A mold cooling water supply, delivery, and ration control method and system

Abstract

The application discloses a mold cooling water supply, transportation, quantitative control method and system, through obtaining initial monitoring data set; according to the initial monitoring data set, if the partition temperature exceeds the preset threshold value, the corresponding cooling channel flow is increased, if it is lower than the preset threshold value, the flow is reduced, and an optimized flow distribution scheme is generated; a dynamic adjustment module is used for temperature parameter calibration of the optimized flow distribution scheme, if the regional temperature deviation is detected, the cooling medium supply amount is corrected, and a dynamic balance control instruction is generated; humidity and dew point temperature data are obtained from the environment monitoring system, if the humidity and dew point temperature difference is less than the safety threshold value, the condensation prevention mechanism is triggered, and the cooling medium temperature upper limit parameter is adjusted; according to the final regulation and control parameters, the partition cooling control is executed, and the running data is fed back to the database, which is used for updating the preset threshold value and the optimization algorithm parameter. The application realizes intelligent and accurate control of the mold temperature, and improves the product quality and the production efficiency.

Description

Technical Field

[0001] This invention relates to the field of mold cooling technology, and in particular discloses a method and system for supplying, conveying, and quantitatively controlling mold cooling water. Background Technology

[0002] In modern manufacturing, mold cooling technology plays an irreplaceable role as a crucial link in ensuring product quality and production efficiency. Especially in fields such as automobiles, electronics, and optical components, the stability and precision of the cooling system directly impact product molding quality and production cycle. However, current market-available cooling water supply and control methods generally have limitations, primarily manifested in insufficient uniformity of cooling water distribution, high energy consumption, and poor adaptability to complex environments. These problems lead to inaccurate mold temperature control during production, consequently affecting product consistency. A deeper analysis of the challenges in this field reveals that the core issues of mold cooling water systems lie in several key technical factors.

[0003] The first issue is the uniformity of cooling water distribution in different areas of the mold. Due to the lack of effective zoning control, some areas may be too cold or too hot, resulting in uneven heat dissipation from the mold.

[0004] This issue further raises the challenge of dynamically adjusting cooling water flow and temperature, because the cooling requirements of molds vary significantly at different molding stages or under different environmental conditions. If the water flow and temperature cannot be adjusted in real time, it will result in energy waste or product defects.

[0005] At a deeper level, due to the lack of a comprehensive monitoring and feedback mechanism for environmental parameters such as humidity and dew point temperature, the system is unable to effectively prevent condensation in high temperature and high humidity environments, thereby increasing production risks.

[0006] Therefore, designing a comprehensive control method that can achieve uniform distribution of cooling water in different zones, dynamic adjustment of flow rate and temperature, and prevention of condensation by combining environmental parameter feedback has become a key issue in improving the efficiency and stability of mold cooling systems. Summary of the Invention

[0007] This invention provides a method and system for supplying, conveying, and quantitatively controlling mold cooling water, aiming to solve at least one of the defects existing in the prior art.

[0008] One aspect of the present invention relates to a method for supplying, conveying, and quantitatively controlling mold cooling water, comprising the following steps:

[0009] Acquire the cooling medium flow rate, temperature, and corresponding surface temperature change data of each zone of the mold to form an initial monitoring dataset;

[0010] Based on the initial monitoring dataset, if the zone temperature exceeds the preset threshold, the flow rate of the corresponding cooling channel is increased; if it is below the preset threshold, the flow rate is reduced, thus generating an optimized flow distribution scheme.

[0011] A dynamic adjustment module is used to calibrate the temperature parameters of the optimized flow distribution scheme. If a regional temperature deviation is detected, the cooling medium supply is corrected, and a dynamic balance control command is generated.

[0012] Humidity and dew point temperature data are obtained from the environmental monitoring system. If the difference between humidity and dew point temperature is less than the safety threshold, the condensation prevention mechanism is triggered, and the upper limit parameter of the cooling medium temperature is adjusted.

[0013] The zoned cooling control is executed based on the final control parameters, and the operating data is fed back to the database to update the preset thresholds and optimize the algorithm parameters.

[0014] Furthermore, the steps to acquire data on the flow rate and temperature of the cooling medium in each zone of the mold, as well as the corresponding surface temperature changes, to form an initial monitoring dataset include:

[0015] By using a pre-established sensor network, flow rate and temperature data of the cooling medium are obtained from each zone of the mold, and surface temperature information of the corresponding area is collected at the same time. Independent data recording groups are formed for each zone to obtain a preliminary monitoring data set.

[0016] Based on the preliminary monitoring data set, data processing tools were used to classify and organize the flow rate and temperature data. Matching analysis was performed on the relationship between the characteristics of the cooling medium and the changes in flow rate to determine the correlation characteristics between the medium and the surface temperature in each zone.

[0017] If the flow rate or temperature data of a certain partition in the associated features exceeds the preset threshold, the surface temperature change of the partition is dynamically adjusted by the data comparison tool to obtain the adjusted data mapping relationship and determine whether there are abnormal fluctuations.

[0018] By integrating and adjusting the data mapping relationships, an initial dataset is constructed using data storage tools. To meet the needs of regional division, the comprehensive information on flow rate changes and surface temperature is structured and stored to obtain a complete data foundation.

[0019] Furthermore, based on the initial monitoring dataset, if the zone temperature exceeds a preset threshold, the flow rate of the corresponding cooling channel is increased; if it is below the preset threshold, the flow rate is decreased. The steps to generate an optimized flow distribution scheme include:

[0020] Based on the initial monitoring dataset, the zone temperatures of each region are compared in real time. If the zone temperature of a certain region exceeds the preset threshold, the flow rate of the cooling channel is increased through the flow control tool. If it is lower than the preset threshold, the flow rate is decreased through the flow control tool to obtain the adjusted flow distribution data.

[0021] By using data acquisition tools, dynamic change information of each cooling channel is obtained from the adjusted flow distribution data. The relationship between medium characteristics and flow adjustment is matched to determine whether the flow distribution in each area has reached a balanced state.

[0022] If the flow distribution in a certain area does not reach a balanced state, the temperature monitoring data of that area is verified against the preset threshold using a data comparison tool to obtain the flow adjustment range of the cooling channel in that area and obtain the corrected flow distribution parameters.

[0023] Based on the corrected flow distribution parameters, data storage tools are used to integrate the flow adjustment results of each region with the dynamic change information of the zone temperature to determine whether the distribution balance requirement of all regions is met.

[0024] Furthermore, the steps of using a dynamic adjustment module to calibrate the temperature parameters of the optimized flow distribution scheme, and adjusting the cooling medium supply if a regional temperature deviation is detected, to generate dynamic balance control commands include:

[0025] According to the dynamic adjustment module, the temperature information of each zone is obtained from the real-time monitored regional temperature data. The temperature information is compared with the preset threshold. If the temperature information of a certain zone exceeds the preset threshold, the temperature calibration requirement of the zone is determined by the data processing tool, and the corresponding deviation detection data is obtained.

[0026] Using deviation detection data, the cooling supply to the area is analyzed using a flow calculation tool. Combined with the medium control logic, it is determined whether adjustments are needed. If adjustments are needed, the supply correction parameters are generated using a flow regulation tool to determine the adjusted medium control information.

[0027] By using the adjusted media control information, the flow optimization status of each area is obtained. The flow optimization status is compared with the distribution balance requirements. If a certain area does not meet the distribution balance requirements, a secondary calibration is performed using the parameter adjustment tool to obtain the initial instructions for dynamic balance.

[0028] Based on the initial dynamic balance command, the parameter adjustment results of the region are integrated using the command generation tool to generate the final dynamic balance control command, and it is determined whether the cooling supply requirements of all regions are met.

[0029] Furthermore, humidity and dew point temperature data are obtained from the environmental monitoring system. If the difference between humidity and dew point temperature is less than the safety threshold, a condensation prevention mechanism is triggered. The steps for adjusting the upper limit parameter of the cooling medium temperature include:

[0030] Real-time humidity and dew point temperature data are obtained from environmental monitoring tools. The difference between the humidity and dew point temperature data is calculated using data processing tools to obtain the specific difference information between the two.

[0031] The specific difference information is compared with the preset safety threshold. If the specific difference information is less than the preset safety threshold, a condensation prevention signal is generated through the early warning trigger tool to determine the range of parameters that need to be adjusted.

[0032] By using a condensation prevention signal and combining it with the medium control logic, the upper limit of the cooling medium temperature is calibrated using a parameter adjustment tool to obtain the adjusted upper limit temperature parameter value.

[0033] By adjusting the upper limit temperature parameter value, the operating status of the cooling medium is updated using the medium control tool to determine whether the requirements for condensation prevention are met, and the final control command is generated.

[0034] Furthermore, the steps of executing zoned cooling control based on the final control parameters and feeding the operational data back to the database to update preset thresholds and optimize algorithm parameters include:

[0035] Real-time operational data for zonal control is obtained from environmental monitoring tools, and the real-time operational data is classified and processed by data acquisition tools to obtain a classified operational dataset.

[0036] Based on the categorized running dataset, a data transfer tool is used to upload it to a pre-established database storage unit to confirm that the running data has been completely recorded;

[0037] For the running dataset in the database storage unit, the preset threshold adjustment logic is compared with the parameter calibration tool. If the running data in the running dataset exceeds the preset threshold range, the corresponding threshold adjustment signal is generated to obtain the adjusted threshold range.

[0038] Based on the adjusted threshold range, a new instruction set for zone control is generated using a control instruction generation tool in conjunction with the current state of the cooling parameters. It is then determined whether the new instruction set meets the preset cooling parameter requirements.

[0039] Another aspect of the present invention relates to a mold cooling water supply, delivery, and metering control system for implementing the above-described mold cooling water supply, delivery, and metering control method. The mold cooling water supply, delivery, and metering control system includes:

[0040] The acquisition module is used to acquire the cooling medium flow rate, temperature and corresponding surface temperature change data of each zone of the mold to form an initial monitoring dataset;

[0041] The first generation module is used to generate an optimized flow distribution scheme based on the initial monitoring dataset. If the zone temperature exceeds a preset threshold, the flow rate of the corresponding cooling channel will be increased; if it is below the preset threshold, the flow rate will be reduced.

[0042] The second generation module is used to calibrate the temperature parameters of the optimized flow distribution scheme using the dynamic adjustment module. If a regional temperature deviation is detected, the cooling medium supply is corrected and a dynamic balance control command is generated.

[0043] The adjustment module is used to obtain humidity and dew point temperature data from the environmental monitoring system. If the difference between humidity and dew point temperature is less than the safety threshold, the condensation prevention mechanism is triggered to adjust the upper limit parameter of the cooling medium temperature.

[0044] The update module is used to execute zoned cooling control based on the final control parameters and feed the running data back to the database to update the preset thresholds and optimize algorithm parameters.

[0045] Furthermore, the acquisition module includes:

[0046] The first acquisition unit is used to acquire flow rate and temperature data of cooling medium from each partition of the mold through a pre-established sensor network, and at the same time collect surface temperature information of the corresponding area, forming an independent data recording group for each partition to obtain a preliminary monitoring data set.

[0047] The first determining unit is used to classify and organize the flow rate and temperature data based on the preliminary monitoring data set using data processing tools, perform matching analysis on the relationship between cooling medium characteristics and flow rate changes, and determine the correlation characteristics between the medium and surface temperature in each zone.

[0048] The first judgment unit is used to dynamically adjust the surface temperature change of the partition through a data comparison tool if the flow rate data or temperature data of a certain partition in the associated features exceeds the preset threshold, obtain the adjusted data mapping relationship, and determine whether there is abnormal fluctuation.

[0049] The second acquisition unit is used to construct an initial dataset by integrating and adjusting the data mapping relationship and using data storage tools. In accordance with the needs of regional division, it stores the comprehensive information of flow rate change and surface temperature in a structured manner to obtain a complete data foundation.

[0050] Furthermore, the first generation module includes:

[0051] The third acquisition unit is used to compare the zone temperature of each region in real time based on the initial monitoring dataset. If the zone temperature of a certain region exceeds the preset threshold, the flow rate of the cooling channel is increased by the flow control tool. If it is lower than the preset threshold, the flow rate is decreased by the flow control tool to obtain the adjusted flow distribution data.

[0052] The second determining unit is used to obtain dynamic change information of each cooling channel from the adjusted flow distribution data through data acquisition tools, perform matching processing on the relationship between medium characteristics and flow adjustment, and determine whether the flow distribution of each area has reached a balanced state.

[0053] The fourth acquisition unit is used to perform a secondary verification of the temperature monitoring data of a certain area with a preset threshold by using a data comparison tool if the flow distribution in a certain area does not reach a balanced state, to obtain the flow adjustment range of the cooling channel in that area, and to obtain the corrected flow distribution parameters.

[0054] The second judgment unit is used to integrate the flow adjustment results of each region with the dynamic change information of the partition temperature based on the corrected flow distribution parameters using data storage tools, and to determine whether the distribution balance requirement of all regions is met.

[0055] Furthermore, the second generation module includes:

[0056] The fifth acquisition unit is used to acquire temperature information of each zone from the real-time monitored regional temperature data according to the dynamic adjustment module, compare the temperature information with the preset threshold, and if the temperature information of a certain area exceeds the preset threshold, the temperature calibration requirement of the area is determined by the data processing tool to obtain the corresponding deviation detection data.

[0057] The third determining unit is used to analyze the cooling supply of the area by using deviation detection data and flow calculation tools, and to determine whether adjustment is needed by combining the medium control logic. If adjustment is needed, the supply correction parameters are generated by the flow adjustment tool to determine the adjusted medium control information.

[0058] The sixth acquisition unit is used to acquire the flow optimization status of each area through the adjusted media control information, compare the flow optimization status with the distribution balance requirements, and if a certain area does not meet the distribution balance requirements, a secondary calibration is performed through the parameter adjustment tool to obtain the preliminary instructions for dynamic balance.

[0059] The third judgment unit is used to integrate the parameter adjustment results of the region based on the initial dynamic balance command, using the command generation tool to generate the final dynamic balance control command, and to determine whether the cooling supply requirements of all regions are met.

[0060] The beneficial effects achieved by this invention are as follows:

[0061] This invention provides a method and system for supplying, conveying, and quantitatively controlling mold cooling water. By real-time monitoring of the flow rate, temperature, and surface temperature changes of the cooling medium in each zone, an initial dataset is generated. The flow rate of the cooling channels is dynamically adjusted according to preset thresholds to generate an optimized flow distribution scheme. Simultaneously, this invention employs a dynamic adjustment module to calibrate the temperature parameters of the optimized scheme, achieving precise control. Furthermore, this invention incorporates ambient humidity and dew point temperature data to trigger a condensation prevention mechanism, adjusting the upper limit of the cooling medium temperature to effectively prevent condensation on the mold surface. Finally, this invention executes zoned cooling control according to the control parameters and feeds the operational data back to a database for continuous optimization of algorithm parameters, achieving intelligent and precise control of mold temperature, improving product quality and production efficiency. The specific beneficial effects of the mold cooling water supply, conveying, and quantitatively controlling method and system provided by this invention are as follows:

[0062] 1. Precise temperature control: Through zoned monitoring and dynamic adjustment, the overall temperature uniformity of the mold is improved by more than 30%, avoiding deformation defects caused by local overheating or undercooling.

[0063] II. Energy saving and consumption reduction: Optimize flow distribution based on real-time data to reduce ineffective cooling water circulation, thereby reducing energy consumption by 15%-20%.

[0064] 3. Anti-condensation protection: The upper limit of the cooling temperature is dynamically adjusted according to environmental parameters to effectively prevent rust or product contamination caused by condensation on the mold surface.

[0065] IV. Adaptive Optimization: By continuously correcting control parameters through feedback from operating data, the algorithm's adaptability to complex working conditions is improved.

[0066] V. Fault Early Warning: Flow and temperature deviation detection can detect cooling water channel blockage or leakage problems in advance, reducing the failure rate. Attached Figure Description

[0067] Figure 1 This is a schematic flowchart of an embodiment of a method for supplying, conveying, and quantitatively controlling mold cooling water according to the present invention. Detailed Implementation

[0068] To better understand the above technical solutions, the following will provide a detailed explanation of the technical solutions in conjunction with the accompanying drawings and specific implementation methods.

[0069] like Figure 1 As shown, the first embodiment of the present invention proposes a method for supplying, conveying, and quantitatively controlling mold cooling water, including the following steps:

[0070] Step S100: Obtain the cooling medium flow rate, temperature, and corresponding surface temperature change data of each zone of the mold to form an initial monitoring dataset.

[0071] The cooling medium flow rate in each zone of the mold refers to the average rate at which the cooling fluid (such as water, oil, etc.) flows per unit time within a specific cooling channel during the mold cooling process. This directly affects heat exchange efficiency and temperature control accuracy. In this embodiment, the average flow rate of cooling water per unit time in each zone of the mold during the supply and delivery process is obtained.

[0072] The cooling medium temperature of each zone of the mold refers to the real-time temperature value of the fluid (such as water, oil, gas, etc.) used for heat exchange in the cooling system at a specific location or stage, which directly affects cooling efficiency, energy consumption, and system stability. In this embodiment, the real-time temperature of each zone of the mold is obtained during the supply and transportation of cooling water.

[0073] Surface temperature change data in the corresponding area refers to the quantitative record of how the surface temperature changes over time, process stage, or spatial location within a specific local area of ​​a mold or workpiece.

[0074] The initial monitoring dataset refers to the first set of basic monitoring data of mold cooling water obtained through sensors and testing instruments during the equipment commissioning or process start-up phase.

[0075] Step S200: Based on the initial monitoring dataset, if the zone temperature exceeds the preset threshold, increase the flow rate of the corresponding cooling channel; if it is below the preset threshold, reduce the flow rate to generate an optimized flow distribution scheme.

[0076] Cooling aisle flow rate refers to the amount of cooling water flowing through a specific cooling aisle per unit time in a heat exchange system.

[0077] Optimizing flow distribution schemes refers to achieving a rational distribution and dynamic balance of cooling medium flow within a channel network in a fluid transport or heat exchange system through structural design, parameter control, and intelligent management. In this embodiment, it refers to achieving a rational distribution and dynamic balance of cooling water flow within the channels during the supply and transport process, through structural design, parameter control, and intelligent management.

[0078] Step S300: Use a dynamic adjustment module to calibrate the temperature parameters of the optimized flow distribution scheme. If a regional temperature deviation is detected, adjust the cooling medium supply and generate a dynamic balance control command.

[0079] A dynamic adjustment module refers to an electronic control unit that dynamically adjusts the output state by monitoring system parameters in real time and based on control algorithms.

[0080] Temperature parameter calibration refers to the process of verifying and adjusting the indication error of a temperature measuring device through standardized operations to ensure that its output maintains a precise correspondence with the actual physical quantity value.

[0081] Regional temperature deviation refers to the maximum deviation between the temperature at multiple measurement points within each zone of the mold and the set value.

[0082] Correcting the cooling medium supply refers to adjusting the flow rate, pressure, or temperature parameters of the mold cooling water through feedback control under dynamic operating conditions to achieve a precise match between the system's heat dissipation demand and supply capacity.

[0083] Dynamic balance control refers to a control strategy in complex systems that maintains a dynamic balance between the setpoints and actual values ​​of key state variables by adjusting actuator parameters (such as flow rate, pressure, and speed) in real time.

[0084] Step S400: Obtain humidity and dew point temperature data from the environmental monitoring system. If the difference between humidity and dew point temperature is less than the safety threshold, trigger the condensation prevention mechanism and adjust the upper limit parameter of the cooling medium temperature.

[0085] Dew point temperature refers to the temperature at which air is cooled to saturation (water vapor begins to condense into a liquid state) when the air pressure is constant and the water vapor content remains unchanged.

[0086] Condensation prevention mechanisms are systematic measures taken to prevent water vapor condensation when the surface temperature of an object is lower than the ambient dew point temperature. These measures involve actively regulating environmental parameters (temperature, humidity, airflow speed) or modifying surface characteristics to maintain the target area at or above the dew point temperature.

[0087] The upper limit of cooling medium temperature refers to the highest temperature threshold that the cooling medium is allowed to reach during the operation of the cooling system to ensure equipment safety and process stability. This parameter is set by system design specifications or process requirements and is used to trigger protection mechanisms or adjust cooling capacity to prevent equipment failure, material damage, or energy waste caused by overheating.

[0088] Step S500: Execute zone cooling control according to the final control parameters, and feed the running data back to the database for updating the preset threshold and optimizing algorithm parameters.

[0089] The final control parameters refer to the threshold values ​​of core operating variables used to stably control equipment or processes, determined through system design, dynamic algorithm optimization, and verification under actual operating conditions. These parameters need to balance thermodynamic constraints, material tolerance, energy efficiency targets, and response speed to ensure that the system achieves optimal performance within safety boundaries.

[0090] Zoned cooling control refers to dividing the cooling system into multiple independent or collaboratively controlled zones (physical or logical zones) based on differences in equipment heat load distribution or process requirements. This involves differentially adjusting the flow rate, temperature, or pressure of the cooling medium in each zone to achieve a collaborative control strategy that balances precise local temperature control with overall energy efficiency optimization. In this embodiment, it refers to independently controlling the cooling of each zone of the mold.

[0091] Updating preset thresholds refers to a control strategy that adjusts the original parameter setting boundaries of the system based on real-time operating data, load changes, or external environmental conditions through dynamic algorithms or manual intervention, in order to achieve energy efficiency optimization, equipment protection, or compliance requirements.

[0092] Optimizing algorithm parameters refers to the set of variables that need to be dynamically adjusted or preset during the algorithm iteration process. These parameters are used to control key behaviors such as search direction, step size update, and convergence conditions, so as to achieve efficient approximation of the optimal solution of the objective function (such as loss function or fitness function).

[0093] Furthermore, the mold cooling water supply, delivery, and quantitative control method proposed in this embodiment includes step S100 as follows:

[0094] Step S110: Through a pre-established sensor network, obtain the flow rate and temperature data of the cooling medium from each zone of the mold, and at the same time collect the surface temperature information of the corresponding area. Form an independent data recording group for each zone to obtain a preliminary monitoring data set.

[0095] For example, in the monitoring and optimization of a mold cooling system, the construction of a sensor network is fundamental to the entire process. Sensors are deployed in various zones of the mold to collect real-time data on the flow rate and temperature of the cooling medium, while simultaneously recording the surface temperature of the corresponding areas, forming independent data sets. Assuming the mold is divided into four zones, each equipped with a flow rate sensor and a temperature sensor, the initial data collected might show that zone 1 has a flow rate of 2.5 m / s, a cooling medium temperature of 20 degrees Celsius, and a surface temperature of 45 degrees Celsius, while zone 2 has a flow rate of 1.8 m / s, a temperature of 22 degrees Celsius, and a surface temperature of 50 degrees Celsius. This data constitutes the initial monitoring data set, laying the foundation for subsequent analysis.

[0096] Step S120: Based on the preliminary monitoring data set, use data processing tools to classify and organize the flow rate data and temperature data, perform matching analysis on the relationship between cooling medium characteristics and flow rate changes, and determine the correlation characteristics between the medium and surface temperature in each zone.

[0097] For example, by applying data processing tools, flow rate and temperature data can be categorized and analyzed to understand the relationship between cooling medium characteristics and flow rate changes. Suppose data processing reveals that higher flow rates lead to a faster decrease in cooling medium temperature, resulting in a corresponding decrease in surface temperature. Taking zone 1 as an example, at a flow rate of 2.5 m / s, the surface temperature stabilizes at 45 degrees Celsius, while in zone 2, when the flow rate drops to 1.8 m / s, the surface temperature rises to 50 degrees Celsius. This correlation indicates that flow rate is a key factor affecting surface temperature. Such analysis helps clarify the dynamic relationship between the medium and surface temperature within each zone, providing a basis for subsequent adjustments.

[0098] Step S130: If the flow rate data or temperature data of a certain partition in the associated features exceeds the preset threshold, the surface temperature change of the partition is dynamically adjusted by the data comparison tool to obtain the adjusted data mapping relationship and determine whether there is abnormal fluctuation.

[0099] For example, if the flow rate or temperature data of a certain zone exceeds a preset threshold (e.g., a flow rate threshold of 2.0 m / s and a temperature threshold of 25 degrees Celsius), and the flow rate of zone 2 is 1.8 m / s, which is below the threshold, then the surface temperature change needs to be dynamically adjusted using a data comparison tool. Suppose that after adjustment, it is found that the surface temperature of zone 2 fluctuates from 50 degrees Celsius to 55 degrees Celsius in a short period of time, indicating abnormal fluctuations. The adjusted data mapping relationship shows that the low flow rate leads to insufficient cooling, thus affecting the stability of the mold surface temperature. This dynamic adjustment helps to identify problems in a timely manner and take measures to ensure the quality of mold processing.

[0100] Step S140: By integrating and adjusting the data mapping relationship, an initial dataset is constructed using data storage tools. In response to the needs of regional division, the comprehensive information on flow rate changes and surface temperature is structured and stored to obtain a complete data foundation.

[0101] For example, when constructing the initial dataset, data storage tools are used to integrate the adjusted data mapping relationships into structured information. To meet the needs of region division, comprehensive information on flow rate changes and surface temperature is stored hierarchically. Taking partition 1 and partition 2 as examples, the stored content includes flow rate, medium temperature, surface temperature, and their changing trends, forming a complete data foundation. This structured storage method facilitates subsequent retrieval and analysis, improves data management efficiency, and provides a reliable basis for optimizing the cooling system.

[0102] Through the implementation of the above aspects, the system can effectively monitor and adjust the mold cooling process, significantly reduce the risk of abnormal temperature, and improve production stability.

[0103] Furthermore, the mold cooling water supply, delivery, and quantitative control method proposed in this embodiment includes step S200 as follows:

[0104] Step S210: Based on the initial monitoring dataset, the zone temperatures of each region are compared in real time. If the zone temperature of a certain region exceeds the preset threshold, the flow rate of the cooling channel is increased by the flow control tool. If it is lower than the preset threshold, the flow rate is decreased by the flow control tool to obtain the adjusted flow distribution data.

[0105] For example, in the monitoring and adjustment of a mold cooling system, real-time temperature comparison for each area is a crucial step. Assume the mold is divided into four zones, each with a temperature threshold of 48 degrees Celsius. When the temperature of a zone exceeds this value, the flow rate of the cooling channels needs to be increased using flow control tools. For instance, if zone 3 reaches a temperature of 52 degrees Celsius, significantly higher than the threshold, the flow rate can be adjusted from 1.5 m / s to 2.2 m / s to enhance cooling. Conversely, for zones with temperatures below the threshold, such as zone 4 at 40 degrees Celsius, the flow rate can be reduced from 2.0 m / s to 1.6 m / s to avoid overcooling and resource waste.

[0106] Step S220: Using a data acquisition tool, obtain dynamic change information of each cooling channel from the adjusted flow distribution data, perform matching processing on the relationship between medium characteristics and flow adjustment, and determine whether the flow distribution of each area has reached a balanced state.

[0107] For example, after acquiring the adjusted flow distribution data, extracting the dynamic changes of each cooling channel using data acquisition tools is particularly important. Regarding the flow adjustment in zone 3, it can be observed that after the flow increased, the temperature gradually decreased from 52 degrees Celsius to 47 degrees Celsius over a period of time, indicating that the adjustment had some effect. In zone 4, after the flow decreased, the temperature rose slightly to 42 degrees Celsius, but remained within a safe range. This dynamic change information helps determine the matching relationship between the medium characteristics and the flow adjustment, and thus assesses whether a balanced distribution has been achieved.

[0108] Step S230: If the flow distribution in a certain area does not reach a balanced state, the temperature monitoring data of the area is compared with the preset threshold using a data comparison tool to obtain the flow adjustment range of the cooling channel in the area and obtain the corrected flow distribution parameters.

[0109] For example, for areas that have not achieved a balanced distribution, a secondary verification is needed using data comparison tools. Taking zone 3 as an example, although the temperature decreased after the flow adjustment, it was still higher than other zones, indicating that the flow distribution was not yet balanced. Further analysis of the temperature monitoring data revealed that the flow adjustment in its cooling channels was insufficient, requiring an additional 0.3 m / s increase in flow, resulting in a corrected parameter of 2.5 m / s. This secondary verification can more accurately pinpoint the problem area and ensure the rationality of the adjustment direction.

[0110] Step S240: Based on the corrected flow distribution parameters, use a data storage tool to integrate the flow adjustment results of each region with the dynamic change information of the zone temperature, and determine whether the distribution balance requirement of all regions is met.

[0111] For example, data storage tools are crucial when integrating flow rate adjustment results with dynamic temperature changes in different zones. For all zones, adjusted flow rate values, temperature trends, and other information can be stored hierarchically. For instance, zone 3 might have a final flow rate of 2.5 m / s and a stable temperature of 47 degrees Celsius, while zone 4 might have a flow rate of 1.6 m / s and a temperature of 42 degrees Celsius. Integrating this data allows us to determine if the distribution is balanced across all zones. If discrepancies remain, further fine-tuning is necessary. This approach provides a clear data foundation for subsequent optimization.

[0112] For example, in the above process, the criterion for judging balanced flow distribution can be based on how close each zone's temperature is to the threshold. Assuming the goal is to control the temperature of all zones within a range of 48 degrees Celsius plus or minus 2 degrees Celsius, through multiple adjustments and data comparisons, the temperatures of each zone can eventually become consistent. This method effectively improves the overall stability of the cooling system, providing reliable assurance for mold processing.

[0113] Furthermore, the mold cooling water supply, delivery, and quantitative control method proposed in this embodiment includes step S300 as follows:

[0114] Step S310: According to the dynamic adjustment module, the temperature information of each zone is obtained from the real-time monitored regional temperature data. The temperature information is compared with the preset threshold. If the temperature information of a certain area exceeds the preset threshold, the temperature calibration requirement of the area is determined by the data processing tool, and the corresponding deviation detection data is obtained.

[0115] For example, in the dynamic adjustment scenario of the mold cooling system, real-time monitoring of the zone temperature and adjustment of the flow rate is a core step.

[0116] To acquire temperature information for each zone, data can be collected every few minutes using sensor devices to ensure real-time data accuracy. For example, assuming the mold is divided into four zones with a preset temperature threshold of 50 degrees Celsius, monitoring reveals that the temperature in zone 1 is 53 degrees Celsius, significantly exceeding the threshold. In this case, data processing tools are needed to analyze the temperature deviation, obtaining a deviation value of 3 degrees Celsius, which will serve as the basis for subsequent adjustments.

[0117] Step S320: Using deviation detection data, analyze the cooling supply of the area through a flow calculation tool, and determine whether adjustment is needed based on the medium control logic. If adjustment is needed, generate supply correction parameters through a flow adjustment tool and determine the adjusted medium control information.

[0118] For example, when applying deviation detection data, the flow calculation tool can analyze whether the cooling supply to region 1 is sufficient, taking into account the characteristics of the cooling medium. Assuming the current flow rate is 1.2 m / s, the analysis reveals that an increase in flow rate is needed to reduce the temperature, and the medium control logic determines that adjustment is necessary. Therefore, the flow adjustment tool generates supply correction parameters, adjusting the flow rate to 1.8 m / s. This adjustment, based on deviation data and medium characteristics, ensures targeted application.

[0119] Step S330: Obtain the flow optimization status of each area through the adjusted medium control information, compare the flow optimization status with the distribution balance requirements, and if a certain area does not meet the distribution balance requirements, perform secondary calibration through the parameter adjustment tool to obtain the preliminary instruction for dynamic balance.

[0120] For example, after obtaining the adjusted media control information, it is necessary to check the flow optimization status of each region. Suppose that after adjustment, the temperature in region 1 drops to 49 degrees Celsius, close to the threshold, but is still slightly higher than other regions, not fully meeting the distribution balance requirement. At this point, a secondary calibration is performed using parameter adjustment tools. Analysis reveals that the flow increase is insufficient, and it is decided to further increase it by 0.2 m / s to 2.0 m / s, forming a preliminary instruction for dynamic balance. This secondary calibration can more accurately approach the balance target.

[0121] For example, regarding the integration of initial dynamic balance commands, the command generation tool can aggregate the final adjustment parameters of Region 1 with data from other regions to generate the final dynamic balance control commands. Assuming the temperatures of Regions 2, 3, and 4 are 48, 47, and 49 degrees Celsius, and the flow rates are 1.5, 1.4, and 1.6 m / s, respectively, a comprehensive assessment reveals that the overall cooling supply demand is basically met, but Region 1 still requires fine-tuning to ensure long-term stability. This integration method facilitates unified management of parameters across regions.

[0122] Step S340: Based on the initial dynamic balance command, use the command generation tool to integrate the parameter adjustment results of the region, generate the final dynamic balance control command, and determine whether the cooling supply requirements of all regions are met.

[0123] For example, when determining whether the cooling supply requirements of all areas are met, this can be assessed by comparing how close each area's temperature is to the threshold. Assuming the target temperature is controlled within a range of 50 degrees Celsius ± 1 degree Celsius, after multiple adjustments, the temperature in area 1 stabilizes at 49.5 degrees Celsius, and other areas are also within the target range, indicating that the overall demand is met. This method effectively improves the system's adaptability and stability, providing reliable support for mold processing.

[0124] Furthermore, the mold cooling water supply, delivery, and quantitative control method proposed in this embodiment includes step S400 as follows:

[0125] Step S410: Obtain real-time humidity data and dew point temperature data from the environmental monitoring tool. Calculate the difference between the humidity data and dew point temperature data using a data processing tool to obtain the specific difference information between them.

[0126] For example, in the operating environment of a mold cooling system, real-time data acquisition by environmental monitoring tools is a crucial aspect.

[0127] For monitoring humidity and dew point temperature data, environmental information can be collected every few minutes using high-precision sensors to ensure the timeliness of the data.

[0128] Assuming the current humidity is 75% and the dew point temperature is 18 degrees Celsius, while the actual ambient temperature is 20 degrees Celsius, the difference between the two is calculated to be 2 degrees Celsius using data processing tools.

[0129] Step S420: Compare the specific difference information with the preset safety threshold. If the specific difference information is less than the preset safety threshold, generate a condensation prevention signal through the early warning triggering tool to determine the range of parameters that need to be adjusted.

[0130] If the preset safety threshold is 3 degrees Celsius, the difference is obviously less than the threshold, indicating that there is a risk of condensation and preventive measures need to be taken.

[0131] For example, during the comparison process between the difference calculation and the safety threshold, the early warning triggering tool generates a condensation prevention signal based on the difference information. If the difference remains consistently below the threshold, the system automatically marks the current environment as high-risk and determines the range of parameters that need adjustment, such as reducing the upper limit of the cooling medium temperature by 2 degrees Celsius. This signal generation method can promptly alert the system to potential problems, providing a basis for subsequent adjustments.

[0132] Step S430: Using the condensation prevention signal and in conjunction with the medium control logic, the upper limit of the cooling medium temperature is calibrated using the parameter adjustment tool to obtain the adjusted upper limit temperature parameter value.

[0133] For example, in the application of condensation prevention signals, the upper limit of the cooling medium's temperature is calibrated using parameter adjustment tools in conjunction with the medium control logic. Assuming the original upper limit was 25 degrees Celsius, and given the current relatively small difference between humidity and dew point, the system analysis determines that the upper limit should be adjusted to 23 degrees Celsius to reduce the likelihood of condensation. This calibration process considers environmental factors and medium characteristics, ensuring that the adjustment is targeted.

[0134] Step S440: By adjusting the upper limit temperature parameter value, the operating status of the cooling medium is updated using the medium control tool to determine whether the requirements for condensation prevention are met, and the final control command is generated.

[0135] For example, the media control tool will update the operating status of the cooling medium in response to the adjusted upper temperature limit parameter value.

[0136] Assuming the adjusted medium temperature stabilizes at 22.5 degrees Celsius, the system continuously monitors changes in ambient humidity and dew point temperature to determine if condensation prevention requirements are met. If the humidity drops to 70% and the difference increases to 3.5 degrees Celsius, indicating that the risk level has been exceeded, the system generates a final control command to maintain the current state. This dynamic update mechanism helps maintain environmental stability.

[0137] For example, during the generation of the final control command, the system comprehensively evaluates all relevant parameters. Assuming that after multiple monitoring sessions, the humidity is stable at 68%, the dew point temperature is 17 degrees Celsius (a difference of 4 degrees Celsius, far exceeding the safety threshold), and the cooling medium temperature remains below 23 degrees Celsius, the system confirms that the risk of condensation has been eliminated, and the final command will lock the current parameter configuration. This comprehensive judgment method ensures the stable operation of the mold cooling system in complex environments, providing reliable protection for the processing.

[0138] For example, continuous monitoring of humidity and dew point temperature data can extract trend information from historical data to aid decision-making. Suppose that the humidity dropped from 80% to 75% in the past hour while the dew point temperature remained stable, the system can predict a low risk of condensation in the short term, thereby optimizing the adjustment frequency and reducing unnecessary parameter fluctuations. This trend-based analysis further enhances the system's adaptability and ensures the rational allocation of resources.

[0139] Furthermore, the mold cooling water supply, delivery, and quantitative control method proposed in this embodiment includes step S500:

[0140] Step S510: Obtain real-time operational data for zonal control from the environmental monitoring tool, and classify the real-time operational data using the data acquisition tool to obtain the classified operational dataset.

[0141] For example, in the application of environmental monitoring tools, the acquisition of real-time operational data for zonal control is a crucial step.

[0142] Environmental monitoring tools can be deployed in different areas of the mold cooling system to collect real-time operational data such as temperature and humidity in each zone. Assuming a mold cooling system is divided into three zones, the monitoring tool for each zone collects data every 5 minutes to ensure real-time data accuracy. This method allows for a clear understanding of the operational status of each zone, providing a foundation for subsequent classification and processing.

[0143] For example, when classifying real-time operational data using data acquisition tools, the data can be divided according to partitions and data types. Suppose the collected data includes region 1 with a temperature of 22 degrees Celsius and humidity of 70%, and region 2 with a temperature of 24 degrees Celsius and humidity of 78%. The data acquisition tool will classify this data by region and indicator, forming a structured operational dataset. This classification method facilitates subsequent analysis and uploading, ensuring the systematic nature of data processing.

[0144] Step S520: Based on the classified running dataset, use a data transmission tool to upload it to a pre-established database storage unit to confirm that the running data has been completely recorded.

[0145] For example, in data transfer tools, the categorized runtime dataset is uploaded to a pre-established database storage unit. Assuming the system uses a cloud database, the data transfer tool uploads the dataset via an encrypted channel, ensuring data security during transmission. Upon completion of the upload, the system automatically generates a confirmation message indicating that the data has been completely stored. This approach facilitates centralized data management and subsequent retrieval.

[0146] Step S530: For the running dataset in the database storage unit, compare the preset threshold adjustment logic with the parameter calibration tool. If the running data in the running dataset exceeds the preset threshold range, generate the corresponding threshold adjustment signal to obtain the adjusted threshold range.

[0147] For example, when comparing a running dataset within a database storage unit, the parameter calibration tool uses preset threshold adjustment logic. Suppose the preset humidity threshold for region 2 is 75%, while the actual data is 78%, exceeding the range. The parameter calibration tool will generate a threshold adjustment signal, suggesting that the humidity control target for that region be adjusted to 73%. This dynamic comparison and adjustment mechanism can promptly identify potential problems.

[0148] Step S540: Based on the adjusted threshold range, generate a new instruction set for zone control using the control instruction generation tool in conjunction with the current state of the cooling parameters, and determine whether the new instruction set meets the preset cooling parameter requirements.

[0149] For example, after generating a threshold adjustment signal, the system, based on the adjusted threshold range, uses a control instruction generation tool to generate a new instruction set for zone control, taking into account the current state of the cooling parameters. Assuming the cooling medium temperature in zone 2 was originally set to 25 degrees Celsius, the instruction set requires it to be reduced to 23 degrees Celsius according to the adjustment signal. After the new instruction set is generated, the system further determines whether it meets the cooling parameter requirements, such as the lower limit of the medium temperature not being lower than 20 degrees Celsius. This judgment process ensures the executability of the instructions.

[0150] For example, from a business perspective, the core of real-time data monitoring and command adjustment in zone control lies in ensuring the stability of the mold cooling system. Assuming that persistently high humidity in zone 2 may lead to localized condensation risks, this risk can be effectively mitigated through the comprehensive management of the data classification, uploading, comparison, and command generation process described above. This business logic forms a closed loop from data acquisition to command execution, ensuring that each link supports the others.

[0151] For example, as an extension, the system can also store historical operating data in the database to assist in optimizing the threshold adjustment logic. Suppose that by analyzing the humidity change trend over the past 24 hours, it is found that the humidity fluctuations in region 2 are relatively large; the system can then further refine the threshold range for that region. This supplementary analysis based on historical data can improve the accuracy of zonal control and provide a more reliable guarantee for system operation.

[0152] This invention relates to a mold cooling water supply, delivery, and quantitative control system for implementing the aforementioned mold cooling water supply, delivery, and quantitative control method. The mold cooling water supply, delivery, and quantitative control system includes an acquisition module, a first generation module, a second generation module, an adjustment module, and an update module. The acquisition module acquires cooling medium flow rate, temperature, and corresponding surface temperature change data for each zone of the mold, forming an initial monitoring dataset. The first generation module, based on the initial monitoring dataset, increases the flow rate of the corresponding cooling channel if the zone temperature exceeds a preset threshold, and decreases the flow rate if it is below the preset threshold, generating an optimized flow distribution scheme. The second generation module uses a dynamic adjustment module to calibrate the temperature parameters of the optimized flow distribution scheme; if a zone temperature deviation is detected, it corrects the cooling medium supply, generating a dynamic balance control command. The adjustment module acquires humidity and dew point temperature data from an environmental monitoring system; if the difference between humidity and dew point temperature is less than a safety threshold, it triggers a condensation prevention mechanism and adjusts the upper limit parameter of the cooling medium temperature. The update module executes zone cooling control according to the final control parameters and feeds the operating data back to the database to update the preset threshold and optimized algorithm parameters.

[0153] Furthermore, the mold cooling water supply, delivery, and quantitative control system provided in this embodiment includes a first acquisition unit, a first determination unit, a first judgment unit, and a second acquisition unit. The first acquisition unit acquires flow rate and temperature data of the cooling medium from each partition of the mold through a pre-established sensor network, and simultaneously collects surface temperature information of the corresponding areas, forming independent data record groups for each partition to obtain a preliminary monitoring data set. The first determination unit classifies and organizes the flow rate and temperature data using data processing tools based on the preliminary monitoring data set, performs matching analysis on the relationship between cooling medium characteristics and flow rate changes, and determines the correlation characteristics between the medium and surface temperature in each partition. The first judgment unit dynamically adjusts the surface temperature change of the partition using a data comparison tool if the flow rate or temperature data of a certain partition exceeds a preset threshold, obtains the adjusted data mapping relationship, and determines whether there are abnormal fluctuations. The second acquisition unit integrates the adjusted data mapping relationship, constructs an initial dataset using data storage tools, and structurally stores the comprehensive information of flow rate changes and surface temperature according to the needs of regional division, obtaining a complete data foundation.

[0154] Preferably, the mold cooling water supply, delivery, and quantitative control system provided in this embodiment includes a first generation module comprising a third acquisition unit, a second determination unit, a fourth acquisition unit, and a second judgment unit. The third acquisition unit is used to compare the zone temperatures of each region in real time based on the initial monitoring dataset. If the zone temperature of a certain region exceeds a preset threshold, the flow rate of the cooling channel is increased using a flow control tool; if it is below the preset threshold, the flow rate is decreased using the flow control tool, resulting in adjusted flow distribution data. The second determination unit is used to acquire dynamic change information of each cooling channel from the adjusted flow distribution data using a data acquisition tool, and to match the relationship between medium characteristics and flow adjustment to determine whether the flow distribution of each region has reached a balanced state. The fourth acquisition unit is used to perform a secondary verification of the temperature monitoring data of a certain region against a preset threshold using a data comparison tool if the flow distribution of a certain region has not reached a balanced state, acquiring the flow adjustment range of the cooling channel in that region and obtaining corrected flow distribution parameters. The second judgment unit is used to integrate the flow adjustment results of each region with the dynamic change information of the zone temperature using a data storage tool based on the corrected flow distribution parameters, to determine whether the distribution balance requirements of all regions are met.

[0155] Furthermore, the mold cooling water supply, delivery, and quantitative control system provided in this embodiment includes a second generation module comprising a fifth acquisition unit, a third determination unit, a sixth acquisition unit, and a third judgment unit. The fifth acquisition unit is used to acquire temperature information for each zone from real-time monitored regional temperature data based on the dynamic adjustment module. It compares the temperature information with a preset threshold. If the temperature information for a certain zone exceeds the preset threshold, it determines the temperature calibration requirement for that zone using a data processing tool, obtaining corresponding deviation detection data. The third determination unit uses the deviation detection data to analyze the cooling supply to that zone using a flow calculation tool, combined with… The media control logic determines whether adjustment is needed. If adjustment is needed, it generates supply correction parameters through the flow regulation tool to determine the adjusted media control information. The sixth acquisition unit is used to acquire the flow optimization status of each region through the adjusted media control information, compare the flow optimization status with the distribution balance requirements, and if a region does not meet the distribution balance requirements, it performs secondary calibration through the parameter adjustment tool to obtain the preliminary dynamic balance command. The third judgment unit is used to integrate the parameter adjustment results of the region according to the preliminary dynamic balance command, and generate the final dynamic balance control command to determine whether the cooling supply requirements of all regions are met.

[0156] The mold cooling water supply, delivery, and quantitative control method and system provided in this embodiment, compared with the prior art, forms an initial dataset by real-time monitoring of the flow rate, temperature, and surface temperature changes of the cooling medium in each zone, and dynamically adjusts the cooling channel flow rate according to a preset threshold to generate an optimized flow distribution scheme. Simultaneously, this invention uses a dynamic adjustment module to calibrate the temperature parameters of the optimized scheme, achieving precise control. Furthermore, this embodiment combines ambient humidity and dew point temperature data to trigger a condensation prevention mechanism, adjusting the upper limit of the cooling medium temperature to effectively prevent condensation on the mold surface. Finally, this embodiment executes zoned cooling control according to the control parameters and feeds the operating data back to the database for continuous optimization of algorithm parameters, achieving intelligent and precise control of mold temperature, improving product quality and production efficiency. The specific beneficial effects of the mold cooling water supply, delivery, and quantitative control method and system provided in this embodiment are as follows:

[0157] 1. Precise temperature control: Through zoned monitoring and dynamic adjustment, the overall temperature uniformity of the mold is improved by more than 30%, avoiding deformation defects caused by local overheating or undercooling.

[0158] II. Energy saving and consumption reduction: Optimize flow distribution based on real-time data to reduce ineffective cooling water circulation, thereby reducing energy consumption by 15%-20%.

[0159] 3. Anti-condensation protection: The upper limit of the cooling temperature is dynamically adjusted according to environmental parameters to effectively prevent rust or product contamination caused by condensation on the mold surface.

[0160] IV. Adaptive Optimization: By continuously correcting control parameters through feedback from operating data, the algorithm's adaptability to complex working conditions is improved.

[0161] V. Fault Early Warning: Flow and temperature deviation detection can detect cooling water channel blockage or leakage problems in advance, reducing the failure rate.

[0162] Although preferred embodiments of the invention have been described, those skilled in the art, upon learning the basic inventive concept, can make other changes and modifications to these embodiments. Therefore, the appended claims are intended to be interpreted as including both the preferred embodiments and all changes and modifications falling within the scope of the invention. Clearly, those skilled in the art can make various alterations and modifications to the invention without departing from its spirit and scope. Thus, if these modifications and modifications of the invention fall within the scope of the claims and their equivalents, the invention is also intended to include these modifications and modifications.

Claims

1. A method for supplying, conveying, and quantitatively controlling mold cooling water, characterized in that, Includes the following steps: The flow rate and temperature of the cooling medium in each zone of the mold, as well as the surface temperature change data of the corresponding areas, are obtained to form an initial monitoring dataset. Based on the initial monitoring dataset, if the zone temperature exceeds a preset threshold, the flow rate of the corresponding cooling channel is increased; if it is below the preset threshold, the flow rate is reduced, thus generating an optimized flow distribution scheme. The temperature parameters of the optimized flow distribution scheme are calibrated using a dynamic adjustment module. If a regional temperature deviation is detected, the cooling medium supply is corrected, and a dynamic balance control command is generated. Humidity and dew point temperature data are obtained from the environmental monitoring system. If the difference between humidity and dew point temperature is less than the safety threshold, the condensation prevention mechanism is triggered, and the upper limit parameter of the cooling medium temperature is adjusted. The zoned cooling control is executed according to the final control parameters, and the operating data is fed back to the database to update the preset thresholds and optimize the algorithm parameters. Based on the initial monitoring dataset, the zone temperatures of each region are compared in real time. If the zone temperature of a certain region exceeds the preset threshold, the flow rate of the cooling channel is increased by the flow control tool. If it is lower than the preset threshold, the flow rate is decreased by the flow control tool to obtain the adjusted flow distribution data. By using data acquisition tools, dynamic change information of each cooling channel is obtained from the adjusted flow distribution data. The relationship between medium characteristics and flow adjustment is matched to determine whether the flow distribution in each area has reached a balanced state. If the flow distribution in a certain area does not reach a balanced state, the temperature monitoring data of that area is verified against the preset threshold using a data comparison tool to obtain the flow adjustment range of the cooling channel in that area and obtain the corrected flow distribution parameters. Based on the corrected flow distribution parameters, data storage tools are used to integrate the flow adjustment results of each region with the dynamic change information of the zone temperature to determine whether the distribution balance requirement of all regions is met.

2. The method for supplying, conveying, and quantitatively controlling mold cooling water as described in claim 1, characterized in that, The steps of acquiring the cooling medium flow rate, temperature, and corresponding surface temperature change data of each zone of the mold to form an initial monitoring dataset include: By using a pre-established sensor network, flow rate and temperature data of the cooling medium are obtained from each zone of the mold, and surface temperature information of the corresponding area is collected at the same time. Independent data recording groups are formed for each zone to obtain a preliminary monitoring data set. Based on the preliminary monitoring data set, data processing tools were used to classify and organize the flow rate data and temperature data, and a matching analysis was performed on the relationship between the characteristics of the cooling medium and the flow rate changes to determine the correlation characteristics between the medium and the surface temperature in each zone. If the flow rate data or temperature data of a certain partition in the associated features exceeds the preset threshold, the surface temperature change of the partition is dynamically adjusted by the data comparison tool to obtain the adjusted data mapping relationship and determine whether there is abnormal fluctuation. By integrating and adjusting the data mapping relationships, an initial dataset is constructed using data storage tools. To meet the needs of regional division, the comprehensive information on flow rate changes and surface temperature is structured and stored to obtain a complete data foundation.

3. The method for supplying, conveying, and quantitatively controlling mold cooling water as described in claim 1, characterized in that, The steps of using a dynamic adjustment module to calibrate the temperature parameters of the optimized flow distribution scheme, and correcting the cooling medium supply if a regional temperature deviation is detected, and generating a dynamic balance control command include: According to the dynamic adjustment module, the temperature information of each zone is obtained from the real-time monitored regional temperature data. The temperature information is compared with a preset threshold. If the temperature information of a certain zone exceeds the preset threshold, the temperature calibration requirement of the zone is determined by the data processing tool, and the corresponding deviation detection data is obtained. Using the aforementioned deviation detection data, the cooling supply to the area is analyzed using a flow calculation tool. Combined with the medium control logic, it is determined whether adjustments are needed. If adjustments are needed, the supply correction parameters are generated using a flow adjustment tool to determine the adjusted medium control information. By using the adjusted media control information, the flow optimization status of each region is obtained. The flow optimization status is compared with the distribution balance requirements. If a region does not meet the distribution balance requirements, a secondary calibration is performed using the parameter adjustment tool to obtain the initial instructions for dynamic balance. Based on the initial dynamic balance command, the parameter adjustment results of the region are integrated using the command generation tool to generate the final dynamic balance control command, and it is determined whether the cooling supply requirements of all regions are met.

4. The method for supplying, conveying, and quantitatively controlling mold cooling water as described in claim 1, characterized in that, The steps of acquiring humidity and dew point temperature data from the environmental monitoring system, and triggering a condensation prevention mechanism if the difference between humidity and dew point temperature is less than a safety threshold, and adjusting the upper limit parameter of the cooling medium temperature, include: Real-time humidity and dew point temperature data are obtained from environmental monitoring tools. For the humidity data and the dew point temperature data, the difference is calculated using data processing tools to obtain the specific difference information between the two. The specific difference information is compared with a preset safety threshold. If the specific difference information is less than the preset safety threshold, a condensation prevention signal is generated through an early warning triggering tool to determine the range of parameters that need to be adjusted. Using the aforementioned condensation prevention signal, combined with the medium control logic, the upper limit of the cooling medium temperature is calibrated through a parameter adjustment tool to obtain the adjusted upper limit temperature parameter value. By adjusting the upper limit temperature parameter value, the operating status of the cooling medium is updated using the medium control tool to determine whether the requirements for condensation prevention are met, and the final control command is generated.

5. The method for supplying, conveying, and quantitatively controlling mold cooling water as described in claim 1, characterized in that, The step of executing zoned cooling control based on the final control parameters and feeding back the operating data to the database for updating the preset threshold and optimization algorithm parameters includes: Real-time operational data for zonal control is obtained from environmental monitoring tools, and the real-time operational data is classified and processed by data acquisition tools to obtain a classified operational dataset. Based on the categorized running dataset, a data transfer tool is used to upload it to a pre-established database storage unit to confirm that the running data has been completely recorded; For the running dataset in the database storage unit, the preset threshold adjustment logic is compared with the parameter calibration tool. If the running data in the running dataset exceeds the preset threshold range, the corresponding threshold adjustment signal is generated to obtain the adjusted threshold range. Based on the adjusted threshold range, a new instruction set for zone control is generated by the control instruction generation tool in conjunction with the current state of the cooling parameters, and it is determined whether the new instruction set meets the preset cooling parameter requirements.

6. A mold cooling water supply, delivery, and metering control system, used to implement the mold cooling water supply, delivery, and metering control method as described in any one of claims 1 to 5, characterized in that, The mold cooling water supply, delivery, and metering control system includes: The acquisition module is used to acquire the cooling medium flow rate, temperature and corresponding surface temperature change data of each zone of the mold to form an initial monitoring dataset; The first generation module is used to generate an optimized flow distribution scheme based on the initial monitoring dataset, which increases the flow rate of the corresponding cooling channel if the zone temperature exceeds a preset threshold, and decreases the flow rate if it is below the preset threshold. The second generation module is used to calibrate the temperature parameters of the optimized flow distribution scheme using the dynamic adjustment module. If a regional temperature deviation is detected, the cooling medium supply is corrected, and a dynamic balance control command is generated. The adjustment module is used to obtain humidity and dew point temperature data from the environmental monitoring system. If the difference between humidity and dew point temperature is less than the safety threshold, the condensation prevention mechanism is triggered to adjust the upper limit parameter of the cooling medium temperature. The update module is used to execute zoned cooling control based on the final control parameters and feed the running data back to the database to update the preset thresholds and optimize algorithm parameters.

7. The mold cooling water supply, delivery, and metering control system as described in claim 6, characterized in that, The acquisition module includes: The first acquisition unit is used to acquire flow rate and temperature data of cooling medium from each partition of the mold through a pre-established sensor network, and at the same time collect surface temperature information of the corresponding area, forming an independent data recording group for each partition to obtain a preliminary monitoring data set. The first determining unit is used to classify and organize the flow rate data and temperature data according to the preliminary monitoring data set using data processing tools, perform matching analysis on the relationship between cooling medium characteristics and flow rate changes, and determine the correlation characteristics between the medium and surface temperature in each zone. The first judgment unit is used to dynamically adjust the surface temperature change of the partition through a data comparison tool if the flow rate data or temperature data of a certain partition in the associated features exceeds a preset threshold, obtain the adjusted data mapping relationship, and determine whether there is abnormal fluctuation. The second acquisition unit is used to construct an initial dataset by integrating and adjusting the data mapping relationship and using data storage tools. In accordance with the needs of regional division, it stores the comprehensive information of flow rate change and surface temperature in a structured manner to obtain a complete data foundation.

8. The mold cooling water supply, delivery, and metering control system as described in claim 6, characterized in that, The first generation module includes: The third acquisition unit is used to compare the partition temperature of each region in real time based on the initial monitoring dataset. If the partition temperature of a certain region exceeds the preset threshold, the flow rate of the cooling channel is increased by the flow control tool. If it is lower than the preset threshold, the flow rate is decreased by the flow control tool to obtain the adjusted flow distribution data. The second determining unit is used to obtain dynamic change information of each cooling channel from the adjusted flow distribution data through data acquisition tools, perform matching processing on the relationship between medium characteristics and flow adjustment, and determine whether the flow distribution of each area has reached a balanced state. The fourth acquisition unit is used to perform a secondary verification of the temperature monitoring data of a certain area with a preset threshold by using a data comparison tool if the flow distribution in a certain area does not reach a balanced state, to obtain the flow adjustment range of the cooling channel in that area, and to obtain the corrected flow distribution parameters. The second judgment unit is used to integrate the flow adjustment results of each region with the dynamic change information of the partition temperature based on the corrected flow distribution parameters using data storage tools, and to determine whether the distribution balance requirement of all regions is met.

9. The mold cooling water supply, delivery, and metering control system as described in claim 6, characterized in that, The second generation module includes: The fifth acquisition unit is used to acquire temperature information of each zone from the real-time monitored regional temperature data according to the dynamic adjustment module, compare the temperature information with a preset threshold, and if the temperature information of a certain area exceeds the preset threshold, determine the temperature calibration requirement of the area through the data processing tool and obtain the corresponding deviation detection data. The third determining unit is used to analyze the cooling supply of the area using the deviation detection data and the flow calculation tool, and determine whether adjustment is needed in combination with the medium control logic. If adjustment is needed, the supply correction parameters are generated by the flow adjustment tool to determine the adjusted medium control information. The sixth acquisition unit is used to acquire the flow optimization status of each region through the adjusted medium control information, compare the flow optimization status with the distribution balance requirements, and if a region does not meet the distribution balance requirements, perform secondary calibration through the parameter adjustment tool to obtain the preliminary instruction for dynamic balance. The third judgment unit is used to integrate the parameter adjustment results of the region based on the initial dynamic balance command, using the command generation tool to generate the final dynamic balance control command, and to determine whether the cooling supply requirements of all regions are met.

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