A temperature intelligent control method and system based on closed buffer tank

Abstract

The present invention relates to the field of temperature control, and in particular to a temperature intelligent control method and system based on a closed buffer tank, which obtains the temperature parameters of the fluid in the buffer tank; determines whether a preset temperature value is reached based on the temperature parameters; when the temperature parameters do not reach the preset temperature value, obtains the insulation parameters in the buffer tank and the environmental parameters outside the buffer tank; determines the heat leakage coefficient of the fluid based on the insulation parameters and the environmental parameters; constructs a temperature analysis model, inputs the heat leakage coefficient, the temperature parameters, and the preset temperature value into the temperature analysis model, calculates the temperature adjustment parameter through the temperature analysis model, and outputs the calculated result; based on the calculated result, sends the temperature adjustment parameter to the buffer tank, and the buffer tank adjusts the temperature according to the temperature adjustment parameter. By obtaining the insulation parameters and environmental parameters, the system can automatically evaluate the heat loss situation and adjust the heating or cooling strategy in a timely manner.

Description

Technical Field

[0001] The present application relates to the field of temperature control technology, and in particular to a temperature intelligent control method and system based on a closed buffer tank. Background Art

[0002] Closed buffer tank temperature control is a technology used in industrial process control. In many industrial processes, such as chemical, food processing, and pharmaceutical, precise temperature control is required to ensure product quality and production efficiency.

[0003] In industries such as chemical, food processing and pharmaceuticals, the accuracy of temperature control directly affects the quality of products. For example, in chemical reactions, temperatures that are too high or too low may cause changes in the reaction rate and even the generation of by-products. This lack of accuracy often stems from the fact that traditional methods are not sensitive to environmental changes, resulting in a large deviation between the actual temperature and the set value. In a rapidly changing production environment, temperature changes can be very rapid. Traditional temperature control methods often rely on fixed control logic, which is difficult to adjust in real time. The heating or cooling equipment has a slow response speed, which can easily cause the temperature to lag behind actual demand. This can lead to losses and the production of defective products in demanding production processes. Secondly, existing technologies often lack the ability to dynamically adjust the buffer tank to the temperature requirements of different usage periods, making it difficult to effectively respond to changes in fluid usage patterns, resulting in unnecessary energy consumption.

[0004] Therefore, the existing technology has defects and needs to be improved. Summary of the Invention

[0005] In order to solve one or several problems in the prior art, the main purpose of this application is to provide a temperature intelligent control method and system based on a closed buffer tank.

[0006] In order to achieve the above-mentioned object of the invention, the present application proposes a temperature intelligent control method based on a closed buffer tank, the method comprising:

[0007] Obtain the temperature parameters of the fluid in the buffer tank;

[0008] Determining whether a preset temperature value is reached according to the temperature parameter;

[0009] When the temperature parameter does not reach the preset temperature value, obtaining the insulation parameter inside the buffer tank and the environmental parameter outside the buffer tank;

[0010] determining a heat leakage coefficient of the fluid according to the thermal insulation parameters and the environmental parameters;

[0011] Constructing a temperature analysis model, inputting the heat leakage coefficient, temperature parameter and preset temperature value into the temperature analysis model respectively, calculating the temperature adjustment parameter through the temperature analysis model, and outputting the calculated result;

[0012] Based on the calculation result, the temperature adjustment parameter is sent to the buffer tank, and the buffer tank performs temperature adjustment according to the temperature adjustment parameter.

[0013] The present application also provides an intelligent temperature control system based on a closed buffer tank, comprising:

[0014] A first acquisition module is used to obtain the temperature parameters of the fluid in the buffer tank;

[0015] A judgment module, configured to judge whether a preset temperature value has been reached according to the temperature parameter;

[0016] A second acquisition module is used to acquire the insulation parameters in the buffer tank and the environmental parameters outside the buffer tank when the temperature parameter does not reach the preset temperature value;

[0017] A determination module, configured to determine a heat leakage coefficient of the fluid according to the insulation parameters and the environmental parameters;

[0018] An input module is used to construct a temperature analysis model, input the heat leakage coefficient, temperature parameter and preset temperature value into the temperature analysis model respectively, calculate the temperature adjustment parameter through the temperature analysis model, and output the calculated result;

[0019] The regulating module is configured to send the temperature regulating parameter to the buffer tank based on the calculated result, so that the buffer tank performs temperature regulation according to the temperature regulating parameter.

[0020] The present application also provides a computer device, comprising a memory and a processor, wherein the memory stores a computer program, and the processor implements the steps of any of the above methods when executing the computer program.

[0021] The present application also provides a computer-readable storage medium having a computer program stored thereon, and when the computer program is executed by a processor, the steps of any of the above-mentioned methods are implemented.

[0022] The temperature intelligent control method and system based on the closed buffer tank of the embodiment of the present application continuously monitors temperature changes and combines intelligent algorithms to analyze data in real time. This feature ensures the accuracy of temperature control, minimizes the deviation between the set value and the actual value, and thus ensures temperature stability during the production process. By obtaining insulation parameters and environmental parameters, the system can automatically evaluate heat loss and adjust the heating or cooling strategy in a timely manner. This dynamic adaptability improves the system's responsiveness in rapidly changing environments and ensures the timeliness of temperature regulation. The use of optimized control algorithms reduces unnecessary energy consumption and improves the energy efficiency of the system. The integrated analysis model can make intelligent decisions based on historical data and real-time conditions, reducing dependence on manual operations. BRIEF DESCRIPTION OF THE DRAWINGS

[0023] Figure 1 This is a flow chart of a temperature intelligent control method based on a closed buffer tank according to one embodiment of the present application;

[0024] Figure 2 This is a flow chart of a temperature intelligent control method based on a closed buffer tank according to one embodiment of the present application;

[0025] Figure 3 This is a schematic block diagram of the structure of a temperature intelligent control system based on a closed buffer tank according to one embodiment of the present application;

[0026] Figure 4 This is a schematic block diagram of the structure of a computer device according to an embodiment of the present application.

[0027] The realization of the objectives, functional features and advantages of this application will be further explained in conjunction with embodiments and with reference to the accompanying drawings. DETAILED DESCRIPTION

[0028] In order to make the purpose, technical solutions and advantages of this application more clear, the following further describes this application in detail with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain this application and are not intended to limit this application.

[0029] Reference Figure 1 In an embodiment of the present application, a temperature intelligent control method based on a closed buffer tank is provided, the method comprising:

[0030] S1. Obtain the temperature parameters of the fluid in the buffer tank;

[0031] S2. Determine whether the preset temperature value is reached according to the temperature parameter;

[0032] S3. When the temperature parameter does not reach the preset temperature value, obtaining the insulation parameters inside the buffer tank and the environmental parameters outside the buffer tank;

[0033] S4. Determine the heat leakage coefficient of the fluid according to the insulation parameters and environmental parameters;

[0034] S5. Constructing a temperature analysis model, inputting the heat leakage coefficient, temperature parameter, and preset temperature value into the temperature analysis model respectively, calculating the temperature adjustment parameter through the temperature analysis model, and outputting the calculated result;

[0035] S6. Based on the calculation result, the temperature adjustment parameter is sent to the buffer tank, and the buffer tank performs temperature adjustment according to the temperature adjustment parameter.

[0036] As described in steps S1-S3 above, a temperature sensor can be used to monitor the temperature of the fluid in the buffer tank in real time. This ensures that the system understands the current state of the fluid. By accurately acquiring the fluid temperature, the system can promptly determine whether the current temperature meets production requirements. For example, in a chemical reaction, small changes in temperature can affect the reaction rate and product quality, making real-time monitoring crucial. The acquired temperature parameter is compared with a preset temperature value. If the temperature does not reach the preset value, the system proceeds to the next step for analysis and adjustment. This judgment mechanism ensures that the system only makes adjustments when necessary, avoiding unnecessary energy waste. For example, in food processing, heating is only initiated when the temperature falls below the ideal value to maintain energy efficiency. The system uses additional sensors to obtain data on the insulation effectiveness of the buffer tank (such as the thermal conductivity of the insulation material) and the external environment temperature and humidity. Taking these factors into account allows for a more accurate assessment of the fluid's heat loss. For example, if the external environment is very low and the insulation is poor, the system can predict the potential for a drop in fluid temperature earlier, allowing for proactive adjustments.

[0037] As described in steps S3-S6 above, the system uses the insulation and environmental parameters to calculate the fluid's heat leakage coefficient—the fluid's ability to lose heat under current conditions. This coefficient can help predict temperature changes in a specific environment. An accurate heat leakage coefficient can help the system make better temperature adjustment decisions in dynamic environments, reducing energy loss and improving efficiency. For example, in a low-temperature environment, the heat leakage coefficient may increase, and the system can automatically increase heating intensity. The system constructs a temperature analysis model that takes the heat leakage coefficient, current temperature, and preset temperature values ​​as input. It processes and analyzes the data and calculates the required temperature adjustment parameters. This model provides a scientific temperature adjustment strategy, enabling the system to intelligently adjust based on real-time data. For example, if the actual fluid temperature falls below the preset value, the model calculates the required temperature increase and heating time to ensure a rapid return to the target temperature. The system converts the analysis results into specific control instructions and sends them to the buffer tank's heating or cooling equipment to implement temperature adjustment. Through real-time feedback and adjustments, the system can precisely control temperature and avoid the risk of overheating or undercooling. This intelligent adjustment mechanism can significantly improve product consistency and quality and reduce resource waste in fields such as chemical production and food processing.

[0038] As mentioned above, the system continuously monitors temperature changes and uses intelligent algorithms to analyze data in real time. This feature ensures precise temperature control, minimizing deviations between setpoints and actual values, thereby guaranteeing temperature stability during production. By acquiring insulation and environmental parameters, the system automatically assesses heat loss and adjusts heating or cooling strategies promptly. This dynamic adaptability improves the system's responsiveness in rapidly changing environments and ensures timely temperature regulation. The optimized control algorithm reduces unnecessary energy consumption and improves system energy efficiency. Integrated analytical models enable intelligent decision-making based on historical data and real-time conditions, reducing reliance on manual operations.

[0039] Reference Figure 2 In one embodiment, after the step of regulating the temperature of the buffer tank according to the temperature adjustment parameter, the method includes:

[0040] S61, obtaining the adjusted temperature parameter of the fluid in the buffer tank;

[0041] S62, judging whether the preset temperature value is reached according to the adjusted temperature parameter;

[0042] S63: If the adjusted temperature parameter does not reach the preset temperature value, it is determined that there is an error in the temperature adjustment parameter;

[0043] S64, calculating the temperature difference between the adjusted temperature parameter and a preset temperature value based on the judgment result;

[0044] S65, constructing a temperature compensation model, inputting the temperature difference value, the preset temperature value, and the heat leakage coefficient into the temperature compensation model respectively, analyzing the compensation temperature value required to achieve the preset temperature value through the temperature compensation model, and outputting the analysis result;

[0045] S66: Generate compensation parameters from the compensated temperature value and send them to the buffer tank, so that the buffer tank performs temperature adjustment according to the compensation parameters.

[0046] As described in the above steps, a temperature sensor monitors the temperature of the fluid in the buffer tank in real time to obtain temperature data. Accurate temperature data provides a timely reflection of the current status, enabling the system to react immediately during the adjustment process and avoid lags. The system compares the acquired temperature parameters with the preset target temperature to determine whether they meet the requirements. If the temperature reaches the preset value, the system stops adjustment; otherwise, it continues adjustment, ensuring production efficiency and product quality. By comparing the adjusted temperature with the preset value, the system identifies any deviations from the target temperature adjustment parameters (such as heating power and cooling flow). Promptly identifying any deviations helps the system adjust its control strategy, ensuring more accurate subsequent adjustments and avoiding production problems caused by temperature deviations. The system calculates the difference between the current temperature and the target temperature to quantify the degree of error. This process can be considered as establishing the basic data for temperature adjustment. Quantifying the temperature difference provides a clear target, enabling subsequent compensation measures to accurately address the current temperature deviation. Parameters such as the temperature difference, the preset temperature value, and the heat leakage coefficient are input into a temperature compensation model. This model uses previous data for analysis and prediction. Through model analysis, the system dynamically calculates the compensation temperature required to achieve the preset temperature, enabling more efficient regulation and avoiding over- or under-adjustment. After model analysis, the system outputs the compensation temperature value, which the system then converts into actual compensation parameters. Converting the model output into actual control parameters enables automated regulation, enhancing the system's intelligence and adaptability. The system transmits the calculated compensation parameters to the buffer tank to adjust the heating or cooling intensity or flow rate. Through precise compensation parameter adjustment, the system can quickly and effectively adjust the temperature to the preset value, ensuring the stability and efficiency of the production process.

[0047] In one embodiment, the generating of compensation parameters from the compensated temperature value and sending the compensation parameters to the buffer tank comprises:

[0048] Analyzing the preset temperature value and the compensation temperature value, and determining the required adjustment time according to the preset temperature value and the compensation temperature value;

[0049] According to the adjustment time, the temperature compensation process is divided into a first stage and a second stage, and each stage is matched with a corresponding temperature interval to obtain a segmented supplementary parameter, wherein the first stage is used to quickly adjust the temperature parameter to a value close to the preset temperature value, and the second stage is used to slowly adjust the temperature following the first stage to the preset temperature value;

[0050] The segmented supplementary parameters are used to generate compensation parameters, and the compensation parameters are sent to the buffer tank.

[0051] As described above, the preset temperature value is compared with the offset temperature value, and the difference between the two is calculated. Based on this difference and the current heating / cooling capacity, the time required to reach the target temperature is estimated. For example, if the current temperature differs significantly from the target temperature, the system will determine that a longer adjustment time is required. By accurately calculating the adjustment time, the system can more effectively schedule the temperature adjustment process, improving response speed and efficiency, and avoiding temperature fluctuations caused by overly rapid or slow adjustments. Based on the determined adjustment time, the system divides the temperature adjustment process into two stages: the first stage quickly approaches the target temperature, and the second stage slowly reaches the target temperature. This staged adjustment method can reduce the impact of temperature adjustment. Staged adjustment effectively controls the rate of temperature change, avoiding system oscillation caused by rapid changes, ensuring a smooth process, and improving product quality and safety. The system sets specific temperature ranges for each stage based on the characteristics of the first and second stages. For example, the first stage may have a wider range for rapid adjustment, while the second stage may have a narrower range to ensure precise approach to the target temperature. This range matching ensures targeted adjustment methods in different stages, making the entire adjustment process more scientific and reasonable, and optimizing temperature adjustment efficiency. The system combines the temperature ranges for the first and second stages with the corresponding regulation strategies to generate specific supplementary parameters. For example, the heating power or cooling flow rate might be set based on the temperature change rate at each stage. These segmented supplementary parameters provide precise guidance for actual operation, improving regulation accuracy and making the temperature adjustment process more controllable. The system then transmits these compensation parameters to the buffer tank via the control system, adjusting the operating status of the heating or cooling device to ensure temperature regulation.

[0052] In one embodiment, the method for determining the heat leakage coefficient of the fluid based on the insulation parameters and the environmental parameters includes:

[0053] Acquiring historical usage data of the buffer tank, analyzing usage patterns of the buffer tank under different environmental parameters based on the historical usage data, and extracting peak usage periods based on the usage patterns;

[0054] Obtain the thermal insulation parameters of the fluid during the historical peak period, and construct the initial heat leakage coefficient based on the thermal insulation parameters of the fluid during the historical peak period and the environmental parameters;

[0055] acquiring current environmental parameters and usage patterns in real time, inputting the current environmental parameters, current usage patterns, and initial heat leakage coefficient into a preset dynamic adjustment model, adjusting the initial heat leakage coefficient according to the current environmental parameters and usage patterns through the dynamic adjustment model, and outputting the result;

[0056] The heat leakage coefficient of the current fluid is obtained based on the output results.

[0057] As mentioned above, historical usage data for the buffer tank can be extracted from the database, including information such as temperature, usage duration, and environmental conditions. This data is used to analyze the buffer tank's performance under different environmental parameters. Time series analysis can identify usage patterns, such as which time periods are most frequently used. By analyzing historical data, the system can identify peak usage periods, allowing the model to more accurately reflect actual usage. After obtaining historical data, the system uses data mining techniques (such as cluster analysis or time series analysis) to identify peak usage periods. For example, if usage frequency during certain hourly periods is significantly higher than that during other periods, the system marks these periods as peak periods. The extracted peak periods provide key data for optimizing the calculation of the heat leakage coefficient, making the model more targeted under high-load conditions. The system collects fluid insulation parameters during historical peak periods, such as the initial fluid temperature, insulation time, and ambient temperature. This data is used to construct an initial heat leakage coefficient model, making the model more realistic. By combining the insulation parameters from historical peak periods with environmental parameters, for example, by analyzing heat loss under specific environmental conditions, the system can calculate a preliminary heat leakage coefficient. Establishing an initial heat leakage coefficient provides the foundational data for the dynamic adjustment model, avoiding the errors associated with using a fixed coefficient. The system uses sensors and a data acquisition system to monitor current environmental parameters (such as temperature and humidity) and usage patterns (such as flow rate and pressure) in real time. This real-time data provides a timely reflection of the current operating status. Acquiring real-time data enables the system to quickly respond to environmental changes, providing the latest information for dynamic adjustment of the heat leakage coefficient. The current environmental parameters, usage patterns, and initial heat leakage coefficient are input into a pre-set dynamic adjustment model. The model processes the input data using algorithms (such as regression analysis and machine learning) to calculate a new heat leakage coefficient. This dynamic adjustment model allows the system to adjust the heat leakage coefficient in a timely manner, ensuring optimal fluid insulation, reducing energy consumption, and improving efficiency. After the model calculation is complete, it outputs the new heat leakage coefficient for subsequent use in the temperature control system. This result accurately reflects the current heat loss situation. Based on the real-time calculated heat leakage coefficient, the system can effectively adjust the temperature to ensure that the fluid remains within the ideal temperature range during transportation or storage, thereby optimizing energy efficiency and extending equipment life.

[0058] In one embodiment, the method further comprises:

[0059] Obtaining the historical usage data and the historical usage peak period, and predicting the usage peak period under the current environmental parameters based on the historical usage data and the historical usage peak period;

[0060] Generate insulation strategies based on the prediction results.

[0061] As mentioned above, historical usage data is extracted from the database, including information such as temperature, flow rate, time, and environmental conditions. For example, if a buffer tank was frequently used on a previous winter night, the system will record all relevant data from those periods. This historical usage data provides a foundation for subsequent analysis, enabling the system to understand past usage patterns and operational performance. This allows for in-use data to be used as a reference when formulating insulation strategies, mitigating the risks of policy implementation. By analyzing the time series of historical data, the system identifies peak usage periods. For example, using cluster analysis techniques, the system may discover that usage increases significantly during certain time periods (such as morning and evening peaks). After identifying peak periods, the system can focus on optimizing insulation strategies during these critical times, improving efficiency and avoiding wasting resources during low-usage periods. Combining historical data with current environmental parameters, the system uses predictive algorithms (such as time series prediction and machine learning models) to predict future peak usage periods. For example, if the current temperature drops, the system may be able to predict peak usage periods for the next few days based on historical data. This predictive capability enables the system to proactively adjust strategies, preparing insulation for anticipated peak periods, reducing energy consumption, and improving response speed. Based on predicted peak hours, the system develops appropriate insulation strategies, such as pre-heating or adjusting insulation materials during the upcoming peak usage period. This may involve setting equipment operating modes or adjusting valve openings. The resulting insulation strategy effectively addresses upcoming usage, ensuring that fluid temperatures remain at ideal levels during peak periods, minimizing heat loss and improving energy efficiency while ensuring stable system operation.

[0062] In one embodiment, the method of obtaining the historical usage data and the historical usage peak period, and predicting the usage peak period under current environmental parameters based on the historical usage data and the historical usage peak period includes:

[0063] performing statistical analysis on the historical usage data to determine the usage frequency and usage pattern of the buffer tank;

[0064] determining historical peak usage periods based on the usage frequency and usage pattern of the buffer tank;

[0065] Based on historical peak usage periods, obtain corresponding environmental parameters;

[0066] Based on time series analysis, the environmental change characteristics of historical peak usage periods are extracted;

[0067] Based on the extracted results, a prediction model is constructed to predict the peak usage period under the current environmental parameters;

[0068] The current environment parameters are obtained, the current environment parameters are input into the prediction model, the peak usage period under the current environment is predicted by the prediction model, and the prediction result is output.

[0069] As mentioned above, by statistically analyzing historical usage data, the system can identify the frequency and patterns of buffer tank usage. For example, the system can calculate daily or hourly usage times and identify peaks and troughs. Descriptive statistics (such as mean and standard deviation) can be used to summarize usage. Understanding the frequency and patterns of buffer tank usage helps managers identify common usage patterns and optimize management and maintenance strategies. For example, if a particular period of high usage is observed, managers can focus resource allocation on that period. After analyzing usage frequency and patterns, the system can use clustering algorithms or threshold methods to identify historical peak usage periods. For example, the system might set a usage threshold, and when usage exceeds this threshold, it is considered a peak period. Identifying peak periods can help optimize thermal insulation strategies. For example, the system can initiate thermal insulation measures before peak periods to ensure that the fluid maintains an appropriate temperature during use and reduce heat loss. The system correlates historical peak periods with environmental parameters (such as temperature and humidity). Through database queries and data matching, the environmental characteristics of these periods are extracted. Obtaining environmental parameters helps understand the impact of external conditions on usage behavior and provides a data foundation for subsequent predictive models. For example, if usage frequency increases under certain temperatures, the system can use this information for subsequent forecasting. The system uses time series analysis to extract environmental variation characteristics during historical peak usage periods. This can be achieved through techniques such as calculating moving averages and seasonal decomposition, revealing the relationship between environmental factors and usage patterns. The extracted environmental variation characteristics provide a key basis for building more accurate predictive models. Understanding how usage patterns vary under different environmental conditions helps improve the model's predictive accuracy. Based on the extracted environmental variation characteristics, the system constructs a predictive model (such as a regression model or machine learning model). The model inputs historical environmental parameters and usage data, and outputs predictions for future peak usage periods. By building an effective predictive model, the system can accurately predict peak usage periods under current environmental parameters. This forward-looking capability enables the system to make proactive adjustments to ensure efficient equipment operation. Current environmental parameters (such as temperature and humidity) are obtained in real time and input into the predictive model. The model then calculates the peak usage period under the current environment. Through real-time data input and model calculations, the system can quickly respond and adjust insulation strategies. This ensures that the buffer tank maintains the ideal temperature during upcoming peak usage periods.

[0070] In one embodiment, the insulation strategy includes timed insulation during the peak usage period and heat recovery during the low-frequency usage period. During the peak usage period determined by analysis, the system will automatically start insulation measures to ensure that the fluid in the buffer tank is maintained at an appropriate temperature. Based on the predicted peak period, the system can start the heating equipment in advance to ensure that the fluid is at an appropriate temperature when in use. The fluid temperature is monitored in real time, and once the temperature is lower than the set value, the system can automatically heat it to prevent the temperature from being too low. Ensure that the fluid can be extracted at the optimal temperature during peak usage, improve user experience, and reduce energy consumption. During periods of low frequency of use, the system can implement heat recovery measures to recover and reuse the remaining heat. This includes: using specialized heat exchange equipment to recover unused heat in the buffer tank for heating other fluids or supplying it to other equipment. During low-frequency periods, according to actual needs, the heating temperature is lowered or the heating frequency is adjusted to reduce energy consumption.

[0071] The temperature intelligent control method based on a closed buffer tank of the present application continuously monitors temperature changes and combines intelligent algorithms to analyze data in real time. This feature ensures the accuracy of temperature control, minimizes the deviation between the set value and the actual value, and thus ensures temperature stability during the production process. By obtaining insulation parameters and environmental parameters, the system can automatically evaluate heat loss and adjust heating or cooling strategies in a timely manner. This dynamic adaptability improves the system's responsiveness in rapidly changing environments and ensures the timeliness of temperature regulation. The use of optimized control algorithms reduces unnecessary energy consumption and improves the energy efficiency of the system. The integrated analysis model can make intelligent decisions based on historical data and real-time conditions, reducing dependence on manual operations.

[0072] Reference Figure 3 In an embodiment of the present application, a temperature intelligent control system based on a closed buffer tank is further provided, comprising:

[0073] The first acquisition module 1 is used to obtain the temperature parameters of the fluid in the buffer tank;

[0074] A judgment module 2 is used to judge whether a preset temperature value is reached according to the temperature parameter;

[0075] The second acquisition module 3 is used to acquire the insulation parameters in the buffer tank and the environmental parameters outside the buffer tank when the temperature parameter does not reach the preset temperature value;

[0076] Determination module 4, for determining the heat leakage coefficient of the fluid according to the insulation parameters and environmental parameters;

[0077] An input module 5 is used to construct a temperature analysis model, input the heat leakage coefficient, temperature parameter and preset temperature value into the temperature analysis model respectively, calculate the temperature adjustment parameter through the temperature analysis model, and output the calculated result;

[0078] The regulating module 6 is configured to send the temperature regulating parameter to the buffer tank based on the calculation result, so that the buffer tank performs temperature regulation according to the temperature regulating parameter.

[0079] As described above, it can be understood that the various components of the temperature intelligent control system based on the closed buffer tank proposed in this application can realize the functions of any of the temperature intelligent control methods based on the closed buffer tank described above, and the specific structure will not be repeated.

[0080] Reference Figure 4 In the embodiment of the present application, a computer device is also provided. The computer device may be a server, and its internal structure may be as follows: Figure 4 As shown. The computer device includes a processor, a memory, a network interface and a database connected via a system bus. The processor of the computer design is used to provide computing and control capabilities. The memory of the computer device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system, a computer program and a database. The internal memory provides an environment for the operation of the operating system and the computer program in the non-volatile storage medium. The database of the computer device is used to store data such as monitoring data. The network interface of the computer device is used to communicate with an external terminal via a network connection. When the computer program is executed by the processor, a temperature intelligent control method based on a closed buffer tank is implemented.

[0081] The above-mentioned processor executes the above-mentioned temperature intelligent control method based on the closed buffer tank, including: obtaining the temperature parameters of the fluid in the buffer tank; judging whether the preset temperature value is reached according to the temperature parameters; when the temperature parameters do not reach the preset temperature value, obtaining the insulation parameters in the buffer tank and the environmental parameters outside the buffer tank; determining the heat leakage coefficient of the fluid according to the insulation parameters and the environmental parameters; constructing a temperature analysis model, inputting the heat leakage coefficient, temperature parameters and preset temperature value into the temperature analysis model respectively, calculating the temperature adjustment parameter through the temperature analysis model, and outputting the calculation result; based on the calculation result, sending the temperature adjustment parameter to the buffer tank, and performing temperature adjustment according to the temperature adjustment parameter through the buffer tank.

[0082] The aforementioned intelligent temperature control method based on a closed buffer tank continuously monitors temperature changes and uses intelligent algorithms to analyze data in real time. This feature ensures precise temperature control, minimizing the deviation between setpoints and actual values, thereby guaranteeing temperature stability during production. By acquiring insulation and environmental parameters, the system can automatically assess heat loss and promptly adjust heating or cooling strategies. This dynamic adaptability improves the system's responsiveness in rapidly changing environments and ensures timely temperature regulation. The use of an optimized control algorithm reduces unnecessary energy consumption and improves the system's energy efficiency. The integrated analytical model enables intelligent decision-making based on historical data and real-time conditions, reducing reliance on manual operations.

[0083] An embodiment of the present application also provides a computer-readable storage medium having a computer program stored thereon. When the computer program is executed by a processor, a temperature intelligent control method based on a closed buffer tank is implemented, including the following steps: obtaining the temperature parameters of the fluid in the buffer tank; judging whether a preset temperature value is reached according to the temperature parameters; when the temperature parameters do not reach the preset temperature value, obtaining the insulation parameters in the buffer tank and the environmental parameters outside the buffer tank; determining the heat leakage coefficient of the fluid according to the insulation parameters and the environmental parameters; constructing a temperature analysis model, inputting the heat leakage coefficient, temperature parameters and preset temperature value into the temperature analysis model respectively, calculating the temperature adjustment parameter through the temperature analysis model, and outputting the calculation result; based on the calculation result, sending the temperature adjustment parameter to the buffer tank, and performing temperature adjustment through the buffer tank according to the temperature adjustment parameter.

[0084] Those skilled in the art will understand that all or part of the processes in the above-mentioned embodiment methods can be implemented by instructing the relevant hardware through a computer program, and the computer program can be stored in a non-volatile computer-readable storage medium. When the computer program is executed, it can include the processes of the embodiments of the above-mentioned methods. Among them, any reference to memory, storage, database or other media provided in this application and used in the embodiments may include non-volatile and / or volatile memory. Non-volatile memory may include read-only memory (ROM), programmable ROM (PROM), electrically programmable ROM (EPROM), electrically erasable programmable ROM (EEPROM) or flash memory. Volatile memory may include random access memory (RAM) or external cache memory. By way of illustration and not limitation, RAM is available in many forms, such as static RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), double data rate SDRAM (SSRSDRAM), enhanced SDRAM (ESDRAM), Synchronous Link DRAM (SLDRAM), Rambus direct RAM (RDRAM), direct RAM bus dynamic RAM (DRDRAM), and RAM bus dynamic RAM (RDRAM), etc.

[0085] It should be noted that, in this document, the terms "comprises," "includes," or any other variations thereof are intended to encompass non-exclusive inclusion, such that a process, apparatus, article, or method comprising a series of elements includes not only those elements but also other elements not explicitly listed, or elements inherent to such process, apparatus, article, or method. In the absence of further limitations, an element defined by the phrase "comprising a ..." does not exclude the presence of other identical elements in the process, apparatus, article, or method comprising the element.

[0086] The above description is only a preferred embodiment of the present application and does not limit the patent scope of the present application. Any equivalent structure or equivalent process transformation made using the contents of the present application specification and drawings, or directly or indirectly applied in other related technical fields, are also included in the patent protection scope of the present application.

Claims

1. A temperature intelligent control method based on a closed buffer tank, characterized in that: The method comprises: Obtain the temperature parameters of the fluid in the buffer tank; Determining whether a preset temperature value is reached according to the temperature parameter; When the temperature parameter does not reach the preset temperature value, obtaining the insulation parameter inside the buffer tank and the environmental parameter outside the buffer tank; determining a heat leakage coefficient of the fluid according to the thermal insulation parameters and the environmental parameters; Constructing a temperature analysis model, inputting the heat leakage coefficient, temperature parameter and preset temperature value into the temperature analysis model respectively, calculating the temperature adjustment parameter through the temperature analysis model, and outputting the calculated result; Based on the calculated result, the temperature adjustment parameter is sent to the buffer tank, and the buffer tank performs temperature adjustment according to the temperature adjustment parameter; The method for determining the heat leakage coefficient of the fluid based on the insulation parameters and environmental parameters includes: obtaining historical usage data of the buffer tank, analyzing the usage pattern of the buffer tank under different environmental parameters based on the historical usage data, in this case, the usage model includes which time periods are used more frequently; and extracting peak usage periods based on the usage pattern; obtaining the insulation parameters of the fluid during the historical peak period, and constructing an initial heat leakage coefficient based on the insulation parameters and environmental parameters of the fluid during the historical peak period; obtaining the current environmental parameters and usage pattern in real time, in this case, the usage pattern includes flow rate and pressure, inputting the current environmental parameters, current usage pattern and initial heat leakage coefficient into a preset dynamic adjustment model, adjusting the initial heat leakage coefficient according to the current environmental parameters and usage pattern through the dynamic adjustment model, and outputting the result; and obtaining the heat leakage coefficient of the current fluid based on the output result; After the step of performing temperature adjustment according to the temperature adjustment parameter by the buffer tank, the method includes: obtaining the adjusted temperature parameter of the fluid in the buffer tank; judging whether the preset temperature value is reached according to the adjusted temperature parameter; if the adjusted temperature parameter does not reach the preset temperature value, judging that there is an error in the temperature adjustment parameter; based on the result of the judgment, calculating the temperature difference between the adjusted temperature parameter and the preset temperature value; constructing a temperature compensation model, inputting the temperature difference, the preset temperature value and the heat leakage coefficient into the temperature compensation model respectively, analyzing the compensation temperature value required to reach the preset temperature value through the temperature compensation model, and outputting the analysis result; generating a compensation parameter from the compensation temperature value and sending it to the buffer tank, and performing temperature adjustment according to the compensation parameter by the buffer tank.

2. The temperature intelligent control method based on a closed buffer tank according to claim 1 is characterized in that: The method of generating compensation parameters from the compensation temperature value and sending them to the buffer tank includes: Analyzing the preset temperature value and the compensation temperature value, and determining the required adjustment time according to the preset temperature value and the compensation temperature value; According to the adjustment time, the temperature compensation process is divided into a first stage and a second stage, and each stage is matched with a corresponding temperature interval to obtain a segmented supplementary parameter, wherein the first stage is used to quickly adjust the temperature parameter to a value close to the preset temperature value, and the second stage is used to slowly adjust the temperature following the first stage to the preset temperature value; The segmented supplementary parameters are used to generate compensation parameters, and the compensation parameters are sent to the buffer tank.

3. The temperature intelligent control method based on a closed buffer tank according to claim 1 is characterized in that: The method further comprises: Obtaining the historical usage data and the historical usage peak period, and predicting the usage peak period under the current environmental parameters based on the historical usage data and the historical usage peak period; Generate insulation strategies based on the prediction results.

4. The temperature intelligent control method based on a closed buffer tank according to claim 3 is characterized in that: The method of obtaining the historical usage data and the historical usage peak period, and predicting the usage peak period under current environmental parameters based on the historical usage data and the historical usage peak period includes: performing statistical analysis on the historical usage data to determine the usage frequency and usage pattern of the buffer tank; determining historical peak usage periods based on the usage frequency and usage pattern of the buffer tank; Based on historical peak usage periods, obtain corresponding environmental parameters; Based on time series analysis, the environmental change characteristics of historical peak usage periods are extracted; Based on the extracted results, a prediction model is constructed to predict the peak usage period under the current environmental parameters; The current environment parameters are obtained, the current environment parameters are input into the prediction model, the peak usage period under the current environment is predicted by the prediction model, and the prediction result is output.

5. The temperature intelligent control method based on a closed buffer tank according to claim 3 is characterized in that: The heat preservation strategy includes timed heat preservation during the peak usage period and heat recovery during the low usage period.

6. A temperature intelligent control system based on a closed buffer tank, used in the method according to any one of claims 1 to 5, characterized in that: include: A first acquisition module is used to obtain the temperature parameters of the fluid in the buffer tank; A judgment module, configured to judge whether a preset temperature value has been reached according to the temperature parameter; A second acquisition module is used to acquire the insulation parameters in the buffer tank and the environmental parameters outside the buffer tank when the temperature parameter does not reach the preset temperature value; A determination module, configured to determine a heat leakage coefficient of the fluid according to the insulation parameters and the environmental parameters; An input module is used to construct a temperature analysis model, input the heat leakage coefficient, temperature parameter and preset temperature value into the temperature analysis model respectively, calculate the temperature adjustment parameter through the temperature analysis model, and output the calculated result; The regulating module is configured to send the temperature regulating parameter to the buffer tank based on the calculated result, so that the buffer tank performs temperature regulation according to the temperature regulating parameter.

7. A computer device comprising a memory and a processor, wherein the memory stores a computer program, wherein: When the processor executes the computer program, the steps of the method according to any one of claims 1 to 5 are implemented.

8. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the steps of the method according to any one of claims 1 to 5 are implemented.

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