Electromechanical installation engineering automatic debugging method and system based on intelligent control

Abstract

The invention discloses an electromechanical installation engineering automatic debugging method and system based on intelligent control, and the method comprises the steps: enabling an intelligent control system to monitor the energy consumption of equipment in real time, optimizing an operation mode, adjusting the working state according to data, and enabling the equipment to work cooperatively through the intelligent control system; deploying temperature and humidity, pressure, current and voltage sensors for stable wiring, and configuring parameters by adopting an intelligent debugging tool; based on the preliminary data, optimizing the system by applying an adaptive control algorithm, adjusting a strategy, fusing multi-dimensional data, and identifying performance bottleneck and abnormality; the centralized monitoring platform monitors the equipment state in real time and sends out an early warning signal; after multiple rounds of debugging, the energy-saving effect and performance are evaluated, a control algorithm is optimized, and optimization is continuously carried out according to historical data and environmental changes; according to the electromechanical installation engineering automatic debugging method and system based on intelligent control, the adaptive control algorithm and the machine learning technology are introduced, and the strategy is dynamically optimized according to the equipment operation state, the environment change and the historical data.

Description

Technical Field

[0001] The invention belongs to the field of intelligent control of electromechanical installation engineering automation debugging, and in particular relates to an intelligent control-based electromechanical installation engineering automation debugging method and system. Background Art

[0002] The automatic commissioning of electromechanical installation engineering based on intelligent control refers to the use of intelligent control technology to automatically debug and optimize various electromechanical systems (such as air conditioning, lighting, power supply, fire protection, etc.) during the installation and commissioning of electromechanical equipment, so as to improve the commissioning efficiency, reduce manual intervention, ensure the stability and energy saving of the system, and monitor and adjust the working status of the equipment in real time through embedded systems, sensors, automatic controllers and other technologies to ensure that the equipment operates in the best state; in traditional commissioning methods, commissioning personnel need to manually adjust various parameters and conduct repeated tests, while intelligent control systems can automatically optimize and adjust through algorithms, reduce manual intervention, and improve commissioning accuracy and speed; the system can collect the operating data of the equipment in real time (such as temperature, pressure, current, voltage, etc.), and predict the possible fault points of the system through data analysis, make corrections in advance, and improve the reliability of the equipment; the intelligent control system can not only automatically adjust parameters, but also perform fault diagnosis when a fault occurs, and self-repair or alarm through the automatic recovery function to reduce downtime; the intelligent control system can dynamically adjust the operating status of the equipment according to the real-time load demand and environmental changes, so as to achieve energy saving and emission reduction and reduce operating costs; all data and equipment status during the commissioning process can be presented through a visual interface, so that operators can monitor and manage the commissioning process of the equipment in real time.

[0003] However, although the automated debugging of electromechanical installation projects based on intelligent control has many advantages, there are also some defects and challenges in practical applications. During the debugging process, intelligent control systems usually rely on preset algorithms and models to optimize and adjust the operating parameters of the equipment. In reality, equipment and environments often have some unexpected situations (such as equipment aging, changes in the external environment, etc.). At this time, the existing algorithms may not be able to adapt and respond quickly, resulting in reduced system debugging efficiency or failures. Summary of the invention

[0004] In view of the deficiencies in the prior art, the purpose of the present invention is to provide an automated debugging method and system for electromechanical installation projects based on intelligent control. By introducing adaptive control algorithms and machine learning techniques, the intelligent control system dynamically adjusts the optimization strategy according to the operating status of the equipment, environmental changes and historical data.

[0005] The technical solution adopted by the present invention to solve its technical problem is:

[0006] The automated debugging method of electromechanical installation engineering based on intelligent control includes:

[0007] By real-time monitoring of equipment energy consumption, optimizing operation mode and load scheduling, intelligently adjusting working status according to real-time data of equipment, different electromechanical equipment can work together through intelligent control systems and communication protocols;

[0008] Adopt PLC, DCS, and SCADA intelligent control systems according to the type, scale, and control requirements of electromechanical equipment, and deploy temperature, humidity, pressure, current, and voltage sensors according to engineering design;

[0009] The electromechanical equipment and control modules are installed according to the design plan, the electrical wiring is stabilized, the sensors and actuators are connected to the control system, and the intelligent debugging tools and platforms are used to perform preliminary system parameter configuration, including sensor calibration, control strategy setting, and debugging priority setting;

[0010] Based on the preliminary operation data, the adaptive control algorithm is used to dynamically optimize the system. The control strategy is adjusted in real time according to the load and environmental change factors of different equipment. The collected multi-dimensional data is integrated, and the system performance bottlenecks or abnormal points are identified through the intelligent analysis platform to adjust the debugging strategy.

[0011] The centralized monitoring platform can be used to view equipment status and operating parameters in real time. The intelligent control system can analyze data in real time, identify potential equipment failures, and issue early warning signals.

[0012] Through multiple rounds of debugging and operation, the system is comprehensively tested for performance, and key indicators such as energy saving effect, response speed, and stability are evaluated. Based on data analysis and actual operation feedback during the debugging process, system parameters are adjusted and control algorithms are optimized.

[0013] Based on the real-time operation data collected in the later stage of debugging, the system is continuously optimized, the control strategy is adjusted, and the equipment scheduling is optimized. Through machine learning and adaptive optimization, the intelligent control system is continuously optimized and adjusted according to the historical data of the equipment and environmental changes during operation.

[0014] As a preferred method, by real-time monitoring of the energy consumption of the equipment, optimizing the operation mode and load scheduling, intelligently adjusting the working state according to the real-time data of the equipment, and the method for different electromechanical equipment to work together through intelligent control systems and communication protocols is as follows:

[0015] Real-time monitoring of electromechanical equipment through temperature, humidity, current, voltage, and pressure sensors to obtain data on the equipment's energy consumption, load, and working status;

[0016] Based on real-time data, the system analyzes the operating status and energy efficiency performance of the equipment through a preset optimization algorithm, and adjusts the operating mode of the equipment to optimize the operating mode of air conditioning, lighting, and ventilation equipment;

[0017] According to the equipment load and operation requirements, the intelligent system dispatches the load of different equipment. Different electromechanical equipment can work together through PLC, DCS, and SCADA intelligent control systems. Equipment can exchange data and transfer information through standard Modbus, BACnet, Zigbee, and MQTT communication protocols to share operation data.

[0018] The system automatically adjusts the working status of the equipment based on real-time monitoring data. When it identifies that the energy consumption of a certain equipment is too high or its performance has declined, the system automatically adjusts its operating parameters or issues a warning signal.

[0019] As a preferred method, PLC, DCS, SCADA intelligent control systems are used according to the type, scale and control requirements of electromechanical equipment, and the method of deploying temperature, humidity, pressure, current and voltage sensors according to engineering design is as follows:

[0020] Determine the size and complexity of the control system based on the types of motors, pumps, fans, air conditioning system electromechanical equipment involved in the system and the size of the system consisting of a single device or multiple devices;

[0021] According to the working principle, operating status and temperature control, pressure control, and energy efficiency optimization control objectives of the equipment, clarify the required control strategy and data collection requirements;

[0022] Based on the above, select the corresponding PLC, DCS, and SCADA control systems;

[0023] According to the engineering design, temperature, humidity, pressure, current and voltage sensors are used and deployed at the monitoring location. The sensors transmit real-time data to the PLC, DCS or SCADA system, and the working status and operating parameters of the equipment are displayed through real-time monitoring.

[0024] Preferably, the electromechanical equipment and control module are installed according to the design plan to ensure reliable electrical connection, sensors and actuators are connected to the control system, and intelligent debugging tools and platforms are used to perform preliminary system parameter configuration, including sensor calibration, control strategy setting, and debugging priority setting methods:

[0025] Electromechanical equipment and control modules Install pumps, fans, motors, valve electromechanical equipment and PLC, DCS, SCADA control modules according to the design plan, and stabilize the electrical wiring, including the connection of power, signal lines, and control lines between equipment;

[0026] According to the engineering design, temperature, humidity, pressure, current, voltage sensors and electric valves, motor actuators are connected to the corresponding PLC, DCS, SCADA control systems;

[0027] Adopt intelligent debugging tools corresponding to the system type, including PLC programming software, DCS configuration software, and SCADA system configuration tools, and perform preliminary system parameter configuration on the debugging platform, including setting the input and output parameters of the controller, defining the operation mode of the equipment, inputting the range of the sensor, and setting the alarm threshold;

[0028] After the system is installed, the sensors are calibrated through intelligent debugging tools. The calibration methods include temperature sensor calibration, pressure sensor calibration, and current / voltage sensor calibration. The control signal is simulated through the debugging platform to identify the response of valves and motor actuators.

[0029] According to the system design requirements, set and program control strategies, including PID control, logic control, energy-saving optimization strategies, and set scheduling priorities among multiple devices based on the importance of the system's key devices and functions.

[0030] As a preferred method, based on the preliminary operation data, the adaptive control algorithm is used to dynamically optimize the system, and the control strategy is adjusted in real time according to the load and environmental change factors of different equipment. The collected multi-dimensional data is integrated, and the system performance bottlenecks or abnormal points are identified through the intelligent analysis platform. The method of adjusting the debugging strategy is as follows:

[0031] After the system is put into operation, the temperature, pressure, current, voltage, and flow rate of the equipment are collected in real time through sensors and transmitted to the control system or data center. Basic analysis is performed on the collected preliminary operation data, including average value, maximum value, minimum value, and fluctuation range.

[0032] The adaptive control algorithms used include model reference adaptive control, gain scheduling control, fuzzy adaptive control, and neural network adaptive control. The adaptive control algorithm dynamically adjusts the control parameters according to the real-time load changes of the equipment and the changes in environmental conditions;

[0033] Clean, denoise and standardize the collected data. Through data fusion technology, data from different devices and sensors are integrated into a global state and a comprehensive view of the system is generated.

[0034] By analyzing the response time, energy efficiency, and load fluctuation index operating data of the equipment, we can identify the performance bottlenecks in the system. We can also use machine learning algorithms to build models based on normal operating data to identify abnormal behaviors in the system that are inconsistent with the normal operating mode.

[0035] Combined with the real-time operation data of the equipment, environmental change data, and control signal information, through data mining and pattern recognition, the system's operating status can be fully diagnosed and potential risk points or failure modes can be identified;

[0036] According to the identification results of system performance bottlenecks and anomalies, the debugging strategy is adjusted by optimizing control parameters, load balancing and scheduling, early warning and protection mechanisms, and dynamic optimization strategies. The control strategy is continuously optimized through closed-loop feedback of adaptive control algorithms and data fusion.

[0037] As a preferred method, the centralized monitoring platform is used to view the equipment status and operating parameters in real time, and the intelligent control system analyzes the data in real time, identifies potential equipment failures, and issues early warning signals in advance:

[0038] Through the hardware installed on each device and sensor, the operating status and key parameters of the device are collected in real time, and all the collected device data are aggregated into a centralized monitoring platform, which displays the real-time status, operating parameters and historical data of the device through a graphical interface;

[0039] Through real-time data collection, the intelligent control system uses algorithms to monitor the operating status of the equipment and identify whether there are abnormal fluctuations or abnormal values. Fault diagnosis and prediction include:

[0040] Rule-based diagnosis sets the normal range and threshold for equipment operation. If a parameter exceeds the set range, an early warning is triggered.

[0041] Model-based prediction: predicting the future state of the equipment by building a mathematical model of the equipment operation;

[0042] Machine learning and data mining: Apply machine learning algorithms to predict faults and identify patterns. By training historical fault data models, characteristic patterns related to faults can be identified.

[0043] Anomaly detection, based on machine learning or deep learning models to identify abnormal behavior, using methods such as isolation forest, K-means clustering, and PCA to identify data that deviates from normal patterns during operation;

[0044] Different levels of early warning rules are set in the system. Different levels of early warning signals are issued according to the degree of deviation of equipment operating parameters. Based on the historical data, process requirements and maintenance experience of the equipment, the upper and lower limit alarm thresholds of temperature, vibration, pressure and current are set.

[0045] When it is detected that the parameters exceed the set threshold, the system will promptly notify relevant personnel of the fault warning through email, SMS, mobile application push, and sound and light alarms.

[0046] As a preferred method, through multiple rounds of debugging and operation, the system is comprehensively tested for performance, and key indicators such as energy saving effect, response speed, and stability are evaluated. According to the data analysis and actual operation feedback during the debugging process, the system parameters are adjusted and the control algorithm is optimized as follows:

[0047] According to multiple rounds of debugging and operation under different working conditions, the data of the system under different working conditions are collected, including load changes, operation methods, and environmental conditions. The collected system data are processed using Excel, MATLAB, and Python data analysis tools to analyze various performance indicators;

[0048] Calculate the energy efficiency ratio based on the system's energy consumption data and compare it with the benchmark performance to evaluate the energy saving effect. By measuring the system's response time, analyze the gap between it and the expected target, and further evaluate the stability of the system fluctuation, overshoot, and steady-state error.

[0049] The algorithm is adjusted through PID regulation, adaptive control, and optimal control algorithm. The model predictive control algorithm is adopted to predict the future behavior of the system by establishing a mathematical model, and dynamic adjustments are made based on the prediction results.

[0050] As a preferred method, the system is continuously optimized based on the real-time operation data collected in the later stage of debugging, the control strategy is adjusted, and the equipment scheduling is optimized. Through machine learning and adaptive optimization, the intelligent control system is continuously optimized and adjusted according to the historical data of the equipment and environmental changes during operation. The method is:

[0051] During system operation, the device status, power consumption, load, response time, and environmental change operation data are collected in real time through sensors, device logs, and operation records;

[0052] Train machine learning models with historical operating data, compare real-time data with historical data, and identify potential trends and anomalies;

[0053] According to the real-time collected data and system changes, the control parameters and algorithms are adaptively adjusted. The system senses the load changes, external interference, and changes in the equipment wear environment in real time, and dynamically adjusts the operation mode of the equipment.

[0054] Use machine learning or optimization algorithms to optimize the operation scheduling of equipment and allocate tasks and loads through intelligent scheduling algorithms;

[0055] Based on data analysis and model prediction, the system produces optimization suggestions or automatically executes certain optimization decisions. Different optimization goals are set according to energy efficiency optimization, response speed, and system stability goals, and the optimization strategy is adjusted through machine learning.

[0056] Through Q-learning and deep reinforcement learning algorithms, the system improves and optimizes itself through interaction with the environment.

[0057] The automatic debugging system of electromechanical installation engineering based on intelligent control includes:

[0058] Intelligent debugging and adaptive control module, which is used to automatically adjust control strategies and parameters according to the real-time operation data of the equipment, and coordinate and optimize the scheduling between multiple devices through intelligent algorithms;

[0059] Data collection and real-time monitoring module, which is used to collect equipment operation data in real time through sensors and transmit the data to the intelligent control platform for real-time analysis and decision-making. Based on the equipment operation data, equipment health monitoring is carried out through machine learning and data mining technology;

[0060] The fault diagnosis and early warning module is used to compare the collected data with the preset fault model, and the intelligent control system identifies the faults in the equipment. When the system detects an abnormal operating state, it automatically triggers an alarm;

[0061] Automatic reporting and data analysis module, after debugging is completed, the system automatically generates a detailed debugging report, and uses the data analysis platform to deeply mine the data during the debugging process to identify performance bottlenecks and equipment operation trends;

[0062] The optimization module is used to automatically perform optimization adjustments based on real-time operating data. After debugging is completed, the system automatically generates a detailed report to record parameter changes, fault diagnosis results and operating conditions.

[0063] Another technical problem to be solved by the present invention is to provide an electronic device, including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein when the processor executes the program, an automated debugging method and system for electromechanical installation engineering based on intelligent control as described above is implemented.

[0064] Another technical problem to be solved by the present invention is to provide a computer-readable storage medium on which a computer program is stored, and when the program is executed by a processor, an automated debugging method and system for electromechanical installation engineering based on intelligent control is implemented.

[0065] The beneficial effects of the present invention are:

[0066] By adjusting the working status according to the real-time data of the equipment through the intelligent control system, it is possible to effectively avoid overloading or no-load operation of the equipment, improve energy utilization, and intelligently adjust the working mode and load scheduling so that each device always works in the optimal state, avoiding the inefficiency of manual scheduling; the scheme uses an adaptive control algorithm to enable the system to be dynamically optimized and adjust the control strategy in time according to the load changes and environmental changes of different equipment. This can effectively avoid the static adjustment problem in traditional control methods and improve the response speed and adaptability.

[0067] By real-time monitoring of the energy consumption and working status of the equipment, the intelligent control system can adjust the operating mode and load scheduling of the equipment, thereby reducing unnecessary energy consumption. This is particularly important for large-scale electromechanical equipment systems, especially equipment with high energy consumption (such as air conditioners, elevators, pump equipment, etc.), and can significantly improve the overall energy utilization efficiency; during the debugging process, multiple rounds of comprehensive performance tests are conducted to evaluate the energy-saving effect, and the system parameters are adjusted based on data analysis and actual feedback to achieve continuous optimization and maximize the energy-saving effect; the intelligent control system can identify potential faults and issue early warning signals by real-time monitoring of equipment status and operating parameters. This early fault detection can help avoid greater losses caused by equipment failures and improve the reliability of the equipment.

[0068] Through the integration of multi-dimensional data (such as temperature, humidity, voltage and other sensor data), the system can perform more accurate analysis to help identify potential anomalies, thereby adjusting the control strategy in a timely manner and effectively avoiding the occurrence of faults; through the adjustment of adaptive control algorithms and debugging strategies, the system can maintain stable operation under different environmental and load conditions, reducing unstable factors caused by changes in the external environment; through the collaborative work of communication protocols and intelligent control systems, different electromechanical equipment can be dynamically scheduled according to demand, and the collaboration between devices can avoid waste of resources and optimize the use efficiency of equipment, especially when multiple devices are running together, it can avoid resource conflicts or idleness caused by improper scheduling.

[0069] Through machine learning and adaptive optimization, the control system can continuously optimize equipment scheduling based on historical data and environmental changes to ensure that the system always operates in the best condition; through machine learning and adaptive optimization, the system can continuously learn and adjust the control strategy. As the debugging process continues to deepen, the system can better adapt to the operating characteristics and environmental changes of the equipment and achieve more precise control and scheduling; the intelligent scheduling and decision-making system can automatically make decisions based on real-time data, the working status of the equipment and changes in the external environment. This is more efficient and accurate than traditional manual scheduling and avoids human errors. BRIEF DESCRIPTION OF THE DRAWINGS

[0070] Figure 1It is a flow chart of the artificial intelligence-based power proxy service system of the present invention. DETAILED DESCRIPTION

[0071] The principles and features of the present invention are described below, and the examples are only used to explain the present invention and are not used to limit the scope of the present invention. The present invention is described more specifically by way of example in the following paragraphs. According to the following description and claims, the advantages and features of the present invention will become clearer.

[0072] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as those commonly understood by those skilled in the art of the present invention. The terms used herein in the specification of the present invention are only for the purpose of describing specific embodiments and are not intended to limit the present invention. The term "and / or" used herein includes any and all combinations of one or more related listed items.

[0073] Example

[0074] The automated debugging method of electromechanical installation engineering based on intelligent control includes:

[0075] By real-time monitoring of equipment energy consumption, optimizing operation mode and load scheduling, intelligently adjusting working status according to real-time data of equipment, different electromechanical equipment can work together through intelligent control systems and communication protocols;

[0076] Adopt PLC, DCS, and SCADA intelligent control systems according to the type, scale, and control requirements of electromechanical equipment, and deploy temperature, humidity, pressure, current, and voltage sensors according to engineering design;

[0077] The electromechanical equipment and control modules are installed according to the design plan, the electrical wiring is stabilized, the sensors and actuators are connected to the control system, and the intelligent debugging tools and platforms are used to perform preliminary system parameter configuration, including sensor calibration, control strategy setting, and debugging priority setting;

[0078] Based on the preliminary operation data, the adaptive control algorithm is used to dynamically optimize the system. The control strategy is adjusted in real time according to the load and environmental change factors of different equipment. The collected multi-dimensional data is integrated, and the system performance bottlenecks or abnormal points are identified through the intelligent analysis platform to adjust the debugging strategy.

[0079] The centralized monitoring platform can be used to view equipment status and operating parameters in real time. The intelligent control system can analyze data in real time, identify potential equipment failures, and issue early warning signals.

[0080] Through multiple rounds of debugging and operation, the system is comprehensively tested for performance, and key indicators such as energy saving effect, response speed, and stability are evaluated. Based on data analysis and actual operation feedback during the debugging process, system parameters are adjusted and control algorithms are optimized.

[0081] Based on the real-time operation data collected in the later stage of debugging, the system is continuously optimized, the control strategy is adjusted, and the equipment scheduling is optimized. Through machine learning and adaptive optimization, the intelligent control system is continuously optimized and adjusted according to the historical data of the equipment and environmental changes during operation.

[0082] Real-time monitoring of equipment energy consumption and adjustment of its working state according to the real-time data of the equipment effectively avoids energy waste during equipment operation. For example, intelligent optimization of load scheduling and operating mode can ensure that the equipment only runs when necessary, avoiding idling or overloading, thereby saving energy; the dynamic adjustment and continuous optimization system will adjust the equipment's working strategy and load distribution according to real-time feedback data, further improve energy utilization efficiency, and ensure that the electromechanical system can always remain in the optimal energy efficiency range under different working conditions; using adaptive control algorithms, dynamically adjust the control strategy according to load changes and environmental factors of different equipment, which enables the system to cope with changing working environments, maintain long-term stable operation, and avoid equipment failures due to fluctuations in external conditions; through a centralized monitoring platform, real-time analysis of equipment status and operating parameters, the intelligent control system can identify potential equipment failures in advance and issue early warning signals, so that maintenance or adjustments can be carried out before failures occur, reducing downtime and extending equipment life.

[0083] By real-time monitoring of equipment energy consumption, optimizing operation mode and load scheduling, intelligently adjusting the working state according to the real-time data of the equipment, and coordinating the work between different electromechanical equipment through intelligent control systems and communication protocols, the following methods are used:

[0084] Real-time monitoring of electromechanical equipment through temperature, humidity, current, voltage, and pressure sensors to obtain data on the equipment's energy consumption, load, and working status;

[0085] Based on real-time data, the system analyzes the operating status and energy efficiency performance of the equipment through a preset optimization algorithm, and adjusts the operating mode of the equipment to optimize the operating mode of air conditioning, lighting, and ventilation equipment;

[0086] According to the equipment load and operation requirements, the intelligent system dispatches the load of different equipment. Different electromechanical equipment can work together through PLC, DCS, and SCADA intelligent control systems. Equipment can exchange data and transfer information through standard Modbus, BACnet, Zigbee, and MQTT communication protocols to share operation data.

[0087] The system automatically adjusts the working status of the equipment based on real-time monitoring data. When it identifies that the energy consumption of a certain equipment is too high or its performance has declined, the system automatically adjusts its operating parameters or issues a warning signal.

[0088] By real-time monitoring of the operating data of electromechanical equipment through a variety of sensors such as temperature, humidity, current, voltage, and pressure, the system can accurately understand the working status and energy consumption of the equipment. Based on real-time data analysis, the system automatically adjusts the operating mode of air-conditioning, lighting, ventilation and other equipment to optimize energy consumption. This dynamic adjustment can ensure that the equipment operates in the optimal energy efficiency range, thereby achieving energy-saving goals; by analyzing equipment operating data through optimization algorithms, the system can identify low energy efficiency of equipment and automatically adjust its operating mode. For example, when the load of air-conditioning or ventilation equipment is low, its power consumption is reduced; when the load is high, its operating efficiency is improved to ensure maximum energy utilization; according to the equipment load and operating requirements, the intelligent system performs load scheduling for different equipment to ensure that there is no overload or idleness between the equipment, PLC, DCS, and SCADA Intelligent control systems such as Modbus, BACnet, Zigbee, MQTT, etc. can work together in real time and share data and information through communication protocols (such as Modbus, BACnet, Zigbee, MQTT, etc.), so that devices can complete tasks collaboratively under different working conditions, thereby improving the overall automation level of the system; the automation of the entire control process reduces the complexity and possible errors of manual operations, reduces human intervention, thereby reducing labor costs and improving system reliability; the system monitors the working status of the equipment in real time and analyzes the equipment's energy consumption, load and other data. When it is found that a certain device has abnormal energy consumption or performance degradation, the intelligent control system can automatically adjust its working status, or issue a fault warning, and take measures in advance for maintenance or replacement. This early warning mechanism can effectively prevent the impact of equipment failures on the overall system and avoid downtime or major failures.

[0089] According to the type, scale and control requirements of electromechanical equipment, PLC, DCS, SCADA intelligent control systems are adopted, and according to the engineering design, the method of deploying temperature, humidity, pressure, current and voltage sensors is as follows:

[0090] Determine the size and complexity of the control system based on the types of motors, pumps, fans, air conditioning system electromechanical equipment involved in the system and the size of the system consisting of a single device or multiple devices;

[0091] According to the working principle, operating status and temperature control, pressure control, and energy efficiency optimization control objectives of the equipment, clarify the required control strategy and data collection requirements;

[0092] Based on the above, select the corresponding PLC, DCS, and SCADA control systems;

[0093] According to the engineering design, temperature, humidity, pressure, current and voltage sensors are used and deployed at the monitoring location. The sensors transmit real-time data to the PLC, DCS or SCADA system, and the working status and operating parameters of the equipment are displayed through real-time monitoring.

[0094] By adopting intelligent control systems such as PLC (Programmable Logic Controller), DCS (Distributed Control System), SCADA (Data Acquisition and Supervisory Control System), it is possible to realize automatic control of equipment, reduce human intervention, improve operational efficiency and accuracy, and monitor the operating status and operating parameters of the equipment (such as temperature, humidity, pressure, current, voltage, etc.) in real time to ensure that the equipment operates under the best working conditions and avoid inefficiency or failure caused by human negligence or operating errors; through real-time collection of various working parameters by sensors, the system can perform energy efficiency optimization control and automatically adjust the operating mode of the equipment. For example, when the equipment load is low, the operating power is reduced; when a high load is required, the equipment operating efficiency is improved, thereby reducing energy consumption. Resource waste can be achieved, and energy saving can be achieved. At the same time, the control system can identify energy efficiency problems in equipment operation and provide data support for subsequent optimization analysis to further improve energy utilization efficiency; real-time monitoring and early warning mechanism: temperature, humidity, pressure, current, voltage and other sensors will feed back the equipment's operating parameters to the control system (such as PLC, DCS or SCADA system) in real time. When the equipment fails or operates abnormally (such as overtemperature, overcurrent, etc.), the system can issue an alarm in time and take automatic adjustment measures to prevent the fault from expanding and avoid production stagnation. This automatic monitoring and adjustment mechanism significantly improves the reliability of equipment operation and reduces downtime and maintenance costs caused by improper human operation or equipment failure.

[0095] The electromechanical equipment and control modules are installed according to the design plan to ensure reliable electrical connections. Sensors and actuators are connected to the control system. Intelligent debugging tools and platforms are used to perform preliminary system parameter configuration, including sensor calibration, control strategy setting, and debugging priority setting methods:

[0096] Electromechanical equipment and control modules Install pumps, fans, motors, valve electromechanical equipment and PLC, DCS, SCADA control modules according to the design plan, and stabilize the electrical wiring, including the connection of power, signal lines, and control lines between equipment;

[0097] According to the engineering design, temperature, humidity, pressure, current, voltage sensors and electric valves, motor actuators are connected to the corresponding PLC, DCS, SCADA control systems;

[0098] Adopt intelligent debugging tools corresponding to the system type, including PLC programming software, DCS configuration software, and SCADA system configuration tools, and perform preliminary system parameter configuration on the debugging platform, including setting the input and output parameters of the controller, defining the operation mode of the equipment, inputting the range of the sensor, and setting the alarm threshold;

[0099] After the system is installed, the sensors are calibrated through intelligent debugging tools. The calibration methods include temperature sensor calibration, pressure sensor calibration, and current / voltage sensor calibration. The control signal is simulated through the debugging platform to identify the response of valves and motor actuators.

[0100] According to the system design requirements, set and program control strategies, including PID control, logic control, energy-saving optimization strategies, and set scheduling priorities among multiple devices based on the importance of the system's key devices and functions.

[0101] By ensuring the stability of electrical connections (including the connection of power supply, signal lines, and control lines), communication interruptions, signal loss, or equipment failures caused by poor wiring are avoided, thereby ensuring the stability and reliability of the system. Temperature, humidity, pressure, current, and voltage sensors and actuators (such as electric valves and motor actuators) are correctly connected to the control system (PLC, DCS, and SCADA), ensuring accurate monitoring and response of the equipment. By using intelligent debugging tools corresponding to the system type (such as PLC programming software, DCS configuration software, and SCADA system configuration tools), the system debugging process can be completed efficiently. These tools support rapid configuration of input and output parameters, setting of equipment operating modes, adjustment of sensor ranges, and setting of alarm thresholds, thereby speeding up the debugging and startup of the system. The sensor calibration process ensures the monitoring accuracy of the system, reduces measurement errors, and ensures that the real-time monitoring data of the equipment is accurate and reliable. Accurate calibration of temperature, pressure, and current / voltage sensors helps to improve the response speed of the control system to the equipment status. The debugging platform simulates control signals and identifies the responses of valves and motor actuators, which can detect potential actuator problems in advance and ensure the normal operation of the equipment's execution functions. According to the design requirements of the system, control methods such as PID control, logic control, and energy-saving optimization strategies are set to effectively improve the automation level of the system. PID control can accurately adjust various parameters in the system to ensure the stability and response speed of the equipment during operation. Logic control can help the system make decisions automatically and realize complex equipment operations. Energy-saving optimization strategies can intelligently adjust the working status of equipment according to load changes, thereby improving the overall energy efficiency of the system. Scheduling priorities are set according to the importance of key equipment and functions in the system to ensure that the system can give priority to the normal operation of key equipment during operation, while reasonably arranging the work of other equipment to avoid unnecessary waste of resources. Through real-time monitoring and calibration of sensors, potential equipment failures and performance problems can be discovered in advance, which facilitates early maintenance measures to avoid equipment failures during operation.

[0102] Based on the preliminary operation data, the adaptive control algorithm is used to dynamically optimize the system. According to the load of different equipment and environmental change factors, the control strategy is adjusted in real time. The collected multi-dimensional data is integrated, and the system performance bottlenecks or abnormal points are identified through the intelligent analysis platform. The method of adjusting the debugging strategy is as follows:

[0103] After the system is put into operation, the temperature, pressure, current, voltage, and flow rate of the equipment are collected in real time through sensors and transmitted to the control system or data center. Basic analysis is performed on the collected preliminary operation data, including average value, maximum value, minimum value, and fluctuation range.

[0104] The adaptive control algorithms used include model reference adaptive control, gain scheduling control, fuzzy adaptive control, and neural network adaptive control. The adaptive control algorithm dynamically adjusts the control parameters according to the real-time load changes of the equipment and the changes in environmental conditions;

[0105] Clean, denoise and standardize the collected data. Through data fusion technology, data from different devices and sensors are integrated into a global state and a comprehensive view of the system is generated.

[0106] By analyzing the response time, energy efficiency, and load fluctuation index operating data of the equipment, we can identify the performance bottlenecks in the system. We can also use machine learning algorithms to build models based on normal operating data to identify abnormal behaviors in the system that are inconsistent with the normal operating mode.

[0107] Combined with the real-time operation data of the equipment, environmental change data, and control signal information, through data mining and pattern recognition, the system's operating status can be fully diagnosed and potential risk points or failure modes can be identified;

[0108] According to the identification results of system performance bottlenecks and anomalies, the debugging strategy is adjusted by optimizing control parameters, load balancing and scheduling, early warning and protection mechanisms, and dynamic optimization strategies. The control strategy is continuously optimized through closed-loop feedback of adaptive control algorithms and data fusion.

[0109] By using adaptive control algorithms, the solution can automatically adjust control parameters according to the real-time load changes of the equipment and changes in environmental conditions, thereby ensuring that the system can maintain optimal operation under various working conditions. Traditional static control strategies may not be able to cope with dynamic changes, while adaptive control can make adjustments based on real-time data to improve the flexibility and adaptability of the system; through data fusion technology, multi-dimensional data (such as temperature, pressure, current, voltage, flow, etc.) from different devices and sensors are integrated into a global state, providing a more comprehensive view of the entire system, which not only helps to more accurately understand the health status of each device and the overall system, but also can reflect the changing trends of the system in real time, facilitating faster response; using machine learning and data mining technology, the system can build models based on normal operating data and identify abnormal behaviors, and promptly discover system performance bottlenecks or potential failures. This intelligent diagnosis and anomaly detection capability greatly improves the system's fault prediction and prevention capabilities and reduces the occurrence of sudden failures.

[0110] The method of checking the equipment status and operating parameters in real time through the centralized monitoring platform, and the intelligent control system analyzing the data in real time, identifying potential equipment failures, and issuing early warning signals is as follows:

[0111] Through the hardware installed on each device and sensor, the operating status and key parameters of the device are collected in real time, and all the collected device data are aggregated into a centralized monitoring platform, which displays the real-time status, operating parameters and historical data of the device through a graphical interface;

[0112] Through real-time data collection, the intelligent control system uses algorithms to monitor the operating status of the equipment and identify whether there are abnormal fluctuations or abnormal values. Fault diagnosis and prediction include:

[0113] Rule-based diagnosis sets the normal range and threshold for equipment operation. If a parameter exceeds the set range, an early warning is triggered.

[0114] Model-based prediction: predicting the future state of the equipment by building a mathematical model of the equipment operation;

[0115] Machine learning and data mining: Apply machine learning algorithms to predict faults and identify patterns. By training historical fault data models, characteristic patterns related to faults can be identified.

[0116] Anomaly detection, based on machine learning or deep learning models to identify abnormal behavior, using methods such as isolation forest, K-means clustering, and PCA to identify data that deviates from normal patterns during operation;

[0117] Different levels of early warning rules are set in the system. Different levels of early warning signals are issued according to the degree of deviation of equipment operating parameters. Based on the historical data, process requirements and maintenance experience of the equipment, the upper and lower limit alarm thresholds of temperature, vibration, pressure and current are set.

[0118] When it is detected that the parameters exceed the set threshold, the system will promptly notify relevant personnel of the fault warning through email, SMS, mobile application push, and sound and light alarms.

[0119] Through the centralized monitoring platform, managers can view the operating status and key parameters of each device in real time, forming a comprehensive equipment management view. The graphical interface can intuitively display the equipment operation data, simplify the monitoring process, reduce manual intervention, and improve management efficiency. Through a variety of advanced algorithms and models, the system can analyze equipment data in real time and predict potential faults. The system can not only detect abnormal fluctuations in equipment, but also identify impending faults in advance through fault diagnosis and prediction, prevent system shutdown or equipment damage, and reduce sudden shutdowns in production. Based on the combination of rule-based diagnosis, model prediction and machine learning algorithms, the system can timely discover potential risks of equipment and deal with them in advance through early warning. This prediction and early warning capability greatly improves the reliability of equipment, reduces unplanned downtime and maintenance, and increases the service life of equipment. The system supports different levels of early warning rules and can issue different levels of alarms according to the degree of deviation of equipment operating parameters. In this way, managers can take different countermeasures according to the severity of the alarm, thereby optimizing the allocation of maintenance resources and ensuring smooth production. By analyzing the historical data, process requirements and maintenance experience of the equipment, the system can set more accurate alarm thresholds, so that the early warning rules are more in line with the actual situation of the equipment, avoid excessive early warning or missed reports, and improve the accuracy and practicality of fault prediction.

[0120] Through multiple rounds of debugging and operation, the system is comprehensively tested for performance, and key indicators such as energy saving effect, response speed, and stability are evaluated. Based on data analysis and actual operation feedback during the debugging process, the system parameters are adjusted and the control algorithm is optimized as follows:

[0121] According to multiple rounds of debugging and operation under different working conditions, the data of the system under different working conditions are collected, including load changes, operation methods, and environmental conditions. The collected system data are processed using Excel, MATLAB, and Python data analysis tools to analyze various performance indicators;

[0122] Calculate the energy efficiency ratio based on the system's energy consumption data and compare it with the benchmark performance to evaluate the energy saving effect. By measuring the system's response time, analyze the gap between it and the expected target, and further evaluate the stability of the system's fluctuation, overshoot, and steady-state error.

[0123] The algorithm is adjusted through PID regulation, adaptive control, and optimal control algorithm. The model predictive control algorithm is adopted to predict the future behavior of the system by establishing a mathematical model, and dynamic adjustments are made based on the prediction results.

[0124] Through multiple rounds of debugging and operation under different working conditions, the system can conduct comprehensive performance tests under a variety of actual operating conditions and evaluate various key indicators (such as energy saving effect, response speed, stability, etc.). This comprehensive test can ensure the stability and efficiency of the system in actual applications and help accurately identify performance bottlenecks; through data analysis tools (such as Excel, MATLAB, Python), the data in the debugging process can be processed and analyzed to help evaluate the performance of the system under different loads, operating modes and environmental conditions. This process can accurately identify factors that affect performance, thereby providing a reliable basis for subsequent parameter optimization and algorithm adjustment; based on energy consumption data, the energy efficiency ratio is calculated and compared with the benchmark performance to quantify the energy saving effect. This method can not only It can help identify room for energy efficiency improvement and ensure that the energy efficiency of the system is effectively evaluated and optimized to achieve energy-saving goals. By measuring the response time of the system and comparing it with the expected target, it can identify whether the response speed meets expectations, thereby providing guidance for subsequent optimization. Indicators such as fluctuations, overshoot, and steady-state errors can help the system optimize its dynamic characteristics and ensure stability and efficiency in actual operation. Through PID adjustment, adaptive control, optimal control and other algorithm adjustments, the system can be dynamically adjusted according to changes in actual working conditions, thereby optimizing control accuracy and stability. At the same time, the model predictive control (MPC) algorithm is used to predict the future behavior of the system by establishing a mathematical model, so that the system can be adjusted in advance to reduce response time and overshoot, and further optimize the overall performance of the system.

[0125] According to the real-time operation data collected in the later stage of debugging, the system is continuously optimized, the control strategy is adjusted, and the equipment scheduling is optimized. Through machine learning and adaptive optimization, the intelligent control system continuously optimizes and adjusts according to the historical data of the equipment and environmental changes during operation. The method is as follows:

[0126] During system operation, the device status, power consumption, load, response time, and environmental change operation data are collected in real time through sensors, device logs, and operation records;

[0127] Train machine learning models with historical operating data, compare real-time data with historical data, and identify potential trends and anomalies;

[0128] According to the real-time collected data and system changes, the control parameters and algorithms are adaptively adjusted. The system senses the load changes, external interference, and changes in the equipment wear environment in real time, and dynamically adjusts the operation mode of the equipment.

[0129] Use machine learning or optimization algorithms to optimize the operation scheduling of equipment and allocate tasks and loads through intelligent scheduling algorithms;

[0130] Based on data analysis and model prediction, the system produces optimization suggestions or automatically executes certain optimization decisions. Different optimization goals are set according to energy efficiency optimization, response speed, and system stability goals, and the optimization strategy is adjusted through machine learning.

[0131] Through Q-learning and deep reinforcement learning algorithms, the system improves and optimizes itself through interaction with the environment.

[0132] This solution uses real-time operation data to continuously optimize the system. By collecting data such as device status, power consumption, load, response time, etc., it can understand the system operation status in real time and make dynamic adjustments based on changes. This adaptive control enables the system to automatically respond to factors such as load fluctuations, external interference and equipment wear, thereby maintaining long-term stable and efficient operation. The use of machine learning algorithms to optimize the operation scheduling of equipment can improve the system's work efficiency and reduce resource waste. Through intelligent scheduling, tasks and loads can be intelligently allocated to ensure that equipment operates in the optimal state. This not only improves energy efficiency, but also reduces equipment overload or idle status. By comprehensively considering the goals of energy efficiency optimization, response speed and system stability, machine learning algorithms can identify energy-saving potential and provide optimization decisions or automatic Automatically execute optimization measures, which will significantly improve energy efficiency, reduce energy costs, and improve the operating performance of the system; machine learning models trained with historical data can identify potential trends and anomalies, such as early warning of equipment failure, reduced system efficiency, etc. By comparing real-time data with historical data, the system can promptly detect problems and adjust control strategies to avoid equipment damage or system performance degradation; based on advanced algorithms such as deep reinforcement learning (Q-learning, deep reinforcement learning), the system can self-improve and optimize in interaction with the environment. By learning from past experience, the system gradually optimizes the decision-making process and realizes optimization control from reactive to proactive. Each decision is based on historical data, real-time feedback and system status, making the control strategy more and more accurate and efficient.

[0133] The automatic debugging system of electromechanical installation engineering based on intelligent control includes:

[0134] Intelligent debugging and adaptive control module, which is used to automatically adjust control strategies and parameters according to the real-time operation data of the equipment, and coordinate and optimize the scheduling between multiple devices through intelligent algorithms;

[0135] Data collection and real-time monitoring module, which is used to collect equipment operation data in real time through sensors and transmit the data to the intelligent control platform for real-time analysis and decision-making. Based on the equipment operation data, equipment health monitoring is carried out through machine learning and data mining technology;

[0136] The fault diagnosis and early warning module is used to compare the collected data with the preset fault model, and the intelligent control system identifies the faults in the equipment. When the system detects an abnormal operating state, it automatically triggers an alarm;

[0137] Automatic reporting and data analysis module, after debugging is completed, the system automatically generates a detailed debugging report, and uses the data analysis platform to deeply mine the data during the debugging process to identify performance bottlenecks and equipment operation trends;

[0138] The optimization module is used to automatically perform optimization adjustments based on real-time operating data. After debugging is completed, the system automatically generates a detailed report to record parameter changes, fault diagnosis results and operating conditions.

[0139] This embodiment also provides an electronic device, including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein when the processor executes the computer program, the method and system for automated debugging of electromechanical installation projects based on intelligent control as described above are implemented.

[0140] This embodiment also provides a computer-readable storage medium on which a computer program is stored. When the program is executed by a processor, the method and system for automated debugging of electromechanical installation projects based on intelligent control as described above are implemented.

[0141] Those skilled in the art can understand that all or part of the processes in the above-mentioned embodiment methods can be completed by instructing the relevant hardware through a computer program, and the computer program can be stored in a non-volatile computer-readable storage medium. When the computer program is executed, it can include the processes of the embodiments of the above-mentioned methods. Among them, any reference to memory, storage, database or other media used in the embodiments provided in this application can include non-volatile and / or volatile memory. Non-volatile memory may include read-only memory (ROM), programmable ROM (PROM), electrically programmable ROM (EPROM), electrically erasable programmable ROM (EEPROM) or flash memory. Volatile memory may include random access memory (RAM) or external cache memory. As an illustration and not limitation, RAM is available in many forms, such as static RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), double data rate SDRAM (DDRSDRAM), enhanced SDRAM (ESDRAM), synchronous link (Synchlink) DRAM (SLDRAM), memory bus (Rambus) direct RAM (RDRAM), direct memory bus dynamic RAM (DRDRAM), and memory bus dynamic RAM (RDRAM).

[0142] Those skilled in the art will clearly understand that for the sake of convenience and brevity of description, only the division of the above-mentioned functional units and modules is used as an example. In actual applications, the above-mentioned functions can be distributed and completed by different functional units and modules as needed, that is, the internal structure of the system can be divided into different functional units or modules to complete all or part of the functions described above.

[0143] The above embodiments of the present invention are not intended to limit the protection scope of the present invention, and the implementation modes of the present invention are not limited thereto. All other modifications, replacements or changes made to the above structures of the present invention based on the above contents of the present invention, in accordance with common technical knowledge and customary means in the art, without departing from the above basic technical ideas of the present invention, should fall within the protection scope of the present invention.

Claims

1. An automated debugging method for electromechanical installation engineering based on intelligent control, characterized in that: Included are: By real-time monitoring of equipment energy consumption, optimizing operation mode and load scheduling, intelligently adjusting working status according to real-time data of equipment, different electromechanical equipment can work together through intelligent control systems and communication protocols; Adopt PLC, DCS, and SCADA intelligent control systems according to the type, scale, and control requirements of electromechanical equipment, and deploy temperature, humidity, pressure, current, and voltage sensors according to engineering design; The electromechanical equipment and control modules are installed according to the design plan, the electrical wiring is stabilized, the sensors and actuators are connected to the control system, and the intelligent debugging tools and platforms are used to perform preliminary system parameter configuration, including sensor calibration, control strategy setting, and debugging priority setting; Based on the preliminary operation data, the adaptive control algorithm is used to dynamically optimize the system. The control strategy is adjusted in real time according to the load and environmental change factors of different equipment. The collected multi-dimensional data is integrated, and the system performance bottlenecks or abnormal points are identified through the intelligent analysis platform to adjust the debugging strategy. The centralized monitoring platform can be used to view equipment status and operating parameters in real time. The intelligent control system can analyze data in real time, identify potential equipment failures, and issue early warning signals. Through multiple rounds of debugging and operation, the system is comprehensively tested for performance, and key indicators such as energy saving effect, response speed, and stability are evaluated. Based on data analysis and actual operation feedback during the debugging process, system parameters are adjusted and control algorithms are optimized. Based on the real-time operation data collected in the later stage of debugging, the system is continuously optimized, the control strategy is adjusted, and the equipment scheduling is optimized. Through machine learning and adaptive optimization, the intelligent control system is continuously optimized and adjusted according to the historical data of the equipment and environmental changes during operation.

2. The method for automatic debugging of electromechanical installation engineering based on intelligent control according to claim 1, characterized in that: By real-time monitoring of equipment energy consumption, optimizing operation mode and load scheduling, intelligently adjusting the working state according to the real-time data of the equipment, and coordinating the work between different electromechanical equipment through intelligent control systems and communication protocols, the following methods are used: Real-time monitoring of electromechanical equipment through temperature, humidity, current, voltage, and pressure sensors to obtain data on the equipment's energy consumption, load, and working status; Based on real-time data, the system analyzes the operating status and energy efficiency performance of the equipment through a preset optimization algorithm, and adjusts the operating mode of the equipment to optimize the operating mode of air conditioning, lighting, and ventilation equipment; According to the equipment load and operation requirements, the intelligent system dispatches the load of different equipment. Different electromechanical equipment can work together through PLC, DCS, and SCADA intelligent control systems. Equipment can exchange data and transfer information through standard Modbus, BACnet, Zigbee, and MQTT communication protocols to share operation data. The system automatically adjusts the working status of the equipment based on real-time monitoring data. When it identifies that the energy consumption of a certain equipment is too high or its performance has declined, the system automatically adjusts its operating parameters or issues a warning signal.

3. The method for automatic debugging of electromechanical installation engineering based on intelligent control according to claim 2 is characterized in that: According to the type, scale and control requirements of electromechanical equipment, PLC, DCS, SCADA intelligent control systems are adopted, and according to the engineering design, the method of deploying temperature, humidity, pressure, current and voltage sensors is as follows: Determine the size and complexity of the control system based on the types of motors, pumps, fans, air conditioning system electromechanical equipment involved in the system and the size of the system consisting of a single device or multiple devices; According to the working principle, operating status and temperature control, pressure control, and energy efficiency optimization control objectives of the equipment, clarify the required control strategy and data collection requirements; Based on the above, select the corresponding PLC, DCS, and SCADA control systems; According to the engineering design, temperature, humidity, pressure, current and voltage sensors are used and deployed at the monitoring location. The sensors transmit real-time data to the PLC, DCS or SCADA system, and the working status and operating parameters of the equipment are displayed through real-time monitoring.

4. The method for automatic debugging of electromechanical installation engineering based on intelligent control according to claim 3 is characterized in that: The electromechanical equipment and control modules are installed according to the design plan to ensure reliable electrical connections. Sensors and actuators are connected to the control system. Intelligent debugging tools and platforms are used to perform preliminary system parameter configuration, including sensor calibration, control strategy setting, and debugging priority setting methods: Electromechanical equipment and control modules Install pumps, fans, motors, valve electromechanical equipment and PLC, DCS, SCADA control modules according to the design plan, and stabilize the electrical wiring, including the connection of power, signal lines, and control lines between equipment; According to the engineering design, temperature, humidity, pressure, current, voltage sensors and electric valves, motor actuators are connected to the corresponding PLC, DCS, SCADA control systems; Adopt intelligent debugging tools corresponding to the system type, including PLC programming software, DCS configuration software, and SCADA system configuration tools, and perform preliminary system parameter configuration on the debugging platform, including setting the input and output parameters of the controller, defining the operation mode of the equipment, inputting the range of the sensor, and setting the alarm threshold; After the system is installed, the sensors are calibrated through intelligent debugging tools. The calibration methods include temperature sensor calibration, pressure sensor calibration, and current / voltage sensor calibration. The control signal is simulated through the debugging platform to identify the response of valves and motor actuators. According to the system design requirements, set and program control strategies, including PID control, logic control, energy-saving optimization strategies, and set scheduling priorities among multiple devices based on the importance of the system's key devices and functions.

5. The method for automatic debugging of electromechanical installation engineering based on intelligent control according to claim 4 is characterized in that: Based on the preliminary operation data, the adaptive control algorithm is used to dynamically optimize the system. According to the load of different equipment and environmental change factors, the control strategy is adjusted in real time. The collected multi-dimensional data is integrated, and the system performance bottlenecks or abnormal points are identified through the intelligent analysis platform. The method of adjusting the debugging strategy is as follows: After the system is put into operation, the temperature, pressure, current, voltage, and flow rate of the equipment are collected in real time through sensors and transmitted to the control system or data center. Basic analysis is performed on the collected preliminary operation data, including average value, maximum value, minimum value, and fluctuation range. The adaptive control algorithms used include model reference adaptive control, gain scheduling control, fuzzy adaptive control, and neural network adaptive control. The adaptive control algorithm dynamically adjusts the control parameters according to the real-time load changes of the equipment and the changes in environmental conditions; Clean, denoise and standardize the collected data. Through data fusion technology, data from different devices and sensors are integrated into a global state and a comprehensive view of the system is generated. By analyzing the response time, energy efficiency, and load fluctuation index operating data of the equipment, we can identify the performance bottlenecks in the system. We can also use machine learning algorithms to build models based on normal operating data to identify abnormal behaviors in the system that are inconsistent with the normal operating mode. Combined with the real-time operation data of the equipment, environmental change data, and control signal information, through data mining and pattern recognition, the system's operating status can be fully diagnosed and potential risk points or failure modes can be identified; According to the identification results of system performance bottlenecks and anomalies, the debugging strategy is adjusted by optimizing control parameters, load balancing and scheduling, early warning and protection mechanisms, and dynamic optimization strategies. The control strategy is continuously optimized through closed-loop feedback of adaptive control algorithms and data fusion.

6. The method for automatic debugging of electromechanical installation engineering based on intelligent control according to claim 5 is characterized in that: The method of checking the equipment status and operating parameters in real time through the centralized monitoring platform, and the intelligent control system analyzing the data in real time, identifying potential equipment failures, and issuing early warning signals is as follows: Through the hardware installed on each device and sensor, the operating status and key parameters of the device are collected in real time, and all the collected device data are aggregated into a centralized monitoring platform, which displays the real-time status, operating parameters and historical data of the device through a graphical interface; Through real-time data collection, the intelligent control system uses algorithms to monitor the operating status of the equipment and identify whether there are abnormal fluctuations or abnormal values. Fault diagnosis and prediction include: Rule-based diagnosis sets the normal range and threshold for equipment operation. If a parameter exceeds the set range, an early warning is triggered. Model-based prediction: predicting the future state of the equipment by building a mathematical model of the equipment operation; Machine learning and data mining: Apply machine learning algorithms to predict faults and identify patterns. By training historical fault data models, characteristic patterns related to faults can be identified. Anomaly detection, based on machine learning or deep learning models to identify abnormal behavior, using methods such as isolation forest, K-means clustering, and PCA to identify data that deviates from normal patterns during operation; Different levels of early warning rules are set in the system. Different levels of early warning signals are issued according to the degree of deviation of equipment operating parameters. Based on the historical data, process requirements and maintenance experience of the equipment, the upper and lower limit alarm thresholds of temperature, vibration, pressure and current are set. When it is detected that the parameters exceed the set threshold, the system will promptly notify relevant personnel of the fault warning through email, SMS, mobile application push, and sound and light alarms.

7. The method for automatic debugging of electromechanical installation engineering based on intelligent control according to claim 6, characterized in that: Through multiple rounds of debugging and operation, the system is comprehensively tested for performance, and key indicators such as energy saving effect, response speed, and stability are evaluated. Based on data analysis and actual operation feedback during the debugging process, the system parameters are adjusted and the control algorithm is optimized as follows: According to multiple rounds of debugging and operation under different working conditions, the data of the system under different working conditions are collected, including load changes, operation methods, and environmental conditions. The collected system data are processed using Excel, MATLAB, and Python data analysis tools to analyze various performance indicators; Calculate the energy efficiency ratio based on the system's energy consumption data and compare it with the benchmark performance to evaluate the energy saving effect. By measuring the system's response time, analyze the gap between it and the expected target, and further evaluate the stability of the system fluctuation, overshoot, and steady-state error. The algorithm is adjusted through PID regulation, adaptive control, and optimal control algorithm. The model predictive control algorithm is adopted to predict the future behavior of the system by establishing a mathematical model, and dynamic adjustments are made based on the prediction results.

8. The method for automatic debugging of electromechanical installation engineering based on intelligent control according to claim 7, characterized in that: According to the real-time operation data collected in the later stage of debugging, the system is continuously optimized, the control strategy is adjusted, and the equipment scheduling is optimized. Through machine learning and adaptive optimization, the intelligent control system continuously optimizes and adjusts according to the historical data of the equipment and environmental changes during operation. The method is as follows: During system operation, the device status, power consumption, load, response time, and environmental change operation data are collected in real time through sensors, device logs, and operation records; Train machine learning models with historical operating data, compare real-time data with historical data, and identify potential trends and anomalies; According to the real-time collected data and system changes, the control parameters and algorithms are adaptively adjusted. The system senses the load changes, external interference, and changes in the equipment wear environment in real time, and dynamically adjusts the operation mode of the equipment. Use machine learning or optimization algorithms to optimize the operation scheduling of equipment and allocate tasks and loads through intelligent scheduling algorithms; Based on data analysis and model prediction, the system produces optimization suggestions or automatically executes certain optimization decisions. Different optimization goals are set according to energy efficiency optimization, response speed, and system stability goals, and the optimization strategy is adjusted through machine learning. Through Q-learning and deep reinforcement learning algorithms, the system improves and optimizes itself through interaction with the environment.

9. The mechanical and electrical installation engineering automation debugging system based on intelligent control is characterized by: Included are: Intelligent debugging and adaptive control module, which is used to automatically adjust control strategies and parameters according to the real-time operation data of the equipment, and coordinate and optimize the scheduling between multiple devices through intelligent algorithms; Data collection and real-time monitoring module, which is used to collect equipment operation data in real time through sensors and transmit the data to the intelligent control platform for real-time analysis and decision-making. Based on the equipment operation data, equipment health monitoring is carried out through machine learning and data mining technology; The fault diagnosis and early warning module is used to compare the collected data with the preset fault model, and the intelligent control system identifies the faults in the equipment. When the system detects an abnormal operating state, it automatically triggers an alarm; Automatic reporting and data analysis module, after debugging is completed, the system automatically generates a detailed debugging report, and uses the data analysis platform to deeply mine the data during the debugging process to identify performance bottlenecks and equipment operation trends; The optimization module is used to automatically perform optimization adjustments based on real-time operating data. After debugging is completed, the system automatically generates a detailed report to record parameter changes, fault diagnosis results and operating conditions.

10. An electronic device, characterized in that: The invention comprises a memory, a processor and a computer program stored in the memory and executable on the processor. When the processor executes the program, the automatic debugging method for electromechanical installation engineering based on intelligent control as described in any one of claims 1 to 8 is implemented.

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