Intelligent temperature control method and system based on closed buffer tank

Abstract

The invention relates to the field of temperature control, in particular to an intelligent temperature control method and system based on a closed buffer tank. Judging whether a preset temperature value is reached or not according to the temperature parameter; when the temperature parameter does not reach the preset temperature value, a heat preservation parameter in the buffer tank and an environment parameter outside the buffer tank are obtained; determining the heat leakage coefficient of the fluid according to the heat preservation parameters and the environmental parameters; constructing a temperature analysis model, respectively inputting the heat leakage coefficient, the temperature parameter and a preset temperature value into the temperature analysis model, calculating a temperature adjustment parameter through the temperature analysis model, and outputting a calculation result; and based on a calculation result, sending a temperature adjustment parameter to the buffer tank, and performing temperature adjustment through the buffer tank according to the temperature adjustment parameter. By obtaining heat preservation parameters and environment parameters, the system can automatically evaluate the heat loss condition and adjust the heating or cooling strategy in time.

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 kind of industrial process control. In many industrial processes, such as chemical industry, food processing, pharmaceutical industry, etc., temperature needs to be precisely controlled 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 reaction rates or 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 may lead to losses and the production of defective products in demanding production processes. Secondly, existing technologies often lack the ability to dynamically adjust the temperature requirements of buffer tanks during different periods of use, making it difficult to effectively respond to changes in fluid usage patterns and cause unnecessary energy consumption.

[0004] Therefore, the prior art 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 the present 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 invention object, the present application proposes a temperature intelligent control method based on a closed buffer tank, the method comprising:

[0007] Obtaining 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 in 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 calculation 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 embodiment of the present application also provides a temperature intelligent control system based on a closed buffer tank, comprising:

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

[0015] A judgment module, used to judge whether a preset temperature value is reached according to the temperature parameter;

[0016] A second acquisition module, used for acquiring 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, used to determine the heat leakage coefficient of the fluid according to the thermal insulation parameters and environmental parameters;

[0018] An input module, 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 used to send the temperature adjustment parameter to the buffer tank based on the calculation result, and the buffer tank performs temperature regulation according to the temperature adjustment 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 one 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 analyzes data in real time in combination with intelligent algorithms. This feature ensures the accuracy of temperature control, minimizes the deviation between the set value and the actual value, and thus ensures the temperature stability during the production process. By obtaining insulation parameters and environmental parameters, the system can automatically evaluate the heat loss situation and adjust the heating or cooling strategy in time. This dynamic adaptability improves the system's responsiveness in a rapidly changing environment 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 reliance 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 an 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 an 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 an embodiment of the present application;

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

[0027] The realization of the purpose, 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 solution and advantages of the present application more clearly understood, the present application is further described in detail below in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present application and are not used to limit the present 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. Obtaining the temperature parameters of the fluid in the buffer tank;

[0031] S2. judging whether a preset temperature value has been reached according to the temperature parameter;

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

[0033] S4, determining 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 calculation 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 the above steps S1-S3, a temperature sensor can be used to monitor the temperature of the fluid in the buffer tank in real time. Ensure that the system understands the current state of the fluid. By accurately obtaining the fluid temperature, the system can promptly determine whether the current temperature meets the production requirements. For example, in a chemical reaction, a slight change in temperature may affect the reaction rate and product quality, so real-time monitoring is very critical. Compare the obtained temperature parameters with the preset temperature values. If the temperature does not reach the preset value, the system will enter the next step for analysis and adjustment. This judgment mechanism ensures that the system is adjusted only when necessary to avoid unnecessary energy waste. For example, in food processing, the heating program will only be started when the temperature is lower than the ideal value to maintain energy efficiency. The system obtains the insulation effect in the buffer tank (such as the thermal conductivity of the insulation material) and the temperature, humidity and other data of the external environment through additional sensors. Considering insulation and environmental factors, the heat loss of the fluid can be more accurately evaluated. For example, if the external environment temperature is very low and the insulation effect is poor, the system can predict earlier that the fluid may cool down, so as to make adjustments in advance.

[0037] As described in the above steps S3-S6, using the insulation and environmental parameters, the system calculates the heat leakage coefficient of the fluid, that is, the heat loss capacity of the fluid under the current conditions. This coefficient can help predict the temperature change of the fluid under a specific environment. An accurate heat leakage coefficient can help the system make better temperature adjustment decisions in a dynamic environment, reduce energy loss and improve efficiency. For example, in a low temperature environment, the heat leakage coefficient may increase, and the system can automatically increase the heating intensity. The system builds a temperature analysis model, takes the heat leakage coefficient, the current temperature and the preset temperature value as input, performs data processing and analysis, and calculates the required temperature adjustment parameters. This model can provide a scientific temperature adjustment strategy so that the system can be adjusted intelligently according to real-time data. For example, if the actual temperature of the fluid is lower than the preset value, the model will calculate the temperature value and heating time that need to be increased to ensure a rapid recovery to the target temperature. The system converts the analysis results into specific control instructions and sends them to the heating or cooling equipment of the buffer tank to achieve temperature adjustment. Through real-time feedback and adjustment, the system can accurately control the temperature and avoid the risk of overheating or overcooling. This intelligent adjustment mechanism can significantly improve the consistency and quality of products and reduce resource waste in fields such as chemical production and food processing.

[0038] As mentioned above, by continuously monitoring temperature changes and combining 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 a rapidly changing environment 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 reliance on manual operations.

[0039] Reference Figure 2 In one embodiment, after the step of adjusting 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, based on the judgment result, calculating the temperature difference between the adjusted temperature parameter and the preset temperature value;

[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 reach the preset temperature value through the temperature compensation model, and outputting the analysis result;

[0045] S66: Generate compensation parameters from the compensation 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, the temperature of the fluid in the buffer tank can be monitored in real time by a temperature sensor to obtain temperature data. Accurate temperature data can reflect the current state in a timely manner, helping the system to respond in real time during the adjustment process and avoid lag. The system compares the obtained temperature parameters with the preset target temperature to determine whether the requirements are met. If the temperature reaches the preset value, the system will stop adjusting; otherwise, it will continue to adjust to ensure the efficiency of the production process and product quality. By comparing the adjusted temperature with the preset value, the system identifies the situation that the standard is not met and the temperature adjustment parameters (such as heating power, cooling flow, etc.) have deviations. Timely identification of the existence of errors helps the system adjust the control strategy to ensure that subsequent adjustments can be more accurate, thereby avoiding production problems caused by non-compliance with the temperature. The system calculates the difference between the current temperature and the target temperature to quantify the degree of error. This process can be regarded as the basic data for establishing temperature adjustment. Quantifying the temperature difference provides a clear goal, so that subsequent compensation measures can accurately respond to 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. The model uses previous data for analysis and prediction. Through model analysis, the system can dynamically calculate the compensation temperature value required to achieve the preset temperature, so as to adjust more effectively and avoid excessive or insufficient adjustment. After model analysis, the compensation temperature value is output, and the system converts this information into actual compensation parameters. The model output is converted into actual control parameters to achieve automatic adjustment, which improves the intelligence and adaptability of the system. The system transmits the calculated compensation parameters to the buffer tank to adjust the intensity or flow of heating or cooling. Through precise compensation parameter adjustment, the system can quickly and effectively adjust the temperature to the preset value to ensure the stability and efficiency of the production process.

[0047] In one embodiment, the generating of compensation parameters from the compensation temperature value and sending them 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 temperature 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 compensation temperature value, the difference between the two is calculated, and the time required to reach the target temperature is estimated based on the difference and the current heating / cooling capacity. For example, if the current temperature is significantly different from the target, the system will determine that a longer adjustment time is required. Through accurate adjustment time calculation, the system can arrange the temperature adjustment process more reasonably, improve response speed and efficiency, and avoid temperature fluctuations caused by too fast or too slow adjustment. According to 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 segmented adjustment method can reduce the impact of temperature adjustment. Staged adjustment can effectively control the rate of temperature change, avoid system shock caused by rapid changes, ensure a smooth process, and improve product quality and safety. The system sets a specific temperature range for each stage based on the characteristics of the first and second stages. For example, a wider range may be set in the first stage for rapid adjustment, while a narrower range may be set in the second stage to ensure accurate approach to the target temperature. This range matching ensures that the adjustment methods in different stages are targeted, making the entire adjustment process more scientific and reasonable, and optimizing the efficiency of temperature adjustment. The system combines the temperature ranges of the first and second stages with the corresponding adjustment strategies to generate specific supplementary parameters. For example, the corresponding heating power or cooling flow rate may be set according to the temperature change rate of each stage. The generated segmented supplementary parameters can accurately guide the actual operation, improve the accuracy of the adjustment, and make the temperature adjustment process more controllable. The system sends the generated compensation parameters to the buffer tank through the control system to adjust the working status of the heating or cooling device to ensure the implementation of temperature adjustment.

[0052] In one embodiment, the method for determining the heat leakage coefficient of the fluid according to the insulation parameter and the environmental parameter includes:

[0053] Acquire historical usage data of the buffer tank, analyze usage patterns of the buffer tank under different environmental parameters according to the historical usage data, and extract peak usage periods according to the usage patterns;

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

[0055] Acquire current environmental parameters and usage mode in real time, input the current environmental parameters, current usage mode and initial heat leakage coefficient into a preset dynamic adjustment model, adjust the initial heat leakage coefficient according to the current environmental parameters and usage mode through the dynamic adjustment model, and output the result;

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

[0057] As described above, historical usage data of the buffer tank can be extracted from the database, including information such as temperature, usage time, and environmental conditions. These data are used to analyze the performance of the buffer tank under different environmental parameters. Through time series analysis, usage patterns can be identified, such as which time periods are used more frequently. By analyzing historical data, the system can identify peak usage periods, so that the model can 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 the usage frequency in certain hour periods is significantly higher than other periods, the system marks them as peak periods. The extracted peak periods provide key data for optimizing the calculation of the heat leakage coefficient, which makes the model more targeted under high load conditions. The system collects the insulation parameters of the fluid during the historical peak period, such as the initial temperature of the fluid, the insulation time, and the ambient temperature. These data are used to construct the initial heat leakage coefficient model, making the model closer to the actual situation. Combining the insulation parameters and environmental parameters of the historical peak period, for example, by analyzing the heat loss under specific environmental conditions, the system can calculate a preliminary heat leakage coefficient. The establishment of the initial heat leakage coefficient provides basic data for the dynamic adjustment model, avoiding the error caused by the use of fixed coefficients. The system monitors the current environmental parameters (such as temperature, humidity) and usage patterns (such as flow, pressure) in real time through sensors and data acquisition systems. This real-time data can reflect the current operating status in a timely manner. The acquisition of real-time data enables the system to respond quickly to environmental changes and provides the latest information for the dynamic adjustment of the heat leakage coefficient. The current environmental parameters, usage patterns and initial heat leakage coefficient are input into the preset dynamic adjustment model. The model processes the input data through algorithms (such as regression analysis and machine learning) to calculate a new heat leakage coefficient. Through the dynamic adjustment model, the system can adjust the heat leakage coefficient in a timely manner to ensure that the thermal insulation effect of the fluid is always in the best state, reduce energy consumption and improve efficiency. After the model calculation is completed, the new heat leakage coefficient is output for use by the subsequent temperature control system. This result can accurately reflect the current heat loss situation. According to the real-time calculated heat leakage coefficient, the system can make effective temperature adjustments 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] Acquire the historical usage data and the historical usage peak period, and predict the usage peak period under the current environmental parameters according to the historical usage data and the historical usage peak period;

[0060] Generate a heat preservation strategy based on the prediction results.

[0061] As mentioned above, the past usage data is extracted from the database, which includes information such as temperature, flow, time, and environmental conditions. For example, if a buffer tank was frequently used on a past winter night, the system will record all relevant data during these periods. The historical usage data provides a basis for subsequent analysis, enabling the system to understand past usage patterns and operating conditions, so that actual data can be referenced when formulating insulation strategies to reduce the risk of strategy implementation. Through time series analysis of historical data, the system identifies peak usage periods. For example, using cluster analysis techniques, the system may find that the frequency of use in certain time periods (such as morning peak and evening peak) has increased significantly. After identifying the peak periods, the system can focus on optimizing insulation strategies during these key time periods to improve efficiency and avoid wasting resources during low usage periods. Combining historical data with current environmental parameters, the system uses prediction algorithms (such as time series prediction, machine learning models, etc.) to predict future peak usage periods. For example, when the current temperature drops, the system may infer the peak usage periods in the next few days based on historical data. This prediction capability enables the system to proactively adjust strategies, make insulation preparations in advance during expected peak periods, reduce energy consumption, and improve response speed. Based on the predicted peak hours, the system formulates corresponding insulation strategies, such as preheating or adjusting the use of insulation materials during the upcoming peak usage period. This may involve setting the working mode of the equipment or adjusting the opening degree of the valve. The generated insulation strategy can effectively respond to the upcoming usage demand, ensure that the temperature of the fluid is maintained at an ideal level during the peak period, avoid heat loss, thereby improving energy efficiency and ensuring the stable operation of the system.

[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 the current environmental parameters according to 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 a historical peak usage period based on the usage frequency and usage pattern of the buffer tank;

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

[0066] Based on time series analysis, the environmental change characteristics during 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 described above, by statistically analyzing the historical usage data, the system can identify the usage frequency and pattern of the buffer tank. For example, the system can calculate the number of uses per day or per hour and identify the peaks and valleys of use. Descriptive statistical methods (such as mean and standard deviation) can be used to summarize the usage. Understanding the usage frequency and pattern of the buffer tank helps managers identify common usage habits and optimize management and maintenance strategies. For example, if it is found that the usage frequency is particularly high during a certain period, managers can centrally configure resources for that period. After completing the analysis of the usage frequency and pattern, the system can determine the historical peak usage period through clustering algorithms or threshold methods. For example, the system may set a threshold for the number of uses, and when the number of uses exceeds this value, it is considered to enter the peak period. Identifying the peak period can help optimize the insulation strategy. For example, before the peak period, the system can start insulation measures in advance to ensure that the fluid maintains a suitable temperature during use and reduce heat loss. The system associates the historical peak periods with the environmental parameters at that time (such as temperature, humidity, etc.). Through database query and data matching, the environmental characteristics of these periods are extracted. Obtaining environmental parameters can help understand the impact of external conditions on usage behavior and provide a data basis for subsequent prediction models. For example, if the frequency of use increases at a certain temperature, the system can use this information for subsequent predictions. The system uses time series analysis methods to extract environmental change characteristics during historical peak usage periods. This can be achieved by calculating moving averages, seasonal decomposition and other techniques to reveal the relationship between environmental factors and usage patterns. The extracted environmental change characteristics provide an important basis for building more accurate prediction models. Understanding the changes in usage patterns under different environmental conditions helps to improve the prediction accuracy of the model. Based on the extracted environmental change characteristics, the system builds a prediction model (such as a regression model, a machine learning model, etc.). The input of the model is historical environmental parameters and usage data, and the output is the prediction results of future peak hours. By building an effective prediction model, the system can accurately predict the peak usage period under the current environmental parameters. This forward-looking capability allows the system to make adjustments in advance to ensure efficient operation of the equipment. Obtain the current environmental parameters (such as temperature, humidity, etc.) in real time, input these parameters into the prediction model, and calculate the peak usage period under the current environment through the model. Through real-time data input and model calculation, the system can respond quickly and adjust the insulation strategy. This ensures that the buffer tank is maintained at 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 obtained by analysis, the system will automatically start insulation measures to ensure that the fluid in the buffer tank is maintained at an appropriate temperature. According to the predicted peak period, the system can start the heating equipment in advance to ensure that the temperature of the fluid is appropriate when it is used. The fluid temperature is monitored in real time. 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 special 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, reduce the heating temperature or adjust the heating frequency 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 and minimizes the deviation between the set value and the actual value, thereby ensuring the temperature stability during the production process. By obtaining insulation parameters and environmental parameters, the system can automatically evaluate the heat loss and adjust the heating or cooling strategy in time. This dynamic adaptability improves the system's responsiveness in a rapidly changing environment 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 reliance 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 also provided, comprising:

[0073] The first acquisition module 1 is used to acquire 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] A determination module 4, used to determine the heat leakage coefficient of the fluid according to the insulation parameters and the 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 calculation result;

[0078] The regulating module 6 is used to send the temperature adjustment parameter to the buffer tank based on the calculation result, and the buffer tank performs temperature regulation according to the temperature adjustment 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 one 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 an 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 through a system bus. Among them, the processor designed by the computer 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 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 through 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 calculated result; based on the calculated result, sending the temperature adjustment parameter to the buffer tank, and adjusting the temperature according to the temperature adjustment parameter through the buffer tank.

[0082] The above-mentioned temperature intelligent control method based on closed buffer tank 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 the temperature stability in the production process. By obtaining insulation parameters and environmental parameters, the system can automatically evaluate the heat loss and adjust the heating or cooling strategy in time. This dynamic adaptability improves the system's responsiveness in a rapidly changing environment 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.

[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 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 insulation parameters in the buffer tank and environmental parameters outside the buffer tank; determining a heat leakage coefficient of the fluid according to the insulation parameters and 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 a temperature adjustment parameter through the temperature analysis model, and outputting the calculated result; based on the calculated result, sending the temperature adjustment parameter to the buffer tank, and adjusting the temperature through the buffer tank according to the temperature adjustment parameter.

[0084] Those of ordinary skill in the art can understand that all or part of the processes in the above-mentioned embodiment methods can be completed 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 (Synchlink) DRAM (SLDRAM), memory bus (Rambus) direct RAM (RDRAM), direct memory bus dynamic RAM (DRDRAM), and memory bus dynamic RAM (RDRAM), etc.

[0085] It should be noted that, in this article, the terms "include", "comprises" or any other variations thereof are intended to cover non-exclusive inclusion, so that a process, device, article or method including a series of elements includes not only those elements, but also other elements not explicitly listed, or also includes elements inherent to such process, device, article or method. In the absence of further restrictions, an element defined by the sentence "includes a ..." does not exclude the existence of other identical elements in the process, device, article or method including 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: Obtaining 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 in 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 calculation result; 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.

2. The temperature intelligent control method based on the closed buffer tank according to claim 1 is characterized in that: After the step of adjusting the temperature of the buffer tank according to the temperature adjustment parameter, the method includes: Obtaining the adjusted temperature parameter of the fluid in the buffer tank; Determine 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, it is determined that there is an error in the temperature adjustment parameter; Based on the result of the determination, a temperature difference between the adjusted temperature parameter and a preset temperature value is calculated; 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 reach the preset temperature value through the temperature compensation model, and outputting the analysis result; The compensation temperature value is used to generate a compensation parameter which is sent to the buffer tank, and the buffer tank performs temperature adjustment according to the compensation parameter.

3. The temperature intelligent control method based on the closed buffer tank according to claim 2 is characterized in that: The method of generating compensation parameters from the compensation temperature value and sending them to the buffer tank comprises: 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 temperature 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.

4. The temperature intelligent control method based on the closed buffer tank according to claim 1 is characterized in that: The method of determining the heat leakage coefficient of the fluid according to the insulation parameter and the environmental parameter comprises: Acquire historical usage data of the buffer tank, analyze usage patterns of the buffer tank under different environmental parameters according to the historical usage data, and extract peak usage periods according to the usage patterns; Obtain the thermal insulation parameters of the fluid during the historical peak period, and construct the initial heat leakage coefficient according to the thermal insulation parameters of the fluid during the historical peak period and the environmental parameters; Acquire current environmental parameters and usage mode in real time, input the current environmental parameters, current usage mode and initial heat leakage coefficient into a preset dynamic adjustment model, adjust the initial heat leakage coefficient according to the current environmental parameters and usage mode through the dynamic adjustment model, and output the result; The heat leakage coefficient of the current fluid is obtained based on the output results.

5. The temperature intelligent control method based on the closed buffer tank according to claim 4 is characterized in that: The method further comprises: Acquire the historical usage data and the historical usage peak period, and predict the usage peak period under the current environmental parameters according to the historical usage data and the historical usage peak period; Generate a heat preservation strategy based on the prediction results.

6. The temperature intelligent control method based on the closed buffer tank according to claim 5 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 the current environmental parameters according to 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 a historical peak usage period based on the usage frequency and usage pattern of the buffer tank; Based on the historical peak usage period, obtain the corresponding environmental parameters; Based on time series analysis, the environmental change characteristics during 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.

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

8. A temperature intelligent control system based on a closed buffer tank, characterized in that: include: A first acquisition module is used to acquire the temperature parameter of the fluid in the buffer tank; A judgment module, used to judge whether a preset temperature value is reached according to the temperature parameter; A second acquisition module, used for acquiring 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, used to determine the heat leakage coefficient of the fluid according to the thermal insulation parameters and environmental parameters; An input module, 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 used to send the temperature adjustment parameter to the buffer tank based on the calculation result, and the buffer tank performs temperature regulation according to the temperature adjustment parameter.

9. 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 7 are implemented.

10. 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 7 are implemented.

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