Intelligent Detection System and Method for Foundry Cooling System

Abstract

The present invention relates to the technical field of detection systems, and particularly to an intelligent detection system and method for a casting cooling system, including: a data acquisition unit that acquires a data set during the operation of the casting cooling system, the data set including detection data on the stability of the cooling system and detection data on the coolant in the cooling system within a specified time period; a data integration unit that integrates the acquired data set in chronological order and forms an information packet; an information transmission unit that converts the information in the data packet into a digital signal and transmits it; through intelligent and automated detection and control means, the present invention effectively solves the deficiencies in the detection and control of traditional cooling systems, improves the stability, flexibility, and adaptability of the cooling system, and thereby improves the quality and production efficiency of castings.

Description

Technical Field

[0001] The present invention relates to the technical field of detection systems, and particularly to an intelligent detection system and method for a casting cooling system. Background Art

[0002] In the process of casting production, the cooling system is one of the key links to ensure the quality of castings. The stability of the cooling system directly affects the internal structure, surface quality and mechanical properties of castings. If the cooling system operates unstably, it may lead to a series of problems such as uneven internal stress distribution, cracks, and deformation in castings, thereby reducing the qualified rate and production efficiency of castings. Therefore, how to effectively detect and control the operating state of the cooling system is an important technical challenge faced by casting enterprises.

[0003] Currently, the detection and control of casting cooling systems mainly rely on traditional manual monitoring and simple automated control methods. These methods have the following main problems:

[0004] Lack of intelligent control: Existing control systems usually adopt fixed parameter settings and cannot dynamically adjust parameters such as the flow rate and temperature of the coolant according to real-time data. This rigid control method is difficult to adapt to the complex and changeable production conditions in the casting process.

[0005] Insufficient data integration and analysis capabilities: Traditional methods lack the ability to integrate and deeply analyze multi-source data, and cannot predict the operating trend of the cooling system and take preventive measures in advance by comparing historical data and real-time data.

[0006] Lack of adaptive adjustment mechanism: Existing systems usually cannot dynamically adjust the allowable range of control parameters according to the deviation between historical data and real-time data, resulting in the system lacking flexibility and adaptability in the face of emergencies. Summary of the Invention

[0007] To solve the above technical problems, the present invention provides an intelligent detection system and method for a casting cooling system, and the specific technical solutions adopted are as follows:

[0008] On the one hand, the present application provides an intelligent detection system for a casting cooling system, and the system includes:

[0009] A data acquisition unit that acquires a data set during the operation of the casting cooling system, and the data set includes detection data on the stability of the cooling system and detection data on the coolant in the cooling system within a specified time period;

[0010] A data integration unit that integrates the acquired data set in chronological order and forms an information packet;

[0011] An information transmission unit that converts the information in the data packet into a digital signal and transmits it;

[0012] An analysis unit that receives the digital signal and draws a first curve and a second curve based on the digital signal and the timeline. The first curve is used to represent the detection data of the cooling system stability, and the second curve is used to represent the detection data of the coolant in the cooling system;

[0013] A control unit that adjusts the data of the coolant in the cooling system according to the first curve and represents it in real time through the second curve;

[0014] A revision unit that sets the allowable variation range of the first curve and the allowable variation range of the second curve based on the information in the historical information packet. Based on the allowable variation range of the first curve, when the detection data of the coolant is outside the allowable variation range of the second curve, the allowable variation range of the second curve is revised;

[0015] A storage unit that stores the allowable variation range of the first curve and the allowable variation range of the second curve revised after a specified time, and automatically adjusts the subsequent cooling system according to the two ranges.

[0016] Based on the above embodiments, the detection data of the coolant in the cooling system includes flow rate, pressure, pre-cooling temperature, post-cooling temperature, and casting surface temperature.

[0017] Based on the above embodiments, the detection data of the cooling system stability includes system operation state data, equipment health data, system pressure fluctuation data, coolant circulation efficiency data, system energy consumption data, fault alarm data, and system response time data.

[0018] Based on the above embodiments, the specific acquisition method of the detection data of the cooling system stability includes:

[0019] Using sensors to detect various parameters of the cooling system in real time;

[0020] Converting the analog signal collected by the sensor into a digital signal;

[0021] Transmitting the collected data to the analysis unit;

[0022] Storing the collected data for subsequent analysis and processing;

[0023] Displaying the collected data in real time and providing an alarm function.

[0024] Based on the above embodiments, the specific process of the analysis unit analyzing the data includes:

[0025] Receiving the digital signal from the information transmission unit, including the detection data of the cooling system stability and the coolant;

[0026] Preprocess the received data;

[0027] Analyze the received data;

[0028] Use visualization to draw curves;

[0029] Display the drawn curves on the monitoring interface in real time and trigger an alarm when an anomaly is detected.

[0030] Based on the above embodiments, the preprocessing of the data includes denoising, normalization, and time alignment.

[0031] Based on the above embodiments, the specific implementation steps of the control unit include:

[0032] Receive the first curve and second curve data output by the analysis unit;

[0033] Set the allowable range for the first curve data and the second curve data;

[0034] Formulate a dynamic adjustment strategy based on the real-time data of the first curve;

[0035] Send a control command to the cooling system according to the adjustment strategy;

[0036] During the adjustment process, monitor the changes in the coolant parameters in real time, update the second curve, and display the adjustment effect in real time;

[0037] Receive the allowable change range of the first curve and the allowable change range of the second curve from the revision unit, and dynamically adjust the adjustment strategy according to the revised allowable range to ensure that the first curve and the second curve operate within the allowable range.

[0038] Based on the above embodiments, the control unit triggers an alarm and regulates the cooling system when an anomaly in the cooling system is detected.

[0039] Based on the above embodiments, the specific working steps of the revision unit include:

[0040] Receive the historical information packet and the real-time information packet from the storage unit and the analysis unit respectively;

[0041] Initial set the allowable change range of the first curve and the allowable change range of the second curve based on the data in the historical information packet;

[0042] Dynamically revise the two change ranges according to the real-time data packet information;

[0043] Store the revised two change ranges in the storage unit for subsequent system adjustment, and send the revised two change ranges to the control unit to dynamically adjust the adjustment strategy.

[0044] On the other hand, the present application also provides an intelligent detection method for a casting cooling system, specifically including the following steps;

[0045] Obtain a data set during the working process of the casting cooling system, where the data set includes detection data on the stability of the cooling system and detection data on the coolant in the cooling system within a specified time period;

[0046] Integrate the collected data sets in chronological order and form information packets;

[0047] Convert the information in the data packets into digital signals and transmit them;

[0048] Draw a first curve and a second curve based on the digital signals and the time line. The first curve is used to represent the detection data on the stability of the cooling system, and the second curve is used to represent the detection data on the coolant in the cooling system;

[0049] Adjust the data on the coolant in the cooling system according to the first curve and represent it in real time through the second curve;

[0050] Set the allowable variation range of the first curve and the allowable variation range of the second curve based on the information in the historical information packets. Based on the allowable variation range of the first curve, when the detection data on the coolant is outside the allowable variation range of the second curve, revise the allowable variation range of the second curve;

[0051] Store the allowable variation range of the first curve and the allowable variation range of the second curve revised after a specified time, and automatically adjust the subsequent cooling system according to the two variation ranges.

[0052] The advantages of the present invention are as follows:

[0053] Through intelligent and automated detection and control means, the present invention effectively solves the deficiencies in the detection and control of traditional cooling systems, improves the stability, flexibility and adaptability of the cooling system, and thus improves the quality and production efficiency of castings. BRIEF DESCRIPTION OF THE DRAWINGS

[0054] Figure 1 is a control diagram of an intelligent detection system for a casting cooling system;

[0055] Figure 2 is a flowchart of the analysis process of the analysis unit;

[0056] Figure 3 is a flowchart of the implementation process of the control unit;

[0057] Figure 4 is a flowchart of the working steps of the revision unit;

[0058] Figure 5It is a flowchart of an intelligent detection method for a casting cooling system. Detailed implementation mode

[0059] Next, the technical solutions in the embodiments of the present invention will be clearly and completely described in conjunction with the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are only a part of the embodiments of the present invention, rather than all the embodiments.

[0060] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by those of ordinary skill in the technical field to which this invention belongs. The terms used in the description 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 of the related listed items.

[0061] The present application will be described in detail below in conjunction with the accompanying drawings in the present application.

[0062] Embodiment 1:

[0063] As Figures 1 to 5 shown, the intelligent detection system for a casting cooling system of the present invention specifically includes:

[0064] A data acquisition unit that acquires a data set during the operation of the casting cooling system. The data set includes detection data on the stability of the cooling system and detection data on the coolant in the cooling system within a specified time period.

[0065] A data integration unit that integrates the acquired data set in chronological order and forms an information packet.

[0066] An information transmission unit that converts the information in the data packet into a digital signal and transmits it.

[0067] An analysis unit that receives the digital signal and draws a first curve and a second curve based on the digital signal and the time line. The first curve is used to represent the detection data on the stability of the cooling system, and the second curve is used to represent the detection data on the coolant in the cooling system.

[0068] A control unit that adjusts the data on the coolant in the cooling system according to the first curve and represents it in real time through the second curve.

[0069] A revision unit that sets the allowable variation range of the first curve and the allowable variation range of the second curve based on the information in the historical information packet. Based on the allowable variation range of the first curve, when the detection data on the coolant is outside the allowable variation range of the second curve, the allowable variation range of the second curve is revised.

[0070] A storage unit stores the allowable first curve variation range and the allowable second curve variation range revised after a specified time, and automatically adjusts the subsequent cooling system according to these two ranges.

[0071] In the process of implementing the above technical solution, the following advantages exist:

[0072] Dynamic adjustment ability: The system can dynamically adjust the parameters of the coolant according to the real-time collected data, avoiding the rigid control method of traditional fixed parameter setting. This intelligent control can better adapt to the complex and changeable production conditions in the casting process, improving the flexibility and adaptability of the system.

[0073] Adaptive adjustment mechanism: The system can dynamically adjust the allowable range of control parameters according to the deviation between historical data and real-time data, ensuring that it can respond in a timely manner in the face of emergencies and reducing the quality problems of castings caused by parameter deviation.

[0074] Multi-source data integration: The system integrates multi-source data in chronological order through a data integration unit to form information packets, facilitating subsequent analysis and processing. This integration ability enables the system to comprehensively grasp the operating status of the cooling system.

[0075] In-depth data analysis: The analysis unit can draw curves based on digital signals and timelines to intuitively display the stability of the cooling system and the state of the coolant. Through the comparative analysis of historical data and real-time data, the system can predict the operating trend of the cooling system and take preventive measures in advance to avoid potential problems.

[0076] Real-time representation and adjustment: The control unit can adjust the data of the coolant in real time according to the first curve and display the adjustment effect in real time through the second curve. This real-time monitoring and feedback mechanism can ensure that the cooling system is always in the best operating state, reducing the quality problems of castings caused by the instability of the cooling system.

[0077] Revision and optimization: The revision unit can set and dynamically revise the allowable curve variation range based on historical data, ensuring that the system can automatically optimize control parameters in the face of different production conditions and improving the adaptive ability of the system.

[0078] Automatic adjustment and storage: The storage unit can store the allowable curve variation range revised after a specified time and automatically adjust the subsequent cooling system according to these ranges. This automatic storage and adjustment mechanism reduces the need for manual intervention, improves the automation level of the system, and at the same time ensures the continuous optimization of control parameters.

[0079] Reduce quality issues: Through intelligent detection and control, the system can effectively reduce problems such as uneven internal stress distribution, cracks, and deformation in castings caused by unstable cooling systems, and improve the qualified rate of castings.

[0080] Improve production efficiency: The automation and intelligent control of the system reduce the need for manual monitoring and adjustment, improve production efficiency, and reduce production costs.

[0081] Prediction and prevention: The system can analyze historical and real-time data to predict the operation trend of the cooling system, discover potential problems in advance and take preventive measures, reduce equipment failures and downtime, and improve the continuity and stability of production.

[0082] In summary, the present invention effectively solves the deficiencies in the detection and control of traditional cooling systems through intelligent and automated detection and control means, improves the stability, flexibility, and adaptability of the cooling system, and thereby improves the quality and production efficiency of castings.

[0083] It should be noted that the detection data of the coolant in the cooling system includes flow rate, pressure, pre-cooling temperature, post-cooling temperature, surface temperature of the casting, etc., and the acquisition of the detection data can be achieved through conventional sensor detection devices such as flow meters, thermometers, and pressure gauges, which will not be elaborated here. By using the detected data, the specific operation status of the cooling system can be intuitively displayed, and it is convenient to be more precise during subsequent adjustments. For example, one or more of the collected and detected data can be revised to keep the first curve within the specified range and improve the smooth operation of the cooling system.

[0084] The detection data for the stability of the cooling system includes system operation status data, equipment health data, system pressure fluctuation data, coolant circulation efficiency data, system energy consumption data, fault alarm data, and system response time data, etc. By using the above data, the stability of the system can be intuitively and effectively reflected, so as to facilitate the accurate determination of system stability, timely discover and solve problems, and ensure the quality of castings and production efficiency.

[0085] For easy understanding, the following provides a detailed explanation of the various detection data for system stability:

[0086] System operation status data: Status information such as the startup, shutdown, and operation mode (such as automatic / manual mode) of the cooling system, which can reflect whether the system is operating normally, whether there are abnormal shutdowns or mode switching problems, and the specific parameters include running, stopped, faulty, etc.;

[0087] Equipment health data: The operating status and health of key equipment in the cooling system (such as water pumps, cooling towers, heat exchangers, etc.). The equipment health directly affects the stability of the system, and equipment failures may lead to system failures. Specific parameters include water pump vibration data (reflecting whether the water pump is operating normally), motor current data (reflecting whether the motor load is normal), and equipment temperature data (reflecting whether the equipment is overheating);

[0088] System pressure fluctuation data: The fluctuation of pressure in the cooling system. Excessive pressure fluctuations may indicate problems such as system leakage, blockage, or insufficient pumping capacity. Specific parameters include the pressure fluctuation range (unit: Pa or bar);

[0089] Coolant circulation efficiency data: The circulation efficiency of the coolant in the system, including circulation time, circulation volume, etc. Low circulation efficiency may lead to insufficient cooling effect and affect the quality of castings. Specific parameters include circulation time (unit: seconds or minutes) and circulation volume (unit: L / min or m³ / h);

[0090] System energy consumption data: The energy consumed by the cooling system during operation. Abnormal increase in energy consumption may indicate a decrease in system efficiency or a fault. Specific parameters include energy consumption (unit: kW·h);

[0091] Fault alarm data: Fault alarm information triggered by the cooling system during operation. Fault alarm data can detect system problems in a timely manner and avoid production interruptions or casting quality problems caused by faults. Specific parameters include fault type (such as water pump fault, cooling tower fault, pressure anomaly, etc.) and fault occurrence time;

[0092] System response time data: The response time of the cooling system to control instructions. Excessive response time may indicate insufficient system control ability and affect the cooling effect. Specific parameters include response time (unit: seconds).

[0093] The specific data acquisition process for detecting the stability of the cooling system includes:

[0094] Using sensors to detect various parameters of the cooling system in real time;

[0095] Converting the analog signals collected by the sensors into digital signals;

[0096] Transmitting the collected data to the analysis unit (such as an industrial computer or a cloud server);

[0097] Storing the collected data for subsequent analysis and processing;

[0098] Displaying the collected data in real time and providing an alarm function.

[0099] When implementing data acquisition, the following methods can be specifically adopted:

[0100] System operating status data acquisition: The startup, stop, and operating mode of the system are recorded in real time through a PLC (Programmable Logic Controller) or DCS (Distributed Control System);

[0101] Equipment health data acquisition: The vibration data of the water pump is detected by vibration sensors installed on the water pump bearing or housing, the circuit data is collected by current sensors installed on the motor power line, and the temperature data of the equipment is detected by thermocouples installed on the equipment surface;

[0102] System pressure fluctuation data acquisition: Fluctuation data is collected by pressure sensors or pressure transmitters installed on the main pipeline of the cooling system;

[0103] Coolant circulation efficiency data acquisition: The circulation time of the coolant is calculated by a flow sensor and a timer, and the circulation volume is collected by an electromagnetic flowmeter or a turbine flowmeter installed on the coolant pipeline;

[0104] System energy consumption data acquisition: Energy consumption is collected by an electric energy meter or a power sensor installed on the power input end of the cooling system;

[0105] Fault alarm data acquisition: The system status is monitored in real time through sensors and control systems, and an alarm is triggered when an abnormality is detected;

[0106] System response time data acquisition: The time from issuing a control instruction to system execution is recorded by the control system.

[0107] Optimized based on the above implementation, the specific process of the analysis unit analyzing the data includes:

[0108] Receiving digital signals from the information transmission unit, including the detection data of the cooling system stability and the coolant;

[0109] Preprocessing the received data;

[0110] Analyzing the received data;

[0111] Using a visualization method to draw curves

[0112] Displaying the drawn curves on the monitoring interface in real time, and triggering an alarm when an abnormality is detected.

[0113] Among them, the preprocessing of the data includes denoising, normalization, and time alignment. Specifically, denoising is to use filtering algorithms (such as low-pass filtering, Kalman filtering) to remove the noise in the signal; normalization is to normalize data with different dimensions to the same range (such as 0 to 1) for subsequent analysis; time alignment is to ensure that the timestamps of all data are aligned for drawing timeline curves.

[0114] In the step of analyzing the received data, the following methods can be specifically adopted:

[0115] For the stability data of the cooling system: Use time series analysis algorithms to analyze the operation status trend; Use anomaly detection models to identify anomaly points; Use regression models to evaluate the equipment health.

[0116] For the coolant detection data: Use sliding window algorithms to calculate real-time parameters; Use time series prediction models to predict future trends; Use anomaly detection models to identify outliers.

[0117] The advantages of the above implementation process: Through scientific data processing, advanced analysis algorithms, and real-time monitoring functions, the analysis process of the analysis unit can significantly improve the intelligent level of the casting cooling system, enhance the accuracy and reliability of data analysis, strengthen the real-time monitoring ability of the system, improve the intelligent level of the system, optimize the system operation efficiency, improve the quality of castings and production efficiency, support data-driven continuous improvement, and thus help enterprises achieve more efficient, stable, and intelligent casting production, and enhance product quality and market competitiveness.

[0118] Optimized based on the above implementation, the specific implementation steps of the control unit include:

[0119] Receive the first curve and second curve data output by the analysis unit;

[0120] Set the allowable range for the first curve data and the second curve data;

[0121] According to the real-time data of the first curve, formulate dynamic adjustment strategies. For example, when the system stability is lower than the allowable range, increase the coolant flow rate or lower the coolant temperature; when the coolant temperature is too high, start the cooling tower or increase the coolant circulation volume; when the coolant pressure is too low, adjust the pumping power or check whether the pipeline is blocked;

[0122] Send control instructions to the cooling system according to the adjustment strategy;

[0123] During the adjustment process, monitor the changes in coolant parameters in real time, update the second curve, and display the adjustment effect in real time;

[0124] Receive the allowable change range of the first curve and the allowable change range of the second curve from the revision unit, and dynamically adjust the adjustment strategy according to the revised allowable range to ensure that the first curve and the second curve operate within the allowable range.

[0125] The purpose of this step is that the control unit formulates a dynamic adjustment strategy by receiving the data from the analysis unit, precisely adjusts the coolant parameters, and displays the adjustment effect in real time through the second curve. At the same time, through the collaborative work with the revision unit, the control unit can dynamically adjust the adjustment strategy to ensure the stable operation of the system within the allowable range. This intelligent control method can significantly improve the stability, efficiency, and reliability of the casting cooling system, ensuring the casting quality and production efficiency.

[0126] Furthermore, the control unit triggers an alarm and regulates the cooling system when detecting an abnormality in the cooling system. Specifically, when the coolant temperature is too high and cannot be resolved through adjustment, an emergency shutdown is triggered. When the system stability exceeds the specified time range and continuously remains below the allowable range, a maintenance alarm is triggered.

[0127] Optimized based on the above implementation, the specific working steps of the revision unit include:

[0128] Receive the historical information packet and the real-time information packet from the storage unit and the analysis unit respectively;

[0129] Based on the data in the historical information packet, initially set the allowable variation range of the first curve and the allowable variation range of the second curve;

[0130] According to the real-time data packet information, dynamically revise the two variation ranges;

[0131] Store the revised two variation ranges in the storage unit for subsequent system adjustment use, and send the revised two variation ranges to the control unit to dynamically adjust the adjustment strategy.

[0132] The purpose of this step is that the revision unit sets and dynamically adjusts the allowable variation range of the first curve and the allowable variation range of the second curve by receiving historical data and real-time data, ensuring the stable operation of the system under complex and changeable production conditions. The working steps of the revision unit include data reception and parsing, setting of the initial allowable range, real-time data deviation analysis, dynamic adjustment of the allowable range, storage of the revision results, and exception handling and alarm. By dynamically adjusting the allowable range, the revision unit can significantly improve the adaptability and control accuracy of the system, ensuring the casting quality and production efficiency.

[0133] Embodiment 2:

[0134] The intelligent detection method for the casting cooling system of the present invention specifically includes the following steps;

[0135] Obtain a data set during the operation of the casting cooling system, where the data set includes the detection data of the cooling system stability within a specified time period and the detection data of the coolant in the cooling system;

[0136] Integrate the collected data sets in chronological order and form an information packet;

[0137] Convert the information in the data packet into a digital signal and transmit it;

[0138] Draw a first curve and a second curve based on the digital signal and the timeline. The first curve is used to represent the detection data of the cooling system stability, and the second curve is used to represent the detection data of the coolant in the cooling system;

[0139] Adjust the data of the coolant in the cooling system according to the first curve and represent it in real time through the second curve;

[0140] Set the allowable change range of the first curve and the allowable change range of the second curve based on the information in the historical information packet. Based on the allowable change range of the first curve, when the detection data of the coolant is outside the allowable change range of the second curve, revise the allowable change range of the second curve;

[0141] Store the allowable change range of the first curve and the allowable change range of the second curve revised after a specified time, and automatically adjust the subsequent cooling system according to the two change ranges.

[0142] The various change methods and specific embodiments of the intelligent detection system for the casting cooling system in the foregoing Embodiment 1 are equally applicable to the intelligent detection method for the casting cooling system in this embodiment. Through the foregoing detailed description of the intelligent detection system for the casting cooling system, those skilled in the art can clearly know the implementation method of the intelligent detection method for the casting cooling system in this embodiment. Therefore, for the sake of brevity of the specification, it will not be described in detail here.

[0143] The above is only the preferred embodiment of the present invention. It should be noted that for those of ordinary skill in the art, without departing from the technical principle of the present invention, several improvements and modifications can still be made, and these improvements and modifications should also be regarded as the protection scope of the present invention.

Claims

1. Intelligent detection system for casting cooling system, characterized in that: The system comprises: A data collection unit collects a data set during the operation of the casting cooling system, wherein the data set includes detection data on the stability of the cooling system and detection data on the coolant in the cooling system within a specified time period; A data integration unit, which integrates the collected data groups in chronological order and forms an information package; An information transmission unit, which converts the information in the data packet into a digital signal and transmits it; an analysis unit, receiving the digital signal, and drawing a first curve and a second curve based on the digital signal and a timeline, wherein the first curve is used to express detection data of the stability of the cooling system, and the second curve is used to express detection data of the coolant in the cooling system; A control unit, which adjusts the data of the coolant in the cooling system according to the first curve and expresses it in real time through the second curve; A revision unit, which sets the first curve variation range and the second curve variation range based on the information of the history information package, and revises the second curve variation range based on the first curve variation range when the detection data of the coolant is outside the second curve variation range; A storage unit for storing the first curve variation range and the second curve variation range that are revised after a specified time, and automatically adjusting the subsequent cooling system according to the two ranges; The test data of coolant in the cooling system include flow rate, pressure, temperature before cooling, temperature after cooling, and casting surface temperature; The detection data of cooling system stability includes system operation status data, equipment health data, system pressure fluctuation data, coolant circulation efficiency data, system energy consumption data, fault alarm data and system response time data; The specific implementation steps of the control unit include: receiving first curve and second curve data output by the analysis unit; Setting the allowable range for the first curve data and the second curve data; Formulate a dynamic adjustment strategy based on the real-time data of the first curve; Send control instructions to the cooling system according to the regulation strategy; During the adjustment process, the changes in coolant parameters are monitored in real time, the second curve is updated, and the adjustment effect is displayed in real time; receiving the allowed variation range of the first curve and the allowed variation range of the second curve from the revision unit, and dynamically adjusting the adjustment strategy according to the revised allowed range to ensure that the first curve and the second curve operate within the allowed range; The specific work steps of the revision unit include: receiving a historical information packet and a real-time information packet from a storage unit and an analysis unit respectively; Initially setting the allowed first curve variation range and the allowed second curve variation range based on the data of the historical information package; Dynamically revise the two change ranges based on real-time data packet information; The revised two variation ranges are stored in a storage unit for subsequent system adjustment, and the revised two variation ranges are sent to a control unit to dynamically adjust the adjustment strategy.

2. The intelligent detection system for casting cooling system according to claim 1, characterized in that: The specific methods for collecting the detection data of the cooling system stability include: Use sensors to detect various parameters of the cooling system in real time; Convert the analog signal collected by the sensor into a digital signal; transmitting the collected data to an analysis unit; Store the collected data for subsequent analysis and processing; Display the collected data in real time and provide alarm function.

3. The intelligent detection system for casting cooling system according to claim 1 is characterized in that: The specific process of analyzing data by the analysis unit includes: Receiving digital signals from the information transmission unit, including cooling system stability and coolant detection data; Preprocessing the received data; Analyze the received data; Draw curves using visualization methods; The drawn curve is displayed in real time on the monitoring interface, and an alarm is triggered when an abnormality is detected.

4. The intelligent detection system for casting cooling system according to claim 3 is characterized in that: Data preprocessing includes denoising, normalization, and time alignment.

5. The intelligent detection system for casting cooling system according to claim 1, characterized in that: The control unit triggers an alarm and regulates the cooling system when an abnormality is detected in the cooling system.

6. An intelligent detection method for a casting cooling system, characterized in that: The method is applicable to the intelligent detection system for a casting cooling system according to claim 1, and specifically comprises the following steps: Acquire a data set of the casting cooling system during operation, wherein the data set includes detection data of the cooling system stability and detection data of the coolant in the cooling system within a specified time period; Integrate the collected data groups in chronological order and form information packages; Convert the information in the data packet into a digital signal and transmit it; Draw a first curve and a second curve based on the digital signal and the timeline, the first curve is used to express the detection data of the stability of the cooling system, and the second curve is used to express the detection data of the coolant in the cooling system; Adjusting the data of the coolant in the cooling system according to the first curve and expressing it in real time through the second curve; The first curve variation range and the second curve variation range are set based on the information of the historical information package, and based on the first curve variation range, when the detection data of the coolant is outside the second curve variation range, the second curve variation range is revised; The first allowable curve variation range and the second allowable curve variation range revised after a specified time are stored, and the subsequent cooling system is automatically adjusted based on the two variation ranges.

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