Intelligent temperature control method, system, medium and program product applied to fermentation chamber

Abstract

An intelligent temperature control method, system, medium and program product applied to a fermentation bin, which relates to the field of electronic digital data processing. The method includes: the system obtains the microbial activity intensity information and the temperature information in the fermentation bin in real time, and adjusts the temperature of the fermentation bin to a preset temperature range corresponding to the current fermentation stage; then, by dividing the preset temperature range into a preset number of temperature intervals, the temperature of the fermentation bin is adjusted to each temperature interval divided into the preset temperature range in turn, and the corresponding microbial activity intensity information is recorded; the temperature interval with the highest score among all the recorded information is set as the target temperature interval, which is used as the control temperature of the subsequent fermentation bin. The implementation of this method can achieve more accurate control of the temperature of the fermentation bin, maintain the high activity of the microorganisms, and improve the fermentation efficiency and product quality.

Description

Technical Field

[0001] The present application relates to the field of electronic digital data processing, and in particular to an intelligent temperature control method, system, medium and program product applied to a fermentation bin. Background Art

[0002] Fermentation chambers play a key role in many industrial and agricultural fields, especially in the production of biomass energy, food brewing, and agricultural waste treatment. In these processes, temperature is one of the key factors affecting fermentation efficiency and product quality. The appropriate temperature can not only promote the activity of microorganisms and accelerate biochemical reactions, but also enhance the stability and safety of the final product. Therefore, precise control of the temperature in the fermentation chamber is crucial to achieve efficient and sustainable biological fermentation processes.

[0003] In the related art, the temperature control of the fermentation bin is generally performed by continuously monitoring the temperature in the bin through a temperature sensor and sending the data to the central processing unit of the corresponding computer. Based on the preset temperature range, the control actuator (such as the heater and the cooling system) adjusts the temperature of the fermentation bin so that the actual temperature of the fermentation bin is always maintained within the preset temperature range to maintain the optimal conditions required for the fermentation process.

[0004] However, the related art generally only needs to control the temperature of the fermentation bin to maintain within a preset temperature range, which still lacks accuracy in temperature control. Especially when dealing with highly variable biomass materials (such as livestock and poultry manure), since each batch of materials may have large differences in chemical and physical properties, it is difficult to predict the heat generated during anaerobic digestion. As a result, it is difficult for existing temperature control methods to achieve accurate temperature control of the fermentation bin, and large temperature fluctuations are prone to occur. This temperature instability will directly affect the metabolism and reproduction of microorganisms, and thus affect the fermentation efficiency. Summary of the invention

[0005] The present application provides an intelligent temperature control method, system, medium and program product applied to a fermentation bin, which is used to address the problem of inaccurate temperature control system in the fermentation process of livestock and poultry manure in related technologies, which affects the metabolism and reproduction of microorganisms.

[0006] In a first aspect, the present application provides an intelligent temperature control method applied to a fermentation bin, which is applied to an intelligent temperature control system. The method comprises:

[0007] The microbial activity intensity information of the fermentation chamber and the temperature information in the chamber are respectively obtained in real time through the biological sensor and the temperature sensor. The biological sensor includes a pH sensor, a dissolved oxygen sensor and a CO2 concentration sensor. The microbial activity intensity information includes pH value, CO2 release and oxygen consumption rate;

[0008] The temperature of the fermentation bin is adjusted to a preset temperature range corresponding to the current fermentation stage by controlling an actuator, the actuator comprising a heating device and a cooling system;

[0009] Dividing the preset temperature range into a preset number of temperature intervals to obtain a set of temperature intervals, wherein the preset number is proportional to the size of the preset temperature range;

[0010] sequentially adjusting the temperature of the fermentation bin to each temperature interval in the temperature interval set, and recording the microbial activity intensity information in each temperature interval;

[0011] The temperature interval with the highest microbial activity intensity among all the recorded microbial activity intensity information is set as the target temperature interval; and the temperature of the fermentation bin is adjusted to the target temperature interval by controlling the actuator.

[0012] Through the above embodiment, the system divides the preset temperature range into multiple temperature intervals, adjusts the fermentation bin temperature to each interval in turn and records the corresponding microbial activity intensity, finds the target temperature interval with the best microbial activity, and finally controls the fermentation bin temperature to maintain in the target interval. This achieves more precise control of the fermentation bin temperature, maintains the high activity of the microorganisms, and improves the fermentation efficiency and product quality.

[0013] In some embodiments, the step of sequentially adjusting the temperature of the fermentation bin to each temperature interval in the temperature interval set and recording the microbial activity intensity information in each temperature interval specifically includes:

[0014] Adjusting the temperature of the fermentation bin to a first temperature interval, where the first temperature interval is a subset of the temperature interval set;

[0015] After each preset time interval, determining whether the value fluctuation in the microbial activity intensity information is greater than a preset threshold;

[0016] If yes, maintaining the temperature of the fermentation chamber in the first temperature range;

[0017] If not, the microbial activity intensity information is recorded, and the temperature of the fermentation bin is adjusted to a second temperature interval, where the second temperature interval is adjacent to the first temperature interval in the temperature interval set.

[0018] Through the above embodiment, the system continuously monitors and adjusts the temperature of the fermentation chamber to various preset temperature ranges and records the intensity of microbial activity in each range. It can evaluate and determine in real time which temperature range is most suitable for the current fermentation needs, thereby ensuring that the fermentation process is carried out at the most ideal temperature as much as possible, greatly enhancing the controllability and predictability of the fermentation process.

[0019] In some embodiments, before the step of adjusting the temperature of the fermentation bin to the first temperature interval, the method further comprises:

[0020] Calculating a first difference between the current temperature of the fermentation bin and a lower limit of the preset temperature range, and a second difference between an upper limit of the preset temperature range;

[0021] Determining whether the first difference is greater than the second difference;

[0022] If yes, then setting the temperature interval where the upper limit value of the preset temperature range is located as the first temperature interval;

[0023] If not, the temperature interval where the lower limit value of the preset temperature range is located is set as the first temperature interval.

[0024] Through the above embodiment, before controlling the temperature of the fermentation bin to traverse all temperature intervals, the system calculates the difference between the current temperature of the fermentation bin and the boundary value corresponding to the preset temperature range, and sets the temperature interval corresponding to the boundary value closest to the current temperature of the fermentation bin as the initial temperature interval, thereby reducing fluctuations in the temperature adjustment process and improving the stability of the fermentation environment.

[0025] In some embodiments, before the step of adjusting the temperature of the fermentation bin to a preset temperature range corresponding to the current fermentation stage by controlling the actuator, the method further includes:

[0026] Determine the fermentation stage of the fermentation tank based on the data information collected by the biosensor, the data information includes pH value, VFA concentration, and the proportion of CO2 and CH4;

[0027] The corresponding preset temperature range is matched from the database according to the fermentation stage.

[0028] Through the above embodiments, the system can identify the specific stage of fermentation by analyzing the data collected by the biosensor, and match the corresponding preset temperature range from the database accordingly, so that each fermentation stage can be carried out at the most suitable temperature, thereby improving the efficiency of the entire fermentation process and product quality.

[0029] In some embodiments, before the step of matching the corresponding preset temperature range from the database according to the fermentation stage, the method further includes:

[0030] Build a machine learning model based on historical fermentation data, which includes temperature setting information corresponding to each fermentation stage, microbial activity intensity information, warehouse temperature and humidity information, and fermentation product quality;

[0031] The data information collected by the biosensor is input into the machine learning model to obtain the preset temperature range.

[0032] Through the above embodiments, the system uses historical fermentation data to train the machine learning model, so that the system can more accurately predict and set the optimal temperature range of the fermentation stage based on the data information collected by the current biosensor. The accuracy and adaptability of temperature control are improved, so that the system can self-learn and adapt to different fermentation conditions, further improving the stability and reliability of the fermentation process.

[0033] In some embodiments, after the step of recording the microbial activity intensity information and adjusting the temperature of the fermentation bin to the second temperature range, the method further includes:

[0034] Scoring the microbial activity intensity according to preset ratios corresponding to different values ​​in the microbial activity intensity information to obtain a scoring result;

[0035] The microbial activity intensity information and the corresponding scoring result are stored in a database.

[0036] Through the above embodiment, after recording the microbial activity intensity information, the system scores the information through a preset ratio and stores the data and the corresponding score in a database, providing a method to quantify and evaluate the microbial activity intensity, and providing a basis for determining the target temperature range in the future.

[0037] In some embodiments, after the step of adjusting the temperature of the fermentation bin to the target temperature range by controlling the actuator, the method further includes:

[0038] The data collected by the biosensor and the temperature sensor are sent to the user terminal in real time for display;

[0039] receiving, through the user terminal, adjustment parameters of the preset temperature range and the target temperature interval by the user;

[0040] The adjustment parameter is preferentially used to set the preset temperature range and the target temperature interval.

[0041] Through the above embodiment, after adjusting the temperature of the fermentation bin to the target temperature range, the system can send the data monitored by the sensor or sensor to the user terminal in real time, and set the temperature parameters of the preset temperature range and the target temperature range according to the user's adjustment instructions. This enables the user to remotely monitor the fermentation process and customize the temperature control settings.

[0042] In a second aspect, the present application provides an intelligent temperature control system, the intelligent temperature control system comprising: one or more processors and a memory;

[0043] The memory is coupled to the one or more processors, and the memory is used to store computer program code, which includes computer instructions. The one or more processors call the computer instructions so that the intelligent temperature control system can implement an intelligent temperature control method applied to a fermentation bin provided in the above embodiment, which will not be repeated here.

[0044] In a third aspect, the present application provides a computer-readable storage medium, including instructions. When the instructions are executed on an intelligent temperature control system, the intelligent temperature control system can implement an intelligent temperature control method applied to a fermentation bin provided in the above-mentioned embodiment, which will not be repeated here.

[0045] In a fourth aspect, the present application provides a computer program product. When the computer program product runs on an intelligent temperature control system, the intelligent temperature control system can implement an intelligent temperature control method applied to a fermentation bin provided in the above embodiment, which will not be repeated here.

[0046] One or more technical solutions provided in the embodiments of the present application have at least the following technical effects or advantages:

[0047] 1. The system divides the preset temperature range into multiple temperature intervals, sets the temperature interval corresponding to the boundary value closest to the current temperature of the fermentation bin as the initial temperature interval, adjusts the temperature of the fermentation bin to each interval in turn and records the corresponding microbial activity intensity, finds the target temperature interval with the best microbial activity, and finally controls the fermentation bin temperature to maintain in the target interval. This achieves more precise control of the fermentation bin temperature, maintains the high activity of microorganisms, and improves fermentation efficiency and product quality.

[0048] 2. The system uses historical fermentation data to train machine learning models, so that the system can more accurately predict and set the optimal temperature range for the fermentation stage based on the data information collected by the current biosensor. The specific stage of fermentation is then identified by analyzing the data collected by the biosensor, and the corresponding preset temperature range is matched from the database, so that each fermentation stage can be carried out at the most suitable temperature, thereby improving the efficiency of the entire fermentation process and product quality. In addition, after recording the information on the intensity of microbial activity, the system scores this information using a preset ratio, and stores the data and corresponding scores in the database. It provides a method to quantify and evaluate the intensity of microbial activity, providing a basis for judgment for the subsequent selection of the target temperature range.

[0049] 3. After adjusting the temperature of the fermentation chamber to the target temperature range, the system can send the data monitored by the sensor or sensor to the user terminal in real time, and set the temperature parameters of the preset temperature range and the target temperature range according to the user's adjustment instructions. This enables users to remotely monitor the fermentation process and customize the temperature control settings. BRIEF DESCRIPTION OF THE DRAWINGS

[0050] Figure 1 It is a flow chart of an intelligent temperature control method applied to a fermentation bin in an embodiment of the present application;

[0051] Figure 2 It is another flow chart of an intelligent temperature control method applied to a fermentation bin in an embodiment of the present application;

[0052] Figure 3 This is a flow chart of the system determining the preset temperature range corresponding to different fermentation stages in the embodiment of the present application;

[0053] Figure 4 It is a schematic diagram of the structure of a physical device of the intelligent temperature control system in the embodiment of the present application. DETAILED DESCRIPTION

[0054] The terms used in the following embodiments of the present application are only for the purpose of describing specific embodiments, and are not intended to be used as limitations to the present application. As used in the specification and appended claims of the present application, the singular expressions "one", "a kind of", "said", "above", "the" and "this" are intended to also include plural expressions, unless there is a clear indication to the contrary in the context. It should also be understood that the term "and / or" used in the present application refers to any or all possible combinations comprising one or more listed items.

[0055] In the following, the terms "first" and "second" are used for descriptive purposes only and are not to be understood as suggesting or implying relative importance or implicitly indicating the number of the indicated technical features. Thus, a feature defined as "first" or "second" may explicitly or implicitly include one or more of the features, and in the description of the embodiments of the present application, unless otherwise specified, "plurality" means two or more.

[0056] For ease of understanding, the application scenarios of the relevant technical embodiments are introduced below.

[0057] A large livestock breeding enterprise A is facing environmental pollution and resource waste caused by a large amount of livestock and poultry manure. The company decided to invest in the construction of a large-scale biogas project, placing livestock and poultry manure in a fermentation bin and using anaerobic fermentation technology to convert livestock and poultry manure into clean energy biogas and organic fertilizer. However, during the actual operation, the engineering staff found that the temperature control in the fermentation bin has been unstable, resulting in unsatisfactory biogas production and quality. After analysis, the main reason is that the composition of livestock and poultry manure is complex and changeable, and the heat generated during anaerobic digestion is difficult to predict, making it difficult to adapt to the fixed temperature control scheme in the relevant technology (or controlling the temperature of the fermentation bin within a preset temperature range). This seriously affects the economic benefits and environmental protection effects of the biogas project. In this regard, the company urgently needs a more intelligent and precise temperature control system to ensure the stability and efficiency of the fermentation process.

[0058] In order to deal with the problem that the temperature of the fermentation bin is difficult to accurately control in the related art, the present application provides an intelligent temperature control method, system, medium and program product applied to the fermentation bin. The system can monitor the temperature of the fermentation bin and the activity intensity of microorganisms in real time through temperature sensors and biosensors, and divide the preset temperature range into multiple temperature intervals, adjust the temperature of the fermentation bin to each interval in turn and record the corresponding detected microbial activity intensity, find the target temperature interval with the best microbial activity (i.e., the highest microbial activity intensity), and finally control the fermentation bin temperature to maintain in the target interval. More precise control of the fermentation bin temperature is achieved, the high activity of microorganisms is maintained, and the fermentation efficiency and product quality are improved.

[0059] For ease of understanding, the following describes the process of the method provided by this implementation in combination with the above scenario. Figure 1 , which is a flow chart of an intelligent temperature control method applied to a fermentation bin in an embodiment of the present application.

[0060] S101, obtaining the microbial activity intensity information of the fermentation bin and the temperature information in the bin in real time through the biosensor and the temperature sensor respectively.

[0061] The system can fully monitor the fermentation chamber by installing biological sensors and temperature sensors. Among them, biological sensors include but are not limited to pH sensors, dissolved oxygen sensors and CO2 concentration sensors, which are used to collect key indicators that can reflect the intensity of microbial activity in real time, such as pH value, CO2 release and oxygen consumption rate. At the same time, the temperature sensor is responsible for monitoring the temperature changes inside the fermentation chamber.

[0062] Specifically, the system can control the biosensor or temperature sensor to collect the microbial activity intensity information and the temperature information in the fermentation bin at preset time intervals for storage. Of course, the data collection conditions of the biosensor or temperature sensor can also be defined according to actual conditions. For example, the system can control the temperature sensor to collect the temperature data in the fermentation bin in real time, and control the biosensor to collect the microbial activity intensity information when it is detected that the change in the temperature data exceeds a preset threshold. This is not limited here.

[0063] It is understandable that the pH sensor can monitor the pH changes of the fermentation broth, and different types of microorganisms have different adaptation ranges to pH values. The growth, reproduction and metabolic activities of microorganisms are most vigorous in the most suitable pH range; the dissolved oxygen sensor can reflect the oxygen consumption of microorganisms. In the aerobic fermentation process, dissolved oxygen is an important factor affecting the growth and metabolism of microorganisms. The higher the activity of the microorganism, the greater the demand for oxygen, which is reflected in a higher oxygen consumption rate. Therefore, the activity level of the microorganism can be evaluated by measuring the consumption rate of dissolved oxygen in the fermentation broth through the dissolved oxygen sensor; the CO2 concentration sensor can monitor the release of CO2 during the fermentation process, and the CO2 release rate can directly reflect the intensity of the microbial respiration, and then indirectly characterize the number and activity of the microorganism. When other conditions are relatively stable, the higher the CO2 release rate, the more microorganisms there are and the more vigorous the metabolism.

[0064] S102, adjusting the temperature of the fermentation bin to a preset temperature range corresponding to the current fermentation stage by controlling the actuator.

[0065] The system adjusts the temperature of the fermentation chamber to the preset temperature range of the corresponding fermentation stage by controlling the heating device and cooling system and other actuators according to the fermentation stage of the current fermentation process. The user can directly set the fermentation stage of the current fermentation process through the terminal device or the display connected to the system, and the system will match the corresponding preset temperature range from the database according to the fermentation stage set by the user.

[0066] For ease of understanding, the following uses the anaerobic fermentation of livestock and poultry manure to produce biogas as an example to introduce the four main fermentation stages in the fermentation process: hydrolysis and acidification stage, acetic acid production stage, methanogenesis stage and decay stage. The division of fermentation stages can be adjusted according to different fermentation materials or actual fermentation conditions, and is not limited here.

[0067] It is understandable that the hydrolysis and acidification stage of the above four fermentation stages generally requires a higher temperature (such as about 55°C) to promote the decomposition of organic matter and the release of soluble substances; in the methanogenesis stage, the temperature needs to be controlled at about 35°C to ensure the activity of methanogens. If the temperature is too high or too low, it will inhibit the activity of key microorganisms in a specific stage and affect the biogas production and quality. Therefore, users can reasonably set and store the preset temperature range of each stage in the database according to factors such as the actual fermentation raw material characteristics, microbial flora characteristics and process requirements. For example, the temperature range of the hydrolysis and acidification stage can be preset to 50-60°C, and the temperature range of the methanogenesis stage can be preset to 30-38°C, etc.

[0068] Specifically, when the system detects that the fermentation process has entered a certain fermentation stage, the system matches the preset temperature range corresponding to the specific fermentation stage from the database, and then controls the actuator to start the corresponding heating or cooling adjustment, so that the temperature in the fermentation bin reaches and stabilizes within the preset temperature range as soon as possible. Among them, the heating device may include an electric heating rod, a hot water circulation system, etc. to increase the temperature of the fermentation liquid through heat exchange; the cooling system may use cold water spraying, cold air circulation, etc. to take away excess heat, which is not limited here. At the same time, the system continuously monitors and adjusts the working status of the actuator through the PID control algorithm, so that the temperature is always maintained within the preset temperature range until the end of this stage.

[0069] S103: Divide the preset temperature range into a preset number of temperature intervals to obtain a temperature interval set.

[0070] After determining the preset temperature range corresponding to the current fermentation stage, the system further divides the preset temperature range into multiple more detailed temperature intervals, a temperature interval set, which is used to more finely control the temperature of the fermentation bin and find the temperature range that is most conducive to microbial activity.

[0071] It should be noted that the number of temperature intervals to be divided requires comprehensive consideration of multiple factors. Specifically, the more temperature intervals there are, the higher the accuracy of temperature control, but it also means that the system needs to spend more time switching between different temperature intervals and collecting data. In addition, if the number of temperature intervals is too small, it may not be possible to fully reveal the correlation between temperature and microbial activity. Therefore, in the actual temperature interval division process, the system will set the number of temperature intervals based on the size of the preset temperature range. Of course, the user can also directly define the number of temperature intervals corresponding to different preset temperature ranges, which is not limited here.

[0072] The following is an example of how the system dynamically adjusts the granularity of the interval division based on the size of the preset temperature range. For example, the preset temperature range of a certain fermentation stage is 30-40°C, with a total span of 10°C. If it is divided into 5 intervals, the width of each interval is 2°C; if the preset temperature range is reduced to 35-38°C, with a span of only 3°C, it can be divided into 6 intervals, and the width of each interval is reduced to 0.5°C. It can be seen that the number of preset intervals is positively correlated with the size of the preset temperature range, which can ensure that the granularity of the temperature interval division in different fermentation stages is basically consistent.

[0073] Taking the methane production stage of anaerobic fermentation of livestock and poultry manure to produce biogas as an example, the preset temperature range is 30-38°C, and the system can divide it into 9 temperature intervals according to the step length of 1°C: 30-31°C, 31-32°C, 32-33°C, 33-34°C, 34-35°C, 35-36°C, 36-37°C, 37-38°C. The temperature interval set formed in this way can cover the most likely optimal temperature area in the methane production stage, providing a basis for judgment for subsequent temperature adjustment and microbial activity evaluation.

[0074] S104, adjusting the temperature of the fermentation bin to each temperature interval in the temperature interval set in turn, and recording the microbial activity intensity information in each temperature interval.

[0075] The system adjusts the actual temperature of the fermentation chamber to each temperature range in turn according to the preset temperature range set, and monitors and records the intensity information of microbial activity in each temperature range. It is used to comprehensively evaluate the changing rules of microbial activity under different temperature conditions and find the temperature range that is most conducive to the current fermentation stage.

[0076] Specifically, the system first adjusts the temperature of the fermentation chamber to the first temperature range, such as 30-31°C. After keeping the temperature stable for a period of time, the system collects a series of data reflecting the intensity of microbial activity through biosensors, such as pH value, CO2 release, oxygen consumption rate, etc. These data are recorded together with the temperature information to form a microbial activity data set in this temperature range.

[0077] After the system completes data collection in the first temperature range, it adjusts the temperature of the fermentation chamber to the second range, such as 31-32°C, and repeats the above process. Therefore, the system can switch the temperature range in sequence and obtain the microbial activity data in each range one by one. During the entire temperature range switching process, the system coordinates and controls the heating and cooling devices to maintain or slowly adjust the temperature of the fermentation chamber within the target range, reducing the impact of large fluctuations on microbial activity.

[0078] It should be noted that in each temperature range, the system will continuously monitor the fluctuation of microbial activity intensity. If the data fluctuation exceeds the preset threshold, such as a sudden sharp drop in CO2 release, it may mean that the temperature range is not suitable for microbial growth. The system will adjust to the next range in time to avoid wasting time by maintaining the temperature in an unfavorable range. On the contrary, if the data is relatively stable, the system will stay in this range for a longer period of time to obtain a more reliable evaluation result of microbial activity intensity.

[0079] S105. The temperature interval with the highest corresponding score of microbial activity intensity among all recorded microbial activity intensity information is set as the target temperature interval.

[0080] Based on the microbial activity data sets under different temperature ranges recorded in step S104, the system comprehensively evaluates these data according to preset scoring rules, obtains scores for the microbial activity intensity corresponding to different temperature ranges, and sets the temperature range with the highest score as the target temperature range for the subsequent fermentation process.

[0081] Taking the anaerobic fermentation of livestock and poultry manure as an example, the system obtains the microbial activity data under 9 temperature ranges through step S104. For the methanogenic stage, the system determines that in the range of 34-35°C, the pH value is stable at 7.0-7.2, the CO2 release rate reaches 1.2L / L, and the methane concentration exceeds 60%, and the comprehensive score is the highest, reaching 92 points. Therefore, the system determines 34-35°C as the target temperature range for fermentation in the methanogenic stage.

[0082] The following introduces the above preset scoring rules using the pH value, CO2 release and oxygen consumption rate in the microbial activity intensity information as the core indicators for scoring. Specifically, the system sets an optimal value for each indicator at different fermentation stages, and the optimal value can be set by the user. The system calculates the ratio between each core indicator and the corresponding optimal value in each temperature range as the basis for scoring. For example, if the pH ratio is between 0.95 and 1.05, the pH score is 100; if the pH ratio is between 0.90 and 0.95 or the pH ratio is between 1.05 and 1.10, etc., the specific scoring criteria can be adjusted according to actual conditions and are not limited here. Similarly, with the pH value, scores for other core indicators (such as CO2 release and oxygen consumption rate) can be obtained.

[0083] Furthermore, since different indicators have different effects on microbial activity, they need to be assigned different weights. The weights can be determined based on the relative importance and relevance of the indicators. Generally, the sum of the weights is required to be 1, which is not limited here. For example, the system can set pH weight = 0.4, CO2 weight = 0.35, and O2 weight = 0.25. Then, the comprehensive score of the current temperature range is obtained according to the formula. Specifically, the comprehensive score calculation formula can be:

[0084] Comprehensive score = pH score × pH weight + CO2 score × CO2 weight + O2 score × O2 weight.

[0085] In addition, the preset scoring rules can also be adjusted according to the actual needs of the user, which is not limited here.

[0086] S106, adjusting the temperature of the fermentation bin to a target temperature range by controlling the actuator.

[0087] Since the activity intensity of microorganisms in the fermentation chamber is the highest (i.e., the activity of microorganisms is the highest) in the temperature range corresponding to the target temperature interval, it is conducive to the metabolism and reproduction of microorganisms. Therefore, after determining the target temperature interval, the system controls the actuator through instructions to adjust the actual temperature of the fermentation chamber to stabilize within the target temperature interval.

[0088] The system divides the preset temperature range into multiple temperature intervals, adjusts the fermentation chamber temperature to each interval in turn, records the corresponding microbial activity intensity, finds the target temperature interval with the best microbial activity, and finally controls the fermentation chamber temperature to maintain it in the target interval. This achieves more precise control of the fermentation chamber temperature, maintains the high activity of microorganisms, and improves fermentation efficiency and product quality.

[0089] In addition, the system will also send the data collected by the biological sensor and temperature sensor to the user terminal for display in real time, so that users can understand the internal information of the fermentation warehouse in real time and formulate or adjust subsequent strategies in time.

[0090] Furthermore, the intelligent temperature control system provided by the present application is wirelessly connected to the user's terminal device and can receive control instructions sent by the user through the terminal device. For example, the user can send adjustment parameters of the preset temperature range and the target temperature interval to the system through the terminal device. After receiving the adjustment parameters, the system will preferentially use the adjustment parameters to set the preset temperature range and the target temperature interval, that is, the priority of the adjustment parameters sent by the user is higher than the priority of the corresponding parameters stored in the database, which is not limited here.

[0091] The following is a more detailed description of the process of the method provided by this implementation. Figure 2, is another flow chart of an intelligent temperature control method applied to a fermentation bin in an embodiment of the present application.

[0092] S201, calculating a first difference between the current temperature of the fermentation bin and a lower limit of a preset temperature range, and a second difference between an upper limit of the preset temperature range.

[0093] Before starting the temperature range traversal program, the system first needs to determine a reasonable starting temperature range. Specifically, the system first obtains the real-time temperature data of the fermentation bin through the temperature sensor installed in the fermentation bin. Considering the uneven temperature distribution in the fermentation bin, the system generally deploys multiple sensors at different positions and depths of the bin, and obtains the comprehensive temperature value through the weighted average algorithm to improve the measurement accuracy.

[0094] After obtaining the current temperature of the fermentation chamber, the system retrieves the boundary value of the preset temperature range. Taking the methane production stage of anaerobic fermentation of livestock and poultry manure as an example, the preset temperature range is 30-38°C. The system will calculate the difference between the current temperature and 30°C and 38°C respectively, and obtain the first difference (the difference with the lower limit) and the second difference (the difference with the upper limit).

[0095] S202: Whether the first difference is greater than the second difference.

[0096] After obtaining two differences (a first difference and a second difference) between the current temperature and the lower limit and the upper limit of the preset temperature range, the system further compares the two differences to determine the first traversed temperature interval.

[0097] Specifically, if the first difference is greater than the second difference, it means that the current temperature is closer to the upper limit of the preset temperature range. At this time, it is preferred to start traversing from the temperature interval containing the upper limit, that is, enter step S203; otherwise, it is started from the temperature interval containing the lower limit, that is, enter step S204.

[0098] S203: Set the temperature interval where the upper limit value of the preset temperature range is located as the first temperature interval.

[0099] After detecting that the first difference is greater than the second difference, the system sets the temperature interval where the upper limit value of the preset temperature range is located as the first temperature interval.

[0100] Continuing with the methane production stage of anaerobic fermentation of livestock and poultry manure as an example, assuming that the system detects that the current temperature is 34.5°C, the preset temperature range is 30-38°C, the first difference (the difference from the lower limit of 30°C) is 4.5°C, and the second difference (the difference from the upper limit of 38°C) is 3.5°C. By comparing the values, it can be seen that 4.5°C>3.5°C, that is, the first difference is larger. The system starts from the interval of 37-38°C, which contains the upper limit value, in the 9 temperature intervals (30-31°C, 31-32°C, ..., 37-38°C) divided by 1°C steps, and then adjusts downward to other intervals in turn until the entire preset temperature range is covered.

[0101] S204: Set the temperature interval where the lower limit value of the preset temperature range is located as the first temperature interval.

[0102] After detecting that the first difference is greater than the second difference, the system sets the temperature interval where the upper limit value of the preset temperature range is located as the first temperature interval.

[0103] S205, adjusting the temperature of the fermentation bin to the first temperature range.

[0104] After determining the first temperature interval, the system controls the actuator through instructions to adjust the temperature of the fermentation bin to a temperature range corresponding to the first temperature interval.

[0105] Before controlling the temperature of the fermentation bin to traverse all temperature intervals, the system calculates the difference between the current temperature of the fermentation bin and the boundary value corresponding to the preset temperature range, and sets the temperature interval corresponding to the boundary value closest to the current temperature of the fermentation bin as the initial temperature interval, thereby reducing fluctuations in the temperature adjustment process and improving the stability of the fermentation environment.

[0106] S206: Whether the time interval is greater than a preset time interval.

[0107] After the system adjusts the temperature of the fermentation bin to the first temperature interval, the system maintains the temperature of the fermentation bin within the temperature range corresponding to the first temperature interval, and simultaneously performs timing to obtain the interval time. When it is detected that the interval time is greater than the preset interval time, the system proceeds to step S207; otherwise, the system proceeds to step S208.

[0108] In addition, while timing, the system will also obtain real-time information on the intensity of microbial activity and the temperature in the warehouse detected by the biological sensor and temperature sensor at specific time intervals, and record the data information obtained each time.

[0109] It should be noted that the above-mentioned specific time interval is smaller than the preset time interval. The smaller the specific time interval is, the higher the frequency at which the system collects data information through the sensor or sensor. Both the specific time interval and the preset time interval can be set by the user according to actual conditions and are not limited here.

[0110] S207: Whether the numerical fluctuation in the microbial activity intensity information is greater than a preset threshold.

[0111] If it is detected that the time interval is greater than the preset time interval, the system retrieves the microbial activity intensity information recorded within the time interval to determine whether the numerical fluctuation in the microbial activity intensity information is greater than the preset threshold. Specifically, the system generates a surface graph based on the various data in the microbial activity intensity information to display the changes in the various data in the microbial activity intensity information over time. Furthermore, when it is detected that the slope of the curve corresponding to each data no longer changes or the amplitude of the change is lower than the preset threshold, it is determined that the numerical fluctuation in the microbial activity intensity information is less than or equal to the preset threshold, that is, the process proceeds to step S209; otherwise, it is determined that the numerical fluctuation in the microbial activity intensity information is greater than the preset threshold, that is, the process proceeds to step S208.

[0112] S208. Maintain the temperature of the fermentation bin in the first temperature range.

[0113] It can be understood that when the system determines that the numerical fluctuation in the microbial activity intensity information is greater than the preset threshold, it means that the microbial activity intensity information detected by the current biosensor is not an accurate value in the first temperature range, and the temperature of the fermentation bin needs to be maintained in the first temperature range until the numerical fluctuation in the microbial activity intensity information is less than or equal to the preset threshold.

[0114] S209, recording the microbial activity intensity information, and adjusting the temperature of the fermentation bin to the second temperature range.

[0115] It is understandable that when the system determines that the value fluctuation in the microbial activity intensity information is less than or equal to the preset threshold, it means that the microbial activity intensity information currently detected by the biosensor is an accurate value in the first temperature range. At this time, the system will record the last acquired microbial activity intensity information and save it to the database, and then adjust the temperature of the fermentation bin to the second temperature range, and continue to execute steps S206 to S207 until each temperature range in the temperature range set is traversed.

[0116] It should be noted that the second temperature interval is the next temperature interval adjacent to the first temperature interval. After recording the microbial activity intensity information corresponding to the second temperature interval, the system updates the second temperature interval to the first temperature interval, sets the next adjacent temperature interval as the new second temperature interval, and executes the judgment of steps S206 to S207 until the current second temperature interval is the last temperature interval divided by the preset temperature range.

[0117] The system continuously monitors and adjusts the temperature of the fermentation chamber to various preset temperature ranges and records the intensity of microbial activity in each range. It can evaluate and determine in real time which temperature range is most suitable for the current fermentation needs, thereby ensuring that the fermentation process is carried out at the most ideal temperature as much as possible, greatly enhancing the controllability and predictability of the fermentation process.

[0118] The following is a more detailed description of the process of the method provided by this implementation. Figure 3 , which is a flow chart of a system determining a preset temperature range corresponding to different fermentation stages in an embodiment of the present application.

[0119] S301. Determine the fermentation stage of the fermentation bin based on the data information collected by the biosensor.

[0120] The system analyzes the data information collected by the biosensor in real time, such as pH value, volatile fatty acid (VFA) concentration, ratio of carbon dioxide (CO2) and methane (CH4), etc., to comprehensively judge the progress status of the fermentation process and then determine the fermentation stage of the fermentation tank.

[0121] Taking the anaerobic fermentation of livestock and poultry manure to produce biogas as an example, its fermentation stage mainly includes four stages: hydrolysis and acidification, acetic acid production, methanogenesis and decay. In the hydrolysis and acidification stage, macromolecular organic matter is decomposed into soluble small molecular substances, the pH value is low (generally 4.5-6.0), the VFA concentration rises rapidly, and CO2 dominates; in the acetic acid production stage, acetic acid bacteria further convert small molecular substances into acetic acid, the pH value begins to rise to neutral (6.5-7.5), and the VFA concentration reaches a peak; in the methanogenesis stage, methanogens use acetic acid and other precursors to generate a large amount of methane, the pH value is stable at 7.0-8.0, the VFA concentration decreases, and the CH4 ratio exceeds 50%; in the decay stage, the available substrate decreases, various indicators tend to be stable, and the methane production gradually decreases.

[0122] Therefore, the system can compare and analyze the real-time pH value, VFA concentration, CO2 and CH4 ratio data collected by the biosensor with the above-mentioned characteristic indicators, and determine the most likely fermentation stage of the fermentation chamber based on the data change trend and threshold judgment. For example, if it is detected that the pH value rises rapidly from 5.5 to 7.0, the VFA concentration is close to the peak value, the CO2 content decreases and the CH4 ratio begins to rise, it can be determined that the fermentation process is in the transition stage from acetic acid production to methanogenesis. This intelligent recognition method overcomes the limitations of single indicator judgment, improves the reliability of fermentation stage division through multi-parameter joint analysis, and provides a decision-making basis for subsequent matching of appropriate temperature control ranges.

[0123] S302. Build a machine learning model based on historical fermentation data.

[0124] After determining the fermentation stage, the system needs to further predict the optimal temperature control range (preset temperature range) for that stage. Traditional methods mainly rely on expert experience to set the temperature range for each stage, but it is difficult to adapt to the dynamic changes under different raw materials and process conditions. In order to improve the intelligence level and adaptability of the prediction, the system can use historical fermentation data to build a machine learning model, and continuously optimize and adjust the temperature control strategy in a data-driven way.

[0125] Specifically, the system obtains a large amount of historical fermentation case data from the database, including but not limited to the temperature setting information corresponding to each fermentation stage, microbial activity intensity information, warehouse temperature and humidity information, fermentation product quality and other data information. After preprocessing these data information, the system constructs a training sample set, and divides the training set into a training set, a test set, and a validation set. A machine learning model is constructed based on a machine learning algorithm (such as a support vector machine), the training set is used to train the machine learning model, the test set is used to test the machine learning model, and the model parameters are adjusted based on the test results. The validation set is then used to validate and evaluate the machine learning model, and validation indicators such as mean square error (MSE) and coefficient of determination (R 2 ) The model that meets the preset index standards predicts and evaluates the preset temperature range of each fermentation stage.

[0126] S303: Input the data information collected by the biosensor into the machine learning model to obtain a preset temperature range.

[0127] Specifically, when the biosensor collects various data information reflecting the current fermentation status in real time, the system passes it as an input parameter to the built machine learning prediction model. The model can quickly infer the optimal temperature control range suitable for the current fermentation stage, that is, the preset temperature range, by comprehensively analyzing the similarity and mapping relationship between the input data and historical samples, and store the fermentation stage and the corresponding preset temperature range in the database.

[0128] S304, matching the corresponding preset temperature range from the database according to the fermentation stage.

[0129] After the machine learning model successfully predicts the optimal temperature control range for the current fermentation stage based on the input data from the biosensor, if a change in the current fermentation stage is detected, the system matches the preset temperature range corresponding to the current fermentation stage from the database.

[0130] The system uses historical fermentation data to train machine learning models, allowing the system to more accurately predict and set the optimal temperature range for the fermentation stage based on the data information collected by the current biosensor. This improves the accuracy and adaptability of temperature control, allowing the system to self-learn and adapt to different fermentation conditions, further improving the stability and reliability of the fermentation process.

[0131] The intelligent temperature control system of the embodiment of the present invention is applied to electronic equipment. Figure 4 A schematic diagram of the architecture of an electronic device suitable for implementing an embodiment of the present invention is shown.

[0132] It should be noted that Figure 4 The electronic device shown is only an example and should not bring any limitation to the functions and scope of use of the embodiments of the present invention.

[0133] Those skilled in the art will appreciate that all or part of the steps in the various methods of the above embodiments can be completed by instructions (computer programs), or by controlling related hardware through instructions (computer programs), and the instructions can be stored in a computer-readable storage medium and loaded and executed by a processor. The electronic device of this embodiment includes a storage medium and a processor, wherein a plurality of instructions are stored in the storage medium, and the instructions can be loaded by the processor to execute any step of the method provided in the embodiment of the present invention.

[0134] Specifically, the storage medium and the processor are electrically connected directly or indirectly to realize data transmission or interaction. For example, these elements can be electrically connected to each other through one or more signal lines. The storage medium stores computer execution instructions for implementing the data access control method, including at least one software function module that can be stored in the storage medium in the form of software or firmware. The processor executes various functional applications and data processing by running the software program and module stored in the storage medium. The storage medium can be, but is not limited to, random access storage medium (Random Access Memory, referred to as: RAM), read-only storage medium (Read Only Memory, referred to as: ROM), programmable read-only storage medium (Programmable Read-Only Memory, referred to as: PROM), erasable read-only storage medium (Erasable Programmable Read-Only Memory, referred to as: EPROM), electrically erasable read-only storage medium (Electric Erasable Programmable Read-Only Memory, referred to as: EEPROM), etc. Among them, the storage medium is used to store programs, and the processor executes the program after receiving the execution instruction.

[0135] Furthermore, the software programs and modules in the above-mentioned storage medium may also include an operating system, which may include various software components and / or drivers for managing system tasks (such as memory management, storage device control, power management, etc.), and may communicate with various hardware or software components to provide an operating environment for other software components. The processor may be an integrated circuit chip having signal processing capabilities. The above-mentioned processor may be a general-purpose processor, including a central processing unit (CPU), a network processor (NP), etc., which may implement or execute the various methods, steps, and logic flow diagrams disclosed in this embodiment. The general-purpose processor may be a microprocessor or the processor may also be any conventional processor, etc.

[0136] Since the instructions stored in the storage medium can execute the steps in any method provided in the embodiments of the present invention, the beneficial effects of any method provided in the embodiments of the present invention can be achieved. Please refer to the previous embodiments for details and will not be repeated here.

[0137] The above is only a preferred specific embodiment of the present invention, but the protection scope of the present invention is not limited thereto. Any changes or substitutions that can be easily thought of by a person skilled in the art within the technical scope disclosed by the present invention should be included in the protection scope of the present invention. Therefore, the protection scope of the present invention should be based on the protection scope of the claims.

Claims

1. An intelligent temperature control method applied to a fermentation bin, applied to an intelligent temperature control system, characterized in that: The method comprises: obtaining in real time the microbial activity intensity information of the fermentation bin and the temperature information in the bin through a biological sensor and a temperature sensor, respectively, wherein the biological sensor comprises a pH sensor, a dissolved oxygen sensor and a CO2 concentration sensor, and the microbial activity intensity information comprises a pH value, a CO2 release amount and an oxygen consumption rate; The temperature of the fermentation bin is adjusted to a preset temperature range corresponding to the current fermentation stage by controlling the actuator, wherein the actuator includes a heating device and a cooling system; Dividing the preset temperature range into a preset number of temperature intervals to obtain a set of temperature intervals, wherein the preset number is proportional to the size of the preset temperature range; Calculating a first difference between the current temperature of the fermentation bin and a lower limit of the preset temperature range, and a second difference between an upper limit of the preset temperature range; Determining whether the first difference is greater than the second difference; If yes, setting the temperature interval where the upper limit value of the preset temperature range is located as the first temperature interval; If not, setting the temperature interval where the lower limit value of the preset temperature range is located as the first temperature interval; Adjusting the temperature of the fermentation bin to a first temperature interval, wherein the first temperature interval is a subset of the temperature interval set; After each preset time interval, determining whether the value fluctuation in the microbial activity intensity information is greater than a preset threshold; If yes, maintaining the temperature of the fermentation bin within the first temperature range; If not, the microbial activity intensity information is recorded, and the temperature of the fermentation bin is adjusted to a second temperature interval, wherein the second temperature interval is adjacent to the first temperature interval in the temperature interval set; Scoring the microbial activity intensity according to preset ratios corresponding to different values ​​in the microbial activity intensity information to obtain a scoring result; Storing the microbial activity intensity information and the corresponding scoring results in a database; The temperature interval with the highest microbial activity intensity among all the recorded microbial activity intensity information is set as the target temperature interval; The temperature of the fermentation bin is adjusted to the target temperature range by controlling the actuator.

2. The method according to claim 1, characterized in that Before the step of adjusting the temperature of the fermentation bin to a preset temperature range corresponding to the current fermentation stage by controlling the actuator, the method further includes: Determining the fermentation stage of the fermentation chamber according to the data information collected by the biosensor, wherein the data information includes pH value, VFA concentration, and the proportion of CO2 and CH4; The corresponding preset temperature range is matched from the database according to the fermentation stage.

3. The method according to claim 2, characterized in that Before the step of matching the corresponding preset temperature range from the database according to the fermentation stage, the method further includes: Constructing a machine learning model based on historical fermentation data, wherein the historical fermentation data includes temperature setting information corresponding to each fermentation stage, microbial activity intensity information, temperature and humidity information in the warehouse, and fermentation product quality; The data information collected by the biosensor is input into the machine learning model to obtain the preset temperature range.

4. The method according to claim 1, characterized in that: After the step of adjusting the temperature of the fermentation bin to the target temperature range by controlling the actuator, the method further includes: Sending the data collected by the biosensor and the temperature sensor to the user terminal in real time for display; Receiving, through the user terminal, adjustment parameters of the preset temperature range and the target temperature interval from a user; The adjustment parameters are preferentially used to set the preset temperature range and the target temperature interval.

5. An intelligent temperature control system, characterized in that: The intelligent temperature control system includes: one or more processors and a memory; the memory is coupled to the one or more processors, the memory is used to store computer program code, the computer program code includes computer instructions, and the one or more processors call the computer instructions to enable the intelligent temperature control system to execute the method described in any one of claims 1-4.

6. A computer-readable storage medium comprising instructions, characterized in that: When the instruction is executed on the intelligent temperature control system, the intelligent temperature control system executes the method according to any one of claims 1 to 4.

7. A computer program product, characterized in that When the computer program product runs on an intelligent temperature control system, the intelligent temperature control system executes the method according to any one of claims 1 to 4.

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