A Fermentation Vat Backfill Soil Monitoring System Based on a Sensor Network

Abstract

The present invention relates to the technical field of sensor monitoring, and specifically to a monitoring system for the backfill soil of fermentation vats based on a sensor network. The system includes: a temperature and humidity prediction module, a communication optimization module, a data acquisition module, an anomaly detection module, and a leakage monitoring module. In the present invention, by analyzing the microbial metabolic characteristics, the temperature and humidity benchmarks during the fermentation process are identified, providing a data basis for anomaly data detection. The optimization of the data flow path improves the communication stability and energy efficiency ratio, reduces data transmission latency, dynamically adjusts the data acquisition frequency, realizes a fine data acquisition strategy, optimizes the accuracy and real-time performance of data acquisition, calculates the rate of change of temperature gradient and the trend of gas concentration change, combines with the humidity state of the backfill soil, enhances the accuracy of anomaly detection, reduces false alarms and missed alarms, and by identifying the gas diffusion path, leaks are detected in real time, improving the accuracy and rapid response ability of leakage monitoring, and enhancing the comprehensive performance and applicability of the monitoring system.

Description

Technical Field

[0001] The present invention relates to the technical field of sensor monitoring, and particularly to a monitoring system for the backfill soil of a fermentation tank based on a sensor network. Background Art

[0002] The technical field of sensor monitoring includes technologies for real-time monitoring, collection, and processing of physical, chemical, biological, and other environmental parameters based on various sensor devices. The core content includes multiple links such as data acquisition, signal conversion, data transmission, and analysis and processing, and is applied to multiple fields such as industrial automation, environmental monitoring, and intelligent manufacturing. In the industrial production process, sensor monitoring technology is used to detect multiple key parameters such as temperature, humidity, pressure, gas composition, and liquid level to ensure the stability of the production environment and improve the accuracy of process control. With the improvement of sensor accuracy and the development of wireless communication technology, the monitoring system based on a sensor network has become an important support for intelligent manufacturing and refined management. Through remote monitoring, automatic control, and data processing of multi-sensor fusion, the reliability and real-time performance of the monitoring system are improved.

[0003] Among them, the monitoring system for the backfill soil of a fermentation tank based on a sensor network refers to using multiple types of sensors to collect data on the temperature, humidity, and gas composition of the backfill soil of the fermentation tank, and sending the monitoring data to a central processing system for analysis and storage through wireless transmission technology. The system covers wireless temperature and humidity sensors, gas detection sensors, data acquisition modules, low-power wireless communication modules, and remote monitoring terminals to form a complete monitoring network. The monitoring system periodically collects the environmental parameters inside the backfill soil through distributed sensing nodes, and uses edge computing technology to perform data preprocessing locally, and then transmits the data to a data management platform through a wireless network. In the data management platform, the data undergoes multi-channel filtering processing to reduce environmental noise interference, and a threshold setting method is used for abnormal state detection to facilitate real-time monitoring and early warning. The system uses a low-power communication protocol to reduce energy consumption, and combines data storage management technology to achieve archiving and historical analysis of long-term monitoring data, providing data support for the fermentation process.

[0004] The traditional monitoring technology for the backfill soil of fermentation vats relies on a fixed data collection frequency and fails to dynamically adjust according to different environmental change conditions. When the environmental parameters change rapidly, there is a lag in data collection. When the environment is stable, unnecessary data redundancy occurs, reducing the efficiency of data collection. The data transmission path depends on a preset network structure and cannot be optimized and adjusted in combination with real-time signal quality and energy consumption conditions. As a result, nodes with high energy consumption or severe signal attenuation are overloaded, affecting the stability of data transmission. In terms of anomaly detection, it relies on a single threshold setting for status judgment and does not fully consider the temperature and humidity change trends and the impact of soil humidity on gas diffusion, resulting in a high false alarm rate in the detection of abnormal states and reducing the reliability of the system. In terms of gas leakage monitoring, it does not fully utilize the gas diffusion path information for leakage risk assessment and only relies on the change in single-point concentration for judgment, resulting in limited leakage identification ability in complex environments, leading to misjudgment or missed judgment and affecting the overall performance of the monitoring system. In practical applications, it may lead to problems such as data lag, unstable transmission, inaccurate anomaly detection, and untimely response to leakage monitoring, reducing the reliability and practicality of the monitoring system in the fields of industrial production and environmental monitoring. Summary of the Invention

[0005] The object of the present invention is to solve the deficiencies existing in the prior art and propose a monitoring system for the backfill soil of fermentation vats based on a sensor network.

[0006] To achieve the above object, the present invention adopts the following technical solutions: A monitoring system for the backfill soil of fermentation vats based on a sensor network includes:

[0007] The temperature and humidity prediction module obtains microbial metabolic characteristic data, calculates the metabolic heat release rate and water vapor release rate at multiple fermentation stages, analyzes the temperature and humidity change characteristics, and establishes a temperature and humidity benchmark prediction model;

[0008] The communication optimization module calls the temperature and humidity benchmark prediction model, obtains the geographical location, wireless signal strength, and energy consumption level of the sensor nodes, adjusts and optimizes the data flow path by calculating the cost and signal quality of data transmission between nodes, and generates an optimized communication path;

[0009] The data collection module calls the optimized communication path, uses sensors to obtain the temperature and humidity data of the backfill soil and the concentration data of multiple gases in real time, adjusts the data collection frequency of the sensors by analyzing the change rate of the data, and obtains the collection frequency parameter;

[0010] The anomaly detection module uses the collection frequency parameter to calculate the temperature gradient change rate and gas concentration change trend of multiple regions in real time, combines the humidity state of the backfill soil, analyzes and detects abnormal heat diffusion paths, and generates a heat transfer anomaly determination value.

[0011] As a further solution of the present invention, the temperature and humidity reference prediction model specifically includes the metabolic heat release rate, the water vapor release rate, and the temperature and humidity change trend. The optimized communication path includes the data transmission cost between nodes, the signal quality, and the data flow path. The acquisition frequency parameter specifically includes the temperature and humidity data change rate, the change rates of various gas concentrations, and the sensor acquisition frequency adjustment value. The heat transfer anomaly determination value includes the temperature gradient change rate, the gas concentration change trend, and the abnormal heat diffusion path.

[0012] As a further solution of the present invention, the temperature and humidity prediction module includes:

[0013] The metabolic heat calculation sub-module obtains the microbial metabolic characteristic data, including the oxygen consumption rate and the carbon dioxide release rate. According to the heat change of oxygen consumption and carbon dioxide release, the formula is used:

[0014] ;

[0015] Calculate the metabolic heat release rate per unit volume to obtain the heat release rate;

[0016] Among them, represents the metabolic heat release rate, represents the calorific value of oxygen combustion, represents the th moment of the oxygen consumption rate, represents the calorific value of carbon dioxide combustion, represents the th moment of the carbon dioxide release rate, represents the unit volume, represents the total number of time steps, represents the index of the time step;

[0017] The water vapor release calculation sub-module calls the heat release rate and calculates the water vapor release rate by using the oxygen consumption rate and the carbon dioxide release rate;

[0018] The temperature and humidity prediction modeling sub-module calls the water vapor release rate, combines the heat release rate data of each fermentation stage, records the temperature and humidity change characteristics of multiple fermentation stages, and constructs a temperature and humidity reference prediction model.

[0019] As a further solution of the present invention, the communication optimization module includes:

[0020] The data acquisition sub-module calls the temperature and humidity reference prediction model, obtains the geographical location, wireless signal strength, and energy consumption level of the sensor nodes, and generates a node information data set;

[0021] The signal quality analysis sub-module calls the node information data set, analyzes and records the cost and signal quality of transmissions between multiple nodes, and generates a signal quality evaluation value;

[0022] The path score calculation sub-module, based on the signal quality evaluation value, uses the formula:

[0023] ;

[0024] Calculates the scores of multiple signal transmission paths, adjusts and optimizes the data flow path by comparing the scores of multiple paths, and generates an optimized communication path;

[0025] Among them, represents the path score, represents the signal quality between the b-th pair of nodes in the path, represents the transmission cost between the b-th pair of nodes in the path, represents the weight factor of signal quality, represents the weight factor of cost, represents the total number of node pairs in the path, represents the index of the node pair in the path.

[0026] As a further solution of the present invention, the data acquisition module includes:

[0027] The sensor data acquisition sub-module calls the optimized communication path, obtains the temperature and humidity data of each sensor node and the concentration data of multiple gases, and generates the original acquisition data;

[0028] The change rate analysis sub-module calls the original acquisition data, analyzes the change rate of the data according to the temperature and humidity data and gas concentration data collected by the sensor, and evaluates the change trend, and generates a data change rate;

[0029] The acquisition frequency adjustment sub-module, based on the data change rate, uses the formula:

[0030] ;

[0031] Calculates the acquisition frequency adjustment value, adjusts the data acquisition frequency, and obtains the acquisition frequency parameter;

[0032] Among them, represents the adjusted acquisition frequency, represents the change amount of temperature data, represents the change amount of gas concentration data, represents the time change amount, represents the temperature change weight, represents the humidity change weight, represents the gas concentration change weight

[0033] As a further solution of the present invention, the anomaly detection module includes:

[0034] The temperature gradient calculation sub-module uses the acquisition frequency parameter to obtain the temperature data of multiple regions in real time, calculates the temperature difference between each region, combines the position coordinates, identifies the speed and direction of heat diffusion, and obtains the temperature gradient change rate;

[0035] The specific formula for obtaining the temperature gradient change rate is:

[0036] ;

[0037] Calculate the temperature gradient change rate;

[0038] Wherein, is the temperature gradient change rate, is the temperature difference between different measurement points within the region, is the distance between the measurement points;

[0039] The gas concentration trend analysis sub-module calculates the change trend of the concentrations of multiple gases in each region in real time according to the temperature gradient change rate, uses the gas concentration data, analyzes the diffusion speed of the gas, and obtains the gas concentration trend offset;

[0040] The specific formula for obtaining the gas concentration trend offset is:

[0041] ;

[0042] Calculate the gas concentration trend offset;

[0043] Wherein, is the gas concentration trend offset, is the gas concentration difference between adjacent measurement points, is the distance between the measurement points;

[0044] Based on the gas concentration trend offset, the heat transfer anomaly determination sub-module combines the measured value of the backfill soil humidity and uses the formula:

[0045] ;

[0046] Calculate the heat diffusion path anomaly coefficient and generate a heat transfer anomaly determination value;

[0047] Wherein, represents the heat diffusion path anomaly coefficient, represents the gas concentration trend offset, represents the temperature gradient change rate, represents the measured value of the backfill soil humidity, represents the data acquisition time interval, represents the absolute value of the humidity change rate.

[0048] As a further aspect of the present invention, the system further includes:

[0049] The leakage monitoring module calls the heat transfer anomaly determination value, and based on the real-time change rates of carbon dioxide and oxygen concentrations, in combination with the backfill soil porosity and the real-time humidity state, by identifying and analyzing the gas diffusion path, it detects gas leakage in real time and generates a leakage monitoring result;

[0050] The leakage monitoring result specifically refers to the carbon dioxide concentration change rate, the oxygen concentration change rate, and the gas diffusion path.

[0051] As a further aspect of the present invention, the leakage monitoring module includes:

[0052] The gas diffusion resistance calculation sub-module calls the heat transfer anomaly determination value, combines the backfill soil porosity and the real-time humidity state data, evaluates the degree of gas restriction, calculates the restricted situation of gas flow, evaluates the gas diffusion ability in multiple regions, and obtains gas diffusion resistance data;

[0053] The gas flow direction determination sub-module calls the gas diffusion resistance data, combines the gas concentration change trend data, calculates the diffusion gradient between multiple measurement points, compares the diffusion resistance of each region, analyzes the gas flow direction, and obtains gas flow direction information;

[0054] The leakage risk assessment sub-module calls the gas flow direction information, combines the gas diffusion resistance data, identifies the gas distribution in multiple regions, and uses the formula:

[0055] ;

[0056] Calculates the leakage risk index of each region, detects gas leakage, and generates a leakage monitoring result;

[0057] Wherein, is the leakage risk index of the th region, is the target gas concentration of the th measurement point in the th region, is the gas diffusion gradient of the th measurement point in the th region, is the gas diffusion resistance of the th measurement point in the th region, is the th measurement point in the Environmental impact factors of each measurement point, is the total number of measurement points in the area ;

[0058] The specific formula for calculating the gas diffusion gradient is:

[0059] ;

[0060] wherein, is the gas diffusion gradient, is the gas concentration at measurement point 2, is the gas concentration at measurement point 1, is the distance between measurement point 1 and measurement point 2;

[0061] The specific formula for calculating the gas diffusion resistance is:

[0062] ;

[0063] wherein, is the gas diffusion resistance, is the thickness of the soil layer through which the gas passes, is the gas diffusion coefficient.

[0064] Compared with the prior art, the advantages and positive effects of the present invention are as follows:

[0065] In the present invention, by analyzing the microbial metabolic characteristics, the temperature and humidity benchmarks during the fermentation process are identified, providing a data basis for abnormal data detection. The optimization of the data flow path improves the communication stability and energy efficiency ratio, reduces data transmission delay, dynamically adjusts the data acquisition frequency, realizes a fine data acquisition strategy, optimizes the accuracy and real-time performance of data acquisition, calculates the temperature gradient change rate and the gas concentration change trend, combines with the moisture state of the backfill soil, enhances the accuracy of abnormal detection, reduces false alarms and missed alarms, and improves the accuracy and rapid response ability of leakage monitoring by identifying the gas diffusion path, thereby enhancing the comprehensive performance and applicability of the monitoring system. Description of the Drawings

[0066] Figure 1 is the system flow chart of the present invention;

[0067] Figure 2 is the flow chart of the temperature and humidity prediction module of the present invention;

[0068] Figure 3 is the flow chart of the communication optimization module of the present invention;

[0069] Figure 4 is the flow chart of the data acquisition module of the present invention;

[0070] Figure 5Flowchart of the anomaly detection module of the present invention;

[0071] Figure 6 Flowchart of the leakage monitoring module of the present invention. Detailed implementation manners

[0072] In order to make the objectives, technical solutions and advantages of the present invention clearer and more understandable, the present invention will be further described in detail below in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present invention and are not used to limit the present invention.

[0073] In the description of the present invention, it should be understood that the orientation or positional relationships indicated by the terms "length", "width", "upper", "lower", "front", "rear", "left", "right", "vertical", "horizontal", "top", "bottom", "inner", "outer", etc. are based on the orientation or positional relationships shown in the accompanying drawings. They are only for the convenience of describing the present invention and simplifying the description, rather than indicating or implying that the device or element referred to must have a specific orientation, be constructed and operated in a specific orientation, and thus should not be construed as a limitation of the present invention. In addition, in the description of the present invention, the meaning of "a plurality of" is two or more unless otherwise specifically defined.

[0074] Embodiment 1: Please refer to Figure 1 , a fermentation cellar backfill soil monitoring system based on a sensor network includes:

[0075] The temperature and humidity prediction module obtains microbial metabolic characteristic data, calculates the metabolic heat release rate and water vapor release rate in multiple fermentation stages, analyzes the temperature and humidity change characteristics, and establishes a temperature and humidity baseline prediction model;

[0076] The communication optimization module calls the temperature and humidity baseline prediction model, obtains the geographical location, wireless signal strength, and energy consumption level of the sensor nodes, adjusts and optimizes the data flow path by calculating the cost and signal quality of data transmission between nodes, and generates an optimized communication path;

[0077] The data acquisition module calls the optimized communication path, uses sensors to obtain the temperature and humidity data of the backfill soil and the concentration data of various gases in real time, adjusts the data acquisition frequency of the sensors by analyzing the change rate of the data, and obtains the acquisition frequency parameter;

[0078] The anomaly detection module uses the acquisition frequency parameter to calculate the temperature gradient change rate and gas concentration change trend in multiple regions in real time, combines the humidity state of the backfill soil, analyzes and detects the abnormal heat diffusion path, and generates a heat transfer anomaly determination value;

[0079] The leakage monitoring module calls the heat transfer anomaly determination value, and based on the real-time change rates of carbon dioxide and oxygen concentrations, combines the porosity of the backfill soil and the real-time humidity state, and detects gas leakage in real time by identifying and analyzing the gas diffusion path, generating a leakage monitoring result.

[0080] The temperature and humidity reference prediction model specifically includes the metabolic heat release rate, water vapor release rate, and temperature and humidity change trend. The optimized communication path includes the data transmission cost between nodes, signal quality, and data flow path. The acquisition frequency parameter specifically includes the change rate of temperature and humidity data, the change rate of various gas concentrations, and the sensor acquisition frequency adjustment value. The heat transfer anomaly determination value includes the temperature gradient change rate, gas concentration change trend, and abnormal heat diffusion path. The leakage monitoring result specifically refers to the change rate of carbon dioxide concentration, the change rate of oxygen concentration, and the gas diffusion path.

[0081] Please refer to Figure 2 , the temperature and humidity prediction module includes:

[0082] The metabolic heat calculation sub-module obtains microbial metabolic characteristic data, including the oxygen consumption rate and carbon dioxide release rate. According to the heat changes of oxygen consumption and carbon dioxide release, the formula is used:

[0083] ;

[0084] Calculate the metabolic heat release rate per unit volume to obtain the heat release rate;

[0085] Among them, represents the metabolic heat release rate, represents the calorific value of oxygen combustion, represents the th moment of oxygen consumption rate, represents the calorific value of carbon dioxide combustion, represents the th moment of carbon dioxide release rate, represents the unit volume, represents the total number of time steps, represents the index of the time step;

[0086] In the process of calculating the metabolic heat release rate, it is first necessary to obtain microbial metabolic characteristic data, including the oxygen consumption rate and carbon dioxide release rate , the acquisition of these data usually relies on real-time online gas analyzers. Such devices measure the oxygen and carbon dioxide concentrations in the exhaust gas of the fermenter and calculate the gas exchange rate per hour by combining the gas flow rate. To ensure the accuracy of the measured data, equipment calibration is required before measurement, and drift correction is performed on the measured data to eliminate the influence of environmental temperature and pressure changes on the measured values. Subsequently, the metabolic heat release rate per unit volume is calculated. When calculating, the calorific value of oxygen combustion needs to be considered. And the calorific value of carbon dioxide combustion. The heat release caused by oxygen consumption is calculated separately. And the heat loss caused by carbon dioxide generation. For all time steps. Sum them up and divide by the unit volume. To obtain the metabolic heat release rate. Thus, the heat release rates at different time periods are obtained.

[0087] The metabolic heat release rate is calculated using the formula:

[0088] ;

[0089] Where, represents the metabolic heat release rate ( ), represents the calorific value of oxygen combustion (kJ / mol), represents the oxygen consumption rate at the th moment (mmol / L·h), represents the calorific value of carbon dioxide combustion (kJ / mol), represents the carbon dioxide release rate at the th moment (mmol / L·h), represents the unit volume ( ), represents the total number of time steps, represents the index of time.

[0090] Set kJ / mol, kJ / mol, unit volume , time point When, the oxygen consumption rate mmol / L·h, the carbon dioxide release rate mmol / L·h, then calculate :

[0091] ;

[0092] This result indicates that at the time point When the metabolic heat release rate per unit volume is 50 , it indicates that the oxygen consumption of microorganisms is relatively active during this stage, and the heat released during the metabolic process is relatively high. If the continues to increase at subsequent time points, it may mean that the microbial metabolic activity is accelerating. If decreases, it may indicate a slowdown in metabolism or a reduction in nutrients in the culture medium.

[0093] The water vapor release calculation sub-module calls the heat release rate and calculates the water vapor release rate using the oxygen consumption rate and carbon dioxide release rate;

[0094] During the process of calculating the water vapor release rate, the metabolic heat release rate needs to be called first, and then combined with the oxygen consumption rate and the carbon dioxide release rate to estimate the water vapor release rate . The calculation of the water vapor release rate depends on the water vapor generation coefficient . This coefficient can be obtained by fitting the experimental data of water loss during the fermentation process. The generation of water vapor mainly comes from the respiration of microorganisms and the evaporation of water in the fermentation products. The temperature and air flow rate in the fermenter will also affect the amount of water vapor released. Therefore, when calculating the water vapor release rate, it is necessary to comprehensively consider the oxygen consumption and carbon dioxide generation, and make a correction calculation in combination with the value measured experimentally.

[0095] The water vapor release rate is calculated using the formula:

[0096] ;

[0097] where, is the water vapor release rate (g / L·h), is the water vapor generation coefficient, is the oxygen consumption rate (mmol / L·h) at the th moment, is the carbon dioxide release rate (mmol / L·h) at the th moment, is the total number of time steps.

[0098] Set , at time point , the oxygen consumption rate mmol / L·h, and the carbon dioxide release rate mmol / L·h, then calculate the water vapor release rate :

[0099] ;

[0100] The results show that the water vapor release rate per unit volume is 1.5 g / L·h. If this value continues to increase, it indicates that the water vapor concentration in the fermenter is rising, and it may be necessary to adjust the ventilation rate or temperature control strategy to prevent excessive water loss from affecting the medium concentration.

[0101] The temperature and humidity prediction modeling sub-module calls the water vapor release rate, combines it with the heat release rate data of each fermentation stage, records the temperature and humidity change characteristics of multiple fermentation stages, and constructs a temperature and humidity baseline prediction model;

[0102] When calculating the trend change of temperature and humidity, the water vapor release rate needs to be called first , combined with the heat release rate of each fermentation stage for calculation. The calculation of the temperature change rate uses as the driving factor, combined with the temperature change coefficient to calculate the change of temperature over time. The humidity change is calculated through combined with the humidity change coefficient to calculate the humidity values at different time points. The temperature change coefficient reflects the impact of heat release on the ambient temperature, while the humidity change coefficient represents the contribution of the water vapor release rate to the humidity change. Both can be obtained through experimental measurement and adjusted according to the temperature and humidity control strategy of the fermentation environment.

[0103] Use the formula to calculate the trend change of temperature and humidity:

[0104] ;

[0105] where is the temperature at the th moment, is the temperature at the previous moment, is the temperature change coefficient, is the metabolic heat release rate, is the humidity at the th moment, is the humidity at the previous moment, is the humidity change coefficient, is the water vapor release rate.

[0106] Set , , the initial temperature , the initial humidity , the time point when calculating and :

[0107] ;

[0108] ;

[0109] After calculation, it is found that at the temperature reaches 38.5 °C and the humidity rises to 80.5%.

[0110] At time point it is calculated that and , indicating that under the action of metabolic heat release and water vapor release, both the temperature and humidity increase. If they continue to rise at subsequent time points, it may be necessary to adjust the cooling system or dehumidification system to maintain a suitable fermentation environment. If the temperature and humidity are too high, it may inhibit the growth of microorganisms, and if too low, it may affect metabolic activity.

[0111] Please refer to Figure 3 , the communication optimization module includes:

[0112] The data acquisition sub-module calls the temperature and humidity benchmark prediction model to obtain the geographical location, wireless signal strength, and energy consumption level of the sensor node, and generates a node information data set;

[0113] In the application in the sensor network, first, it is necessary to obtain the geographical location, wireless signal strength, and energy consumption level from the sensor node. These data can help determine the basic environmental conditions of each node and serve as the basis for subsequent calculations. To ensure the accuracy and real-time nature of these data, the sensor node will regularly upload its location data, and obtain the accurate location of the node through the GPS module or a positioning algorithm based on known locations. Usually, the location data unit is longitude and latitude (for example: the location of node 1 is (30.2672°N, 97.7431°W)). The wireless signal strength is usually measured by the RSSI (Received Signal Strength Indication) value, with the unit of dBm, and common values may be 60 dBm, 70 dBm, etc., reflecting the communication quality between nodes. The energy consumption level is obtained by measuring the changes in current and voltage through the sensor. After obtaining the node location, wireless signal strength, and energy consumption data, they are input into the system for subsequent analysis and serve as the basis for transmission cost and signal quality analysis.

[0114] Assume that the geographical location of node 1 is (30.2672°N, 97.7431°W), the wireless signal strength is 65 dBm, and the energy consumption level is 150 mAh. We input these data into the system for subsequent calculations. Assume that the transmission signal strength is 65 dBm, the distance is 100 meters, and the energy consumption is 150 mAh. As a numerical example in practical applications, these data will affect the transmission path selection and energy optimization calculation.

[0115] The signal quality analysis sub-module calls the node information data set, analyzes and records the transmission cost and signal quality between multiple nodes, and generates a signal quality evaluation value;

[0116] Next, by analyzing the transmission cost and signal quality between multiple nodes, the feasibility of each communication path is evaluated. The transmission cost is usually determined by factors such as the distance between nodes and the required energy. A common calculation method is to estimate the cost based on the distance between nodes and the battery consumption of the current node. For example: if the distance between two nodes is 100 meters, the battery power is 200 mAh, and the energy consumption for transmitting data through a certain protocol is 50 mAh, then the cost of this path can be expressed as 50 mAh. Signal quality analysis is based on the wireless signal strength (RSSI value) to judge the reliability of the signal. A lower RSSI value (e.g., 90 dBm) may mean poor signal quality, while a higher value (e.g., 50 dBm) indicates better signal quality. By calculating the mean and variance of the signal quality between nodes, the network stability and the reliability of data transmission can be determined. These data will be an important basis for optimizing the communication path.

[0117] The transmission cost is calculated by the following formula:

[0118] ;

[0119] where, is the transmission cost of the path, is the distance between nodes (unit: meter), is the energy required for a single transmission (unit: mAh), is the total energy of the node (unit: mAh).

[0120] Assume the distance between nodes meters, the transmission energy mAh, the total energy of the node mAh, then the transmission cost is:

[0121] ;

[0122] This indicates that the transmission cost of this path is 25 mAh.

[0123] The path score calculation sub-module calculates the scores of multiple signal transmission paths according to the signal quality evaluation value, using the formula:

[0124] ;

[0125] By comparing the scores of multiple paths, the data flow path is adjusted and optimized to generate an optimized communication path;

[0126] where, Represents the path score, Represents the signal quality between the b-th pair of nodes in the path, Represents the transmission cost between the b-th pair of nodes in the path, Represents the weight factor of signal quality, Represents the weight factor of cost, Represents the total number of node pairs in the path, Represents the index of the node pair in the path;

[0127] Finally, by calculating the scores of multiple paths, the data flow path is adjusted and optimized. The scoring of a path is usually completed by weighted summation of the transmission cost and signal quality between each pair of nodes in each path. Specifically, the path score is calculated as follows:

[0128] ;

[0129] where, is the path score, is the signal quality between the b-th pair of nodes, is the transmission cost between the b-th pair of nodes, and are the weights of signal quality and cost respectively. The setting of weights is usually adjusted according to actual needs. For example, when signal quality is more important, can be set to a larger value (e.g., 0.7), while the weight of transmission cost ( ) can be set to a smaller value (e.g., 0.3). These weights reflect the relative importance of signal quality and cost in path selection.

[0130] Suppose there are 3 paths, each path contains 3 node pairs, and the signal quality and transmission cost are respectively:

[0131] Path 1, dBm, mAh, Path 2, dBm, mAh, Path 3, dBm, mAh;

[0132] The weights are set as, , ;

[0133] According to the formula, the path scoring is calculated as follows:

[0134] ;

[0135] ;

[0136] ;

[0137] Finally, the score of Path 1 is the highest, so Path 1 is selected as the optimized path.

[0138] Please refer to Figure 4 , the data acquisition module includes:

[0139] The sensor data acquisition sub-module calls the optimized communication path to obtain the temperature and humidity data and the concentration data of various gases of each sensor node, and generates the original acquisition data;

[0140] During the data acquisition process, the role of the sensor node is to periodically collect and transmit environmental data, usually including temperature, humidity, and gas concentration, etc. The collection of these data is carried out by the set sensor devices at predetermined time intervals. Each sensor node monitors environmental changes within its working area and regularly sends the collected data. The sensor devices generally transmit the collected environmental data to the central processing system in real time through wireless communication protocols (such as WiFi, LoRa, Zigbee, etc.). Through the data acquisition function, the sensors can monitor multiple physical quantities simultaneously and adjust the data acquisition strategy according to different acquisition conditions (such as time interval, environmental changes, etc.). For example, the temperature sensor records the current temperature value each time it acquires data, the humidity sensor records the air humidity value, and the gas sensor can detect and record the concentrations of gases such as carbon dioxide and nitrogen dioxide. For each data point, the sensor measures and records the value regularly. For example, assume that at a certain moment, the temperature value recorded by the sensor is 25°C, the humidity is 60%, and the carbon dioxide concentration is 400 ppm. These data are transmitted to the central system in real time and processed through the data communication module. These raw data provide the basis for subsequent analysis and optimization. During the transmission process, through measures such as data caching and redundancy checking, the stability and accuracy of the data are ensured. These data acquisition operations continue to ensure that the environmental changes in the monitoring area can be real-time feedback and processed.

[0141] The change rate analysis sub-module calls the original acquisition data, analyzes the change rate of the data according to the temperature and humidity data and the gas concentration data collected by the sensors, and evaluates the change trend, and generates the data change rate;

[0142] As data collection progresses, the next step is to analyze the rate of change of the collected data. The calculation of the rate of change is usually used to quantify the speed of change of environmental parameters, so that the collection frequency can be adjusted and resource utilization optimized based on the rate of change in the follow-up. The specific process of rate-of-change analysis includes two aspects: First, obtain data at consecutive time points, then calculate the change amount between these data. Next, combine the change amount with the time interval to obtain the rate of change. This process is carried out independently for each type of data (temperature, humidity, and gas concentration). For example, if the change in temperature between two time points is 5°C and the time interval is 2 hours, the temperature rate of change is:

[0143] ;

[0144] where, and represent the current value and the previous value of the temperature respectively, is the time interval. Assuming the temperature change is 5°C and the time interval is 2 hours, the temperature rate of change is:

[0145] ;

[0146] After obtaining the rates of change of temperature, humidity, and gas concentration, the system will be able to identify the speed of environmental changes, and then determine whether it is necessary to increase the data collection frequency to cope with environmental fluctuations, ensuring data accuracy and timely response.

[0147] Based on the data rate of change, the collection frequency adjustment sub-module uses the formula:

[0148] ;

[0149] to calculate the collection frequency adjustment value, adjust the data collection frequency, and obtain the collection frequency parameter;

[0150] where, represents the adjusted collection frequency, represents the change amount of temperature data, represents the change amount of gas concentration data, represents the change amount of time, represents the temperature change weight, represents the humidity change weight, represents the gas concentration change weight;

[0151] Finally, based on the calculated rate of change results, the system will adjust the data acquisition frequency. The adjustment of the acquisition frequency is dynamically optimized according to the speed of data change: when the environment changes rapidly, the acquisition frequency needs to be increased to ensure that all changes are captured quickly; when the environment changes relatively smoothly, the acquisition frequency can be reduced to save computing resources and battery energy. Specifically, the adjustment of the acquisition frequency depends on the calculation results of the rate of change. When the temperature rate of change is higher than a preset threshold (such as 5°C / h), the system will adjust the acquisition frequency to once per minute; if the rate of change is lower (such as less than 0.5°C / h), the system will adjust it to once every 10 minutes. To achieve this adjustment, the system uses the following formula to calculate the acquisition frequency:

[0152] ;

[0153] where, represents the adjusted acquisition frequency, represents the change in temperature data, represents the change in gas concentration data, represents the change in time, represents the temperature change weight, represents the humidity change weight, represents the gas concentration change weight

[0154] For example, assume that the temperature change is 5°C, the time interval is 1 hour, the humidity change is 3%, the gas concentration change is 10 ppm, and the corresponding weights are set as: (temperature change weight), (humidity change weight), (gas concentration change weight).

[0155] According to the formula, the calculated result of the acquisition frequency is:

[0156] ;

[0157] This result indicates that under the current temperature, humidity, and gas concentration change conditions, the system needs to adjust the acquisition frequency to 10 times per second to ensure the real-time and accuracy of capturing data changes.

[0158] Please refer to Figure 5 , the anomaly detection module includes:

[0159] The temperature gradient calculation sub-module uses the acquisition frequency parameter to obtain the temperature data of multiple regions in real time, calculates the temperature difference between each region, combines the position coordinates, identifies the speed and direction of heat diffusion, and obtains the temperature gradient rate of change;

[0160] To obtain temperature data for multiple regions, it is necessary to collect data from multiple sensor nodes. First, determine the coordinates of each measurement point and record its relative position, such as , , etc. For the temperature change within a certain time interval , based on the data recorded by the sensor, obtain the time and corresponding temperature values and . Calculate the temperature difference between different measurement points within the region. The formula is as follows:

[0161] ;

[0162] Then, calculate the temperature gradient change rate , that is

[0163] ;

[0164] Among them, is the distance between measurement points. The distance between measurement points can be calculated using the spatial Euclidean distance, that is

[0165] ;

[0166] Assume that a certain sensor A is at the position and the temperature is , and sensor B is at the position and the temperature is . Then

[0167] ;

[0168] ;

[0169] ;

[0170] This calculation shows that the temperature gradient change rate is , which can be used for subsequent gas concentration trend analysis.

[0171] The gas concentration trend analysis sub-module calculates the change trend of the concentrations of multiple gases in each region in real time according to the temperature gradient change rate, analyzes the diffusion speed of the gas, and obtains the gas concentration trend offset;

[0172] After the temperature gradient change rate is calculated, use the gas concentration data to perform real-time calculations, compare the gas concentration data of multiple measurement points, and calculate the concentration change between adjacent measurement points. The formula is as follows:

[0173] ;

[0174] Among them, is the gas concentration trend offset (unit: %), is the difference in gas concentration between adjacent measurement points (unit: ppm), is the distance between measurement points (unit: m).

[0175] Suppose the carbon dioxide concentration at a certain measurement point A is 400 ppm, the concentration at measurement point B is 420 ppm, and the distance between measurement point A and measurement point B is 3 m, then there is

[0176] ;

[0177] ;

[0178] The calculated value indicates that the gas concentration has changed by 6.67% within a distance of 3 m, which can be used for further analysis of the gas diffusion rate.

[0179] Table 1: Temperature and humidity data of measurement points and calculation results table

[0180] ;

[0181] As shown in Table 1, both the temperature gradient and the gas concentration trend offset have been calculated and can be used for further analysis of heat transfer anomalies.

[0182] The heat transfer anomaly determination sub-module is based on the gas concentration trend offset, combined with the measured value of the backfill soil humidity, and uses the formula:

[0183] ;

[0184] Calculate the heat diffusion path anomaly coefficient and generate a heat transfer anomaly determination value;

[0185] Among them, represents the heat diffusion path anomaly coefficient, represents the gas concentration trend offset, represents the temperature gradient change rate, represents the measured value of the backfill soil humidity, represents the data acquisition time interval, represents the absolute value of the humidity change rate;

[0186] Based on the gas concentration trend offset and the measured value of the backfill soil humidity , calculate the heat diffusion path anomaly coefficient , and use the formula:

[0187] ;

[0188] Among them, represents the abnormal coefficient of the heat diffusion path, is the offset of the gas concentration trend, is the change rate of the temperature gradient, is the measured value of the moisture content of the backfill soil, is the data acquisition time interval, is the absolute value of the moisture change rate. Assume the parameter values are as follows, , , , , , substitute into the calculation:

[0189] ;

[0190] ;

[0191] ;

[0192] ;

[0193] The calculated abnormal coefficient of the heat diffusion path , indicating that there is a certain degree of heat diffusion abnormality in this area. This value can be compared with a preset threshold to determine whether to trigger an alarm or further optimize the data acquisition strategy.

[0194] Please refer to Figure 6 , the leakage monitoring module includes:

[0195] The gas diffusion resistance calculation sub-module calls the heat transfer anomaly determination value, combines the porosity of the backfill soil and the real-time moisture state data, evaluates the degree of restriction of the gas, calculates the restricted situation of the gas flow, evaluates the diffusion ability of the gas in multiple regions, and obtains the gas diffusion resistance data;

[0196] In the fermentation tank backfill soil monitoring system, first obtain the heat transfer anomaly determination value. This value is obtained by the sensor to monitor the temperature changes in each area of the backfill soil in real time and calculate the change rate of the temperature gradient. The temperature gradient can be calculated by the following formula:

[0197] ;

[0198] Among them, is the temperature gradient (℃ / cm), is the temperature of measuring point 2 (℃), is the temperature of measuring point 1 (℃), is the distance between measuring point 1 and measuring point 2 (cm).

[0199] Set the assumed value: ℃, °C, cm, then the calculation is as follows:

[0200] ;

[0201] The calculated temperature gradient is 0.5 °C / cm, and this value is relatively high, indicating that there is a relatively large heat transfer anomaly in this area.

[0202] Next, obtain the porosity data of the backfill soil. Porosity represents the ratio of the pore volume in the soil to the total soil volume and can be calculated using the following formula:

[0203] ;

[0204] where, is the soil porosity (%), is the pore volume (cubic centimeters), is the total soil volume (cubic centimeters).

[0205] Set the assumed values: cubic centimeters, cubic centimeters, then the calculation is as follows:

[0206] ;

[0207] The calculated porosity is 40%, indicating that the soil in this area contains a relatively high proportion of pores, which may affect gas diffusion.

[0208] Subsequently, obtain the real-time humidity status data. Humidity affects the gas diffusion ability in the soil and can be measured in real time by a humidity sensor. For example, the humidity sensor in a certain area shows a humidity of 20%. Then, evaluate the degree of gas restriction, analyze the relationship between the temperature anomaly area and porosity and humidity, and determine the gas flow restriction in these areas.

[0209] Next, calculate the gas flow restriction using the gas diffusion resistance calculation formula:

[0210] ;

[0211] where, is the gas diffusion resistance (s / cm²), is the thickness of the soil layer through which the gas passes (cm), is the gas diffusion coefficient (cm² / s).

[0212] Set the assumed values: cm, cm² / s, then the calculation is as follows:

[0213] ;

[0214] The calculated gas diffusion resistance is 1500 s / cm², which is a relatively large value, indicating that gas diffusion in this area is relatively difficult.

[0215] The gas flow direction determination sub-module calls the gas diffusion resistance data, combines it with the gas concentration change trend data, calculates the diffusion gradient between multiple measurement points, compares the diffusion resistance of each area, analyzes the gas flow direction, and obtains the gas flow direction information;

[0216] In the fermentation cellar backfill soil monitoring system, first, the gas diffusion resistance data is called to represent the resistance degree of the soil in each area to gas diffusion. Then, the gas concentration change trend data is obtained by real-time monitoring of the gas concentration in each area through sensors and recording its change over time. Then, the diffusion gradient between multiple measurement points is calculated using the following formula:

[0217] ;

[0218] Among them, is the gas diffusion gradient (% / cm), is the gas concentration at measurement point 2 (%), is the gas concentration at measurement point 1 (%), is the distance (cm) between measurement point 1 and measurement point 2.

[0219] Set the assumed values: , , cm, then the calculation is as follows:

[0220] ;

[0221] The calculated gas diffusion gradient is 0.004% / cm, indicating that the gas concentration in this area changes greatly.

[0222] Finally, analyze the gas flow direction, combine the concentration gradient and diffusion resistance data to determine the gas flow direction information.

[0223] The leakage risk assessment sub-module calls the gas flow direction information, combines it with the gas diffusion resistance data, identifies the gas distribution in multiple areas, and uses the formula:

[0224] ;

[0225] Calculate the leakage risk index of each area, detect gas leakage, and generate leakage monitoring results;

[0226] Among them, is the leakage risk index of the th area, is the target gas concentration of the th measurement point in the th area, is the gas diffusion gradient of the th measurement point in the th area, is the gas diffusion resistance of the th measurement point in the th area, is the environmental impact factor of the th measurement point in the th area, is the total number of measurement points in area .

[0227] In the monitoring system for the backfill soil of the fermentation tank, first call the gas flow direction information, which is obtained through the aforementioned steps and represents the gas flow trend in different areas. Then, combine the gas diffusion resistance data to identify the gas distribution in multiple areas.

[0228] Next, use the following formula to calculate the leakage risk index for each area:

[0229] ;

[0230] where, is the leakage risk index of the th area, is the target gas concentration (%) of the th measurement point in the th area, is the gas diffusion gradient (% / cm) of the th measurement point in the th area, is the gas diffusion resistance (s / cm²) of the th measurement point in the th area, is the environmental impact factor of the th measurement point in the th area, is the total number of measurement points in area .

[0231] Set hypothetical values: , , ; , , ; , , ; , , 。

[0232] Substitute into the formula for calculation:

[0233] ;

[0234] The calculation result is used to evaluate the leakage risk level of this area.

[0235] The above are only the preferred embodiments of the present invention, and do not limit the present invention in other forms. Any person skilled in the art may use the technical content disclosed above to make changes or modifications into equivalent embodiments with equivalent changes and apply them to other fields. However, as long as it does not depart from the technical solution content of the present invention, any simple modification, equivalent change and modification made to the above embodiments based on the technical essence of the present invention still fall within the protection scope of the technical solution of the present invention.

Claims

1. A fermentation tank backfill soil monitoring system based on a sensor network, characterized in that: The system comprises: The temperature and humidity prediction module obtains microbial metabolic characteristic data, calculates the metabolic heat release rate and water vapor release rate in multiple fermentation stages, analyzes the temperature and humidity change characteristics, and establishes a temperature and humidity benchmark prediction model; The communication optimization module calls the temperature and humidity benchmark prediction model to obtain the geographical location, wireless signal strength, and energy consumption level of the sensor node, and adjusts and optimizes the data flow path by calculating the cost and signal quality of data transmission between nodes to generate an optimized communication path; The data acquisition module calls the optimized communication path and uses sensors to obtain the temperature and humidity data of the backfill soil and the concentration data of various gases in real time, and adjusts the data acquisition frequency of the sensor by analyzing the rate of change of the data to obtain the acquisition frequency parameters; The anomaly detection module uses the acquisition frequency parameters to calculate the temperature gradient change rate and gas concentration change trend of multiple regions in real time, analyzes and detects abnormal heat diffusion paths in combination with the backfill soil moisture status, and generates a heat transfer anomaly judgment value; The anomaly detection module comprises: The temperature gradient calculation submodule uses the acquisition frequency parameters to obtain temperature data of multiple regions in real time, calculates the temperature difference between each region, identifies the speed and direction of heat diffusion in combination with the position coordinates, and obtains the temperature gradient change rate. The specific formula for obtaining the temperature gradient change rate is: ,in, is the temperature gradient change rate, is the temperature difference between different measuring points in the area, is the distance between the measuring points; The gas concentration trend analysis submodule calculates the change trend of multiple gas concentrations in each area in real time based on the temperature gradient change rate and the gas concentration data, analyzes the gas diffusion rate, and obtains the gas concentration trend offset. The specific formula for obtaining the gas concentration trend offset is: ,in, is the gas concentration trend offset, is the gas concentration difference between adjacent measuring points, is the distance between the measuring points; The heat transfer anomaly determination submodule adopts the formula based on the gas concentration trend offset and the measured value of backfill soil humidity: , calculate the heat diffusion path anomaly coefficient and generate the heat transfer anomaly judgment value, where, represents the heat diffusion path anomaly coefficient, Represents the gas concentration trend offset, represents the rate of change of temperature gradient, Represents the measured value of backfill soil moisture, represents the data collection time interval, Represents the absolute value of the humidity change rate.

2. The fermentation tank backfill soil monitoring system based on sensor network according to claim 1 is characterized in that: The temperature and humidity benchmark prediction model specifically includes metabolic heat release rate, water vapor release rate, and temperature and humidity change trend; the optimized communication path includes data transmission cost, signal quality, and data flow path between nodes; the acquisition frequency parameters specifically include temperature and humidity data change rate, multiple gas concentration change rate, and sensor acquisition frequency adjustment value; the heat transfer anomaly judgment value includes temperature gradient change rate, gas concentration change trend, and abnormal heat diffusion path.

3. The fermentation tank backfill soil monitoring system based on sensor network according to claim 1 is characterized in that: The temperature and humidity prediction module includes: The metabolic heat calculation submodule obtains microbial metabolic characteristic data, including oxygen consumption rate and carbon dioxide release rate. According to the heat changes of oxygen consumption and carbon dioxide release, the formula is used: ； Calculate the metabolic heat release rate per unit volume to obtain the heat release rate; in, represents the metabolic heat release rate, represents the calorific value of oxygen combustion, Representative The oxygen consumption rate at a given moment is represents the calorific value of carbon dioxide combustion, Representative The carbon dioxide release rate at a given moment is represents the unit volume, represents the total number of time steps, represents the index of the time step; The water vapor release calculation submodule calls the heat release rate and calculates the water vapor release rate using the oxygen consumption rate and the carbon dioxide release rate; The temperature and humidity prediction modeling submodule calls the water vapor release rate, combines the heat release rate data of each fermentation stage, records the temperature and humidity change characteristics of multiple fermentation stages, and constructs a temperature and humidity benchmark prediction model.

4. The fermentation tank backfill soil monitoring system based on sensor network according to claim 1 is characterized in that: The communication optimization module includes: The data acquisition submodule calls the temperature and humidity benchmark prediction model to obtain the geographical location, wireless signal strength, and energy consumption level of the sensor node, and generates a node information data set; The signal quality analysis submodule calls the node information data set, analyzes and records the transmission cost and signal quality between multiple nodes, and generates a signal quality evaluation value; The path score calculation submodule uses the formula: ； Calculate the scores of multiple signal transmission paths, adjust and optimize the data flow path by comparing the scores of multiple paths, and generate an optimized communication path; in, represents the path score, represents the signal quality between the bth pair of nodes in the path, represents the transmission cost between the bth pair of nodes in the path, A weighting factor representing the signal quality, The weight factor representing the cost, represents the total number of node pairs in the path, Represents the index of a node pair in a path.

5. The fermentation tank backfill soil monitoring system based on sensor network according to claim 1 is characterized in that: The data acquisition module comprises: The sensor data acquisition submodule calls the optimized communication path to obtain the temperature and humidity data of each sensor node and the concentration data of multiple gases to generate raw acquisition data; The change rate analysis submodule calls the original collected data, analyzes the change rate of the data according to the temperature and humidity data and gas concentration data collected by the sensor, evaluates the change trend, and generates the data change rate; The acquisition frequency adjustment submodule adopts the formula based on the data change rate: ； Calculate the acquisition frequency adjustment value, adjust the data acquisition frequency, and obtain the acquisition frequency parameters; in, represents the adjusted acquisition frequency, Represents the change in temperature data, Represents the change in gas concentration data, represents the time variation, represents the temperature change weight, represents the humidity change weight, Represents the weight of gas concentration change.

6. The fermentation tank backfill soil monitoring system based on sensor network according to claim 1 is characterized in that: The system further comprises: The leakage monitoring module calls the heat transfer anomaly judgment value, detects gas leakage in real time and generates leakage monitoring results by identifying and analyzing the diffusion path of the gas according to the real-time change rate of carbon dioxide and oxygen concentrations, combined with the porosity and real-time humidity status of the backfill soil; The leakage monitoring results specifically refer to the rate of change of carbon dioxide concentration, the rate of change of oxygen concentration, and the gas diffusion path.

7. The fermentation tank backfill soil monitoring system based on sensor network according to claim 6 is characterized in that: The leakage monitoring module comprises: The gas diffusion resistance calculation submodule calls the heat transfer anomaly judgment value, combines the backfill soil porosity and real-time humidity status data, evaluates the degree of gas restriction, calculates the restriction of gas flow, evaluates the diffusion capacity of gas in multiple areas, and obtains gas diffusion resistance data; The gas flow direction determination submodule calls the gas diffusion resistance data, combines the gas concentration change trend data, calculates the diffusion gradient between multiple measuring points, compares the diffusion resistance of each area, analyzes the gas flow direction, and obtains the gas flow direction information; The leakage risk assessment submodule calls the gas flow direction information, combines the gas diffusion resistance data, identifies the distribution of gas in multiple areas, and uses the formula: ； Calculate the leakage risk index of each area, detect gas leakage, and generate leakage monitoring results; in, For the The leakage risk index of each region, For the In the region The target gas concentration at each measuring point is For the In the region The gas diffusion gradient at each measuring point is For the In the region The gas diffusion resistance at each measuring point is For the In the region Environmental impact factors of each measuring point, For Region The total number of measuring points within The specific formula for calculating the gas diffusion gradient is: ； in, is the gas diffusion gradient, is the gas concentration at measuring point 2, is the gas concentration at measuring point 1, is the distance between measuring point 1 and measuring point 2; The specific formula for calculating the gas diffusion resistance is: ； in, is the gas diffusion resistance, is the thickness of the soil layer through which the gas passes, is the gas diffusion coefficient.

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