Intelligent detection system and method for casting cooling system

Abstract

The invention relates to the technical field of detection systems, in particular to an intelligent detection system and method for a casting cooling system, and the system comprises a data collection unit which collects a data set in the working process of the casting cooling system, detection data of the stability of the cooling system and detection data of cooling liquid in the cooling system are obtained; the data integration unit is used for integrating the collected data groups according to a time sequence and forming an information packet; the information transmission unit is used for converting information in the data packet into a digital signal and transmitting the digital signal; through intelligent and automatic detection and control means, the defects in detection and control of a traditional cooling system are effectively overcome, the stability, flexibility and adaptability of the cooling system are improved, and then the quality and production efficiency of castings are improved.

Description

Technical Field

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

[0002] In the casting production process, 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 the castings. If the cooling system operates unstably, it may lead to a series of problems such as uneven stress distribution, cracks, deformation, etc. in the casting, thereby reducing the qualification rate and production efficiency of the castings. Therefore, how to effectively detect and control the operating status of the cooling system is an important technical challenge facing foundry companies.

[0003] At present, the detection and control of casting cooling systems mainly rely on traditional manual monitoring and simple automatic control methods. These methods have the following main problems: Lack of intelligent control: Existing control systems usually use 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 changing production conditions in the casting process.

[0004] Insufficient data integration and analysis capabilities: Traditional methods lack the ability to integrate and deeply analyze multi-source data, and are unable to predict the operating trends of the cooling system and take preventive measures in advance by comparing historical data with real-time data.

[0005] Lack of adaptive adjustment mechanism: Existing systems are usually unable to dynamically adjust the allowable range of control parameters based on the deviation between historical data and real-time data, resulting in the system's lack of flexibility and adaptability when facing emergencies. Summary of the invention

[0006] In order 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 solution adopted is: In one aspect, the present application provides an intelligent detection system for a casting cooling system, the system comprising: 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; The storage unit stores the first curve variation range and the second curve variation range that are revised after a specified time, and automatically adjusts the subsequent cooling system according to the two ranges.

[0007] Based on the above embodiment, the detection data of the coolant in the cooling system includes flow rate, pressure, temperature before cooling, temperature after cooling, and casting surface temperature.

[0008] Based on the above embodiment, the detection data of the cooling system stability includes system operation status data, equipment health data, system pressure fluctuation data, coolant circulation efficiency data, system energy consumption data, applause alarm data and system response time data.

[0009] Based on the above embodiment, the specific method for collecting the detection data of the cooling system stability includes: 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.

[0010] Based on the above embodiment, the specific process of the analysis unit analyzing the data 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.

[0011] Based on the above embodiment, data preprocessing includes denoising, normalization, and time alignment.

[0012] Based on the above embodiment, 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; The first curve allowed variation range and the second curve allowed variation range are received from the revision unit, and the adjustment strategy is dynamically adjusted according to the revised allowed range to ensure that the first curve and the second curve run within the allowed range.

[0013] Based on the above embodiment, the control unit triggers an alarm and regulates the cooling system when an abnormality in the cooling system is detected.

[0014] Based on the above embodiment, the specific working 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.

[0015] On the other hand, the present application also provides an intelligent detection method for a casting cooling system, which specifically includes 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.

[0016] The advantages of the present invention are: The present invention effectively solves the deficiencies in 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 thus improves the quality and production efficiency of castings. BRIEF DESCRIPTION OF THE DRAWINGS

[0017] Figure 1 It is a control diagram for the intelligent detection system of the casting cooling system; Figure 2 It is a flow chart of the analysis process of the analysis unit; Figure 3 It is a flow chart of the control unit implementation process; Figure 4 It is a flowchart of the work steps of the revision unit; Figure 5 It is a flow chart of an intelligent detection method for a casting cooling system. DETAILED DESCRIPTION

[0018] The technical solutions in the embodiments of the present invention will be described clearly and completely below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, rather than all the embodiments.

[0019] 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 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.

[0020] The present application is described in detail below in conjunction with the drawings in the present application.

[0021] Embodiment 1: like Figures 1 to 5 As shown, the intelligent detection system for a casting cooling system of the present invention specifically includes: 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 integrates the collected data groups in chronological order and forms an information package; An information transmission unit converts the information in the data packet into a digital signal and transmits it; An analysis unit receives the digital signal and draws a first curve and a second curve based on the digital signal and the time line, wherein 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; 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; The storage unit stores the first curve variation range and the second curve variation range that are revised after a specified time, and automatically adjusts the subsequent cooling system according to the two ranges.

[0022] The above technical solution has the following advantages during implementation: Dynamic adjustment capability: The system can dynamically adjust the coolant parameters according to the real-time collected data, avoiding the rigid control method of traditional fixed parameter settings. This intelligent control can better adapt to the complex and changeable production conditions in the casting process and improve the flexibility and adaptability of the system.

[0023] 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 timely response in the face of emergencies and reducing casting quality problems caused by parameter deviations.

[0024] Multi-source data integration: The system integrates multi-source data in chronological order through the data integration unit to form information packages for subsequent analysis and processing. This integration capability enables the system to more comprehensively grasp the operating status of the cooling system.

[0025] 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 status of the coolant. By comparing and analyzing 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.

[0026] Real-time expression and adjustment: The control unit can adjust the coolant data 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 and reduce casting quality problems caused by unstable cooling system.

[0027] 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 when facing different production conditions and improve the system's adaptability.

[0028] 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 system's automation level, and ensures continuous optimization of control parameters.

[0029] Reduce quality problems: Through intelligent detection and control, the system can effectively reduce problems such as uneven stress distribution, cracks, deformation, etc. in castings caused by unstable cooling system, and improve the qualified rate of castings.

[0030] Improve production efficiency: The system's automation and intelligent control reduce the need for manual monitoring and adjustment, improve production efficiency and reduce production costs.

[0031] Prediction and prevention: The system can predict the operating trend of the cooling system through the analysis of historical data and real-time data, detect potential problems in advance and take preventive measures, reduce equipment failures and downtime, and improve production continuity and stability.

[0032] In summary, the present invention effectively solves the deficiencies in 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 of castings and production efficiency.

[0033] It should be pointed out that the detection data of the coolant in the cooling system includes flow rate, pressure, temperature before cooling, temperature after cooling, casting surface temperature, etc., and the acquisition of detection data can be achieved through conventional sensor detection equipment such as flow meter, thermometer, pressure gauge, etc., which will not be elaborated here. By using the detected data, the specific operation status of the cooling system can be intuitively displayed, and subsequent adjustments can be made more accurately. For example, one or more of the collected detection data can be revised to keep the first curve within the specified range, thereby improving the stability of the cooling system operation.

[0034] The detection data of cooling system stability include system operation status data, equipment health data, system pressure fluctuation data, coolant circulation efficiency data, system energy consumption data, applause alarm data and system response time data, etc. The above data can be used to intuitively and effectively reflect the stability of the system, so as to facilitate accurate judgment of system stability, timely discovery and resolution of problems, and ensure casting quality and production efficiency.

[0035] For ease of understanding, here is a detailed explanation of the various test data of system stability: System operation status data: Status information such as the start, stop, 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 specific parameters include running, stopped, fault, etc. Equipment health data: the operating status and health of key equipment in the cooling system (such as water pumps, cooling towers, heat exchangers, etc.). Equipment health directly affects the stability of the system. Equipment failure may cause system failure. 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 overheated); System pressure fluctuation data: Fluctuation of pressure in the cooling system. Excessive pressure fluctuation may indicate leakage, blockage or insufficient pumping capacity. Specific parameters include pressure fluctuation range (unit: Pa or bar); 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 cycle time (unit: seconds or minutes) and circulation volume (unit: L / min or m³ / h); System energy consumption data: The energy consumed by the cooling system during operation. An abnormal increase in energy consumption may indicate a decrease in system efficiency or a fault. Specific parameters include energy consumption (unit: kW·h); Fault alarm data: Fault alarm information triggered by the cooling system during operation. Fault alarm data can detect system problems in a timely manner to avoid production interruptions or casting quality problems caused by faults. Specific parameters include fault type (such as water pump failure, cooling tower failure, pressure abnormality, etc.) and fault occurrence time; System response time data: the response time of the cooling system to the control command. A long response time may indicate that the system control capability is insufficient, affecting the cooling effect. Specific parameters include response time (unit: seconds).

[0036] The specific process of collecting the cooling system stability test data includes: 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; Transmit the collected data to an analysis unit (such as an industrial computer or a cloud server); Store the collected data for subsequent analysis and processing; Display the collected data in real time and provide alarm function.

[0037] When implementing data collection, the following methods can be used: System operation status data collection: Real-time recording of system start, stop and operation mode through PLC (programmable logic controller) or DCS (distributed control system); Equipment health data collection: The vibration data of the water pump is detected by the vibration sensor installed on the bearing or housing of the water pump, the circuit data is collected by the current sensor installed on the motor power line, and the temperature data of the equipment is detected by the thermocouple installed on the surface of the equipment; System pressure fluctuation data collection: collect fluctuation data through the pressure sensor or pressure transmitter installed on the main pipeline of the cooling system; Coolant circulation efficiency data collection: Calculate the coolant circulation time through the flow sensor and timer, and collect the circulation volume through the electromagnetic flowmeter or turbine flowmeter installed on the coolant pipeline; System energy consumption data collection: Energy consumption collection is achieved through an energy meter or power sensor installed on the power input end of the cooling system; Fault alarm data collection: Real-time monitoring of system status through sensors and control systems, triggering alarms when abnormalities are detected; System response time data collection: The control system records the time from issuing a control instruction to the system executing it.

[0038] Optimizing the above implementation, the specific process of the analysis unit analyzing the data 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 a curve using visualization The drawn curve is displayed in real time on the monitoring interface, and an alarm is triggered when an abnormality is detected.

[0039] Among them, data preprocessing includes denoising, normalization, and time alignment. Specifically, denoising is to use filtering algorithms (such as low-pass filtering and Kalman filtering) to remove noise from the signal; normalization is to normalize data of 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 to facilitate drawing timeline curves.

[0040] In the step of analyzing the received data, the following methods can be used: For cooling system stability data: use time series analysis algorithms to analyze operating status trends; use anomaly detection models to identify abnormal points; and use regression models to evaluate equipment health.

[0041] For coolant detection data: use the sliding window algorithm to calculate real-time parameters; use the time series prediction model to predict future trends; use the anomaly detection model to identify outliers.

[0042] The advantages of the above implementation process are as follows: The analysis process of the analysis unit can significantly improve the intelligence level of the casting cooling system through scientific data processing, advanced analysis algorithms and real-time monitoring functions, improve the accuracy and reliability of data analysis, enhance the real-time monitoring capability of the system, improve the intelligence level of the system, optimize the system operation efficiency, improve the casting quality and production efficiency, support data-driven continuous improvement, and thus help enterprises achieve more efficient, more stable and more intelligent casting production, and improve product quality and market competitiveness.

[0043] Optimizing the above implementation, 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; According to the real-time data of the first curve, a dynamic adjustment strategy is formulated. For example, when the system stability is lower than the allowable range, the coolant flow rate is increased or the coolant temperature is reduced. When the coolant temperature is too high, the cooling tower is started or the coolant circulation volume is increased. When the coolant pressure is too low, the pumping power is adjusted or the pipeline is checked for blockage. 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; The first curve allowed variation range and the second curve allowed variation range are received from the revision unit, and the adjustment strategy is dynamically adjusted according to the revised allowed range to ensure that the first curve and the second curve run within the allowed range.

[0044] The purpose of this step is for the control unit to formulate a dynamic adjustment strategy by receiving data from the analysis unit, accurately adjust the coolant parameters, and display the adjustment effect in real time through the second curve. At the same time, by working in collaboration with the revision unit, the control unit can dynamically adjust the adjustment strategy to ensure that the system operates stably within the allowable range. This intelligent control method can significantly improve the stability, efficiency and reliability of the casting cooling system, and ensure the quality of castings and production efficiency.

[0045] Furthermore, the control unit triggers an alarm and adjusts the cooling system when an abnormality is detected in the cooling system. Specifically, when the coolant temperature is too high and cannot be resolved by adjustment, an emergency shutdown is triggered. When the system stability exceeds the specified time range and continues to be lower than the allowable range, a maintenance alarm is triggered.

[0046] Optimized from the above implementation, 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.

[0047] The purpose of this step is that the revision unit sets and dynamically adjusts the allowed variation range of the first curve and the allowed variation range of the second curve by receiving historical data and real-time data, ensuring that the system can still operate stably under complex and changeable production conditions. The working steps of the revision unit include data reception and analysis, setting of the initial allowable range, real-time data deviation analysis, dynamic adjustment of the allowable range, revision result storage, 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 casting quality and production efficiency.

[0048] Embodiment 2: The intelligent detection method for a casting cooling system of the present invention 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.

[0049] The various variations and specific embodiments of the intelligent detection system for a casting cooling system in the aforementioned embodiment 1 are also applicable to the intelligent detection method for a casting cooling system in the present embodiment. Through the aforementioned detailed description of the intelligent detection system for a casting cooling system, those skilled in the art can clearly know the implementation method of the intelligent detection method for a casting cooling system in the present embodiment, so for the sake of brevity of the specification, it will not be described in detail here.

[0050] The above are only preferred embodiments of the present invention. It should be pointed out that, for ordinary technicians in this technical field, several improvements and modifications can be made without departing from the technical principles of the present invention. 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; The storage unit stores the first curve variation range and the second curve variation range that are revised after a specified time, and automatically adjusts the subsequent cooling system according to the two ranges.

2. The intelligent detection system for casting cooling system according to claim 1, characterized in that: The detection data of the coolant in the cooling system include flow rate, pressure, temperature before cooling, temperature after cooling, and casting surface temperature.

3. The intelligent detection system for casting cooling system according to claim 1 is characterized in that: 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, applause alarm data and system response time data.

4. The intelligent detection system for casting cooling system according to claim 3 is 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.

5. The intelligent detection system for casting cooling system according to claim 1, 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.

6. The intelligent detection system for casting cooling system according to claim 5, characterized in that: Data preprocessing includes denoising, normalization, and time alignment.

7. The intelligent detection system for casting cooling system according to claim 1, characterized in that: 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; The first curve allowed variation range and the second curve allowed variation range are received from the revision unit, and the adjustment strategy is dynamically adjusted according to the revised allowed range to ensure that the first curve and the second curve run within the allowed range.

8. The intelligent detection system for casting cooling system according to claim 7, characterized in that: The control unit triggers an alarm and regulates the cooling system when an abnormality is detected in the cooling system.

9. The intelligent detection system for casting cooling system according to claim 1, characterized in that: 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.

10. An intelligent detection method for a casting cooling system, characterized in that: The method is applicable to the intelligent detection system for 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 within a specified time period and detection data of the coolant in the cooling system; 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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