecewise linear function is a piecewise linear function establishing a linear relationship between input and output variables using big data technology. The process of obtaining the airflow piecewise linear function is as follows: Real-time primary airflow rate, real-time secondary airflow rate, and waste pusher speed are collected over historical periods. At each time point, the real-time primary airflow rate and real-time secondary airflow rate are summed to obtain the total airflow rate. Based on the distribution of the total airflow rate and the waste pusher speed, a suitable function form is selected for fitting. For example, a linear function, polynomial function, or exponential function can be chosen to obtain the airflow piecewise linear function. Figure 3 The straight lines at the multiple points shown represent the piecewise linear function of air volume.

[0057] In this optional implementation, obtaining the first pusher speed value based on the real-time value of total air volume and the air volume polygonal function includes: plotting the air volume polygonal function on a two-dimensional coordinate system, where the horizontal and vertical axes of the two-dimensional coordinate system represent the total air volume flow rate and the waste pusher speed, respectively; finding the real-time value of total air volume on the horizontal axis; and finding the waste pusher speed that corresponds to both the air volume polygonal function and the real-time value of total air volume on the vertical axis, thereby obtaining the first pusher speed value.

[0058] Optionally, obtaining the first pusher speed value based on the real-time value of total air volume and the air volume polygonal function includes: obtaining a polygonal function expression based on the air volume polygonal function; and substituting the real-time value of total air volume into the polygonal function expression to obtain the first pusher speed value.

[0059] The optional implementation provides a method for obtaining the speed value of the first pusher by calculating the speed value of the first pusher through a broken-line function of air volume, thus providing a reliable implementation for obtaining the speed value of the first pusher.

[0060] In some optional implementations of this disclosure, the extraction of bed thickness feature values ​​based on the control variable sequence values ​​and the furnace bed pressure difference value includes: clustering the control variable sequence values ​​using a clustering algorithm to obtain clustered data; extracting sub-variable values ​​belonging to the moderate bed type from the clustered data; inputting the sub-variable values ​​into a pre-trained long short-term memory neural network to obtain the predicted bed pressure difference value output by the long short-term memory neural network; and subtracting the predicted bed pressure difference value from the furnace bed pressure difference value to obtain the bed thickness feature value.

[0061] In this optional implementation, the automatic waste combustion control system needs to control the thickness of the waste in the furnace within a suitable range: if the waste thickness is too thick, the primary air cannot penetrate the waste layer, affecting the drying effect and making it difficult for the waste to ignite in the furnace; if the waste thickness is too thin, there will be insufficient fuel in the furnace, and a small amount of waste will burn up quickly, making it difficult to maintain a suitable furnace temperature for a long time. Since there is no sensor in the furnace to directly measure the thickness of the waste layer, only the pressure difference of the furnace layer can be measured. There is a pressure measuring point at the top of the waste layer and at the bottom air chamber of the waste layer in the furnace. The thickness of the waste layer is indirectly reflected by calculating the pressure difference between the upper and lower sides of the furnace layer.

[0062] In this optional implementation, the pressure difference of the material bed is affected by two key factors: the thickness of the material bed and the real-time flow rate of the primary air. The larger the real-time flow rate of the primary air, the larger the pressure difference of the furnace material bed. The thicker the furnace material bed, the larger the pressure difference of the material bed. Therefore, the thickness of the material bed cannot be directly reflected by the pressure difference of the furnace material bed. A material bed thickness feature extraction model is needed to eliminate the influence of the real-time flow rate of the primary air from the pressure difference of the material bed, and the extracted material bed thickness feature is used as the basis to adjust the pusher speed and the cycle of each grate section.

[0063] In this optional implementation, the above-mentioned clustering algorithm is used to cluster the control variable sequence values ​​to obtain clustered data including: using the K-means clustering algorithm, the control variable sequence values ​​are divided into three categories. Specifically, by drawing a scatter plot representing the relationship between the primary blower frequency and the furnace material bed pressure difference, they are classified into three types: material bed too thick, material bed too thin, and material bed moderate.

[0064] In this optional implementation, the extraction of sub-variable values ​​belonging to the moderate material layer type from the clustered data includes: selecting data with moderate material layer, performing data preprocessing (data cleaning, filling missing values), and then obtaining the sub-variable values.

[0065] In this optional implementation, the pre-trained long short-term memory neural network is a model used to predict the pressure difference value of the furnace bed (i.e., the predicted pressure difference value of the bed). The bed thickness feature value can be obtained by predicting the pressure difference value of the bed. Specifically, the input variables for the Long Short-Term Memory Neural Network are: primary air frequency, secondary air frequency, furnace negative pressure left, furnace negative pressure right, drying grate I primary air damper A opening, drying grate I primary air damper B opening, drying grate I primary air damper C opening, combustion grate I primary air damper A opening, combustion grate I primary air damper B opening, combustion grate I primary air damper C opening, combustion grate II primary air damper A opening, combustion grate II primary air damper B opening, combustion grate II primary air damper C opening, combustion grate III primary air damper A opening, combustion grate III primary air damper B opening, combustion grate III primary air damper C opening, burnout grate I primary air damper A opening, burnout grate I primary air damper B opening, burnout grate I primary air damper C opening, burnout grate II primary air damper A opening, burnout grate II primary air damper B opening, burnout grate II primary air damper C opening. The output variable of the long short-term memory neural network is the predicted pressure difference of the material layer. From the perspective of process principle, the output of the long short-term memory neural network is regarded as the predicted pressure difference of the material layer corresponding to the current air volume when the material layer thickness is moderate.

[0066] In this optional implementation, the furnace bed pressure difference value is the actual bed pressure difference value, and the bed thickness characteristic = furnace bed pressure difference value - long short-term memory neural network output variable (the furnace bed pressure difference value corresponding to the current air volume when the bed thickness is moderate). When the actual bed pressure difference is inconsistent with the bed pressure difference output by the long short-term memory neural network under the condition of "moderate bed thickness", it can be considered that the bed thickness is too thick or too thin, and the difference between the two reflects the degree of bed thickness.

[0067] The optional implementation provides a method for obtaining the material layer thickness feature value. By using a long short-term memory neural network to obtain the predicted material layer pressure difference value, the material layer thickness feature value is obtained, which improves the reliability and accuracy of obtaining the material layer thickness feature value.

[0068] In some optional implementations of this disclosure, the above-mentioned calculation of the second pusher speed value based on the material layer thickness feature value includes: obtaining a material layer piecewise linear function that characterizes the relationship between the material layer thickness feature and the waste pusher speed; and obtaining the second pusher speed value based on the material layer thickness feature value and the material layer piecewise linear function.

[0069] In this optional implementation, the material layer piecewise linear function is a piecewise linear function that establishes a linear relationship between input and output variables using big data technology, such as... Figure 2 The process of obtaining the material layer piecewise linear function is as follows: Collect the characteristic values ​​of the material layer thickness and the speed of the waste pusher during historical periods. Based on the distribution of the material layer thickness characteristic values ​​and the waste pusher speed, select a suitable function form for fitting. For example, a linear function, a polynomial function, or an exponential function can be selected to obtain the material layer piecewise linear function. The calculation method of the material layer thickness characteristic has been explained in the above embodiments and will not be repeated here.

[0070] In this optional implementation, obtaining the second pusher speed value based on the material layer thickness characteristic value and the material layer polygonal function includes: plotting the material layer polygonal function on a two-dimensional coordinate system, wherein the horizontal axis and vertical axis of the two-dimensional coordinate system represent the material layer thickness characteristic value and the waste pusher speed, respectively; finding the material layer thickness characteristic value on the horizontal axis; and finding the waste pusher speed that corresponds to both the material layer polygonal function and the material layer thickness characteristic value on the vertical axis, thereby obtaining the second pusher speed value.

[0071] Optionally, obtaining the second pusher speed value based on the material layer thickness characteristic value and the material layer polygonal function includes: obtaining the polygonal function expression based on the material layer polygonal function; and substituting the flow prediction value into the polygonal function expression to obtain the second pusher speed value.

[0072] This optional implementation provides a reliable method for obtaining the speed value of the second pusher by calculating the speed value of the second pusher through a material layer polygonal function.

[0073] In some optional implementations of this disclosure, the calculation of the pusher speed setpoint based on the predicted flow rate, the first pusher speed value, and the second pusher speed value includes: obtaining a flow rate piecewise linear function characterizing the relationship between the main steam flow rate and the waste pusher speed; obtaining the third pusher speed value based on the predicted flow rate and the flow rate piecewise linear function; and performing a weighted summation of the first pusher speed value, the second pusher speed value, and the third pusher speed value to obtain the pusher speed setpoint. Figure 2 As shown, F1(x)-F3(x) are three different piecewise linear functions: air volume piecewise linear function, material layer piecewise linear function, and flow rate piecewise linear function. Through different piecewise linear functions, three different pusher speed values ​​are obtained: the first pusher speed value, the second pusher speed value, and the third pusher speed value. By weighted summing the first pusher speed value, the second pusher speed value, and the third pusher speed value, the pusher speed setpoint is obtained.

[0074] In this optional implementation, the flow rate piecewise linear function is a linear function establishing a linear relationship between input and output variables using big data technology. The process of obtaining the flow rate piecewise linear function is as follows: Collect the main steam flow rate and the waste pusher speed during historical time periods. Set the main steam flow rate and waste pusher speed into a correspondence with a target delay. Under this correspondence, calibrate the points corresponding to the main steam flow rate and waste pusher speed (each point represents a total air volume flow rate and waste pusher speed). Based on the distribution of all points, select a suitable function form for fitting. For example, linear functions, polynomial functions, exponential functions, etc., can be selected to obtain the flow rate piecewise linear function. The method for obtaining the target delay is the same as described in the above embodiments, only the dataset (in this example, the dataset is a collection of multiple total air volume values ​​and waste pusher speeds) is different, and will not be elaborated further here.

[0075] In this optional implementation, obtaining the third pusher speed value based on the flow prediction value and the flow piecewise linear function includes: plotting the flow piecewise linear function on a two-dimensional coordinate system, where the horizontal and vertical axes of the two-dimensional coordinate system represent the main steam flow value and the waste pusher speed, respectively; finding the flow prediction value on the horizontal axis; and finding the waste pusher speed that corresponds to both the flow piecewise linear function and the flow prediction value on the vertical axis, thereby obtaining the third pusher speed value.

[0076] Optionally, obtaining the third pusher speed value based on the flow prediction value and the flow piecewise linear function includes: obtaining the piecewise linear function expression based on the flow piecewise linear function; and substituting the flow prediction value into the piecewise linear function expression to obtain the third pusher speed value.

[0077] In this embodiment, the weights of the first pusher speed value, the second pusher speed value, and the third pusher speed value in the weighted summation can be set as needed. Generally, since the real-time flow rate of the primary air and the real-time flow rate of the secondary air have a greater impact on the speed of the waste pusher, the weight of the first pusher speed value is greater than the weights of the second pusher speed value and the third pusher speed value.

[0078] The optional implementation provides a method for obtaining the speed value of the third pusher by calculating the speed value of the third pusher through a flow rate polygonal function, thus providing a reliable implementation method for obtaining the speed value of the third pusher.

[0079] In some optional implementations of this disclosure, the above-mentioned dynamic correction of the feeder speed setpoint based on the flow deviation to obtain and use the feeder target speed value to control the feeder speed includes: inputting the flow deviation and the feeder speed setpoint to a proportional-integral-derivative controller to obtain the feeder target speed value output by the proportional-integral-derivative controller; and using the feeder target speed value to control the feeder speed.

[0080] In this optional implementation, the proportional-integral-derivative controller adopts a PID closed-loop control architecture, such as... Figure 2 As shown, the flow deviation between the main steam flow setpoint and the flow prediction is used as the control input. The target speed setpoint of the pusher is dynamically corrected through the adjustment mechanism of the proportional-integral-derivative controller to obtain the target speed value of the pusher.

[0081] In this optional implementation, the proportional-integral-derivative controller control principle is as shown in equation (2), and the target speed value Y(s) of the pusher is obtained.

[0082] (2) In equation (2), FF(s) is the speed reference value, which is also the target speed setpoint of the feeder, E(s) is the flow rate deviation, and K... p Ti is the proportionality coefficient, and T is the integral coefficient. d These are the differential coefficients.

[0083] The optional implementation provides a method for obtaining the target speed value of the feeder by inputting the flow deviation and the feeder speed setpoint into a proportional-integral-derivative controller to obtain the target speed value of the feeder output by the proportional-integral-derivative controller. This method is simple and convenient to implement.

[0084] Optionally, before controlling the speed of the pusher using the target speed value, the above-mentioned waste pusher control method further includes limiting the speed value output by the proportional-integral-derivative controller before obtaining the target speed value of the pusher, thereby obtaining the target speed value of the pusher. Specifically, as shown in... Figure 2 As shown, it should be noted that the final output values ​​of all control loops (pusher speed, primary fan frequency, secondary fan frequency, and grate cycle of each section) need to be limited and speed controlled as a safety protection measure after final output. This is to prevent the final output value from exceeding the normal range due to sensor jump failure or other abnormal conditions, which could cause a production accident. The specific limit value should be adjusted by the operator according to the value within the normal range.

[0085] Further reference Figure 4 As an implementation of the methods shown in the above figures, this disclosure provides an embodiment of a waste pusher control device, which is similar to... Figure 1 Corresponding to the method embodiments shown, this device can be specifically applied to various electronic devices.

[0086] like Figure 4As shown, the waste pusher control device 400 provided in this embodiment includes: an acquisition unit 401, a prediction value calculation unit 402, a deviation calculation unit 403, a first speed calculation unit 404, an extraction unit 405, a second speed calculation unit 406, a setpoint calculation unit 407, and a control unit 408. The acquisition unit 401 can be configured to acquire the control parameter sequence values, current flow rate value of the main steam flow, flow rate setpoint value of the main steam flow, real-time primary air flow rate value, real-time secondary air flow rate value, real-time air intake of the waste pit, furnace bed pressure difference value, and control variable sequence values ​​highly correlated with furnace bed pressure difference over historical periods in the waste automatic combustion control system. The prediction value calculation unit 402 can be configured to calculate the predicted flow rate value of the main steam flow based on the control parameter sequence values ​​and the current flow rate value. The deviation calculation unit 403 can be configured to calculate the flow deviation based on the flow rate setpoint value and the flow rate predicted value. The first speed calculation unit 404 can be configured to calculate the speed of the first pusher based on the real-time primary air flow rate, the real-time secondary air flow rate, and the real-time air intake of the waste pit. The extraction unit 405 can be configured to extract the material layer thickness characteristic value based on the control variable sequence value and the furnace material layer pressure difference value. The second speed calculation unit 406 can be configured to calculate the speed of the second pusher based on the material layer thickness characteristic value. The setpoint calculation unit 407 can be configured to calculate the pusher speed setpoint based on the predicted flow rate, the first pusher speed value, and the second pusher speed value. The control unit 408 can be configured to dynamically correct the pusher speed setpoint based on the flow rate deviation, obtain and use the target pusher speed value to control the pusher speed.

[0087] In this embodiment, the specific processing and technical effects of the following components in the waste pusher control device 400—namely, the acquisition unit 401, the prediction value calculation unit 402, the deviation calculation unit 403, the first speed calculation unit 404, the extraction unit 405, the second speed calculation unit 406, the setpoint calculation unit 407, and the control unit 408—can be found in reference [reference needed]. Figure 1 The relevant descriptions of steps 101, 102, 103, 104, 105, 106, 107, and 108 in the corresponding embodiments will not be repeated here.

[0088] In some embodiments of this disclosure, the control parameter sequence values ​​are obtained by a parameter calculation unit (not shown in the figure), which is configured to: acquire the historical parameter set of the waste automatic combustion control system; calculate the correlation coefficient between the historical parameter set and the main steam flow rate under different delays; and determine the control parameter sequence values ​​based on the correlation coefficient.

[0089] In some embodiments of this disclosure, the prediction value calculation unit 402 is configured to: construct feature parameter values ​​based on the control parameter sequence values ​​and the current flow value; input the feature parameter values ​​into a pre-trained main steam flow prediction model to obtain the flow prediction value output by the main steam flow prediction model.

[0090] In some embodiments of this disclosure, the first speed calculation unit 404 is configured to: perform a weighted summation of the real-time primary air flow rate, the real-time secondary air flow rate, and the real-time air intake of the garbage bin to obtain a real-time total air volume value; obtain an air volume piecewise linear function characterizing the relationship between the total air volume flow rate value and the speed of the garbage pusher; and obtain the speed value of the first pusher based on the real-time total air volume value and the air volume piecewise linear function.

[0091] In some embodiments of this disclosure, the extraction unit 405 is configured to: cluster the control variable sequence values ​​using a clustering algorithm to obtain clustered data; extract sub-variable values ​​belonging to the moderate material layer type from the clustered data; input the sub-variable values ​​into a pre-trained long short-term memory neural network to obtain the predicted material layer pressure difference value output by the long short-term memory neural network; and subtract the predicted material layer pressure difference value from the furnace material layer pressure difference value to obtain the material layer thickness feature value.

[0092] In some embodiments of this disclosure, the second speed calculation unit 406 is configured to: obtain a material layer polygonal function characterizing the relationship between the material layer thickness feature and the waste pusher speed; and obtain the second pusher speed value based on the material layer thickness feature value and the material layer polygonal function.

[0093] In some embodiments of this disclosure, the given value calculation unit 407 is configured to: obtain a flow rate piecewise linear function representing the correspondence between the main steam flow rate value and the waste pusher speed; obtain a third pusher speed value based on the flow rate prediction value and the flow rate piecewise linear function; and perform a weighted summation of the first pusher speed value, the second pusher speed value, and the third pusher speed value to obtain a pusher speed given value.

[0094] In some embodiments of this disclosure, the control unit 408 is configured to: input the flow deviation and the feeder speed setpoint to the proportional-integral-derivative controller to obtain the feeder target speed value output by the proportional-integral-derivative controller; and control the feeder speed using the feeder target speed value.

[0095] The waste pusher control device provided in the embodiments of this disclosure includes an acquisition unit 401 that acquires the control parameter sequence values, current flow value, flow setpoint, primary air real-time flow value, secondary air real-time flow value, waste pit real-time air intake, furnace bed pressure difference value, and control variable sequence values ​​related to the furnace bed pressure difference height during a historical period of the waste automatic combustion control system; a prediction value calculation unit 402 that calculates the predicted flow value of the main steam flow based on the control parameter sequence values ​​and the current flow value; a deviation calculation unit 403 that calculates the flow deviation based on the flow setpoint and the predicted flow value; a first speed calculation unit 404 that calculates the first pusher speed value based on the primary air real-time flow value, secondary air real-time flow value, and waste pit real-time air intake; and an extraction unit 405 that extracts the control variable sequence values ​​and furnace bed pressure difference value. The material layer thickness characteristic value; the second speed calculation unit 406 calculates the second pusher speed value based on the material layer thickness characteristic value; the setpoint calculation unit 407 calculates the pusher speed setpoint value based on the flow prediction value, the first pusher speed value, and the second pusher speed value; the control unit 408 dynamically corrects the pusher speed setpoint value based on the flow deviation, obtains and uses the pusher target speed value to control the pusher speed; thus, based on the control parameter sequence value and the current flow value, the flow prediction value is predicted, and the flow prediction value is input into the control loop that controls the pusher speed, which can effectively improve the response speed of the waste automatic combustion control system. The flow deviation calculated by the flow prediction value can compensate for unnecessary fluctuations in the output of the waste automatic combustion control system in advance, thereby solving the overshoot problem caused by response lag, so that the control of the waste pusher can be closer to the actual production situation.

[0096] According to embodiments of this disclosure, this disclosure also provides an electronic device, a readable storage medium, and a computer program product.

[0097] Figure 5 A schematic block diagram of an example electronic device 500 that can be used to implement embodiments of the present disclosure is shown. The electronic device is intended to represent various forms of digital computers, such as laptop computers, desktop computers, workstations, personal digital assistants, servers, blade servers, mainframe computers, and other suitable computers. The electronic device may also represent various forms of mobile devices, such as personal digital processors, cellular phones, smartphones, wearable devices, and other similar computing devices. The components shown herein, their connections and relationships, and their patterns are merely examples and are not intended to limit the implementation of the present disclosure described and / or claimed herein.

[0098] like Figure 5As shown, the electronic device 500 includes a computing unit 501, which can perform various appropriate actions and processes according to a computer program stored in a read-only memory (ROM) 502 or a computer program loaded from a storage unit 508 into a random access memory (RAM) 503. The RAM 503 may also store various programs and data required for the operation of the electronic device 500. The computing unit 501, ROM 502, and RAM 503 are interconnected via a bus 504. An input / output (I / O) interface 505 is also connected to the bus 504.

[0099] Multiple components in electronic device 500 are connected to I / O interface 505, including: input unit 506, such as keyboard, mouse, etc.; output unit 507, such as various types of monitors, speakers, etc.; storage unit 508, such as disk, optical disk, etc.; and communication unit 509, such as network card, modem, wireless transceiver, etc. Communication unit 509 allows electronic device 500 to exchange information / data with other devices through computer networks such as the Internet and / or various telecommunications networks.

[0100] The computing unit 501 can be a variety of general-purpose and / or special-purpose processing components with processing and computing capabilities. Some examples of the computing unit 501 include, but are not limited to, a central processing unit (CPU), a graphics processing unit (GPU), various special-purpose artificial intelligence (AI) computing chips, various computing units running machine learning model algorithms, a digital signal processor (DSP), and any suitable processor, controller, microcontroller, etc. The computing unit 501 performs the various methods and processes described above, such as the waste pusher control method. For example, in some embodiments, the waste pusher control method may be implemented as a computer software program tangibly contained in a machine-readable medium, such as storage unit 508. In some embodiments, part or all of the computer program may be loaded and / or installed on the electronic device 500 via ROM 502 and / or communication unit 509. When the computer program is loaded into RAM 503 and executed by the computing unit 501, one or more steps of the waste pusher control method described above may be performed. Alternatively, in other embodiments, the computing unit 501 may be configured to perform the waste pusher control method by any other suitable means (e.g., by means of firmware).

[0101] Various embodiments of the systems and techniques described above herein can be implemented in digital electronic circuit systems, integrated circuit systems, field-programmable gate arrays (FPGAs), application-specific integrated circuits (ASICs), application-specific standard products (ASSPs), systems-on-a-chip (SoCs), payload-programmable logic devices (CPLDs), computer hardware, firmware, software, and / or combinations thereof. These various embodiments may include implementations in one or more computer programs that can be executed and / or interpreted on a programmable system including at least one programmable processor, which may be a dedicated or general-purpose programmable processor, capable of receiving data and instructions from a storage system, at least one input device, and at least one output device, and transmitting data and instructions to the storage system, the at least one input device, and the at least one output device.

[0102] The program code used to implement the methods of this disclosure may be written in any combination of one or more programming languages. This program code may be provided to a processor or controller of a general-purpose computer, special-purpose computer, or other programmable waste pusher control device, such that when executed by the processor or controller, the program code causes the patterns / operations specified in the flowcharts and / or block diagrams to be implemented. The program code may be executed entirely on a machine, partially on a machine, as a standalone software package partially on a machine and partially on a remote machine, or entirely on a remote machine or server.

[0103] In the context of this disclosure, a machine-readable medium can be a tangible medium that may contain or store a program for use by or in conjunction with an instruction execution system, apparatus, or device. A machine-readable medium can be a machine-readable signal medium or a machine-readable storage medium. A machine-readable medium can be, but is not limited to, electronic, magnetic, optical, electromagnetic, infrared, or semiconductor systems, apparatus, or devices, or any suitable combination of the foregoing. More specific examples of machine-readable storage media include electrical connections based on one or more wires, portable computer disks, hard disks, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), optical fiber, portable compact disk read-only memory (CD-ROM), optical storage devices, magnetic storage devices, or any suitable combination of the foregoing.

[0104] To provide interaction with a user, the systems and techniques described herein can be implemented on a computer having: a display device for displaying information to the user (e.g., a CRT (cathode ray tube) or LCD (liquid crystal display) monitor); and a keyboard and pointing device (e.g., a mouse or trackball) through which the user provides input to the computer. Other types of devices can also be used to provide interaction with the user; for example, feedback provided to the user can be any form of sensory feedback (e.g., visual feedback, auditory feedback, or tactile feedback); and input from the user can be received in any form (including sound input, voice input, or tactile input).

[0105] The systems and technologies described herein can be implemented in computing systems that include backend components (e.g., as a data server), or computing systems that include middleware components (e.g., an application server), or computing systems that include frontend components (e.g., a user computer with a graphical user interface or web browser through which a user can interact with implementations of the systems and technologies described herein), or any combination of such backend, middleware, or frontend components. The components of the system can be interconnected via digital data communication of any form or medium (e.g., a communication network). Examples of communication networks include local area networks (LANs), wide area networks (WANs), and the Internet.

[0106] It should be understood that the various forms of processes shown above can be used to rearrange, add, or delete steps. For example, the steps described in this disclosure can be executed in parallel, sequentially, or in different orders, as long as the desired result of the technical solution disclosed in this disclosure can be achieved, and this is not limited herein.

[0107] The foregoing description of specific exemplary embodiments of this disclosure is for illustrative and explanatory purposes. These descriptions are not intended to limit this disclosure to the precise forms disclosed, and it will be apparent that many changes and variations can be made in accordance with the foregoing teachings. The exemplary embodiments were chosen and described in order to explain the specific principles of this disclosure and their practical application, thereby enabling those skilled in the art to implement and utilize various different exemplary embodiments of this disclosure, as well as various different choices and variations. The scope of this disclosure is intended to be defined by the claims and their equivalents.

Claims

1. A method for controlling a waste pusher, applied to an automatic waste combustion control system, characterized in that, The method includes: The system acquires the sequence values ​​of control parameters related to the main steam flow rate during historical periods of the automatic waste combustion control system, the current flow rate of the main steam flow rate, the flow rate setpoint of the main steam flow rate, the real-time flow rate of the primary air, the real-time flow rate of the secondary air, the real-time air intake of the waste pit, the furnace material layer pressure difference value, and the sequence values ​​of control variables highly related to the furnace material layer pressure difference during historical periods. Based on the control parameter sequence values ​​and the current flow rate value, calculate the predicted flow rate of the main steam flow rate; Calculate the flow deviation based on the flow setpoint and the flow prediction. Based on the real-time primary air flow rate, the real-time secondary air flow rate, and the real-time air intake of the waste pool, the speed value of the first pusher is calculated. Based on the control variable sequence values ​​and the furnace material layer pressure difference values, the material layer thickness characteristic value is extracted; Based on the material layer thickness characteristic value, the speed value of the second pusher is calculated; Based on the predicted flow rate, the first feeder speed value, and the second feeder speed value, calculate the feeder speed setpoint. Based on the flow deviation, the setpoint value of the pusher speed is dynamically corrected to obtain and use the target speed value of the pusher to control the speed of the pusher. The control parameter sequence values ​​are obtained through the following steps: Obtain the historical parameter set of the automatic waste combustion control system; Calculate the correlation coefficient between the historical parameter set and the main steam flow rate under different delays; Based on the correlation coefficient, determine the sequence values ​​of the control parameters; The step of calculating the predicted flow rate of the main steam flow rate based on the control parameter sequence value and the current flow rate value includes: Based on the control parameter sequence values ​​and the current flow rate value, feature parameter values ​​are constructed; The feature parameter values ​​are input into the pre-trained main steam flow prediction model to obtain the flow prediction value output by the main steam flow prediction model; The calculation of the first pusher speed value based on the real-time primary air flow rate, the real-time secondary air flow rate, and the real-time air intake of the waste pool includes: The real-time flow rate of the primary air, the real-time flow rate of the secondary air, and the real-time air intake of the garbage pit are weighted and summed to obtain the real-time total air volume. Obtain a piecewise linear function representing the relationship between the total air volume flow rate and the speed of the waste pusher; Based on the real-time value of the total air volume and the air volume polygonal function, the speed value of the first pusher is obtained; The extraction of material layer thickness feature values ​​based on the control variable sequence values ​​and the furnace material layer pressure difference values ​​includes: The control variable sequence values ​​are clustered using a clustering algorithm to obtain clustered data. Extract sub-variable values ​​belonging to the moderate material layer type from the clustered data; The sub-variable values ​​are input into a pre-trained long short-term memory neural network to obtain the predicted material layer pressure difference value output by the long short-term memory neural network. Subtract the predicted pressure difference from the furnace bed pressure difference value to obtain the bed thickness characteristic value; The calculation of the feeder speed setpoint based on the predicted flow rate, the first feeder speed value, and the second feeder speed value includes: Obtain a flow pattern function that represents the relationship between the main steam flow rate and the speed of the waste pusher; Based on the predicted flow rate and the flow rate polygonal function, the speed value of the third pusher is obtained; The first pusher speed value, the second pusher speed value, and the third pusher speed value are weighted and summed to obtain the pusher speed setpoint.

2. The method according to claim 1, characterized in that, The step of dynamically correcting the feeder speed setpoint based on the flow deviation, and obtaining and using the feeder target speed value to control the feeder speed includes: The flow rate deviation and the feeder speed setpoint are input into the proportional-integral-derivative controller to obtain the feeder target speed value output by the proportional-integral-derivative controller; The speed of the pusher is controlled by the target speed value of the pusher.

3. A waste pusher control device, characterized in that, The apparatus for performing the method as described in any one of claims 1 to 2 includes: The acquisition unit is configured to acquire the sequence values ​​of control parameters of the control parameters in the automatic waste combustion control system, the current flow value of the main steam flow, the flow set value of the main steam flow, the real-time flow value of the primary air, the real-time flow value of the secondary air, the real-time air intake of the waste pool, the pressure difference of the furnace material layer, and the sequence values ​​of control variables that are highly correlated with the pressure difference of the furnace material layer in historical periods. The prediction calculation unit is configured to calculate the predicted flow rate of the main steam flow rate based on the control parameter sequence value and the current flow rate value. The deviation calculation unit is configured to calculate the flow deviation based on the flow setpoint and the flow prediction value; The first speed calculation unit is configured to calculate the speed value of the first pusher based on the real-time flow value of the primary air, the real-time flow value of the secondary air, and the real-time air intake of the waste pool. The extraction unit is configured to extract the material layer thickness feature value based on the control variable sequence value and the furnace material layer pressure difference value; The second speed calculation unit is configured to calculate the speed value of the second pusher based on the material layer thickness characteristic value; The given value calculation unit is configured to calculate a feeder speed given value based on the predicted flow rate value, the first feeder speed value, and the second feeder speed value; The control unit is configured to dynamically correct the feeder speed setpoint based on the flow deviation, and to obtain and use the feeder target speed value to control the feeder speed.

4. An electronic device, characterized in that, include: At least one processor; and a memory communicatively connected to the at least one processor; The memory stores instructions that can be executed by the at least one processor, which, when executed by the at least one processor, enables the at least one processor to perform the method according to any one of claims 1-2.

5. A non-transitory computer-readable storage medium storing computer instructions, characterized in that, The computer instructions are used to cause the computer to perform the method according to any one of claims 1-2.

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