Mold cooling water supply, conveying and quantitative control method and system

Abstract

The invention discloses a mold cooling water supply, conveying and quantitative control method and system. The method comprises the following steps: acquiring an initial monitoring data set; according to the initial monitoring data set, if the partition temperature exceeds a preset threshold value, the flow of the corresponding cooling channel is increased, if the partition temperature is lower than the preset threshold value, the flow is reduced, and an optimized flow distribution scheme is generated; a dynamic adjusting module is adopted to conduct temperature parameter calibration on 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 an environment monitoring system, if the difference value between the humidity and the dew point temperature is smaller than a safety threshold value, a condensation prevention mechanism is triggered, and a cooling medium temperature upper limit parameter is adjusted; and executing partition cooling control according to the final regulation and control parameters, and feeding back the operation data to a database for updating a preset threshold value and optimizing algorithm parameters. Intelligent and accurate control over the mold temperature is achieved, and the product quality and the production efficiency are improved.

Description

Technical Field

[0001] The present invention relates to the technical field of mold cooling, and in particular discloses a method and system for supplying, conveying and quantitatively controlling mold cooling water. Background Art

[0002] In modern manufacturing, mold cooling technology, as a vital link in ensuring product quality and production efficiency, has irreplaceable value. Especially in the automotive, electronic, and optical sectors, the stability and accuracy of the cooling system directly impact the molding quality and production cycle of the product. However, existing cooling water supply and control methods on the market currently have limitations, primarily manifested in insufficient uniformity in cooling water distribution, high energy consumption, and poor adaptability to complex environments. These issues lead to inaccurate mold temperature control during the production process, which in turn affects product consistency. An in-depth analysis of the challenges faced in this area reveals that the core issues of mold cooling water systems are concentrated on several key technical factors.

[0003] The first is the problem of uniform distribution of cooling water in different areas of the mold. Due to the lack of effective zoning control measures, some areas may be overcooled or overheated, resulting in uneven heat dissipation of the mold.

[0004] This problem further raises the difficulty of dynamically adjusting the cooling water flow and temperature, because the mold's cooling requirements vary significantly in different molding stages or environmental conditions. If the water flow and temperature cannot be adjusted in real time, it will cause energy waste or product defects.

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

[0006] Therefore, how to design a comprehensive control method that can achieve uniform distribution of cooling water, dynamically adjust flow rate and temperature, and combine environmental parameter feedback to prevent condensation has become a key issue in improving the efficiency and stability of the mold cooling system. Summary of the Invention

[0007] The present invention provides a method and system for supplying, delivering and quantitatively controlling mold cooling water, aiming to solve at least one defect existing in the above-mentioned prior art.

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

[0009] Obtain the cooling medium flow rate, temperature and surface temperature change data of each mold partition to form an initial monitoring data set;

[0010] Based on the initial monitoring data set, if the partition temperature exceeds the preset threshold, the corresponding cooling channel flow is increased; if it is lower than the preset threshold, the flow is reduced to generate an optimized flow distribution plan;

[0011] The 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 instruction is generated;

[0012] 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 and the upper temperature limit parameter of the cooling medium is adjusted.

[0013] Partition cooling control is performed according to the final control parameters, and the operating data is fed back to the database for updating preset thresholds and optimizing algorithm parameters.

[0014] Furthermore, the steps of obtaining the cooling medium flow rate, temperature, and surface temperature change data of each zone of the mold and forming an initial monitoring data set include:

[0015] Through the pre-established sensor network, the flow rate and temperature data of the cooling medium are obtained from each zone of the mold. At the same time, the surface temperature information of the corresponding area is collected. An independent data record group is formed for each zone to obtain a preliminary monitoring data set.

[0016] Based on the preliminary monitoring data set, data processing tools are used to classify and organize the flow rate data and temperature data, and a matching analysis is conducted on the relationship between the cooling medium characteristics and flow rate changes to determine the correlation characteristics between the medium and surface temperature in each zone;

[0017] If the velocity data or temperature data of a certain partition in the associated feature exceeds the preset threshold, the surface temperature change of the partition is dynamically adjusted through the data comparison tool, and the adjusted data mapping relationship is obtained to determine whether there is any abnormal fluctuation;

[0018] By integrating the adjusted data mapping relationship, the initial data set is constructed using data storage tools. According to the needs of regional division, the comprehensive information of flow rate changes and surface temperature is structured and stored to obtain a complete data foundation.

[0019] Furthermore, based on the initial monitoring data set, if the partition temperature exceeds a preset threshold, the flow rate of the corresponding cooling channel is increased; if it is lower than the preset threshold, the flow rate is reduced. The steps of generating an optimized flow distribution plan include:

[0020] Based on the initial monitoring data set, the zone temperatures of each area are compared in real time. If the zone temperature of a certain area 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 reduced through the flow control tool to obtain the adjusted flow distribution data.

[0021] Through data acquisition tools, the dynamic change information of each cooling channel is obtained from the adjusted flow distribution data. The relationship between the medium characteristics and the 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 is not balanced, the temperature monitoring data of the area is re-verified against 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.

[0023] According to the corrected flow distribution parameters, a data storage tool is used to integrate the flow adjustment results of each area with the dynamic change information of the partition temperature to determine whether the distribution balance requirements of all areas are met.

[0024] Furthermore, 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 amount is corrected. The steps of generating a dynamic balance control instruction include:

[0025] The dynamic adjustment module obtains the temperature information of each zone from the real-time monitored regional temperature data and compares the temperature information with the preset threshold. If the temperature information of a certain zone exceeds the preset threshold, the data processing tool determines the temperature calibration requirement of the zone and obtains the corresponding deviation detection data.

[0026] Deviation detection data is used to analyze the cooling supply volume in the area through a flow calculation tool. Combined with the medium control logic, it is determined whether adjustment is needed. If adjustment is required, the flow adjustment tool generates supply correction parameters and determines the adjusted medium control information.

[0027] The adjusted media control information is used to obtain the flow optimization status of each area. This is then 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 preliminary instructions for dynamic balance.

[0028] Based on the preliminary dynamic balance instructions, the instruction generation tool is used to integrate the parameter adjustment results of the area to generate the final dynamic balance control instructions to determine whether the cooling supply requirements of all areas are met.

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

[0030] Obtain real-time humidity data and dew point temperature data from environmental monitoring tools, and use data processing tools to perform difference calculations on the humidity and dew point temperature data to obtain 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 parameter range that needs to be adjusted;

[0032] Using the condensation prevention signal and the medium control logic, the upper temperature limit of the cooling medium is calibrated through the parameter adjustment tool to obtain the adjusted upper temperature limit parameter value;

[0033] The operating status of the cooling medium is updated with the help of the medium control tool through the adjusted temperature upper limit parameter value to determine whether the requirements for condensation prevention are met and generate the final control instructions.

[0034] Furthermore, the steps of executing zone cooling control according to the final control parameters and feeding back the operating data to the database for updating the preset thresholds and optimizing the algorithm parameters include:

[0035] Obtain real-time operation data for zoning control from environmental monitoring tools, classify and process the real-time operation data using data acquisition tools to obtain a classified operation data set;

[0036] Based on the classified operating data set, use the data transmission tool to upload it to the pre-established database storage unit to ensure that the operating data has been fully recorded;

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

[0038] Based on the adjusted threshold range, a new instruction set for partition control is generated by the control instruction generation tool in combination with the current status of the cooling parameters to determine 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 quantitative control system for implementing the above-mentioned mold cooling water supply, delivery, and quantitative control method. The mold cooling water supply, delivery, and quantitative control system includes:

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

[0041] The first generation module is used to generate an optimized flow distribution plan based on the initial monitoring data set, increasing the flow of the corresponding cooling channel if the partition temperature exceeds a preset threshold, and reducing the flow if it is lower than the preset threshold;

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

[0044] The update module is used to perform partition cooling control according to the final control parameters and feed back the operating data to the database for updating the preset thresholds and optimizing the algorithm parameters.

[0045] Furthermore, the acquisition module includes:

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

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

[0048] The first judgment unit is configured to dynamically adjust the surface temperature change of a partition through a data comparison tool if the flow rate data or temperature data of a partition in the associated feature exceeds a 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 data set by integrating the adjusted data mapping relationship using a data storage tool. According to the needs of regional division, the comprehensive information of flow rate changes and surface temperature is structured and stored to obtain a complete data foundation.

[0050] Furthermore, the first generation module includes:

[0051] The third acquisition unit is used to perform real-time comparison of the zone temperature of each area based on the initial monitoring data set. If the zone temperature of a certain area exceeds a 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 reduced by the flow control tool to obtain adjusted flow distribution data;

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

[0053] A fourth acquisition unit is configured to, if the flow distribution in a certain area does not reach a balanced state, perform a secondary verification of the temperature monitoring data of the area with a preset threshold value using a data comparison tool, obtain the flow adjustment amplitude of the cooling channel in the area, and obtain a corrected flow distribution parameter;

[0054] The second judgment unit is used to integrate the flow adjustment result of each area with the dynamic change information of the zone temperature based on the corrected flow distribution parameters using a data storage tool to determine whether the distribution balance requirements of all areas are met.

[0055] Furthermore, the second generation module includes:

[0056] a fifth acquisition unit, configured 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 value, and if the temperature information of a zone exceeds the preset threshold value, determine the temperature calibration requirement of the zone through a data processing tool, and obtain corresponding deviation detection data;

[0057] A third determining unit is configured to use the deviation detection data to analyze the cooling supply volume of the area using a flow calculation tool, and determine whether adjustment is required in combination with the medium control logic. If adjustment is required, the flow adjustment tool generates a supply correction parameter to determine the adjusted medium control information;

[0058] The sixth acquisition unit is configured to obtain the flow optimization status of each area using the adjusted medium control information, compare the flow optimization status with the distribution balance requirement, and if a certain area does not meet the distribution balance requirement, perform a secondary calibration using a parameter adjustment tool to obtain preliminary instructions for dynamic balance;

[0059] The third judgment unit is used to integrate the parameter adjustment results of the area using the instruction generation tool according to the preliminary dynamic balance instruction, generate the final dynamic balance control instruction, and judge whether the cooling supply demand of all areas is met.

[0060] The beneficial effects achieved by the present invention are:

[0061] The present invention provides a method and system for supplying, delivering, and quantitatively controlling cooling water for a mold. By real-time monitoring of the flow rate, temperature, and surface temperature changes of the cooling medium in each partition, an initial data set is formed, and the cooling channel flow is dynamically adjusted according to a preset threshold value to generate an optimized flow distribution scheme. At the same time, the present invention uses a dynamic adjustment module to calibrate the temperature parameters of the optimization scheme to achieve precise control. In addition, the present invention also combines the ambient humidity and dew point temperature data to trigger a condensation prevention mechanism, adjust the upper limit of the cooling medium temperature, and effectively prevent condensation on the mold surface. Finally, the present invention performs partition cooling control according to the control parameters, and feeds back the operating data to the database for continuous optimization of the algorithm parameters, thereby achieving intelligent and precise control of the mold temperature and improving product quality and production efficiency. The beneficial effects that can be achieved by the method and system for supplying, delivering, and quantitatively controlling cooling water for a mold provided by the present invention are specifically as follows:

[0062] 1. Precise temperature control: Through zone 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 overcooling.

[0063] 2. Energy saving and consumption reduction: Optimize flow distribution based on real-time data, reduce ineffective cooling water circulation, and reduce energy consumption by 15%-20%.

[0064] 3. Anti-condensation protection: Dynamically adjust the upper limit of cooling temperature based on environmental parameters to effectively prevent rust or product contamination caused by condensation on the mold surface.

[0065] 4. Adaptive optimization: Continuously correct control parameters through operational data feedback to improve the algorithm’s adaptability to complex working conditions.

[0066] 5. Fault warning: Flow and temperature deviation detection can detect cooling water channel blockage or leakage problems in advance, reducing the failure rate. BRIEF DESCRIPTION OF THE DRAWINGS

[0067] Figure 1 The figure is a flow chart of an embodiment of a method for supplying, delivering and quantitatively controlling mold cooling water according to the present invention. DETAILED DESCRIPTION

[0068] In order to better understand the above technical solution, the above technical solution will be described in detail below with reference to the accompanying drawings and specific implementation methods.

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

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

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

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

[0073] The corresponding area surface temperature change data refers to the quantitative record of the surface temperature changes over time, process stage or spatial position within a specific local range of the mold or workpiece.

[0074] The initial monitoring data set refers to the first batch of basic monitoring data sets of mold cooling water obtained through sensors and detection instruments during the equipment debugging or process startup phase.

[0075] Step S200: Based on the initial monitoring data set, if the partition temperature exceeds a preset threshold, the flow rate of the corresponding cooling channel is increased; if it is lower than the preset threshold, the flow rate is reduced, and an optimized flow distribution plan is generated.

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

[0077] Optimizing flow distribution refers to achieving reasonable 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, this refers to achieving reasonable distribution and dynamic balance of cooling water flow within the channel network during the cooling water supply and delivery process through structural design, parameter control, and intelligent management.

[0078] Step S300: Use the dynamic adjustment module to calibrate the temperature parameters of the optimized flow distribution solution. If a regional temperature deviation is detected, the cooling medium supply amount is corrected and a dynamic balance control instruction is generated.

[0079] The dynamic adjustment module refers to an electronic control unit that monitors system parameters in real time and dynamically adjusts the output state based on the control algorithm.

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

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

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

[0083] Dynamic balance control instructions refer to a control strategy that maintains a dynamic balance between the set value and the actual value of key state variables in a complex system by adjusting the actuator parameters (such as flow, pressure, speed, etc.) in real time.

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

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

[0086] The condensation prevention mechanism is a systematic measure to prevent water vapor condensation when the surface temperature of an object is lower than the ambient dew point temperature. It actively controls environmental parameters (temperature, humidity, airflow speed) or modifies surface properties to keep the target area above the dew point temperature.

[0087] The upper limit of the cooling medium temperature parameter is the maximum temperature threshold allowed for the cooling medium during cooling system operation 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 due to overheating.

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

[0089] Final control parameters are the thresholds of core operating variables used to stabilize and control equipment or processes, determined through system design, dynamic algorithm optimization, and actual operating conditions verification. These parameters must balance thermodynamic constraints, material tolerances, energy efficiency targets, and response speed to ensure optimal system performance within safety margins.

[0090] Zoned cooling control involves dividing the cooling system into multiple independent or collaboratively controlled zones (physical or logical zones) based on the equipment's heat load distribution or process requirements. By differentially adjusting the cooling medium flow, temperature, or pressure in each zone, this strategy achieves a coordinated control strategy that combines localized precise temperature control with global energy efficiency optimization. In this example, this involves independent cooling control of each zone of the mold.

[0091] Updating preset thresholds means adjusting the system's original parameter setting boundaries through dynamic algorithms or manual intervention based on real-time operating data, load changes, or external environmental conditions to achieve control strategies for energy efficiency optimization, equipment protection, or compliance requirements.

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

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

[0094] Step S110: Obtain the flow rate data and temperature data of the cooling medium from each partition of the mold through the pre-established sensor network, and simultaneously collect the surface temperature information of the corresponding area, form an independent data record group for each partition, and obtain a preliminary monitoring data set.

[0095] For example, in the monitoring and optimization scenario of mold cooling systems, the construction of a sensor network is the foundation of the entire process. Sensors are placed in each partition of the mold to collect real-time flow rate and temperature data of the cooling medium, while also recording the surface temperature of the corresponding area, forming an independent data record group. Assuming that the mold is divided into four partitions, each equipped with a flow rate sensor and a temperature sensor, the preliminary data collected may show that the flow rate of partition 1 is 2.5 m / s, the cooling medium temperature is 20 degrees Celsius, and the surface temperature is 45 degrees Celsius, while the flow rate of partition 2 is 1.8 m / s, the temperature is 22 degrees Celsius, and the surface temperature is 50 degrees Celsius. These data constitute the preliminary monitoring data set, laying the foundation for subsequent analysis.

[0096] Step S120: Based on the preliminary monitoring data set, the flow rate data and temperature data are classified and sorted using data processing tools, and matching analysis is performed on the relationship between the cooling medium characteristics and the flow rate changes to determine the correlation characteristics between the medium and the surface temperature in each partition.

[0097] For example, with the application of data processing tools, flow rate and temperature data can be categorized and organized to analyze the relationship between cooling medium characteristics and flow rate changes. Suppose, through data processing, it is discovered that the higher the flow rate, the faster the cooling medium temperature drops, and the surface temperature also decreases accordingly. Taking partition 1 as an example, when the flow rate is 2.5 m / s, the surface temperature stabilizes at 45 degrees Celsius. However, when the flow rate in partition 2 drops to 1.8 m / s, the surface temperature rises to 50 degrees Celsius. This correlation indicates that flow rate is the key factor affecting surface temperature. Such analysis helps clarify the dynamic relationship between the medium and surface temperature in each partition, providing a basis for subsequent adjustments.

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

[0099] For example, if the flow rate or temperature data for a particular zone exceeds a preset threshold—for example, if the flow rate threshold is set at 2.0 m / s and the temperature threshold is 25 degrees Celsius, and the flow rate of Zone 2, at 1.8 m / s, is below the threshold, the data comparison tool will need to dynamically adjust the surface temperature change. Suppose, after adjustment, the surface temperature of Zone 2 fluctuates from 50 to 55 degrees Celsius in a short period of time, indicating an abnormal fluctuation. The adjusted data mapping relationship indicates that the low flow rate leads to insufficient cooling, which in turn affects the stability of the mold surface temperature. This dynamic adjustment helps to promptly identify problems and take appropriate measures to ensure mold processing quality.

[0100] Step S140: By integrating the adjusted data mapping relationship, an initial data set is constructed using a data storage tool. Based on the requirements of regional division, the comprehensive information of flow rate changes and surface temperature is structured and stored to obtain a complete data foundation.

[0101] For example, when constructing the initial data set, data storage tools were used to integrate the adjusted data mapping relationships into structured information. Based on the regional division requirements, comprehensive information on flow rate changes and surface temperature was stored in layers. Taking Partitions 1 and 2 as an example, the stored content included flow rate, medium temperature, surface temperature, and their changing trends, forming a complete data foundation. This structured storage method facilitates subsequent access 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 temperature anomalies, and improve production stability.

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

[0104] Step S210: Based on the initial monitoring data set, a real-time comparison is performed on the partition temperatures of each area. If the partition temperature of a certain area exceeds the preset threshold, the flow of the cooling channel is increased through the flow control tool. If it is lower than the preset threshold, the flow control tool is used to reduce the flow to obtain the adjusted flow distribution data.

[0105] For example, in the monitoring and adjustment of mold cooling systems, real-time temperature comparison of each zone is a critical step. Assume the mold is divided into four zones, and the temperature threshold for each zone is set at 48 degrees Celsius. When the temperature of a zone exceeds this value, the flow rate in the cooling channel needs to be increased using flow control tools. Assuming the temperature of zone 3 reaches 52 degrees Celsius, significantly higher than the threshold, the flow rate can be adjusted from the original 1.5 m / s to 2.2 m / s to enhance the cooling effect. 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 excessive cooling and waste of resources.

[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 the medium characteristics and the flow adjustment, and determine whether the flow distribution in each area reaches a balanced distribution state.

[0107] For example, after obtaining adjusted flow distribution data, it's particularly important to use data acquisition tools to extract dynamic change information for each cooling channel. For zone 3, after the flow rate was increased, the temperature gradually decreased from 52°C to 47°C over a period of time, indicating that the adjustment was effective. In contrast, after the flow rate was reduced in zone 4, the temperature rose slightly to 42°C, but remained within a safe range. This dynamic change information helps determine the compatibility between media characteristics and flow adjustments, and thus assess whether distribution balance 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 double-checked with the preset threshold through a data comparison tool to obtain the flow adjustment amplitude of the cooling channel in the area and obtain the corrected flow distribution parameters.

[0109] For example, areas where flow distribution has not been balanced require a secondary verification using a data comparison tool. For example, in zone 3, although the temperature has dropped after flow adjustment, it remains higher than in other zones, indicating that flow distribution is not yet balanced. Further analysis of the temperature monitoring data reveals that the flow adjustment in the cooling channel is insufficient, requiring an additional increase of 0.3 m / s, resulting in a corrected parameter of 2.5 m / s. This secondary verification more accurately pinpoints the problem area and ensures the rationality of the adjustment direction.

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

[0111] For example, the use of data storage tools is crucial when integrating flow adjustment results with information on dynamic temperature changes in each zone. For all zones, adjusted flow values, temperature trends, and other information can be stored in a hierarchical manner. For example, the final flow rate for zone 3 is 2.5 m / s and the temperature is stable at 47°C, while the flow rate for zone 4 is 1.6 m / s and the temperature is 42°C. After integrating this data, it can be determined whether the distribution requirements for all zones are met. If any deviations remain, further fine-tuning is required. This approach provides a clear data foundation for subsequent optimization.

[0112] For example, in the above process, flow balance can be determined based on how close each zone's temperature is to a threshold. Assuming the goal is to keep all zone temperatures within a range of 48°C (±2°C), repeated adjustments and data comparisons can ultimately bring the temperatures of each zone into alignment. This approach effectively improves the overall stability of the cooling system and provides reliable assurance for mold processing.

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

[0114] Step S310: According to the dynamic adjustment module, the temperature information of each partition is obtained from the real-time monitored regional temperature data, and 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 to obtain the corresponding deviation detection data.

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

[0116] For temperature information acquisition in each zone, sensors can collect data every few minutes to ensure real-time data. For example, suppose a 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 and determine a deviation of 3 degrees Celsius, which serves as the basis for subsequent adjustments.

[0117] Step S320: Using the deviation detection data, the flow calculation tool is used to analyze the cooling supply volume of the area, and combined with the medium control logic, it is determined whether adjustment is required. If adjustment is required, the flow adjustment tool is used to generate supply correction parameters and determine the adjusted medium control information.

[0118] For example, using deviation detection data, the flow calculation tool can analyze the adequacy of the cooling supply in zone 1, taking into account the characteristics of the cooling medium. For example, if the current flow rate is 1.2 m / s, analysis reveals that an increase in flow rate is necessary to lower the temperature. The medium control logic then determines this adjustment is necessary. The flow adjustment tool then generates supply correction parameters, adjusting the flow rate to 1.8 m / s. This adjustment, based on both deviation data and medium characteristics, ensures targeted response.

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

[0120] For example, after obtaining adjusted media control information, the flow optimization status of each zone needs to be checked. Suppose that after adjustment, the temperature in zone 1 drops to 49°C, approaching the threshold but still slightly higher than in other zones, not fully achieving balanced distribution. A secondary calibration using the parameter adjustment tool reveals that the flow rate increase is insufficient, so a further increase of 0.2 m / s to 2.0 m / s is determined, forming a preliminary indication for dynamic balance. This secondary calibration allows for more precise alignment with the balance target.

[0121] For example, to integrate preliminary dynamic balancing instructions, the instruction generation tool can combine the final adjustment parameters for zone 1 with data from other zones to generate the final dynamic balancing control instructions. Assuming the temperatures in zones 2, 3, and 4 are 48, 47, and 49 degrees Celsius, respectively, and the flow rates are 1.5, 1.4, and 1.6 meters per second, a comprehensive assessment reveals that the overall cooling supply requirements are generally met, but zone 1 still requires fine-tuning to ensure long-term stability. This integration approach facilitates unified management of parameters across zones.

[0122] Step S340: Based on the preliminary dynamic balance instructions, an instruction generation tool is used to integrate the parameter adjustment results of the area to generate a final dynamic balance control instruction to determine whether the cooling supply requirements of all areas are met.

[0123] For example, to determine whether all zones' cooling requirements are being met, the system can compare how closely each zone's temperature approaches a threshold. For example, if the target temperature is to be within 50°C (±1°C), and after multiple adjustments, the temperature in zone 1 stabilizes at 49.5°C, and all other zones are within the target range, indicating that overall requirements are being met. This approach 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:

[0125] Step S410: Acquire real-time humidity data and dew point temperature data from an environmental monitoring tool, perform difference calculation on the humidity data and dew point temperature data through a data processing tool, and obtain specific difference information between the two.

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

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

[0128] Assume that the current humidity is 75%, the dew point temperature is 18 degrees Celsius, and the actual ambient temperature is 20 degrees Celsius. The data processing tool calculates that the difference between the two is 2 degrees Celsius.

[0129] Step S420: compare the specific difference information with a preset safety threshold. If the specific difference information is less than the preset safety threshold, generate a condensation prevention signal through the early warning trigger tool to determine the parameter range that needs 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, when comparing the calculated difference with the safety threshold, the early warning trigger tool generates a condensation prevention signal based on the difference. If the difference remains below the threshold, the system automatically marks the current environment as high-risk and determines the parameter range that requires adjustment, such as lowering the upper temperature limit of the cooling medium by 2 degrees Celsius. This signal generation method can promptly alert the system to potential problems and provide a basis for subsequent adjustments.

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

[0133] For example, in a condensation prevention signal application, the parameter adjustment tool, combined with the media control logic, calibrates the upper temperature limit of the cooling medium. For example, if the original upper temperature limit is 25°C, and the difference between the current humidity and dew point is small, the system analyzes and decides to adjust the upper limit to 23°C to reduce the possibility of condensation. This calibration process takes into account environmental factors and media characteristics, ensuring targeted adjustments.

[0134] Step S440: Using the adjusted temperature upper limit parameter value, the operating status of the cooling medium is updated with the aid of a medium control tool to determine whether the condensation prevention requirement is met and generate a final control instruction.

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

[0136] Assuming the media temperature stabilizes at 22.5°C after adjustment, the system continuously monitors changes in ambient humidity and dew point temperature to determine whether condensation prevention requirements are met. If the humidity drops to 70% and the difference increases to 3.5°C, indicating that the risk level has been exceeded, the system generates final control instructions to maintain the current state. This dynamic update mechanism helps maintain environmental stability.

[0137] For example, when generating the final control command, the system comprehensively evaluates all relevant parameters. Suppose, after multiple monitoring sessions, the humidity stabilizes at 68%, the dew point is 17°C, a difference of 4°C, well above the safety threshold. Meanwhile, the cooling medium temperature remains below 23°C. The system confirms that the condensation risk has been eliminated, and the final command locks in the current parameter configuration. This comprehensive assessment ensures stable operation of the mold cooling system in complex environments, providing reliable protection for the machining process.

[0138] For example, continuous monitoring of humidity and dew point temperature data can also extract trend information from historical data to aid decision-making. For example, if humidity drops from 80% to 75% over the past hour while the dew point temperature remains stable, the system can predict a low risk of condensation in the short term, thereby optimizing adjustment frequency and reducing unnecessary parameter changes. This trend-based analysis further enhances system adaptability and ensures optimal resource allocation.

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

[0140] Step S510: Acquire real-time operation data for partition control from the environmental monitoring tool, and classify and process the real-time operation data through the data acquisition tool to obtain a classified operation data set.

[0141] For example, in the application of environmental monitoring tools, the acquisition of real-time operation data for partition control is a key link.

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

[0143] For example, the data collection tool can categorize real-time operational data by partition and data type. For example, suppose the collected data includes a temperature of 22°C and a humidity of 70% in area 1, and a temperature of 24°C and a humidity of 78% in area 2. The data collection tool will categorize this data by region and indicator, forming a structured operational data set. This categorization facilitates subsequent analysis and upload, ensuring streamlined data processing.

[0144] Step S520: Upload the classified operating data set to a pre-established database storage unit using a data transmission tool to ensure that the operating data has been completely recorded.

[0145] For example, in a data transfer tool, a classified operational dataset is uploaded to a pre-established database storage unit. Assuming the system uses a cloud-based database, the data transfer tool uploads the dataset via an encrypted channel, ensuring data security during transmission. Once the upload is complete, the system automatically generates a record confirmation message, indicating that the data has been fully stored. This approach facilitates centralized data management and subsequent access.

[0146] Step S530: Compare the preset threshold adjustment logic with the operating data set in the database storage unit using a parameter calibration tool. If the operating data in the operating data set exceeds the preset threshold range, generate a corresponding threshold adjustment signal to obtain an adjusted threshold range.

[0147] For example, the parameter calibration tool compares the operating data set in the database storage unit according to the preset threshold adjustment logic. Suppose the humidity threshold for zone 2 is set at 75%, but the actual data is 78%, out of range. The parameter calibration tool generates a threshold adjustment signal, recommending adjusting the humidity control target for that zone to 73%. This dynamic comparison and adjustment mechanism enables timely identification of potential issues.

[0148] Step S540: Based on the adjusted threshold range, a new instruction set for partition control is generated by a control instruction generation tool in combination with the current state of the cooling parameter, and it is determined whether the new instruction set meets the preset cooling parameter requirements.

[0149] For example, after generating a threshold adjustment signal, the system uses the control instruction generation tool to generate a new instruction set for zone control based on the adjusted threshold range and the current cooling parameter status. For example, suppose the cooling medium temperature in zone 2 is originally set to 25 degrees Celsius. Based on the adjustment signal, the instruction set requires it to be lowered to 23 degrees Celsius. 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 must not fall below 20 degrees Celsius. This judgment process ensures the executable nature of the instruction.

[0150] For example, from a business perspective, the real-time data monitoring and instruction adjustment of zone control are crucial for ensuring the stability of the mold cooling system. Assuming that persistently high humidity in Zone 2 could lead to localized condensation, this risk can be effectively mitigated through the comprehensive management process of data classification, upload, comparison, and instruction generation. This business logic forms a closed loop from data collection to instruction execution, ensuring that each link supports each other.

[0151] For example, as an expansion solution, the system can also store historical operating data in the database to assist in optimizing the threshold adjustment logic. For example, if analyzing humidity trends over the past 24 hours reveals significant humidity fluctuations in Zone 2, the system can further refine the threshold range for that zone. This supplementary analysis based on historical data can improve the accuracy of zone control and provide more reliable assurance for system operation.

[0152] The present invention relates to a mold cooling water supply, delivery, and quantitative control system for realizing the above-mentioned mold cooling water supply, delivery, and quantitative control method. The mold cooling water supply, delivery, and quantitative control system comprises an acquisition module, a first generation module, a second generation module, an adjustment module, and an update module, wherein the acquisition module is used to acquire the cooling medium flow rate, temperature, and corresponding regional surface temperature change data of each partition of the mold to form an initial monitoring data set; the first generation module is used to increase the corresponding cooling channel flow rate if the partition temperature exceeds a preset threshold, and reduce the flow rate if it is lower than the preset threshold, to generate an optimized flow distribution scheme based on the initial monitoring data set; the second generation module is used to use a dynamic adjustment module to calibrate the temperature parameters of the optimized flow distribution scheme, and if a regional temperature deviation is detected, correct the cooling medium supply amount and generate a dynamic balance control instruction; the adjustment module is used to acquire humidity and dew point temperature data from an environmental monitoring system, and if the difference between the humidity and the dew point temperature is less than a safety threshold, trigger a condensation prevention mechanism and adjust the cooling medium temperature upper limit parameter; the update module is used to execute partition cooling control according to the final control parameters, and feed back the operation data to a database for updating the preset threshold and optimization algorithm parameters.

[0153] Furthermore, the mold cooling water supply, delivery, and quantitative control system provided in this embodiment includes an acquisition module comprising a first acquisition unit, a first determination unit, a first judgment unit, and a second acquisition unit. The first acquisition unit is configured to acquire flow rate data and temperature data of the cooling medium from each mold partition through a pre-established sensor network, and simultaneously collect surface temperature information of the corresponding area, thereby forming an independent data record group for each partition to obtain a preliminary monitoring data set. The first determination unit is configured to classify and organize the flow rate data and temperature data using a data processing tool based on the preliminary monitoring data set, perform a matching analysis on the relationship between the cooling medium characteristics and the flow rate changes, and determine the correlation characteristics between the medium and the surface temperature in each partition. The first judgment unit is configured to dynamically adjust the surface temperature change of the partition using a data comparison tool if the flow rate data or temperature data of a partition in the correlation characteristics exceeds a preset threshold, obtain an adjusted data mapping relationship, and determine whether there is an abnormal fluctuation. The second acquisition unit is configured to integrate the adjusted data mapping relationship and construct an initial data set using a data storage tool. Based on the requirements of the regional division, the comprehensive information of the flow rate change and the surface temperature is structured and stored to obtain a complete data foundation.

[0154] Preferably, the mold cooling water supply, delivery, and quantitative control system provided in this embodiment comprises a first generation module including a third acquisition unit, a second determination unit, a fourth acquisition unit, and a second judgment unit, wherein the third acquisition unit is configured to perform a real-time comparison of the zone temperature of each region according to the initial monitoring data set. If the zone temperature of a certain region exceeds a preset threshold, the flow rate of the cooling channel is increased by a flow control tool; if it is lower than the preset threshold, the flow rate is reduced by the flow control tool to obtain adjusted flow distribution data; the second determination unit is configured to obtain dynamic change information of each cooling channel from the adjusted flow distribution data by using a data acquisition tool, match the relationship between the medium characteristics and the flow adjustment, and determine whether the flow distribution of each region has reached a balanced distribution state; the fourth acquisition unit is configured to perform a secondary verification of the temperature monitoring data of a certain region with a preset threshold by using a data comparison tool if the flow distribution of a certain region has not reached a balanced distribution state, obtain the flow adjustment amplitude of the cooling channel in the region, and obtain a corrected flow distribution parameter; the second judgment unit is configured to integrate the flow adjustment result of each region with the dynamic change information of the zone temperature by using a data storage tool based on the corrected flow distribution parameter to determine whether the balanced distribution requirements of all regions are met.

[0155] Furthermore, the mold cooling water supply, delivery, and quantitative control system provided by this embodiment has a second generation module including a fifth acquisition unit, a third determination unit, a sixth acquisition unit, and a third judgment unit. The fifth acquisition unit is used to obtain the temperature information of each partition from the real-time monitored regional temperature data according to the dynamic adjustment module, and compare the temperature information with a preset threshold. If the temperature information of a certain area exceeds the preset threshold, the temperature calibration requirement of the area is determined by a data processing tool to obtain corresponding deviation detection data; the third determination unit is used to use the deviation detection data to analyze the cooling supply of the area through a flow calculation tool, and combine it with the flow calculation tool to determine the temperature of the area. The medium control logic determines whether adjustment is required. If adjustment is required, the flow regulation tool is used to generate supply correction parameters to determine the adjusted medium control information; the sixth acquisition unit is used to obtain the flow optimization status of each area through the adjusted medium control information, and compare the flow optimization status with the distribution balance requirements. If a certain area does not meet the distribution balance requirements, a secondary calibration is performed through the parameter adjustment tool to obtain a preliminary instruction for dynamic balance; the third judgment unit is used to integrate the parameter adjustment results of the area according to the preliminary instruction for dynamic balance using the instruction generation tool to generate a final dynamic balance control instruction to determine whether the cooling supply needs of all areas are met.

[0156] Compared with the prior art, the mold cooling water supply, delivery, and quantitative control method and system provided in this embodiment forms an initial data set by real-time monitoring of the cooling medium flow rate, temperature, and surface temperature changes in each partition, and dynamically adjusts the cooling channel flow according to the preset threshold to generate an optimized flow distribution scheme. At the same time, the present invention uses a dynamic adjustment module to calibrate the temperature parameters of the optimization scheme to achieve precise control. In addition, this embodiment also combines the ambient humidity and dew point temperature data to trigger the condensation prevention mechanism, adjust the upper limit of the cooling medium temperature, and effectively prevent condensation on the mold surface. Finally, this embodiment performs partition cooling control according to the control parameters, and feeds back the operating data to the database for continuous optimization of the algorithm parameters, to achieve intelligent and precise control of the mold temperature, and to improve product quality and production efficiency. The mold cooling water supply, delivery, and quantitative control method and system provided in this embodiment can achieve the following beneficial effects:

[0157] 1. Precise temperature control: Through zone 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 overcooling.

[0158] 2. Energy saving and consumption reduction: Optimize flow distribution based on real-time data, reduce ineffective cooling water circulation, and reduce energy consumption by 15%-20%.

[0159] 3. Anti-condensation protection: Dynamically adjust the upper limit of cooling temperature based on environmental parameters to effectively prevent rust or product contamination caused by condensation on the mold surface.

[0160] 4. Adaptive optimization: Continuously correct control parameters through operational data feedback to improve the algorithm’s adaptability to complex working conditions.

[0161] 5. Fault 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 present invention have been described, those skilled in the art may make additional changes and modifications to these embodiments once they are aware of the basic inventive concepts. Therefore, the appended claims are intended to be interpreted as including the preferred embodiments and all changes and modifications that fall within the scope of the invention. Obviously, those skilled in the art may make various changes and modifications to the present invention without departing from the spirit and scope of the invention. Thus, the present invention is intended to include such changes and modifications as fall within the scope of the claims and their equivalents.

Claims

1. A method for supplying, delivering and quantitatively controlling mold cooling water, characterized in that: The following steps are involved: Obtain the cooling medium flow rate, temperature and surface temperature change data of each mold partition to form an initial monitoring data set; Based on the initial monitoring data set, if the partition temperature exceeds a preset threshold, the flow rate of the corresponding cooling channel is increased; if it is lower than the preset threshold, the flow rate is reduced, and an optimized flow distribution plan is generated; A dynamic adjustment module is used to calibrate the temperature parameters of the optimized flow distribution scheme, and if a regional temperature deviation is detected, the cooling medium supply is corrected to generate a dynamic balance control instruction; 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 and the upper temperature limit parameter of the cooling medium is adjusted. Partition cooling control is performed according to the final control parameters, and the operating data is fed back to the database for updating preset thresholds and optimizing algorithm parameters.

2. The mold cooling water supply, delivery, and quantitative control method according to claim 1, characterized in that: The step of obtaining the cooling medium flow rate, temperature and surface temperature change data of the corresponding area of ​​each mold partition to form an initial monitoring data set includes: Through the pre-established sensor network, the flow rate and temperature data of the cooling medium are obtained from each zone of the mold. At the same time, the surface temperature information of the corresponding area is collected. An independent data record group is formed for each zone to obtain a preliminary monitoring data set. Based on the preliminary monitoring data set, the flow rate data and the temperature data are classified and sorted using data processing tools, and a matching analysis is performed on the relationship between the cooling medium characteristics and the flow rate changes to determine the correlation characteristics between the medium and the surface temperature in each partition; If the velocity data or temperature data of a certain partition in the associated features exceeds a preset threshold, the surface temperature change of the partition is dynamically adjusted through a data comparison tool, and the adjusted data mapping relationship is obtained to determine whether there is abnormal fluctuation; By integrating the adjusted data mapping relationship, the initial data set is constructed using data storage tools. According to the needs of regional division, the comprehensive information of flow rate changes and surface temperature is structured and stored to obtain a complete data foundation.

3. The mold cooling water supply, delivery, and quantitative control method according to claim 1, characterized in that: Based on the initial monitoring data set, if the partition temperature exceeds a preset threshold, the flow rate of the corresponding cooling channel is increased; if it is lower than the preset threshold, the flow rate is reduced. The steps of generating an optimized flow distribution plan include: Based on the initial monitoring data set, a real-time comparison is performed on the zone temperatures of each area. If the zone temperature of a certain area exceeds a preset threshold, the flow rate of the cooling channel is increased by a flow control tool. If it is lower than the preset threshold, the flow rate is reduced by the flow control tool to obtain adjusted flow distribution data; Through data acquisition tools, the dynamic change information of each cooling channel is obtained from the adjusted flow distribution data. The relationship between the medium characteristics and the 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 is not balanced, the temperature monitoring data of the area is re-verified against 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. According to the corrected flow distribution parameters, a data storage tool is used to integrate the flow adjustment results of each area with the dynamic change information of the partition temperature to determine whether the distribution balance requirements of all areas are met.

4. The mold cooling water supply, delivery, and quantitative control method according to 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 instruction include: According to the dynamic adjustment module, the temperature information of each zone is obtained from the real-time monitored regional temperature data, and 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 a data processing tool to obtain corresponding deviation detection data; Using the deviation detection data, the flow calculation tool is used to analyze the cooling supply volume of the area, and combined with the medium control logic, it is determined whether adjustment is required. If adjustment is required, the flow adjustment tool is used to generate supply correction parameters and determine the adjusted medium control information; The adjusted media control information is used to obtain the flow optimization status of each area. 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 preliminary instructions for dynamic balance. Based on the preliminary dynamic balance instructions, the instruction generation tool is used to integrate the parameter adjustment results of the area to generate the final dynamic balance control instructions to determine whether the cooling supply requirements of all areas are met.

5. The mold cooling water supply, delivery, and quantitative control method according to claim 1, characterized in that: The step of obtaining humidity and dew point temperature data from the environmental monitoring system, triggering a condensation prevention mechanism if the difference between the humidity and the dew point temperature is less than a safety threshold, and adjusting the upper limit parameter of the cooling medium temperature includes: Acquiring real-time humidity data and dew point temperature data from an environmental monitoring tool, and performing a difference calculation on the humidity data and the dew point temperature data using a data processing tool to obtain specific difference information between the two; Comparing the specific difference information with a preset safety threshold, and if the specific difference information is less than the preset safety threshold, generating a condensation prevention signal through an early warning trigger tool to determine a parameter range that needs to be adjusted; Using the condensation prevention signal and combining it with the medium control logic, the upper temperature limit of the cooling medium is calibrated through a parameter adjustment tool to obtain an adjusted upper temperature limit parameter value; The operating status of the cooling medium is updated with the help of the medium control tool through the adjusted temperature upper limit parameter value to determine whether the requirements for condensation prevention are met and generate the final control instructions.

6. The mold cooling water supply, delivery, and quantitative control method according to claim 1, characterized in that: The steps of executing zone cooling control according to the final control parameters and feeding back the operating data to the database for updating the preset thresholds and optimizing the algorithm parameters include: Obtain real-time operation data for zoning control from environmental monitoring tools, classify and process the real-time operation data using data acquisition tools to obtain a classified operation data set; Based on the classified operating data set, use the data transmission tool to upload it to the pre-established database storage unit to ensure that the operating data has been fully recorded; For the operating data set in the database storage unit, the preset threshold adjustment logic is compared with the parameter calibration tool. If the operating data in the operating data set exceeds the preset threshold range, a corresponding threshold adjustment signal is generated to obtain the adjusted threshold range; According to the adjusted threshold range, a new instruction set for partition control is generated by a control instruction generation tool in combination with the current state of the cooling parameter, and it is determined whether the new instruction set meets the preset cooling parameter requirements.

7. A mold cooling water supply, delivery, and quantitative control system, used to implement the mold cooling water supply, delivery, and quantitative control method according to any one of claims 1 to 6, characterized in that: The mold cooling water supply, delivery, and quantitative control system includes: The acquisition module is used to obtain the cooling medium flow rate, temperature and surface temperature change data of each zone of the mold to form an initial monitoring data set; A first generation module is configured to generate an optimized flow distribution plan based on the initial monitoring data set, increasing the flow of the corresponding cooling channel if the partition temperature exceeds a preset threshold, and reducing the flow if it is lower than the preset threshold; a second generation module, configured to calibrate the temperature parameters of the optimized flow distribution scheme using a dynamic adjustment module, and to correct the cooling medium supply if a regional temperature deviation is detected, thereby generating a dynamic balance control instruction; The adjustment module is used to obtain humidity and dew point temperature data from the environmental monitoring system. If the difference between the 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 update module is used to perform partition cooling control according to the final control parameters and feed back the operating data to the database for updating the preset thresholds and optimizing the algorithm parameters.

8. The mold cooling water supply, delivery, and quantitative control system according to claim 7, characterized in that: The acquisition module includes: The first acquisition unit is used to acquire the flow rate data and temperature data of the cooling medium from each zone of the mold through a pre-established sensor network, and simultaneously collect the surface temperature information of the corresponding area, forming an independent data record group for each zone to obtain a preliminary monitoring data set; a first determining unit configured to classify and organize the flow rate data and the temperature data using a data processing tool based on a preliminary set of monitoring data, perform a matching analysis on the relationship between the cooling medium characteristics and the flow rate changes, and determine the correlation characteristics between the medium and the surface temperature in each partition; A first judgment unit is configured to dynamically adjust the surface temperature change of a partition through a data comparison tool if the flow rate data or temperature data of a partition in the associated feature exceeds a preset threshold, obtain the adjusted data mapping relationship, and determine whether there is an abnormal fluctuation; The second acquisition unit is used to construct an initial data set by integrating the adjusted data mapping relationship using a data storage tool. According to the needs of regional division, the comprehensive information of flow rate changes and surface temperature is structured and stored to obtain a complete data foundation.

9. The mold cooling water supply, delivery, and quantitative control system according to claim 7, characterized in that: The first generation module includes: a third acquisition unit, configured to perform a real-time comparison of the zone temperatures of each region according to the initial monitoring data set; if the zone temperature of a certain region exceeds a preset threshold, the flow rate of the cooling channel is increased by a flow control tool; if the zone temperature is lower than the preset threshold, the flow rate is reduced by the flow control tool, thereby obtaining adjusted flow distribution data; The second determining unit is configured to obtain dynamic change information of each cooling channel from the adjusted flow distribution data using a data acquisition tool, perform matching processing based on the relationship between the medium characteristics and the flow adjustment, and determine whether the flow distribution in each area has reached a balanced distribution state; A fourth acquisition unit is configured to, if the flow distribution in a certain area does not reach a balanced state, perform a secondary verification of the temperature monitoring data of the area with a preset threshold value using a data comparison tool, obtain the flow adjustment amplitude of the cooling channel in the area, and obtain a corrected flow distribution parameter; The second judgment unit is used to integrate the flow adjustment result of each area with the dynamic change information of the zone temperature based on the corrected flow distribution parameters using a data storage tool to determine whether the distribution balance requirements of all areas are met.

10. The mold cooling water supply, delivery, and quantitative control system according to claim 7, characterized in that: The second generation module includes: a fifth acquisition unit, configured 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 zone exceeds the preset threshold, determine the temperature calibration requirement of the zone through a data processing tool, and obtain corresponding deviation detection data; a third determining unit, configured to use the deviation detection data to analyze the cooling supply volume of the area using a flow calculation tool, determine whether adjustment is required in combination with the medium control logic, and if so, generate a supply correction parameter using a flow adjustment tool to determine adjusted medium control information; a sixth acquisition unit configured to obtain the flow optimization status of each zone using the adjusted medium control information, compare the flow optimization status with the distribution balance requirement, and if a zone does not meet the distribution balance requirement, perform secondary calibration using a parameter adjustment tool to obtain preliminary instructions for dynamic balance; The third judgment unit is used to integrate the parameter adjustment results of the area using the instruction generation tool according to the preliminary dynamic balance instruction, generate the final dynamic balance control instruction, and judge whether the cooling supply demand of all areas is met.

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