Bean curd storage intelligent supervision system based on data acquisition

Abstract

The invention discloses an intelligent bean curd storage supervision system based on data acquisition, relates to the technical field of food storage, solves the problem that the storage state of bean curd cannot be efficiently supervised from multiple perspectives, and comprises a preliminary analysis module, a decay analysis module and a comprehensive processing module, the preliminary analysis module is used for performing preliminary analysis on the real-time environment temperature and the real-time environment humidity of the bean curd, and performing analysis to generate a preliminary normal signal or performing analysis to obtain abnormal data of the bean curd; the corruption analysis module is used for performing real-time analysis on the acid-base property and corruption risk of the bean curd surface after receiving the preliminary normal signal, if an acid-base abnormal signal or a corruption risk signal is generated through analysis, sending the signal to the early warning module, and if a monitoring signal is generated through analysis, sending the signal to the comprehensive processing module; and the comprehensive processing module is used for comprehensively processing the real-time environment of the bean curd according to different signals. According to the invention, the storage state of the bean curd is efficiently supervised from multiple angles.

Description

Technical Field

[0001] This invention belongs to the field of food storage technology, specifically an intelligent monitoring system for tofu storage based on data acquisition. Background Technology

[0002] Tofu is a nutritious food with a long history. The public's love for tofu has driven the advancement and development of tofu production technology. The main production process of tofu is twofold: first, making soy milk from soybeans; and second, coagulation, in which the soy milk coagulates into a gel containing a large amount of water under the combined action of heat and coagulant, which is tofu. Tofu contains a variety of trace elements essential to the human body, as well as rich high-quality protein. Tofu, as a common and perishable food, has strict requirements for environmental conditions during storage. Traditional methods of tofu storage supervision mainly rely on regular manual inspections, which have problems such as low supervision efficiency, untimely information, and blind spots in supervision. It is difficult to accurately and in real time monitor and manage the storage status of tofu from multiple perspectives, which makes tofu prone to spoilage and deterioration during storage, resulting in economic losses and food safety hazards. Therefore, this invention proposes an intelligent monitoring system for tofu storage based on data acquisition. Summary of the Invention

[0003] The purpose of this invention is to propose an intelligent monitoring system for tofu storage based on data acquisition, so as to solve the problem mentioned in the background art of being unable to efficiently monitor the storage status of tofu from multiple perspectives.

[0004] The objective of this invention can be achieved through the following technical solutions: The intelligent monitoring system for tofu storage based on data acquisition includes a data acquisition module, a preliminary analysis module, a spoilage analysis module, an early warning module, and a comprehensive processing module. The data acquisition module is used to collect real-time ambient temperature and humidity data of the tofu at different time points and send them to the preliminary analysis module. The preliminary analysis module is used to perform preliminary analysis on the real-time ambient temperature and humidity data of the tofu. If the analysis generates a preliminary normal signal, it is sent to the spoilage analysis module. If the analysis obtains abnormal data of the tofu, it is sent to the early warning module and the comprehensive processing module. The spoilage analysis module is used to analyze the acidity / alkalinity and spoilage risk of the tofu surface in real time after receiving a preliminary normal signal. If the analysis generates an acidity / alkalinity abnormality signal or a spoilage risk signal, it is sent to the early warning module. If the analysis generates a monitoring signal, it is sent to the comprehensive processing module. The comprehensive processing module is used to perform comprehensive processing on the real-time environment of the tofu based on different signals. The early warning module is used to trigger an alarm after receiving an abnormal signal or abnormal data.

[0005] Furthermore, the analysis process of the preliminary analysis module is as follows: The real-time ambient temperature of the tofu at all time points was compared with the storage temperature range at all collection points, and the real-time ambient humidity of the tofu at all time points was compared with the storage humidity range at all collection points. If the real-time ambient temperature of all sampling points on the tofu is within the storage ambient temperature range at all time points, and the real-time ambient humidity of all sampling points on the tofu is within the storage humidity range, then a preliminary normal signal is generated. If the real-time ambient temperature of the tofu at any time point is not within the storage temperature range, or the real-time ambient humidity of the tofu at any time point is not within the storage humidity range, then proceed to the next step.

[0006] Furthermore, the analysis process of the preliminary analysis module also includes: Any sampling point where the real-time ambient temperature of the tofu does not fall within the storage temperature range at any given time point is recorded as a temperature anomaly sampling point; any sampling point where the real-time ambient humidity of the tofu does not fall within the storage temperature range at any given time point is recorded as a humidity anomaly sampling point. The time point when the real-time ambient temperature of the tofu is not within the storage temperature range is recorded as the temperature abnormality node, and the time point when the real-time ambient humidity of the tofu is not within the storage humidity range is recorded as the humidity abnormality node. The temperature anomaly nodes and the real-time ambient temperature collected by the corresponding temperature anomaly collection points are bound together to obtain multiple sets of temperature anomaly packets. Bind the humidity anomaly node and the real-time environmental humidity collected by the corresponding humidity anomaly collection point to obtain multiple sets of humidity anomaly packets. Multiple sets of temperature and humidity anomaly packets constitute the abnormal data for tofu.

[0007] Furthermore, the analysis process of the corruption analysis module is as follows: The pH value at different locations on the surface of tofu is collected in real time and compared with the standard acid-base range. If the pH value at any point on the surface of the tofu is outside the standard acid-base range, an acid-base abnormality signal will be generated. If the pH value at different locations on the surface of the tofu is within the standard acid-base range, proceed to the next step.

[0008] Furthermore, the analysis process of the corruption analysis module also includes: Subtract the pH value at the corresponding location on the tofu surface at the previous time point from the pH value at the current time point and take the absolute value to obtain the absolute value of pH change at different locations on the tofu surface at the current time point (PHB). The absolute values ​​of pH changes at different locations on the surface of tofu at the current time point were analyzed.

[0009] Furthermore, the analysis process of the corruption analysis module also includes: If the absolute value of pH change at different locations on the surface of the tofu at the current time point is less than the pH change threshold, no action will be taken. If the absolute value of the pH change at any location on the surface of the tofu at the current time point is greater than or equal to the pH change threshold, then the spoilage risk value of the tofu at that location at the current time point is calculated.

[0010] Furthermore, the analysis process of the corruption analysis module also includes: Compare the corruption risk value of the tofu at the current time point with the corruption risk threshold; If the corruption risk value of the tofu at the current time point is less than the corruption risk threshold, the location of the tofu is recorded as a potential corruption location and a monitoring signal is generated; if the corruption risk value of the tofu at the current time point is greater than or equal to the corruption risk threshold, a corruption risk signal is generated.

[0011] Furthermore, the calculation process for the corruption risk value includes: The change in ambient temperature of the tofu at the current time point is obtained by subtracting the real-time ambient temperature of the tofu at the previous time point from the real-time ambient temperature of the tofu at the current time point and taking the absolute value. Similarly, by subtracting the real-time environmental humidity of the tofu at the previous time point from the real-time environmental humidity of the tofu at the current time point and taking the absolute value, we can obtain the change in environmental humidity of the tofu at the current time point. Calculate the spoilage risk value of the tofu at the current time point.

[0012] Furthermore, the specific working process of the integrated processing module is as follows: If a monitoring signal is received, the ambient temperature and humidity of the tofu will be continuously monitored. If abnormal data is received regarding tofu, further analysis will be performed on the ambient temperature or humidity of the tofu. Step P22: Adjust the real-time ambient temperature of the tofu according to the temperature adjustment amount and adjust the real-time ambient humidity of the tofu according to the humidity adjustment amount.

[0013] Furthermore, the analysis process of the advanced analysis is as follows: For multiple temperature anomaly packets in the abnormal data, the temperature deviation of the temperature anomaly collection point is obtained by subtracting the standard storage temperature from the real-time ambient temperature of the temperature anomaly collection point and taking the absolute value. The temperature deviation of multiple temperature anomaly collection points is compared and the maximum value of the temperature deviation is obtained and recorded as the temperature adjustment amount of the tofu. For multiple sets of humidity anomaly packets in the abnormal data, the humidity deviation of each humidity anomaly collection point is obtained by subtracting the standard storage humidity from the real-time ambient humidity of each collection point and taking the absolute value. The humidity deviation of multiple humidity anomaly collection points is compared and the maximum value among the humidity deviations is obtained and recorded as the humidity adjustment amount of the tofu.

[0014] In summary, due to the adoption of the above technical solution, the beneficial effects of the present invention are: 1. This invention performs a preliminary analysis of the storage status of tofu by collecting real-time ambient temperature and humidity data at different time points. The analysis results in whether the tofu is in a normal state or abnormal data. This invention achieves efficient monitoring of the tofu storage environment.

[0015] 2. This invention analyzes the acidity and alkalinity of tofu surface based on pH values ​​at different locations, and then calculates the spoilage risk value of tofu based on ambient temperature, ambient humidity, and pH value of tofu surface. The spoilage risk value of tofu is then analyzed to obtain the spoilage risk situation of tofu. This invention achieves effective analysis of tofu spoilage risk from multiple perspectives.

[0016] 3. This invention analyzes the storage environment of tofu based on abnormal data, and obtains the temperature and humidity adjustment amounts for the tofu. Then, the storage environment is adjusted according to the obtained temperature and humidity adjustment amounts. This invention enables timely adjustment of the storage environment when the tofu storage environment is in an abnormal state. Attached Figure Description

[0017] To facilitate understanding by those skilled in the art, the present invention will be further described below with reference to the accompanying drawings.

[0018] Figure 1 This is an overall system block diagram of the present invention; Figure 2 This is a schematic diagram of the data collection points in this invention; Figure 3 This is a flowchart of the method of the present invention. Detailed Implementation

[0019] The technical solution of the present invention will be clearly and completely described below with reference to the embodiments. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0020] Example 1: Please refer to Figure 1 and Figure 2 As shown, the technical solution provided by the present invention is: an intelligent monitoring system for tofu storage based on data acquisition, including a data acquisition module, a preliminary analysis module, a spoilage analysis module, an early warning module, and a comprehensive processing module; The data acquisition module is used to collect real-time ambient temperature and humidity data of the tofu at different time points, and send the data to the preliminary analysis module; for details, please refer to [link / reference needed]. Figure 2 As shown, multiple sampling points were set on the surface of the tofu; Specifically, the preliminary analysis module is used to perform preliminary analysis on the real-time ambient temperature and humidity of the tofu. The analysis process is as follows: The real-time ambient temperature of the tofu at all time points was compared with the storage temperature range at all collection points, and the real-time ambient humidity of the tofu at all time points was compared with the storage humidity range at all collection points. For example, the storage temperature range for tofu is 2 degrees Celsius to 8 degrees Celsius, and the storage humidity range for tofu is 50% to 70%. If the real-time ambient temperature of all sampling points on the tofu is within the storage ambient temperature range at all time points, and the real-time ambient humidity of all sampling points on the tofu is within the storage humidity range, then a preliminary normal signal is generated. If the real-time ambient temperature of the tofu at any time point is not within the storage temperature range, or the real-time ambient humidity of the tofu at any time point is not within the storage humidity range, then proceed to the next step. Any sampling point where the real-time ambient temperature of the tofu does not fall within the storage temperature range at any given time point is recorded as a temperature anomaly sampling point; any sampling point where the real-time ambient humidity of the tofu does not fall within the storage temperature range at any given time point is recorded as a humidity anomaly sampling point. The time point when the real-time ambient temperature of the tofu is not within the storage temperature range is recorded as the temperature abnormality node, and the time point when the real-time ambient humidity of the tofu is not within the storage humidity range is recorded as the humidity abnormality node. The temperature anomaly nodes and the real-time ambient temperature collected by the corresponding temperature anomaly collection points are bound together to obtain multiple sets of temperature anomaly packets. Bind the humidity anomaly node and the real-time environmental humidity collected by the corresponding humidity anomaly collection point to obtain multiple sets of humidity anomaly packets. Multiple sets of temperature and humidity anomaly packets constitute the abnormal data for tofu; If a preliminary normal signal is generated, the preliminary analysis module will send the preliminary normal signal to the spoilage analysis module. If abnormal data of tofu is obtained from the analysis, it will be sent to the early warning module and the comprehensive processing module.

[0021] In this embodiment, the spoilage analysis module is used to perform real-time analysis of the acidity / alkalinity and spoilage risk on the surface of the tofu after receiving a preliminary normal signal. The analysis process is as follows: Please see Figure 2 As shown, the pH value at different locations on the surface of tofu is collected in real time, and the pH value at different locations on the surface of tofu is compared with the standard acid-base range. If the pH value at any location on the surface of the tofu is outside the standard acid-base range, an acid-base abnormality signal is generated; if the pH values ​​at different locations on the surface of the tofu are all within the standard acid-base range, proceed to the next step. Subtract the pH value at the corresponding location on the tofu surface at the previous time point from the pH value at the current time point and take the absolute value to obtain the absolute value of pH change at different locations on the tofu surface at the current time point (PHB). The absolute value of pH change at different locations on the tofu surface corresponding to the current time node is analyzed. If the absolute value of pH change at different locations on the tofu surface corresponding to the current time node is less than the pH change threshold, no operation is performed. For example, in this embodiment, the standard acid-base range is [6.5, 7.0], and the pH change threshold is 0.2. If, at any point on the surface of the tofu at the current time point, the absolute value of the pH change is greater than or equal to the pH change threshold, then the spoilage risk value at that point on the tofu at the current time point is calculated, specifically as follows: The change in ambient temperature of the tofu at the current time point is obtained by subtracting the real-time ambient temperature of the tofu at the previous time point from the real-time ambient temperature of the tofu at the current time point and taking the absolute value. Similarly, the change in ambient humidity of the tofu at the current time point is obtained by subtracting the real-time ambient humidity of the tofu at the previous time point from the real-time ambient humidity of the tofu at the current time point and taking the absolute value. The spoilage risk value FB of the tofu at the current time point is calculated using the formula FB=WD / WDB×k1+SD / SDB×k2+PHB / PHBZ×k3. Here, WDB is the standard value of the change in ambient temperature at the current time point (WDB=1), SDB is the standard value of the change in ambient humidity at the current time point (SDB=1), PHBZ is the standard value of the absolute value of pH change (PHBZ=1), and k1, k2, and k3 are weighting coefficients, k1+k2+k3=1. In this embodiment, k1=0.4, k2=0.4, and k3=0.2. Compare the corruption risk value of the tofu at the current time point with the corruption risk threshold; If the corruption risk value of the tofu at the current time point is less than the corruption risk threshold, the location of the tofu is recorded as a potential corruption location and a monitoring signal is generated; if the corruption risk value of the tofu at the current time point is greater than or equal to the corruption risk threshold, a corruption risk signal is generated. For example, in this embodiment, the corruption risk threshold is set to 1. If an acid-base anomaly signal or a putrefaction risk signal is generated, the putrefaction analysis module will send the signal to the early warning module; if a monitoring signal is generated, the putrefaction analysis module will send the signal to the integrated processing module.

[0022] In this embodiment, the integrated processing module is used to perform integrated processing on the real-time environment of the tofu based on different signals. The specific working process is as follows: If a monitoring signal is received, the ambient temperature and humidity of the tofu will be continuously monitored. If abnormal data is received regarding the tofu, further analysis will be performed on the ambient temperature or humidity of the tofu, specifically: For multiple temperature anomaly packets in the abnormal data, the absolute value of the real-time ambient temperature corresponding to each temperature anomaly sampling point is obtained by subtracting the standard storage temperature from the real-time ambient temperature and taking the absolute value. The temperature deviation of each temperature anomaly sampling point is then compared and the maximum value among the temperature deviations is recorded as the temperature adjustment amount for the tofu. For multiple humidity anomaly packets in the abnormal data, the absolute value of the humidity deviation of each humidity anomaly sampling point is obtained by subtracting the standard storage humidity from the real-time ambient humidity corresponding to each humidity anomaly sampling point and taking the absolute value. The humidity deviation of each humidity anomaly sampling point is then compared and the maximum value among the humidity deviations is recorded as the humidity adjustment amount for the tofu. The real-time ambient temperature of the tofu is adjusted based on the temperature adjustment amount, and the real-time ambient humidity of the tofu is adjusted based on the humidity adjustment amount.

[0023] In this embodiment, the early warning module is used to trigger an alarm after receiving an abnormal signal or abnormal data, wherein the abnormal signal includes acid-base abnormal signal and putrefaction risk signal.

[0024] In this application, if a corresponding calculation formula appears, the above calculation formula is a dimensionless calculation. The weighting coefficient, proportional coefficient and other coefficients in the formula are set to quantify each parameter to obtain a result value. The size of the weighting coefficient and proportional coefficient is only required to not affect the proportional relationship between the parameter and the result value.

[0025] Example 2: Please refer to Figure 3 As shown, based on another concept of the same invention, a smart monitoring method for tofu storage based on data acquisition is proposed, including the following steps: Step S1: Based on the real-time ambient temperature and humidity of different collection points at different time points, conduct a preliminary analysis of the storage status of tofu to determine whether the tofu is in a normal state or abnormal data. In this embodiment, step S1 includes the following sub-steps: Step S11: Compare the real-time ambient temperature of the tofu at all time points with the storage temperature range, and compare the real-time ambient humidity of the tofu at all time points with the storage humidity range. Step S12: If the real-time ambient temperature of all collection points on the tofu is within the storage ambient temperature range at all time points, and the real-time ambient humidity of all collection points on the tofu is within the storage humidity range, then a preliminary normal signal is generated. If the real-time ambient temperature of the tofu at any time point is not within the storage temperature range, or the real-time ambient humidity of the tofu at any time point is not within the storage humidity range, then proceed to the next step. Step S13: The sampling point where the real-time ambient temperature of the tofu does not belong to the storage temperature range at any time point is recorded as a temperature abnormal sampling point, and the sampling point where the real-time ambient humidity of the tofu does not belong to the storage temperature range at any time point is recorded as a humidity abnormal sampling point. Step S14: Record the time node corresponding to when the real-time ambient temperature of the tofu is not within the storage temperature range as a temperature abnormality node, and record the time node corresponding to when the real-time ambient humidity of the tofu is not within the storage humidity range as a humidity abnormality node. Step S15: Bind the temperature anomaly node and the real-time ambient temperature collected by the corresponding temperature anomaly collection point to obtain multiple temperature anomaly packets. Bind the humidity anomaly node and the real-time environmental humidity collected by the corresponding humidity anomaly collection point to obtain multiple sets of humidity anomaly packets. Step S16: Multiple sets of temperature anomaly packets and humidity anomaly packets constitute the abnormal data for tofu.

[0026] Step S2: Analyze the acidity and alkalinity of the tofu surface based on the pH value at different locations to determine whether the acidity and alkalinity of the tofu surface is normal. In this embodiment, step S2 includes the following sub-steps: Step S21: If the pH value at any location on the surface of the tofu is not within the standard acid-base range, an acid-base abnormality signal is generated. Step S22: If the pH values ​​at different locations on the tofu surface are all within the standard acid-base range, then subtract the pH value at the corresponding location on the tofu surface at the previous time point from the pH value at the current time point and take the absolute value to obtain the absolute value of pH change at different locations on the tofu surface at the current time point, PHB.

[0027] Step S3: Calculate the spoilage risk value of the tofu based on the ambient temperature, ambient humidity, and pH value of the tofu surface. In this embodiment, step S3 includes the following sub-steps: Step S31: Analyze the absolute value of pH change at different locations on the surface of tofu corresponding to the current time node; Step S32: If the absolute value of pH change at different locations on the surface of the tofu corresponding to the current time node is less than the pH change threshold, no operation is performed. Step S33: If the absolute value of pH change at any location on the surface of the tofu at the current time node is greater than or equal to the pH change threshold, then calculate the spoilage risk value of the tofu at the corresponding location at the current time node.

[0028] Step S4: Analyze the degree of spoilage risk of tofu based on the spoilage risk value; In this embodiment, step S4 includes the following sub-steps: Step S41: Compare the corruption risk value of the tofu at the current time point with the corruption risk threshold. Step S42: If the spoilage risk value of the tofu at the current time node is less than the spoilage risk threshold, then the location of the tofu is recorded as a potential spoilage location and a monitoring signal is generated. Step S43: If the corruption risk value of the tofu at the current time node is greater than or equal to the corruption risk threshold, a corruption risk signal is generated.

[0029] Step S5: Analyze the storage environment of tofu based on the abnormal data of tofu, obtain the temperature adjustment amount and humidity adjustment amount of tofu, and adjust the storage environment accordingly. In this embodiment, step S5 includes the following sub-steps: Step S51: If a monitoring signal is received, the ambient temperature and humidity of the tofu are continuously monitored. Step S52: If abnormal data of tofu is received, further analysis is performed on the ambient temperature or humidity of the tofu to obtain the temperature adjustment amount and humidity adjustment amount of the tofu. Step S53: Adjust the real-time ambient temperature of the tofu according to the temperature adjustment amount and adjust the real-time ambient humidity of the tofu according to the humidity adjustment amount.

[0030] The preferred embodiments of the present invention disclosed above are merely illustrative of the invention. These preferred embodiments do not exhaustively describe all details, nor do they limit the invention to any specific implementation. Clearly, many modifications and variations can be made based on the content of this specification. This specification selects and specifically describes these embodiments to better explain the principles and practical applications of the invention, thereby enabling those skilled in the art to better understand and utilize the invention. The invention is limited only by the claims and their full scope and equivalents.

Claims

1. An intelligent monitoring system for tofu storage based on data acquisition, characterized in that: It includes a data acquisition module, a preliminary analysis module, a spoilage analysis module, an early warning module, and a comprehensive processing module. The data acquisition module is used to collect the real-time ambient temperature and humidity of the tofu at the collection point at different time points and send them to the preliminary analysis module. The preliminary analysis module is used to perform preliminary analysis on the real-time ambient temperature and humidity of the tofu. If the analysis generates a preliminary normal signal, it is sent to the spoilage analysis module. If the analysis obtains abnormal data of the tofu, it is sent to the early warning module and the comprehensive processing module. The spoilage analysis module is used to analyze the acidity / alkalinity and spoilage risk of the tofu surface in real time after receiving a preliminary normal signal. If the analysis generates an acidity / alkalinity abnormality signal or a spoilage risk signal, it is sent to the early warning module. If the analysis generates a monitoring signal, it is sent to the comprehensive processing module. The comprehensive processing module is used to perform comprehensive processing on the real-time environment of the tofu based on different signals. The early warning module is used to trigger an alarm after receiving an abnormal signal or abnormal data.

2. The intelligent monitoring system for tofu storage based on data acquisition according to claim 1, characterized in that, The analysis process of the preliminary analysis module is as follows: The real-time ambient temperature of the tofu at all time points was compared with the storage temperature range at all collection points, and the real-time ambient humidity of the tofu at all time points was compared with the storage humidity range at all collection points. If the real-time ambient temperature of all sampling points on the tofu is within the storage ambient temperature range at all time points, and the real-time ambient humidity of all sampling points on the tofu is within the storage humidity range, then a preliminary normal signal is generated. If the real-time ambient temperature of the tofu at any time point is not within the storage temperature range, or the real-time ambient humidity of the tofu at any time point is not within the storage humidity range, then proceed to the next step.

3. The intelligent monitoring system for tofu storage based on data acquisition according to claim 2, characterized in that, The analysis process of the preliminary analysis module also includes: Any sampling point where the real-time ambient temperature of the tofu does not fall within the storage temperature range at any given time point is recorded as a temperature anomaly sampling point; any sampling point where the real-time ambient humidity of the tofu does not fall within the storage temperature range at any given time point is recorded as a humidity anomaly sampling point. The time point when the real-time ambient temperature of the tofu is not within the storage temperature range is recorded as the temperature abnormality node, and the time point when the real-time ambient humidity of the tofu is not within the storage humidity range is recorded as the humidity abnormality node. The temperature anomaly nodes and the real-time ambient temperature collected by the corresponding temperature anomaly collection points are bound together to obtain multiple sets of temperature anomaly packets. Bind the humidity anomaly node and the real-time environmental humidity collected by the corresponding humidity anomaly collection point to obtain multiple sets of humidity anomaly packets. Multiple sets of temperature and humidity anomaly packets constitute the abnormal data for tofu.

4. The intelligent monitoring system for tofu storage based on data acquisition according to claim 3, characterized in that, The analysis process of the corruption analysis module is as follows: The pH value at different locations on the surface of tofu is collected in real time and compared with the standard acid-base range. If the pH value at any point on the surface of the tofu is outside the standard acid-base range, an acid-base abnormality signal will be generated. If the pH value at different locations on the surface of the tofu is within the standard acid-base range, proceed to the next step.

5. The intelligent monitoring system for tofu storage based on data acquisition according to claim 4, characterized in that, The analysis process of the corruption analysis module also includes: Subtract the pH value at the corresponding location on the tofu surface at the previous time point from the pH value at the current time point and take the absolute value to obtain the absolute value of pH change at different locations on the tofu surface at the current time point (PHB). The absolute values ​​of pH changes at different locations on the surface of tofu at the current time point were analyzed.

6. The intelligent monitoring system for tofu storage based on data acquisition according to claim 5, characterized in that, The analysis process of the corruption analysis module also includes: If the absolute value of pH change at different locations on the surface of the tofu at the current time point is less than the pH change threshold, no action will be taken. If the absolute value of the pH change at any location on the surface of the tofu at the current time point is greater than or equal to the pH change threshold, then the spoilage risk value of the tofu at that location at the current time point is calculated.

7. The intelligent monitoring system for tofu storage based on data acquisition according to claim 6, characterized in that, The analysis process of the corruption analysis module also includes: Compare the corruption risk value of the tofu at the current time point with the corruption risk threshold; If the corruption risk value of the tofu at the current time point is less than the corruption risk threshold, the location of the tofu is recorded as a potential corruption location and a monitoring signal is generated; if the corruption risk value of the tofu at the current time point is greater than or equal to the corruption risk threshold, a corruption risk signal is generated.

8. The intelligent monitoring system for tofu storage based on data acquisition according to claim 7, characterized in that, The calculation process for the corruption risk value includes: The change in ambient temperature of the tofu at the current time point is obtained by subtracting the real-time ambient temperature of the tofu at the previous time point from the real-time ambient temperature of the tofu at the current time point and taking the absolute value. Similarly, by subtracting the real-time environmental humidity of the tofu at the previous time point from the real-time environmental humidity of the tofu at the current time point and taking the absolute value, we can obtain the change in environmental humidity of the tofu at the current time point. Calculate the spoilage risk value of the tofu at the current time point.

9. The intelligent monitoring system for tofu storage based on data acquisition according to claim 1, characterized in that, The specific working process of the integrated processing module is as follows: If a monitoring signal is received, the ambient temperature and humidity of the tofu will be continuously monitored. If abnormal data is received regarding tofu, further analysis will be performed on the ambient temperature or humidity of the tofu. The real-time ambient temperature of the tofu is adjusted based on the temperature adjustment amount, and the real-time ambient humidity of the tofu is adjusted based on the humidity adjustment amount.

10. The intelligent monitoring system for tofu storage based on data acquisition according to claim 9, characterized in that, The advanced analysis process is as follows: For multiple temperature anomaly packets in the abnormal data, the temperature deviation of the temperature anomaly collection point is obtained by subtracting the standard storage temperature from the real-time ambient temperature of the temperature anomaly collection point and taking the absolute value. The temperature deviation of multiple temperature anomaly collection points is compared and the maximum value of the temperature deviation is obtained and recorded as the temperature adjustment amount of the tofu. For multiple sets of humidity anomaly packets in the abnormal data, the humidity deviation of each humidity anomaly collection point is obtained by subtracting the standard storage humidity from the real-time ambient humidity of each collection point and taking the absolute value. The humidity deviation of multiple humidity anomaly collection points is compared and the maximum value among the humidity deviations is obtained and recorded as the humidity adjustment amount of the tofu.

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