Intelligent regulation and control system for discharging time sequence of calcining furnace

Abstract

The invention provides an intelligent regulation and control system for a discharging time sequence of a calcining furnace. The method comprises the specific steps that firstly, process data of a calcining furnace are collected, the parameter change rate is calculated in real time, and when a certain parameter change rate exceeds a preset threshold value, a system is switched to a high-density collection mode; secondly, the data reconstruction module performs aggregation and time-space node synchronous analysis on the process data flow to generate a multi-node process parameter distribution sequence; then, a material track analysis module identifies the residence time period, the flowing path and the accumulation change of the material, and outputs a material residence interval sequence and an accumulation abnormal point set; finally, the judgment regulation and control module generates a regulation and control instruction according to the material state judgment result, and parameters such as the discharging speed are flexibly adjusted. Through dynamic acquisition mode adjustment and multi-dimensional process parameter analysis, the data acquisition precision and the real-time response capability are improved, and the high efficiency and safety of calcining furnace operation are guaranteed on the basis of solving the problem that a traditional system cannot respond to changing working conditions in time.

Description

Technical Field

[0001] This invention relates to the field of calcining furnace control technology, specifically to an intelligent control system for calcining furnace discharge timing. Background Technology

[0002] Calcining furnaces are common high-temperature equipment in industries such as metallurgy and chemical engineering. They are mainly used to heat raw materials to high temperatures, causing them to undergo physical or chemical changes. During the calcination process, materials are discharged through a grate to ensure uniform heating and transformation. During the operation of the calcining furnace, process parameters such as temperature, pressure, and material concentration change rapidly. Therefore, accurate monitoring and control of these parameters are crucial. With the application of intelligent technology, the discharge control of calcining furnaces is gradually shifting from manual experience and simple automated control systems to more precise and real-time intelligent control systems.

[0003] However, existing calcining furnace discharge control systems generally rely on fixed-time interval process data acquisition, which cannot respond to rapid changes in process parameters in real time, resulting in an inability to capture and adjust in a timely manner when process fluctuations are large. Secondly, existing calcining furnace discharge control systems mostly rely on a single threshold judgment method to identify the residence, flow, and accumulation states of materials, failing to consider the collaborative analysis of multi-dimensional process parameters. Therefore, misjudgments are prone to occur when material dynamics are complex, affecting the accuracy of the system. Finally, existing calcining furnace discharge control systems usually use uniform control commands to deal with various operating conditions, failing to provide personalized adjustments for the specific state of each spatial node, resulting in an inflexible control strategy that is difficult to adapt to changes in different operating conditions.

[0004] Therefore, this invention proposes an intelligent control system for the discharge sequence of a calcining furnace. Summary of the Invention

[0005] In order to solve the technical problems mentioned in the background art, such as slow response speed, low material state recognition accuracy and inflexible control strategy of existing calcining furnace discharge control systems, the purpose of this invention is to provide an intelligent control system for calcining furnace discharge timing.

[0006] To achieve the above objectives, the present invention adopts the following technical solution:

[0007] A calcining furnace discharge timing intelligent control system includes:

[0008] M1: Data sampling module, which collects process data during the operation of the calcining furnace and calculates the rate of change of each parameter in real time within the continuous sampling interval. When the rate of change of any parameter in the process data exceeds the preset rate threshold of the system, it switches to high-density acquisition mode to output process data stream.

[0009] M2: Data reconstruction module, which performs distributed data aggregation and spatiotemporal node synchronization analysis on the process data stream to generate a multi-node process parameter distribution sequence;

[0010] M3: Material trajectory analysis module, based on the process data stream and the multi-node process parameter distribution sequence, identifies the residence time, flow path and accumulation change of materials in each spatial node, so as to output the material residence interval sequence, material movement trajectory sequence and accumulation anomaly point set;

[0011] M4: The discrimination and control module takes the material residence interval sequence, material movement trajectory sequence and accumulation anomaly point set as input to generate the material working condition discrimination result and corresponding intelligent control parameter instructions for each spatial node.

[0012] Furthermore, the process data includes temperature data, pressure data, and material concentration data;

[0013] The temperature data is denoted as , indicating the first Each spatial node Temperature and pressure data at the sampling time; the pressure data is denoted as , indicating the first Each spatial node Pressure values ​​and material concentration data at the sampling time; the material concentration data is denoted as... , indicating the first Each spatial node Material concentration at the time of sampling;

[0014] in, Number the spatial nodes, and , This represents the total number of spatial nodes. The sampling time number is used, and , This represents the total number of sampling times.

[0015] Furthermore, based on the temperature data The rate of temperature change was calculated. , indicating the first Each spatial node The rate of temperature change at the sampling time;

[0016] Based on the pressure data The rate of pressure change was calculated. , indicating the first Each spatial node The rate of pressure change at the sampling time;

[0017] Based on the material concentration data The rate of change of material concentration was calculated. , indicating the first Each spatial node The rate of change of material concentration at the sampling time.

[0018] Furthermore, the acquisition modes are divided into conventional acquisition mode and high-density acquisition mode;

[0019] Corresponding to the temperature change rate Pressure change rate and the rate of change of material concentration Set the temperature change rate threshold to The pressure change rate threshold is The threshold for the rate of change of material concentration is ;

[0020] When any of the temperature change rate, pressure change rate, and material concentration change rate reaches or exceeds the corresponding set threshold, the system switches to high-density acquisition mode; otherwise, the system executes the normal acquisition mode.

[0021] Record the process data stream as , .

[0022] Furthermore, based on the aforementioned process data stream ,calculate Sampling time and spatial nodes The moving average of parameters between adjacent nodes is calculated as follows:

[0023]

[0024] in, For the first Each spatial node Local moving average temperature at time t;

[0025] Based on the local moving average temperature Calculate the spatial local standard deviation of the temperature data:

[0026]

[0027] in, For the first A spatial node and its neighboring points in The local standard deviation of temperature at time t reflects the first The uniformity of temperature between each spatial node and its neighborhood;

[0028] The calculation process for the pressure data and material concentration data is similar, in order to obtain the local standard deviation of the pressure. and local standard deviation of material concentration ;

[0029] The local standard deviation of temperature Local standard deviation of pressure and local standard deviation of material concentration Assembled in chronological order into the multi-node process parameter distribution sequence , represented as:

[0030]

[0031] .

[0032] Furthermore, for each node and each sampling time The residence status indicators for temperature, pressure, and material concentration are defined separately, and the formula is defined as follows:

[0033]

[0034]

[0035]

[0036] in, For the first Each spatial node at time Material retention determination results based on temperature parameters: 1 indicates retention, and 0 indicates non-retention. The threshold for determining the dwell time of temperature parameters; The threshold for auxiliary criterion of local standard deviation in temperature space; For the first Each spatial node at time Material retention determination results based on pressure parameters; The threshold for determining the residence of pressure parameters; The threshold for the auxiliary criterion of local standard deviation in the pressure space; For the first Each spatial node at time Material residence determination results based on material concentration parameters; The threshold for determining the residence of material concentration parameters; The threshold for the auxiliary criterion of local standard deviation of material concentration in space;

[0037] Based on the material retention discrimination results of the aforementioned temperature, pressure, and material concentration parameters, a joint criterion is defined, namely, the joint material retention discrimination result. for:

[0038]

[0039] Based on the joint material residence discrimination result Output the sequence of material residence intervals, expressed by the formula:

[0040]

[0041] in, For the first The set of all maximized continuous residence intervals under the joint criterion of nodes, i.e., the material residence interval sequence; To satisfy the condition within the interval The goal is to maximize the continuous dwell time, with no more dwell times at either end of the interval that meet the conditions.

[0042] Furthermore, in the stay area The results of the joint material residence determination are analyzed. The distribution along the spatiotemporal axis identifies the movement trajectory of materials. If, within a time period, the nodes... Its neighboring nodes Satisfy at consecutive time intervals , This indicates that the material originates from the node. Move to node The final material motion trajectory sequence is defined as follows:

[0043]

[0044] in, This is a sequence of material movement trajectories, representing the material's movement from the first... Each node Time migration to adjacent Node at The set of flow paths at time, and Adjacent nodes;

[0045] During the stay Inside, the first Each node at the sampling time The local standard deviation of the parameter space is expressed as ,and And set the stacking anomaly detection threshold as follows: ,and ,in, The threshold for identifying abnormal accumulation of temperature parameters. The threshold for identifying abnormal accumulation of pressure parameters. The threshold for identifying abnormal accumulation of material concentration parameters;

[0046] The formula for determining the local standard deviation of the parameter space and the stacking anomaly discrimination threshold is defined as follows: If this condition is met, it indicates the presence of a stacking anomaly; otherwise, This indicates that there is no stacking anomaly;

[0047] Finally, output the first... The set of accumulated anomalies within the node's residence interval is:

[0048] .

[0049] Furthermore, regarding the first Each node and sampling time The material residence interval sequence, material movement trajectory sequence, and accumulation anomaly point set are classified and determined, and the working condition discrimination formula is defined as follows:

[0050]

[0051] in, For the first Each node at the sampling time The results of the working condition judgment;

[0052] like This indicates an abnormal accumulation of materials; if This indicates that the material is flowing normally; if This indicates that the material is normally residing there; This indicates a state where there is no material.

[0053] Based on the working condition judgment result Generate corresponding intelligent control commands:

[0054]

[0055] in, For the first Each node at the sampling time Control instructions; The corresponding control command is to speed up the discharge rate to alleviate the accumulation situation; The corresponding control instruction is to maintain the current parameters, and no adjustment is required; The corresponding control instruction is to slow down the discharge speed to avoid the risk of downstream blockage caused by excessively fast material flow; The corresponding control instruction is "no action required," meaning no adjustments are needed.

[0056] Compared with the prior art, the advantages of the present invention are as follows:

[0057] 1. This invention calculates the rate of change of process parameters in real time and dynamically adjusts the acquisition mode according to the set threshold. When the operating conditions fluctuate greatly, the system can quickly switch to a high-density acquisition mode to ensure timely acquisition of key data. This solves the problem of slow response of traditional fixed sampling intervals under rapidly changing operating conditions and improves the accuracy and real-time performance of data acquisition.

[0058] 2. This invention combines multi-dimensional process parameters such as temperature, pressure, and material concentration, and uses local standard deviation and auxiliary criteria to comprehensively analyze the residence, flow path, and accumulation state of materials. This multi-dimensional comprehensive analysis method breaks through the limitations of single threshold judgment in traditional technology, and can accurately judge the dynamic changes of materials under complex calcining furnace conditions, thus improving the accuracy of material state identification.

[0059] 3. This invention generates personalized control instructions based on the material status of each spatial node, and flexibly adjusts control parameters such as discharge speed and temperature, enabling the system to flexibly respond to changes in different working conditions and promptly alleviate abnormal accumulation or material flow problems. The implementation of this intelligent control strategy not only improves the system's adaptability and response speed under complex working conditions, but also ensures the high efficiency and safety of calcining furnace operation. Attached Figure Description

[0060] To more clearly illustrate the technical solutions in the embodiments of this application, the accompanying drawings used in the description of the embodiments will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of this application. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0061] Figure 1 This is a schematic diagram of the system workflow of the present invention;

[0062] Figure 2 This is a schematic diagram of the material residence interval sequence generation process of the present invention;

[0063] Figure 3 This is a schematic diagram of the control command issuance process of the present invention. Detailed Implementation

[0064] To make the objectives, technical solutions, and advantages of the embodiments of this application clearer, the technical solutions of the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of this application, not all embodiments. Based on the embodiments of this application, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this application.

[0065] To achieve the above objectives, the present invention provides an intelligent control system for the discharge sequence of a calcining furnace, such as... Figures 1-3 As shown, the system includes:

[0066] M1: Data sampling module, which collects process data during the operation of the calcining furnace and calculates the rate of change of each parameter in real time within the continuous sampling interval. When the rate of change of any parameter in the process data exceeds the preset rate threshold of the system, it switches to high-density acquisition mode to output the process data stream.

[0067] The process data includes temperature data, pressure data, and material concentration data;

[0068] The temperature data is denoted as , indicating the first Each spatial node Temperature and pressure data at the sampling time; the pressure data is denoted as , indicating the first Each spatial node Pressure values ​​and material concentration data at the sampling time; the material concentration data is denoted as... , indicating the first Each spatial node Material concentration at the time of sampling;

[0069] in, Number the spatial nodes, and , This represents the total number of spatial nodes. The sampling time number is used, and , This represents the total number of sampling times.

[0070] Based on the temperature data The formula for calculating the rate of temperature change is:

[0071]

[0072] in, Indicates the first Each spatial node The rate of temperature change at the sampling time; Indicates the first Each spatial node Temperature value at the time of sampling;

[0073] Based on the pressure data The formula for calculating the rate of pressure change is:

[0074] in, Indicates the first Each spatial node The rate of pressure change at the sampling time; Indicates the first Each spatial node Pressure value at the time of sampling;

[0075] Based on the material concentration data The formula for calculating the rate of change of material concentration is:

[0076]

[0077] in, Indicates the first Each spatial node The rate of change of material concentration at the sampling time; Indicates the first Each spatial node Material concentration at the time of sampling;

[0078] In this embodiment, the acquisition mode is divided into a regular acquisition mode and a high-density acquisition mode. The regular acquisition mode means that the system periodically samples the process data parameters according to a fixed time interval. The high-density acquisition mode means that when the system detects that the process data parameters have reached or exceeded a preset threshold, it will automatically switch the sampling period to a shorter high-density acquisition interval.

[0079] This embodiment provides an example: if the sampling period of the conventional acquisition mode is 5 minutes, then the sampling period of the high-density acquisition mode is shortened to 1 minute;

[0080] Corresponding to the temperature change rate Pressure change rate and the rate of change of material concentration Set the temperature change rate threshold to The pressure change rate threshold is The threshold for the rate of change of material concentration is When any of the temperature change rate, pressure change rate, and material concentration change rate reaches or exceeds the corresponding set threshold, the system switches to high-density acquisition mode; otherwise, the system executes the normal acquisition mode.

[0081] In this embodiment, the temperature change rate threshold The value is 5℃ / min; the pressure change rate threshold The value is 0.05 MPa / min; the threshold value for the rate of change of material concentration. The value is 2% / min;

[0082] The rate of temperature change With the temperature change rate threshold Comparison:

[0083] like Switch to high-density acquisition mode; if Restore normal data collection mode;

[0084] The rate of pressure change With the pressure change rate threshold being Comparison:

[0085] like Switch to high-density acquisition mode; if Restore normal data collection mode;

[0086] The rate of change of the material concentration With the threshold of the rate of change of the material concentration Comparison:

[0087] like Switch to high-density acquisition mode; if Restore normal data collection mode;

[0088] Record the process data stream as , .

[0089] M2, the data reconstruction module, performs distributed data aggregation and spatiotemporal node synchronization analysis on the process data stream to generate a multi-node process parameter distribution sequence.

[0090] Based on the process data stream ,calculate Sampling time and spatial nodes The moving average of parameters between adjacent nodes, taking the temperature data as an example;

[0091] The moving average of the temperature data is calculated as follows:

[0092]

[0093] in, For the first Each spatial node Local moving average temperature at time t;

[0094] Based on the local moving average temperature Calculate the spatial local standard deviation of the temperature data:

[0095]

[0096] in, For the first A spatial node and its neighboring points in The local standard deviation of temperature at time t reflects the first The uniformity of temperature between each spatial node and its neighborhood;

[0097] The calculation process for the pressure data and material concentration data is similar, in order to obtain the local standard deviation of the pressure. and local standard deviation of material concentration ;

[0098] The local standard deviation of temperature Local standard deviation of pressure and local standard deviation of material concentration Assembled in chronological order into the multi-node process parameter distribution sequence , represented as:

[0099]

[0100] .

[0101] M3, the material trajectory analysis module, based on the process data stream and the multi-node process parameter distribution sequence, identifies the residence time, flow path and accumulation changes of materials in each spatial node, so as to output the material residence interval sequence, material movement trajectory sequence and accumulation anomaly point set.

[0102] For each node and each sampling time The residence status indicators for temperature, pressure, and material concentration are defined separately, and the formula is defined as follows:

[0103]

[0104]

[0105]

[0106] in, For the first Each spatial node at time Material retention determination results based on temperature parameters: 1 indicates retention, and 0 indicates non-retention. The threshold for determining the dwell time of temperature parameters; The threshold for auxiliary criterion of local standard deviation in temperature space; For the first Each spatial node at time Material retention determination results based on pressure parameters: 1 indicates retention, and 0 indicates non-retention. The threshold for determining the residence of pressure parameters; The threshold for the auxiliary criterion of local standard deviation in the pressure space; For the first Each spatial node at time Material retention determination results based on material concentration parameters: 1 indicates retention, and 0 indicates non-retention; The threshold for determining the residence of material concentration parameters; The threshold for the auxiliary criterion of local standard deviation of material concentration in space;

[0107] In this embodiment, the temperature parameter dwell time discrimination threshold The value is 10℃; the threshold value for the local standard deviation of the temperature space auxiliary criterion. The value is 2℃; the pressure parameter residence discrimination threshold The value is 0.1 MPa; the threshold value for the local standard deviation of the pressure space auxiliary criterion. The value is 0.01 MPa; the retention threshold of the material concentration parameter. The value is 10%; the value of the auxiliary criterion threshold for the spatial local standard deviation of the material concentration is 1%.

[0108] Based on the material residence determination results of the temperature parameter, pressure parameter, and material concentration parameter, the joint criterion is defined as:

[0109]

[0110] in, To combine the material retention judgment results, the material retention judgment results are combined with temperature parameters, pressure parameters and material concentration parameters. 1 indicates retention and 0 indicates non-retention.

[0111] Based on the joint material residence discrimination result Output the sequence of material residence intervals, expressed by the formula:

[0112]

[0113] in, For the first The set of all maximized continuous residence intervals under the joint criterion of nodes, i.e., the material residence interval sequence; To satisfy the condition within the interval The maximum continuous dwell time is achieved, and there are no more dwell times that meet the conditions at either end of the interval;

[0114] During the stay The results of the joint material residence determination are analyzed. The distribution along the spatiotemporal axis identifies the movement trajectory of materials. If, within a time period, the nodes... Its neighboring nodes Satisfy at consecutive time intervals , This indicates that the material originates from the node. Move to node The final material motion trajectory sequence is defined as follows:

[0115]

[0116] in, This is a sequence of material movement trajectories, representing the material's movement from the first... Each node Time migration to adjacent Node at The set of flow paths at time, and Adjacent nodes;

[0117] During the stay Inside, the first Each node at the sampling time The local standard deviation of the parameter space is expressed as ,and And set the stacking anomaly detection threshold as follows: ,and ,in, The threshold for identifying abnormal accumulation of temperature parameters. The threshold for identifying abnormal accumulation of pressure parameters. The threshold for identifying abnormal accumulation of material concentration parameters;

[0118] In this embodiment, the temperature parameter accumulation anomaly discrimination threshold is... The value is 3℃; the pressure parameter accumulation anomaly discrimination threshold The value is 0.015 MPa; the material concentration parameter accumulation anomaly discrimination threshold. The value is 1%;

[0119] The formula for determining the local standard deviation of the parameter space and the stacking anomaly discrimination threshold is defined as follows: If this condition is met, it indicates the presence of a stacking anomaly; otherwise, This indicates that there is no stacking anomaly;

[0120] Finally, output the first... The set of accumulated anomalies within the node's residence interval is:

[0121] .

[0122] M4, the discrimination and control module, takes the material residence interval sequence, material movement trajectory sequence and accumulation anomaly point set as input, and generates the material working condition discrimination result and corresponding intelligent control parameter instructions for each spatial node.

[0123] Regarding the first Each node and sampling time The material residence interval sequence, material movement trajectory sequence, and accumulation anomaly point set are classified and determined, and the working condition discrimination formula is defined as follows:

[0124]

[0125] in, For the first Each node at the sampling time The results of the working condition judgment;

[0126] like This indicates an abnormal accumulation of materials; if This indicates that the material is flowing normally; if This indicates that the material is normally stationary, with no abnormalities or movement. This indicates a state where there is no material.

[0127] Based on the working condition judgment result Generate corresponding intelligent control commands:

[0128]

[0129] in, For the first Each node at the sampling time Control instructions; The corresponding control command is to speed up the discharge rate to alleviate the accumulation situation; The corresponding control instruction is to maintain the current parameters, and no adjustment is required; The corresponding control instruction is to slow down the discharge speed to avoid the risk of downstream blockage caused by excessively fast material flow; The corresponding control instruction is "no action required," meaning no adjustments are needed.

[0130] The above description is merely a specific embodiment of this application, but the scope of protection of this application is not limited thereto. Any variations or substitutions that can be easily conceived by those skilled in the art within the scope of the technology disclosed in this application should be included within the scope of protection of this application. Therefore, the scope of protection of this application should be determined by the scope of the claims.

[0131] In conclusion, the above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the protection scope of the present invention.

Claims

1. A smart control system for the discharge sequence of a calcining furnace, characterized in that, include: M1: Data sampling module, which collects process data during the operation of the calcining furnace and calculates the rate of change of each parameter in real time within the continuous sampling interval. When the rate of change of any parameter in the process data exceeds the preset rate threshold of the system, it switches to high-density acquisition mode to output process data stream. M2: Data reconstruction module, which performs distributed data aggregation and spatiotemporal node synchronization analysis on the process data stream to generate a multi-node process parameter distribution sequence; M3: Material trajectory analysis module, based on the process data stream and the multi-node process parameter distribution sequence, identifies the residence time, flow path and accumulation change of materials in each spatial node, so as to output the material residence interval sequence, material movement trajectory sequence and accumulation anomaly point set; M4: The discrimination and control module takes the material residence interval sequence, material movement trajectory sequence and accumulation anomaly point set as input to generate the material working condition discrimination result and corresponding intelligent control parameter instructions for each spatial node.

2. The intelligent control system for calcining furnace discharge timing according to claim 1, characterized in that, The process data includes temperature data, pressure data, and material concentration data; The temperature data is denoted as , indicating the first Each spatial node Temperature and pressure data at the sampling time; the pressure data is denoted as , indicating the first Each spatial node Pressure values ​​and material concentration data at the sampling time; the material concentration data is denoted as... , indicating the first Each spatial node Material concentration at the time of sampling; in, Number the spatial nodes, and , This represents the total number of spatial nodes. The sampling time number is used, and , This represents the total number of sampling times.

3. The intelligent control system for calcining furnace discharge timing according to claim 2, characterized in that, Based on the temperature data The rate of temperature change was calculated. , indicating the first Each spatial node The rate of temperature change at the sampling time; Based on the pressure data The rate of pressure change was calculated. , indicating the first Each spatial node The rate of pressure change at the sampling time; Based on the material concentration data The rate of change of material concentration was calculated. , indicating the first Each spatial node The rate of change of material concentration at the sampling time.

4. The intelligent control system for calcining furnace discharge timing according to claim 3, characterized in that, The acquisition modes are divided into conventional acquisition mode and high-density acquisition mode; Corresponding to the temperature change rate Rate of pressure change and the rate of change of material concentration Set the temperature change rate threshold to The pressure change rate threshold is The threshold for the rate of change of material concentration is ; When any of the temperature change rate, pressure change rate, and material concentration change rate reaches or exceeds the corresponding set threshold, the system switches to high-density acquisition mode; otherwise, the system executes the normal acquisition mode. Record the process data stream as , .

5. The intelligent control system for calcining furnace discharge timing according to claim 4, characterized in that, Based on the process data stream ,calculate Sampling time and spatial nodes The moving average of parameters between adjacent nodes is calculated as follows: in, For the first Each spatial node Local moving average temperature at time t; Based on the local moving average temperature Calculate the spatial local standard deviation of the temperature data: in, For the first A spatial node and its neighboring points in The local standard deviation of temperature at time t reflects the first The uniformity of temperature between each spatial node and its neighborhood; The calculation process for the pressure data and material concentration data is similar, in order to obtain the local standard deviation of the pressure. and local standard deviation of material concentration ; The local standard deviation of temperature Local standard deviation of pressure and local standard deviation of material concentration Assembled in chronological order into the multi-node process parameter distribution sequence , represented as: 。 6. The intelligent control system for calcining furnace discharge timing according to claim 5, characterized in that, For each node and each sampling time The residence status indicators for temperature, pressure, and material concentration are defined separately, and the formula is defined as follows: in, For the first Each spatial node at time Material retention determination results based on temperature parameters: 1 indicates retention, and 0 indicates non-retention. The threshold for determining the dwell time of temperature parameters; The threshold for auxiliary criterion of local standard deviation in temperature space; For the first Each spatial node at time Material retention determination results based on pressure parameters; The threshold for determining the residence of pressure parameters; The threshold for the auxiliary criterion of local standard deviation in the pressure space; For the first Each spatial node at time Material residence determination results based on material concentration parameters; The threshold for determining the residence of material concentration parameters; The threshold for the auxiliary criterion of local standard deviation of material concentration in space; Based on the material retention discrimination results of the aforementioned temperature, pressure, and material concentration parameters, a joint criterion is defined, namely, the joint material retention discrimination result. for: Based on the joint material residence discrimination result Output the sequence of material residence intervals, expressed by the formula: in, For the first The set of all maximized continuous residence intervals under the joint criterion of nodes, i.e., the material residence interval sequence; To satisfy the condition within the interval The goal is to maximize the continuous dwell time, with no more dwell times at either end of the interval that meet the conditions.

7. The intelligent control system for calcining furnace discharge timing according to claim 6, characterized in that, During the stay The results of the joint material residence determination are analyzed. The distribution along the spatiotemporal axis identifies the movement trajectory of materials. If, within a time period, the nodes... Its neighboring nodes Satisfy at consecutive time intervals , This indicates that the material originates from the node. Move to node The final material motion trajectory sequence is defined as follows: in, This is a sequence of material movement trajectories, representing the material's movement from the first... Each node Time migration to adjacent Node at The set of flow paths at time, and Adjacent nodes; During the stay Inside, the first Each node at the sampling time The local standard deviation of the parameter space is expressed as ,and And set the stacking anomaly detection threshold as follows: ,and ,in, The threshold for identifying abnormal accumulation of temperature parameters. The threshold for identifying abnormal accumulation of pressure parameters. The threshold for identifying abnormal accumulation of material concentration parameters; The formula for determining the local standard deviation of the parameter space and the stacking anomaly discrimination threshold is defined as follows: If this condition is met, it indicates the presence of a stacking anomaly; otherwise, This indicates that there is no stacking anomaly; Finally, output the first... The set of accumulated anomalies within the node's residence interval is: 。 8. The intelligent control system for calcining furnace discharge timing according to claim 7, characterized in that, Regarding the first Each node and sampling time The material residence interval sequence, material movement trajectory sequence, and accumulation anomaly point set are classified and determined, and the working condition discrimination formula is defined as follows: in, For the first Each node at the sampling time The results of the working condition judgment; like This indicates an abnormal accumulation of materials; if This indicates that the material is flowing normally; if This indicates that the material is normally residing there; This indicates a state where there is no material. Based on the working condition judgment result Generate corresponding intelligent control commands: in, For the first Each node at the sampling time Control instructions; The corresponding control command is to speed up the discharge rate to alleviate the accumulation situation; The corresponding control instruction is to maintain the current parameters, and no adjustment is required; The corresponding control instruction is to slow down the discharge speed to avoid the risk of downstream blockage caused by excessively fast material flow; The corresponding control instruction is "no action required," meaning no adjustments are needed.

Images