Intelligent linkage control method and system of coal conveying and water supply system

Abstract

The invention discloses an intelligent linkage control method and system for a coal conveying and water supply system, and the method comprises the steps: constructing a coal conveying system state sensing network, and collecting the operation data of coal conveying equipment, the material level information of a coal bunker, and the coal blocking state information in real time; a water supply system state sensing network is constructed, and the pump operation state, the water level, the flow and security signals are collected in real time; a dynamic coupling model of a coal conveying system and a water supply system is established, linkage control is executed under specific working conditions based on the dynamic coupling model, and the linkage control comprises the steps that when the coal bunker material level reaches a high threshold value and the coal conveying system needs load reduction operation, a load reduction instruction is sent to the water supply system, and the operation frequency of a pump machine is adjusted to a high-efficiency interval; when the water supply system detects abnormal flow, a speed regulation instruction is sent to the coal conveying system, and the speed of the belt conveyor is adjusted to meet the water supply requirement. According to the invention, through combination of the state sensing network and the dynamic coupling model, intelligent dynamic adjustment of the operation parameters of the coal conveying and water supply system is realized, and the accuracy and reliability of system management and control are improved.

Description

Technical Field

[0001] This invention relates to the field of coal conveying and water supply technology, and in particular to an intelligent linkage control method and system for coal conveying and water supply systems. Background Technology

[0002] In the field of industrial production, coal conveying systems and water supply systems are core auxiliary systems that ensure continuous production. The two are implicitly related in function: changes in the load of the coal conveying system, such as fluctuations in the coal bunker level and adjustments in the belt conveyor speed, will indirectly affect the actual demand of the water supply system, including the flow demand for equipment cooling water and dust removal water. In turn, the operating status of the water supply system will also restrict the safe and efficient operation of the coal conveying system.

[0003] However, in existing technologies, coal conveying systems and water supply systems typically employ independent control modes: the coal conveying system performs closed-loop control based solely on its own equipment operating parameters, adjusting load by regulating belt conveyor speed and starting / stopping the coal feeder, without considering the water supply system's carrying capacity and operating efficiency; the water supply system primarily adjusts pump frequency based on preset water level thresholds, fixed flow requirements, or manual commands, without establishing a linkage with the real-time load changes of the coal conveying system. While simple interlocking protection exists in some scenarios, it is limited to safety management under fault conditions and lacks control logic for coordinated optimization of both systems under normal operating conditions. Because the control logics of the coal conveying and water supply systems are fragmented, the water supply system may operate in an inefficient range, resulting in energy waste; when the coal conveying system experiences abnormal material levels or reduced load, the water supply parameters may mismatch with actual demand, affecting production continuity. Summary of the Invention

[0004] To address at least one of the aforementioned technical problems, this invention provides an intelligent linkage control method and system for a coal conveying and water supply system.

[0005] In a first aspect, the present invention provides an intelligent linkage control method for a coal conveying and water supply system, the method comprising:

[0006] Construct a status perception network for the coal conveying system to collect real-time operating data of coal conveying equipment, coal bunker level information, and coal blockage status information;

[0007] Construct a water supply system status sensing network to collect real-time data on pump operating status, water level, flow rate, and security signals;

[0008] A dynamic coupling model of the coal conveying system and the water supply system is established. Based on the dynamic coupling model, linkage control is executed under specific operating conditions, including:

[0009] When the coal bunker level reaches a high threshold and the coal conveying system needs to reduce its load, a load reduction command is sent to the water supply system to adjust the pump operating frequency to the high-efficiency range.

[0010] When the water supply system detects an abnormal flow rate, it sends a speed adjustment command to the coal conveying system to adjust the belt conveyor speed to match the water supply demand.

[0011] Preferably, establishing the dynamic coupling model between the coal conveying system and the water supply system includes:

[0012] Based on the coal conveying system operation data, the correlation function between the coal conveying system energy consumption and water supply demand is calculated. The correlation function includes the dynamic mapping relationship between coal flow rate, belt conveyor speed and water supply pump power.

[0013] Based on the water supply system operation data, an energy efficiency optimization function for the water supply system is constructed. The energy efficiency optimization function includes the pump efficiency range and constraints on flow rate and pressure.

[0014] The operation status of the coal conveying system and the water supply system is integrated by data fusion algorithm. Based on the correlation function between energy consumption and water demand of the coal conveying system and the energy efficiency optimization function of the water supply system, a collaborative optimization objective function is generated. The collaborative optimization objective function aims to optimize the overall energy efficiency of the system.

[0015] Preferably, the specific operating condition includes the coal bunker feeding stage, and the linkage control during the coal bunker feeding stage specifically includes:

[0016] Before the coal bunker starts loading, the water supply demand curve for a period of time is predicted by a dynamic coupling model based on the coal conveying system plan and real-time coal flow data.

[0017] Based on the water demand curve, the water supply system gradually increases the pump operating frequency in advance to smoothly transition to the high-efficiency operating range.

[0018] Preferably, the construction of the coal conveying system state perception network includes:

[0019] The coal blockage switch status, coal level gauge data and equipment current signal are collected by multiple types of sensors, and the coal level change rate is calculated based on the coal level gauge data.

[0020] The equipment current signal and coal level change rate are input into the adaptive threshold adjustment algorithm to correct the alarm threshold of the coal blockage switch in real time; based on the coal blockage switch status and the corrected alarm threshold of the coal blockage switch, the coal blockage status signal after operating condition calibration is output.

[0021] Based on coal blockage status signals, equipment current signals, and coal level change rate data, multi-parameter correlation analysis is performed to predict the risk of blockage in downstream equipment. The risk level is divided into early warning level and alarm level according to the degree and duration of abnormality of multiple parameters.

[0022] Preferably, the step of classifying the risk level into a warning level and an alarm level based on the degree and duration of abnormality of multiple parameters includes:

[0023] When a risk is diagnosed as a warning level risk, the interlocking logic is triggered to "flexible control mode" to automatically adjust the operating speed of the upstream coal feeder;

[0024] When an alarm-level risk is diagnosed or a coal blockage switch is directly triggered, the interlocking logic switches to "safety protection mode" and executes an emergency shutdown sequence.

[0025] Preferably, the construction of the water supply system state sensing network includes:

[0026] Collect multi-dimensional status signals of pump vibration, temperature, and operating current, as well as data from water level sensors and flow meters;

[0027] Based on the collected data, unattended control logic is designed, which includes:

[0028] The system incorporates automatic pump start / stop based on water level thresholds, pump group rotation strategies based on flow demand, and emergency handling logic integrated with security signals. When a fire alarm signal is received, the system automatically starts the fire pump and shuts down access control in the relevant area.

[0029] Preferably, the correlation function between energy consumption and water supply demand is obtained through training a machine learning algorithm, specifically as follows:

[0030] Using historical operating data such as coal flow rate, belt conveyor speed, ambient temperature, and coal type characteristics as inputs, and the pump power at which the water supply system achieves the lowest energy consumption per unit coal transport volume as output, a regression prediction model is trained.

[0031] Preferably, the collaborative optimization objective function is determined based on the weighted sum of the total system energy consumption, carbon emission cost, coal conveying delay cost caused by equipment maintenance, and mechanical wear cost estimated based on equipment operating time and load rate;

[0032] The constraints of the objective function include: the upper and lower limits of the safe coal bunker level in the coal conveying system, the limit of pressure fluctuation in the water supply system network, and the lower limit of the operating efficiency of each piece of equipment, which shall not be lower than the rated high efficiency range.

[0033] Secondly, the present invention also provides an intelligent linkage control system for a coal conveying and water supply system, the system comprising:

[0034] The coal conveying network construction unit is used to build a coal conveying system status perception network to collect real-time operating data of coal conveying equipment, coal bunker level information, and coal blockage status information.

[0035] The water supply network construction unit is used to build a water supply system status sensing network to collect pump operating status, water level, flow rate and security signals in real time;

[0036] The linkage control unit is used to establish a dynamic coupling model between the coal conveying system and the water supply system, and to execute linkage control under specific operating conditions based on the dynamic coupling model, including:

[0037] When the coal bunker level reaches a high threshold and the coal conveying system needs to reduce its load, a load reduction command is sent to the water supply system to adjust the pump operating frequency to the high-efficiency range.

[0038] When the water supply system detects an abnormal flow rate, it sends a speed adjustment command to the coal conveying system to adjust the belt conveyor speed to match the water supply demand.

[0039] Thirdly, the present invention also provides an electronic device including a processor and a memory, the memory being used to store computer program code, the computer program code including computer instructions, wherein when the processor executes the computer instructions, the electronic device performs the method as described in the first aspect above and any possible implementation thereof.

[0040] Fourthly, the present invention also provides a computer-readable storage medium storing a computer program, the computer program including program instructions that, when executed by a processor of an electronic device, cause the processor to perform a method as described in the first aspect above and any possible implementation thereof.

[0041] Compared with the prior art, the beneficial effects of the present invention are as follows:

[0042] 1) By constructing a state perception network for the coal conveying and water supply systems, comprehensive data collection is achieved on the operation data of the coal conveying equipment, material level / coal blockage status, and the operating parameters, flow rate / water level of the water supply pumps, providing data support for their linkage; based on the dynamic coupling model, a collaborative control logic is established, enabling the water supply system to actively adjust the pump frequency according to the material level status and load adjustment instructions of the coal conveying system, and the coal conveying system to respond to abnormal water supply flow signals and adjust the belt conveyor speed in a timely manner, achieving precise matching of the operating states of the two systems.

[0043] 2) When the coal bunker level reaches a high threshold and the coal conveying system needs to reduce its load, the pump operating frequency is adjusted to the high-efficiency range by sending a load reduction command to the water supply system to avoid ineffective energy consumption. At the same time, when the water supply system flow is abnormal, the coal conveying system reduces the water supply demand by adjusting the belt conveyor speed, thereby reducing the energy loss of the water supply system under overload and significantly improving the overall energy utilization efficiency.

[0044] 3) The dynamic coupling model can correlate the operating status of the coal conveying and water supply systems in real time, enabling rapid response to changes in operating conditions. When the coal conveying system issues a load reduction command, the water supply system can adjust its parameters synchronously. When the water supply flow is abnormal, the coal conveying system can adjust its speed immediately to avoid operational imbalance caused by response lag and ensure production continuity.

[0045] 4) By coordinating the high threshold of coal bunker material level with the water supply during coal conveying load reduction, the risk of leakage caused by excessive pipeline pressure can be avoided; by adjusting the coal conveying speed when the water supply flow is abnormal, the key water supply needs such as equipment cooling and coal blockage treatment can be met, reducing the risk of equipment overheating and coal blockage shutdown, and extending the service life of the equipment.

[0046] 5) By combining state-aware networks with dynamic coupling models, traditional independent control and manual intervention can be replaced to achieve intelligent dynamic adjustment of operating parameters of coal conveying and water supply systems, reduce human error, and improve the accuracy and reliability of system management.

[0047] It should be understood that the above general description and the following detailed description are exemplary and explanatory only, and are not intended to limit this disclosure. Attached Figure Description

[0048] To more clearly illustrate the technical solutions in the embodiments of the present invention or the background art, the accompanying drawings used in the embodiments of the present invention or the background art will be described below.

[0049] The accompanying drawings, which are incorporated in and form part of this specification, illustrate embodiments consistent with this disclosure and, together with the specification, serve to illustrate the technical solutions of this disclosure.

[0050] Figure 1 A flowchart illustrating an intelligent linkage control method for a coal conveying and water supply system provided in an embodiment of the present invention;

[0051] Figure 2 This is a schematic diagram of the structure of an intelligent linkage control system for a coal conveying and water supply system provided in an embodiment of the present invention. Detailed Implementation

[0052] To enable those skilled in the art to better understand the present invention, the technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0053] The terms "first," "second," etc., used in the specification, claims, and accompanying drawings of this invention are used to distinguish different objects, not to describe a specific order. Furthermore, the terms "comprising" and "having," and any variations thereof, are intended to cover non-exclusive inclusion. For example, a process, method, system, product, or apparatus that includes a series of steps or units is not limited to the listed steps or units, but may optionally include steps or units not listed, or may optionally include other steps or units inherent to these processes, methods, products, or apparatuses.

[0054] In this document, the term "embodiment" means that a particular feature, structure, or characteristic described in connection with an embodiment may be included in at least one embodiment of the invention. The appearance of this phrase in various places throughout the specification does not necessarily refer to the same embodiment, nor is it a separate or alternative embodiment mutually exclusive with other embodiments. It will be explicitly and implicitly understood by those skilled in the art that the embodiments described herein can be combined with other embodiments.

[0055] Please see Figure 1 , Figure 1 This is a flowchart illustrating an intelligent linkage control method for a coal conveying and water supply system provided in an embodiment of the present invention. Figure 1 As shown, the method includes:

[0056] S10. Construct a coal conveying system status perception network to collect real-time operating data of coal conveying equipment, coal bunker level information, and coal blockage status information.

[0057] S20. Construct a water supply system status sensing network to collect pump operating status, water level, flow rate and security signals in real time;

[0058] S30. Establish a dynamic coupling model between the coal conveying system and the water supply system, and execute linkage control under specific operating conditions based on the dynamic coupling model, including:

[0059] S301. When the coal bunker level reaches a high threshold and the coal conveying system needs to reduce its load, a load reduction command is sent to the water supply system to adjust the pump operating frequency to the high-efficiency range.

[0060] S302. When the water supply system detects an abnormal flow rate, it sends a speed adjustment command to the coal conveying system to adjust the belt conveyor speed to match the water supply demand.

[0061] In this embodiment, step S10 is used to construct a state perception network for the coal conveying system. First, equipment data is collected. Vibration and temperature sensors and motor protectors are installed on key equipment such as belt conveyors, coal crushers, and coal feeders to monitor their health status (e.g., bearing temperature, vibration amplitude) in real time, enabling predictive maintenance. Machine vision technology (e.g., laser scanning + high-definition cameras) or radar level gauges are used to detect the instantaneous flow rate, cross-sectional shape, and coal level height on the belt conveyor in real time. To address the risk of coal blockage, radar level switches or pressure sensors are installed at easily blocked points such as coal chutes for monitoring. AI inspection robots and fixed-point monitoring cameras are deployed to automatically identify belt misalignment, longitudinal tears, and abnormal conditions such as open flames and smoke using computer vision algorithms. Simultaneously, sensors are deployed to monitor the concentration of harmful gases such as methane and carbon monoxide, as well as dust, within the conveyor corridor. All the dispersed sensor data is aggregated into an edge controller or programmable logic controller (PLC) via fieldbus or industrial Ethernet to form a unified digital mirror. It enables 24 / 7 uninterrupted panoramic monitoring of equipment operation, material flow, and environmental safety, completely changing the passive situation of relying on manual inspections. It can detect potential equipment faults such as idler roller jamming, motor overheating, and abnormal operation at an early stage, providing a data foundation for predictive maintenance and accident prevention, and reducing the number of unplanned downtimes.

[0062] In step S20, a water supply system status sensing network is constructed. Vibration and temperature sensors are installed on the pump body and motor, and current, voltage, and power are monitored through smart meters to assess the pump unit's operating efficiency and health status in real time. Electromagnetic flow meters and pressure transmitters are installed at key nodes of the water supply network, such as pump station outlets and elevated water tank inlets, to collect flow and pressure data in real time. Online water quality analyzers are installed at water sources and water plants to monitor key indicators such as pH, turbidity, and residual chlorine. Through intelligent video analysis, perimeter intrusion alarm systems, and level switches installed at pump stations and water tanks, a security sensing network is formed to ensure water supply safety. Similar to the coal conveying system, distributed I / O modules and communication networks are used to integrate all sensing data into a unified monitoring and data acquisition system or cloud platform. This allows for real-time monitoring of pump operating conditions, optimization of pump start-up and shutdown strategies based on demand changes, ensuring that the pumps always operate within the high-efficiency range, achieving energy saving and consumption reduction. It also enables second-level detection and rapid location of abnormal conditions such as pipe bursts, sudden pressure drops, and sudden changes in water quality, significantly improving emergency response capabilities.

[0063] After establishing the state perception networks for the coal conveying system and the water supply system respectively, step S30 establishes a dynamic coupling model for the coal conveying system and the water supply system, and executes linkage control. Specifically, a digital twin platform is constructed to integrate real-time data, equipment models, and business rules of the coal conveying system and the water supply system, forming a unified decision support environment. The coupling relationship and linkage logic between the two systems are clearly defined in the platform. For example, a water consumption prediction model is established to analyze the cooling water and flushing water demand patterns of the coal conveying system under different loads. When executing linkage control, if the perception network detects that the coal bunker level has reached a high threshold and the system determines that load reduction is necessary, the dynamic coupling model will integrate the current water supply network pressure, water tank level, and water consumption prediction information to calculate an optimal load reduction rate and target water supply pressure / flow rate that meets safety requirements and is within the pump's high-efficiency zone. The linkage control command is sent to the pump room PLC of the water supply system through the platform. The PLC smoothly adjusts the pump operating frequency through the frequency converter, reducing the outlet pressure and flow rate to achieve load reduction operation. When the water supply system detects an abnormal flow rate (such as a sudden surge, which may indicate fire suppression activation or pipe burst), the coupled model quickly assesses the impact of this abnormality on the overall plant's water balance. To prevent a rapid drop in the clear water tank level, the model sends a speed adjustment request to the coal conveying system. Upon receiving the instruction, the main control PLC of the coal conveying system dynamically adjusts the operating speed of the conveyor belt based on the current coal flow information using a fuzzy control algorithm. This reduces the coal feeding rate while ensuring safety, thereby reducing water consumption for related washing and dust removal equipment and helping the water supply system restore balance. This achieves cross-system energy optimization. The water supply pump operates in its high-efficiency zone according to actual needs, avoiding the waste of "overpowered" pumps; the coal conveyor belt intelligently adjusts its speed according to the coal flow, avoiding idling or light-load operation, significantly improving overall energy efficiency. When one system experiences a disturbance or abnormality, the other system can proactively respond and coordinate adjustments, effectively suppressing the escalation of the accident and improving the safety and reliability of the entire industrial production. Through intelligent linkage, manual intervention is reduced, equipment operation strategies are optimized, and equipment lifespan is extended, thereby achieving cost reduction and efficiency improvement.

[0064] In one embodiment, establishing a dynamic coupling model between the coal conveying system and the water supply system includes:

[0065] Based on the coal conveying system operation data, the correlation function between the coal conveying system energy consumption and water supply demand is calculated. The correlation function includes the dynamic mapping relationship between coal flow rate, belt conveyor speed and water supply pump power.

[0066] Based on the water supply system operation data, an energy efficiency optimization function for the water supply system is constructed. The energy efficiency optimization function includes the pump efficiency range and constraints on flow rate and pressure.

[0067] The operation status of the coal conveying system and the water supply system is integrated by data fusion algorithm. Based on the correlation function between energy consumption and water demand of the coal conveying system and the energy efficiency optimization function of the water supply system, a collaborative optimization objective function is generated. The collaborative optimization objective function aims to optimize the overall energy efficiency of the system.

[0068] Real-time data collection of key parameters of the coal conveying system, including coal flow rate (tons / hour), conveyor belt speed (meters / second), and corresponding equipment power. Simultaneously, instantaneous water consumption in relevant components of the coal conveying system (such as dust removal equipment and washing systems) is monitored. Regression analysis or machine learning algorithms (such as random forests and gradient boosting trees) are used to establish a mathematical model relating coal flow rate, conveyor belt speed, and total energy consumption (including electricity and indirect water consumption).

[0069] Association function It can be characterized as:

[0070] ;

[0071] In the formula, This is the total energy consumption equivalent of the coal conveying system. For coal flow rate, For the belt conveyor speed, For the predicted water supply demand, This is the water consumption conversion factor. Through training with historical data, the function can dynamically reflect energy consumption and water supply demand under different coal transportation conditions.

[0072] To construct an energy efficiency optimization function for the water supply system, an efficiency model for each pump is established based on its characteristic curves (QH curve, efficiency curve) and real-time data on pump operating frequency, outlet pressure, flow rate, and motor power. Its high-efficiency operating range (typically the specific flow-pressure range where efficiency is highest) is clearly defined. Physical constraints of the water supply system are considered, such as the minimum service pressure of the pipeline network (ensuring water supply to end users), reservoir water level limits, and the pump's own safe operating range (e.g., minimum flow rate, maximum speed). These constraints are expressed as inequalities in the optimization function. The energy efficiency optimization function can be represented as finding the pump combination and operating parameters that maximize the overall system operating efficiency or minimize total energy consumption while satisfying all the above constraints. For parallel pump sets, their efficiency characteristics during combined operation must also be considered. Even without linkage with the coal conveying system, it can independently guide the water supply system to achieve optimized operation, for example, by adjusting pump start-up and shutdown and inverter frequency to ensure the pumps operate within their high-efficiency range, directly reducing unit water consumption.

[0073] Finally, a collaborative optimization objective function is generated. This involves integrating real-time operational data (coal flow rate, belt speed, predicted water demand) from the coal conveying system and real-time status data (pressure, flow rate, pump frequency, efficiency) from the water supply system through a data platform (such as an industrial IoT-based platform). The collaborative optimization objective function typically aims at achieving "optimal overall system energy efficiency," i.e., minimizing the combined unit production energy consumption of the coal conveying and water supply systems. Its mathematical expression can be simplified as follows:

[0074] ;

[0075] In the formula, Energy consumption of the coal conveying system Energy consumption of the water supply system This can be represented as total coal consumption or total water supply. This is an energy consumption conversion factor or weighting factor used to balance the importance of the two types of energy consumption. Solving this function requires finding the optimal combination of control variables (such as belt conveyor speed, pump operating frequency, etc.) under the constraints of the coal conveying system and the water supply system, respectively.

[0076] In this way, the system can find the lowest energy consumption operation plan from the perspective of the whole plant, avoid the damage to the overall energy efficiency by a single system for local optimization, and the model can dynamically adjust the optimization strategy according to real-time operating data (such as coal quality changes, equipment anomalies), realize adaptive scheduling, and improve the system's robustness in response to changes in operating conditions.

[0077] In one embodiment, the specific operating condition includes a coal bunker feeding stage, and the linkage control during the coal bunker feeding stage specifically includes:

[0078] Before the coal bunker starts loading, the water supply demand curve for a period of time is predicted by a dynamic coupling model based on the coal conveying system plan and real-time coal flow data.

[0079] Based on the water demand curve, the water supply system gradually increases the pump operating frequency in advance to smoothly transition to the high-efficiency operating range.

[0080] In traditional control methods, water supply systems often adjust only after coal bunker loading has begun or water pressure or flow rate changes. This "feedback control" mode exhibits significant lag, easily leading to frequent pump starts and stops or pressure fluctuations. Therefore, the purpose of this embodiment is to move away from passive waiting and instead, based on the predicted water demand curve, gradually increase pump frequency before coal bunker loading. This allows the pumps to enter their efficient operating range earlier during the critical load phase of loading, avoiding operation in inefficient or surge zones, thereby minimizing energy consumption and maximizing operational stability.

[0081] Specifically, the dynamic coupling model acquires real-time data on planned coal loading, conveyor speed, and real-time coal flow from the coal conveying control system and electronic belt scales. Combining historical data with real-time operating conditions, the model calculates the total water supply required for dust suppression and flushing over a future period, including the entire loading process, and its variation patterns, generating a smooth water demand prediction curve. This prediction curve and corresponding control commands are sent to the water supply system controller via the inter-system communication interface. Upon receiving the command, the PLC in the water pump station does not immediately increase the pump frequency to the target value. Instead, it performs a smooth, step-like frequency increase based on a preset acceleration. Through this early and gradual action, when the coal conveying system officially starts and reaches a high load, the pumps are already operating at or very close to their optimal efficiency frequency range. Throughout the loading phase, the system maintains this high-efficiency operation, and the dynamic coupling model fine-tunes itself based on minor fluctuations in actual coal flow to ensure that the water pressure and flow always match the demand.

[0082] This control method ensures that the water pump operates in its high-efficiency range most of the time, effectively reducing power consumption in raw coal production. The smooth, gradual increase in pressure avoids water hammer and mechanical stress on the pumps and pipelines caused by rapid changes in flow and pressure, effectively extending the service life of critical equipment such as motors, bearings, and valves. Stable water supply pressure means better dust suppression spraying and hydraulic flushing effects for the coal conveying system, while reducing the risk of equipment tripping due to pressure fluctuations, thus improving the stability and automation level of the entire production process.

[0083] In one embodiment, constructing the coal conveying system state awareness network includes:

[0084] The coal blockage switch status, coal level gauge data and equipment current signal are collected by multiple types of sensors, and the coal level change rate is calculated based on the coal level gauge data.

[0085] The equipment current signal and coal level change rate are input into the adaptive threshold adjustment algorithm to correct the alarm threshold of the coal blockage switch in real time; based on the coal blockage switch status and the corrected alarm threshold of the coal blockage switch, the coal blockage status signal after operating condition calibration is output.

[0086] Based on coal blockage status signals, equipment current signals, and coal level change rate data, multi-parameter correlation analysis is performed to predict the risk of blockage in downstream equipment. The risk level is divided into early warning level and alarm level according to the degree and duration of abnormality of multiple parameters.

[0087] The deployment of multiple sensor types is as follows:

[0088] Coal blockage switch: A coal blockage switch, such as a rotary paddle or capacitive type, is installed at easily blocked locations such as coal chutes and feed chutes. When material accumulates and touches the detection mechanism, the switch will send a signal.

[0089] Coal level gauge: Radar level gauges or ultrasonic level gauges are used in coal bunkers, buffer silos, and other locations to continuously measure and output coal level height data.

[0090] Equipment current signal: Install intelligent power acquisition modules in the motor control circuits of equipment such as coal feeders and coal crushers to monitor their operating current in real time.

[0091] To calculate the coal level change rate, the control system periodically reads data from the coal level gauge and calculates the amount of coal level change per unit time, i.e., coal level change rate = (current coal level - previous cycle coal level) / time interval. A continuously decreasing or abnormally slow change rate may indicate upstream material supply problems or impending blockage in downstream equipment.

[0092] After obtaining this data, the equipment current signal and coal level change rate are input into the adaptive threshold adjustment algorithm. The algorithm continuously receives the equipment current signal and coal level change rate. Its core logic is that coal blockage is a gradual process, not an instantaneous event. For example, when the feeder current steadily increases due to a slow increase in load, while the coal level change rate decreases significantly, the system can determine that the risk of coal blockage is accumulating even if the coal blockage switch has not yet been triggered. At this time, the algorithm dynamically lowers the alarm threshold sensitivity of the coal blockage switch, for example, shortening the signal duration threshold required for triggering from the default 2 seconds to 1 second, allowing it to issue an alarm earlier in the early stages of risk accumulation. The final output coal blockage status signal is no longer a simple switching quantity, but a highly reliable signal calibrated by multiple operating condition data. This effectively avoids false alarms caused by changes in coal quality (such as wet coal adhesion) or instantaneous coal dust interference.

[0093] Furthermore, the system performs correlation analysis on calibrated coal blockage status signals, real-time equipment current, coal level change rate, and possibly even parameters such as belt speed. For example, if the current of the downstream coal crusher rises abnormally while its inlet coal level continues to rise, the system can predict a high risk of the coal crusher "blocking" even if the coal blockage switch at that location does not activate. The system is triggered when a few parameters show slight anomalies for a short duration. For example, if the coal level change rate is 10% below normal and lasts for 30 seconds, the system may display a "Observe Carefully" message on the control interface but will not take any mandatory action. The system is triggered when multiple parameters show significant anomalies for a duration reaching the set value, or when the parameter anomalies are severe. For example, if the feeder current exceeds the rated value by 20% and the coal level continues to rise for more than one minute, the system will issue an audible and visual alarm and may automatically implement preliminary measures such as reducing the coal feed rate and activating the vibrator to prevent the accident from escalating. Adaptive thresholds and multiple signal verifications greatly reduce false alarms caused by coal dust interference and material adhesion, making maintenance personnel more confident in every alarm issued by the system. By analyzing trend parameters such as current and coal level change rate, risk signs can be identified before physical coal blockage occurs, realizing the transformation from "passive response" to "proactive prevention", buying valuable time for disposal. The continuously accumulated multi-parameter correlation data can be used to analyze the performance degradation law of equipment, predict the remaining life of equipment, and thus formulate more scientific maintenance plans and reduce the probability of unexpected downtime.

[0094] Preferably, the step of classifying the risk level into a warning level and an alarm level based on the degree and duration of abnormality of multiple parameters includes:

[0095] When a risk is diagnosed as a warning level risk, the interlocking logic is triggered to "flexible control mode" to automatically adjust the operating speed of the upstream coal feeder;

[0096] When an alarm-level risk is diagnosed or a coal blockage switch is directly triggered, the interlocking logic switches to "safety protection mode" and executes an emergency shutdown sequence.

[0097] This mode is triggered when the system, through multi-parameter correlation analysis, determines a downstream blockage risk based on factors such as a decrease in the coal level change rate and a slow increase in equipment current, but before actual blockage occurs. Its goal is not immediate shutdown, but rather to proactively and gradually adjust upstream equipment to eliminate the conditions for risk accumulation, attempting to allow the system to automatically return to normal. When multi-parameter analysis indicates a very serious anomaly, such as severely excessive equipment current, a sharp rise in coal level, or the triggering of the most direct hardware sensor (the coal blockage switch), the system determines that blockage has occurred or is about to occur. At this point, the primary objective is to quickly isolate the fault point, interrupt the material flow, and prevent equipment damage and the expansion of the blockage. This maximizes operational continuity, reduces unplanned downtime, and the smooth speed adjustment of "flexible control" avoids the mechanical and electrical shocks caused by emergency shutdowns, contributing to the long-term health of the equipment.

[0098] In one embodiment, constructing the water supply system state awareness network includes:

[0099] Collect multi-dimensional status signals of pump vibration, temperature, and operating current, as well as data from water level sensors and flow meters;

[0100] Based on the collected data, unattended control logic is designed, which includes:

[0101] The system incorporates automatic pump start / stop based on water level thresholds, pump group rotation strategies based on flow demand, and emergency handling logic integrated with security signals. When a fire alarm signal is received, the system automatically starts the fire pump and shuts down access control in the relevant area.

[0102] Vibration and temperature sensors are installed on the pump body and motor to monitor the equipment's health status in real time and prevent mechanical failures. Simultaneously, operating current is collected via smart meters or motor protectors to determine if the pump is under no-load, overload, or other abnormal operating conditions. Level transmitters (monitoring water levels in pools and tanks) and electromagnetic or ultrasonic flow meters (monitoring instantaneous and cumulative flow in the pipe network) are used to accurately grasp the system's hydraulic and water balance. Motion detection from the video surveillance system, perimeter intrusion alarms, fire alarm signals from the fire protection system, and access control signals from the pump room are connected to the control system via I / O modules or protocol interfaces. The system sets high and low water level thresholds for the pool. When the water level drops to the low threshold, the standby pump automatically starts; when the water level recovers to the high threshold, the pumps are stopped in an orderly manner. To avoid frequent pump starts and stops, reasonable delay judgments or dead zones are typically set. This logic can be extended to the linkage of multiple pumps. The control system dynamically calculates demand based on real-time flow changes and automatically determines the number of pumps to start and which pumps to operate based on principles such as "first-in, first-out" and "balanced operating time." This ensures that each pump has a relatively consistent service life, preventing excessive wear and tear on individual devices. When the control center receives a confirmed fire alarm signal, the emergency logic is triggered immediately: First, the fire pumps are automatically started, and the pressure-stabilizing pumps may also be activated to ensure fire water supply pressure. Second, the electric fire doors or access control systems in the relevant areas are closed to create an isolation zone, and fire exits may be unlocked simultaneously. The entire process is completed automatically without manual intervention, buying valuable time for firefighting and rescue operations.

[0103] This embodiment enables the automatic adjustment of equipment operating status according to actual needs, ultimately achieving an "unmanned" or "minimally staffed" operation mode, significantly reducing labor costs. Simultaneously, on-demand pump start-up and shutdown and optimized operating combinations avoid energy waste and help reduce the unit energy consumption of the water supply system. Through multi-dimensional status monitoring, predictive maintenance of critical equipment such as pumps can be performed, identifying abnormal signs and issuing warnings before failures occur, preventing small problems from escalating into major malfunctions. A balanced pump rotation strategy effectively prevents overuse of individual equipment, extending the overall equipment lifespan. In emergencies such as fires, the system can automatically execute a series of critical operations within seconds, with a response speed far exceeding that of manual intervention, greatly improving the level of personal and property safety.

[0104] In one embodiment, the energy consumption and water supply demand correlation function is obtained by training a machine learning algorithm. Specifically, it uses coal flow rate, belt conveyor speed, ambient temperature, and coal type characteristics from historical operating data as inputs and the pump power at which the water supply system achieves the lowest energy consumption per unit coal transport volume as output to train a regression prediction model.

[0105] Specifically, the relationship between energy consumption and water demand is as follows:

[0106] ;

[0107] In the formula, The pump power (kW) required to achieve the lowest energy consumption per unit coal transport volume in the water supply system. Coal flow rate (t / h) The speed of the belt conveyor is (m / s). The ambient temperature (°C) is used. For reference ambient temperature (usually taken as 25°C) The moisture content of the coal is expressed as (%). For reference, the moisture content (%) of coal. The average particle size of coal (mm). The average particle size of coal is taken as a reference (usually 10 mm). These are the coefficients obtained through training with historical operating data.

[0108] In the above formula, In this study, the interaction between ambient temperature and coal moisture content is incorporated into the model. Higher temperatures result in lower coal moisture content, requiring less water spray and thus reducing pump power demand. The study also considered the impact of coal moisture content and particle size on water demand. Higher moisture content and smaller particle size lead to more severe dust problems, requiring more water spraying to suppress dust. The ratio of coal flow rate to belt speed directly affects the coal load per unit length and is a key parameter determining water demand. By introducing exponential and interaction terms, the nonlinear relationship between the coal conveying system and the water supply system is reflected more accurately. This data-driven model can precisely identify the optimal energy efficiency under various complex operating conditions, avoiding biases from control based on fixed rules or experience, and achieving systemic energy conservation.

[0109] During this process, coal flow rate and conveyor speed are collected in real time from the control system to ensure data timestamp synchronization. Temperature and humidity sensors are deployed in key areas such as the coal conveying corridor to record ambient temperature in real time. Coal moisture content is detected in real time using an online microwave moisture meter; the average particle size of the coal is analyzed using a machine vision system installed after the coal crusher or on the conveyor belt, or an online particle size analyzer. Daily coal quality test reports (ash content, calorific value, etc.) are input into the system as supplementary features. Real-time power and total water supply of the water pumps are collected. The target variable is not the power at any given moment, but rather the pump power that minimizes energy consumption per unit of coal conveyed under specific coal conveying conditions, which needs to be calculated and analyzed post-hoc. This typically requires back-calculation using energy efficiency analysis of historical operating data.

[0110] The K-nearest neighbor algorithm is used to detect and clean outliers in the initial dataset. Then, multi-dimensional data fusion is performed to align and integrate data from different sources and frequencies along a unified timeline, forming a well-organized historical dataset. During model training, the model parameters are the coefficients to be determined. The preprocessed historical dataset is divided into training and test sets. A loss function is defined, typically mean squared error (MSE), which is the average of the squares of the difference between the model's predicted pump power and the actual optimal pump power. An optimization algorithm is used to minimize the loss function, thereby solving for the model coefficients. The model's prediction accuracy is evaluated on the test set using metrics such as mean absolute percentage error and root mean square error. This ensures the model performs well even on unseen data. Through data-driven analysis, this model can accurately pinpoint the optimal energy efficiency points under various complex operating conditions, achieving systemic energy savings.

[0111] In one embodiment, the collaborative optimization objective function is determined based on a weighted sum of the total system energy consumption, carbon emission costs, coal conveying delay costs caused by equipment maintenance, and mechanical wear costs estimated based on equipment operating time and load rate.

[0112] The constraints of the objective function include: the upper and lower limits of the safe coal bunker level in the coal conveying system, the limit of pressure fluctuation in the water supply system network, and the lower limit of the operating efficiency of each piece of equipment, which shall not be lower than the rated high efficiency range.

[0113] In this embodiment, the four indicators for constructing the collaborative optimization objective function have the following meanings:

[0114] Total system energy consumption: The sum of electricity consumption of all equipment, including coal conveyor belts and water pumps;

[0115] Carbon emission cost: Carbon emissions calculated based on energy consumption × local carbon price (e.g., how much carbon is emitted from burning 1 kWh of electricity, multiplied by the price of 1 ton of carbon, and environmental-related costs).

[0116] Coal conveying delay costs: If equipment maintenance (such as pump overhaul or belt conveyor cleaning) causes coal conveying to slow down or stop, resulting in production losses (such as the impact of power generation revenue on the supply of 1 ton less coal).

[0117] Mechanical wear and tear costs: estimated losses based on equipment operating time (e.g., how many hours the pump has run) and load rate (e.g., whether the belt conveyor is running at full load) (e.g., belt aging, pump bearing wear, and the cost of replacing parts in the future).

[0118] Then determine the constraints:

[0119] Red lines for coal conveying systems: the coal bunker level cannot be too high (to prevent coal spillage) or too low (to prevent supply disruption), and must be between safe upper and lower limits (e.g., the level should be maintained between 30% and 80%).

[0120] Water supply system red line: The pressure of the pipeline network should not fluctuate too much (for example, the pressure should not exceed 0.6MPa or fall below 0.3MPa) to avoid pipeline leakage or insufficient water supply;

[0121] Equipment efficiency red line: The operating efficiency of all equipment (belt conveyors, pumps, etc.) must not be lower than its "rated high efficiency range lower limit" (for example, if the high efficiency range of a pump is 60%-90% load, it should not be allowed to run for a long time at a load lower than 60%, otherwise it will consume more electricity and be more prone to damage).

[0122] Finally, the "objective function" and "constraints" are input into the linkage control module of the coal conveying and water supply system. The system will automatically make decisions, including:

[0123] 1) The state-aware network collects data in real time, such as coal bunker level at 85%, pump load at 70%, and pipeline pressure at 0.5 MPa;

[0124] 2) The control module substitutes the objective function to calculate the optimal solution with the "minimum total" under the current state, such as how to adjust the belt conveyor speed and pump frequency to minimize energy consumption, carbon cost, delay cost and wear cost;

[0125] 3) At the same time, check whether this optimal solution meets the constraints, such as whether the material level is between 30% and 80% after adjustment, whether the pressure is between 0.3 and 0.6 MPa, and whether the equipment efficiency meets the standards;

[0126] 4) If the conditions are met, execute the control command. For example, if the material level is 85% and coal needs to be transported to reduce the load, adjust the pump frequency from 50Hz to 40Hz, which is exactly in the pump's high-efficiency range. If the water supply flow is insufficient, reduce the belt conveyor speed from 2m / s to 1.5m / s to reduce the water supply demand.

[0127] 5) Continuous loop: Real-time data acquisition - recalculation of the optimal solution - parameter adjustment to ensure that it always operates within the "optimal + safe" range.

[0128] See Figure 2 In one embodiment, the present invention also provides an intelligent linkage control system for a coal conveying and water supply system, the system comprising:

[0129] The coal conveying network construction unit 100 is used to construct a coal conveying system status perception network to collect real-time operating data of coal conveying equipment, coal bunker level information and coal blockage status information.

[0130] Water supply network construction unit 200 is used to construct a water supply system status sensing network to collect pump operating status, water level, flow rate and security signals in real time;

[0131] The linkage control unit 300 is used to establish a dynamic coupling model between the coal conveying system and the water supply system, and to execute linkage control under specific operating conditions based on the dynamic coupling model, including:

[0132] When the coal bunker level reaches a high threshold and the coal conveying system needs to reduce its load, a load reduction command is sent to the water supply system to adjust the pump operating frequency to the high-efficiency range.

[0133] When the water supply system detects an abnormal flow rate, it sends a speed adjustment command to the coal conveying system to adjust the belt conveyor speed to match the water supply demand.

[0134] It is understood that the system provided in this embodiment has functions or includes modules that can be used to execute the methods described in the above method embodiments. The specific implementation can be referred to the description of the above method embodiments, and for the sake of brevity, it will not be repeated here.

[0135] The present invention also provides a computer-readable storage medium storing a computer program, the computer program including program instructions that, when executed by a processor of an electronic device, cause the processor to perform a method as described in any of the above possible implementations.

[0136] Those skilled in the art will recognize that the units and algorithm steps of the various examples described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are implemented in hardware or software depends on the specific application and design constraints of the technical solution. Those skilled in the art can use different methods to implement the described functions for each specific application, but such implementations should not be considered beyond the scope of this invention.

Claims

1. A method for intelligent linkage control of a coal conveying and water supply system, characterized in that, The method includes: Construct a status perception network for the coal conveying system to collect real-time operating data of coal conveying equipment, coal bunker level information, and coal blockage status information; Construct a water supply system status sensing network to collect real-time data on pump operating status, water level, flow rate, and security signals; A dynamic coupling model of the coal conveying system and the water supply system is established. Based on the dynamic coupling model, linkage control is executed under specific operating conditions, including: When the coal bunker level reaches a high threshold and the coal conveying system needs to reduce its load, a load reduction command is sent to the water supply system to adjust the pump operating frequency to the high-efficiency range. When the water supply system detects an abnormal flow rate, it sends a speed adjustment command to the coal conveying system to adjust the belt conveyor speed to match the water supply demand.

2. The intelligent linkage control method for a coal conveying and water supply system according to claim 1, characterized in that, The establishment of the dynamic coupling model between the coal conveying system and the water supply system includes: Based on the coal conveying system operation data, the correlation function between the coal conveying system energy consumption and water supply demand is calculated. The correlation function includes the dynamic mapping relationship between coal flow rate, belt conveyor speed and water supply pump power. Based on the water supply system operation data, an energy efficiency optimization function for the water supply system is constructed. The energy efficiency optimization function includes the pump efficiency range and constraints on flow rate and pressure. The operation status of the coal conveying system and the water supply system is integrated by data fusion algorithm. Based on the correlation function between energy consumption and water demand of the coal conveying system and the energy efficiency optimization function of the water supply system, a collaborative optimization objective function is generated. The collaborative optimization objective function aims to optimize the overall energy efficiency of the system.

3. The intelligent linkage control method for a coal conveying and water supply system according to claim 1, characterized in that, The specific operating condition includes the coal bunker feeding stage, and the linkage control during the coal bunker feeding stage is as follows: Before the coal bunker starts loading, the water supply demand curve for a period of time is predicted by a dynamic coupling model based on the coal conveying system plan and real-time coal flow data. Based on the water demand curve, the water supply system gradually increases the pump operating frequency in advance to smoothly transition to the high-efficiency operating range.

4. The intelligent linkage control method for a coal conveying and water supply system according to claim 1, characterized in that, The construction of the coal conveying system state awareness network includes: The coal blockage switch status, coal level gauge data and equipment current signal are collected by multiple types of sensors, and the coal level change rate is calculated based on the coal level gauge data. The equipment current signal and coal level change rate are input into the adaptive threshold adjustment algorithm to correct the alarm threshold of the coal blockage switch in real time; based on the coal blockage switch status and the corrected alarm threshold of the coal blockage switch, the coal blockage status signal after operating condition calibration is output. Based on coal blockage status signals, equipment current signals, and coal level change rate data, multi-parameter correlation analysis is performed to predict the risk of blockage in downstream equipment. The risk level is divided into early warning level and alarm level according to the degree and duration of abnormality of multiple parameters.

5. The intelligent linkage control method for a coal conveying and water supply system according to claim 4, characterized in that, The risk level is divided into warning level and alarm level based on the degree and duration of abnormality of multiple parameters, including: When a risk is diagnosed as a warning level risk, the interlocking logic is triggered to "flexible control mode" to automatically adjust the operating speed of the upstream coal feeder; When an alarm-level risk is diagnosed or a coal blockage switch is directly triggered, the interlocking logic switches to "safety protection mode" and executes an emergency shutdown sequence.

6. The intelligent linkage control method for a coal conveying and water supply system according to claim 1, characterized in that, The construction of the water supply system state awareness network includes: Collect multi-dimensional status signals of pump vibration, temperature, and operating current, as well as data from water level sensors and flow meters; Based on the collected data, unattended control logic is designed, which includes: The system incorporates automatic pump start / stop based on water level thresholds, pump group rotation strategies based on flow demand, and emergency handling logic integrated with security signals. When a fire alarm signal is received, the system automatically starts the fire pump and shuts down access control in the relevant area.

7. The intelligent linkage control method for a coal conveying and water supply system according to claim 1, characterized in that, The correlation function between energy consumption and water supply demand is obtained through training using a machine learning algorithm, specifically: Using historical operating data such as coal flow rate, belt conveyor speed, ambient temperature, and coal type characteristics as inputs, and the pump power at which the water supply system achieves the lowest energy consumption per unit coal transport volume as output, a regression prediction model is trained.

8. The intelligent linkage control method for a coal conveying and water supply system according to claim 1, characterized in that, The collaborative optimization objective function is determined based on the weighted sum of the total system energy consumption, carbon emission costs, coal conveying delay costs caused by equipment maintenance, and mechanical wear costs estimated based on equipment operating time and load rate. The constraints of the objective function include: the upper and lower limits of the safe coal bunker level in the coal conveying system, the limit of pressure fluctuation in the water supply system network, and the lower limit of the operating efficiency of each piece of equipment, which shall not be lower than the rated high efficiency range.

9. An intelligent linkage control system for a coal conveying and water supply system, characterized in that, The system includes: The coal conveying network construction unit is used to build a coal conveying system status perception network to collect real-time operating data of coal conveying equipment, coal bunker level information, and coal blockage status information. The water supply network construction unit is used to build a water supply system status sensing network to collect pump operating status, water level, flow rate and security signals in real time; The linkage control unit is used to establish a dynamic coupling model between the coal conveying system and the water supply system, and to execute linkage control under specific operating conditions based on the dynamic coupling model, including: When the coal bunker level reaches a high threshold and the coal conveying system needs to reduce its load, a load reduction command is sent to the water supply system to adjust the pump operating frequency to the high-efficiency range. When the water supply system detects an abnormal flow rate, it sends a speed adjustment command to the coal conveying system to adjust the belt conveyor speed to match the water supply demand.

10. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores a computer program, which includes program instructions that, when executed by a processor of an electronic device, cause the processor to perform the intelligent linkage control method for the coal conveying and water supply system according to any one of claims 1 to 8.

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