Food fresh-keeping method, device and equipment and storage medium

Abstract

The invention relates to a food fresh-keeping method, device and equipment and a storage medium, and the method comprises the steps: collecting respiration intensity data and decay gas concentration of target food in a cold chain carriage, carrying out fresh-keeping threshold matching, and generating food fresh-keeping information; real-time environment parameters of the cold chain compartment are collected, a fresh-keeping instruction is generated according to the food fresh-keeping information and the real-time environment parameters, and an air adjusting valve opening instruction and a compressor frequency instruction are obtained; dynamic regulation and control operation is executed based on the air regulating valve opening instruction and the compressor frequency instruction, and putrefying bacterium comprehensive evaluation indexes and compressor power consumption data are obtained in real time; and according to the putrefying bacterium comprehensive evaluation index and the compressor power consumption data, instruction feedback analysis is carried out, strategy construction is carried out, and a fresh-keeping regulation and control strategy is output. The air conditioning and refrigeration strategy can be dynamically adjusted according to the breathing characteristics of different foods and the growth dynamic state of putrefying bacteria.

Description

Technical Field

[0001] The present invention relates to the technical field of food preservation, and in particular to a food preservation method, device, equipment and storage medium. Background Art

[0002] In food preservation, cold chain transportation is a critical link in ensuring the quality and safety of fresh food. Its core is to inhibit the spoilage of fresh fruits and vegetables through precise environmental control. With increasing consumer demand for fresh fruits and vegetables and the widespread adoption of green logistics concepts, dynamically optimizing preservation strategies based on the real-time state of food has become a key research direction in cold chain technology. Currently, the combination of controlled atmosphere (CA) and low-temperature refrigeration technology is widely used, but its control logic often relies on preset environmental parameter thresholds (such as fixed temperature and gas concentration), and fails to fully integrate the food's own biological activity indicators (such as respiration intensity and the proliferation of spoilage bacteria) with the real-time energy consumption data of cold chain equipment. Summary of the Invention

[0003] The main purpose of the present invention is to provide a food preservation method, device, equipment and storage medium, which can dynamically adjust the gas conditioning and refrigeration strategies according to the respiratory characteristics of different foods and the growth dynamics of spoilage bacteria, making the preservation method more flexible.

[0004] To achieve the above object, the present invention provides a food preservation method, comprising: Collect the respiration intensity data and corruption gas concentration of target food in the cold chain compartment, match them with the preservation threshold, and generate food preservation information; Collecting real-time environmental parameters of the cold chain compartment, generating a preservation instruction based on the food preservation information and the real-time environmental parameters, and obtaining an air conditioning valve opening instruction and a compressor frequency instruction; Performing dynamic control operations based on the gas control valve opening instruction and the compressor frequency instruction, and obtaining a comprehensive evaluation index of spoilage bacteria and compressor power consumption data in real time; According to the comprehensive evaluation index of spoilage bacteria and the power consumption data of the compressor, command feedback analysis is performed, and a strategy is constructed to output a freshness-keeping control strategy.

[0005] Furthermore, the collection of respiration intensity data and corruption gas concentration of target food in the cold chain compartment, matching of preservation thresholds, and generation of food preservation information includes: Sampling the target food at multiple locations to obtain carbon dioxide release rate and ethylene concentration, and performing respiratory fusion calculation to obtain respiratory intensity data; Calibrate the gas concentration of the target food according to the respiration intensity data to obtain the corruption gas concentration; Performing threshold interval mapping on the respiratory intensity data and the corrupt gas concentration according to a preset respiratory intensity threshold range and a corrupt gas concentration threshold range to obtain a respiratory intensity deviation and a corrupt gas concentration deviation; A two-dimensional deviation matrix is ​​constructed according to the respiratory intensity deviation and the corruption gas concentration deviation, and dynamic weight distribution is performed to obtain a comprehensive freshness preservation index; The comprehensive freshness-keeping index is evaluated according to a preset index grading table to generate food freshness-keeping information including a modified atmosphere requirement level and an antibacterial requirement level.

[0006] Furthermore, the real-time environmental parameters of the cold chain compartment are collected, and a preservation instruction is generated according to the food preservation information and the real-time environmental parameters to obtain an air conditioning valve opening instruction and a compressor frequency instruction, including: Performing multi-source data recognition on the real-time environmental parameters to obtain temperature data, humidity data, oxygen concentration data, and carbon dioxide concentration data; performing parameter importance grading on the temperature data, the humidity data, the oxygen concentration data, and the carbon dioxide concentration data according to the food preservation information, and generating an environmental parameter grading result; Performing parameter combination evaluation on the temperature data, the humidity data, the oxygen concentration data, and the carbon dioxide concentration data based on the environmental parameter classification result to obtain a comprehensive environmental evaluation value; Dynamically matching a preset gas control valve opening reference table and a compressor frequency reference table based on the comprehensive environmental assessment value to generate an initial gas control valve opening instruction and an initial compressor frequency instruction; According to the food preservation information, breathing compensation correction and spoilage bacteria inhibition correction are performed on the initial gas control valve opening instruction and the initial compressor frequency instruction respectively to obtain the gas control valve opening instruction and the compressor frequency instruction.

[0007] Furthermore, the dynamic control operation is performed based on the gas control valve opening instruction and the compressor frequency instruction, and the comprehensive evaluation index of spoilage bacteria and compressor power consumption data are obtained in real time, including: Performing air-control valve opening adjustment processing on the air-control valve opening instruction to obtain actual air-control valve opening data; performing compressor frequency adjustment processing on the compressor frequency instruction to obtain actual compressor frequency data; Dynamically monitor the gas composition of the cold chain compartment according to the actual gas control valve opening data to obtain real-time gas composition data; Dynamically monitor the temperature distribution of the cold chain compartment according to the actual compressor frequency data to obtain real-time temperature distribution data; Analyzing the growth inhibition effect of spoilage bacteria in the cold chain compartment based on the real-time gas composition data to obtain a comprehensive evaluation index of the spoilage bacteria; The operating power consumption of the compressor of the cold chain compartment is calculated based on the actual compressor frequency data to obtain the compressor power consumption data.

[0008] Furthermore, the command feedback analysis is performed based on the comprehensive evaluation index of spoilage bacteria and the compressor power consumption data, and a strategy is constructed to output a freshness-keeping control strategy, including: Performing a dynamic trend analysis on the comprehensive evaluation index of spoilage bacteria, extracting a change slope of the comprehensive evaluation index of spoilage bacteria, and grading the inhibition effect according to the change slope of the comprehensive evaluation index of spoilage bacteria to generate a spoilage bacteria inhibition grade; Extracting power consumption fluctuation characteristics from the compressor power consumption data, and performing power consumption stability evaluation to generate a compressor power consumption stability index; A correlation relationship between power consumption inhibition is obtained by performing a joint analysis based on the spoilage bacteria inhibition level and the compressor power consumption stability index; Performing a historical instruction backtracking analysis on the gas control valve opening instruction and the compressor frequency instruction according to the power consumption suppression correlation relationship, and performing instruction optimization space calculation to generate instruction optimization parameters; Calculating the dynamic adjustment range of the gas control valve opening instruction according to the instruction optimization parameter to generate the gas control valve opening adjustment interval, and performing frequency step optimization on the compressor frequency instruction to obtain the compressor frequency adjustment step length; A multi-objective control strategy combination is performed based on the gas control valve opening adjustment interval and the compressor frequency adjustment step, and strategy screening is performed to output the freshness preservation control strategy.

[0009] Furthermore, the power consumption inhibition correlation relationship is obtained by performing a joint analysis based on the spoilage bacteria inhibition level and the compressor power consumption stability index, including: performing level quantization encoding on the spoilage bacteria inhibition level to generate a spoilage bacteria inhibition quantization value; A synergistic change trend curve is constructed based on the spoilage bacteria inhibition quantification value and the compressor power consumption stability index value to obtain a synergistic change curve for power consumption inhibition; Segmenting the power consumption suppression coordinated change curve into change intervals to obtain a power consumption suppression segmentation relationship table; Calculate the power consumption stability fluctuation range corresponding to different suppression levels according to the power consumption suppression segment relationship table, and generate a power consumption suppression stability mapping relationship; Determining a power consumption stability threshold corresponding to a suppression level according to the power consumption stability suppression mapping relationship, and performing priority trend fitting to obtain a power consumption suppression priority matching parameter; The power consumption suppression correlation relationship is obtained by performing association construction based on the power consumption suppression coordinated change curve, the power consumption suppression segment relationship table and the power consumption suppression priority matching parameter.

[0010] The present invention further provides a food preservation device, which is applied to any of the above-mentioned food preservation methods, comprising: A collection module is used to collect the respiration intensity data and corruption gas concentration of the target food in the cold chain compartment, match the preservation threshold value, and generate food preservation information; An analysis module, the analysis module is used to collect real-time environmental parameters of the cold chain compartment, generate a preservation instruction based on the food preservation information and the real-time environmental parameters, and obtain an air conditioning valve opening instruction and a compressor frequency instruction; a correlation module, the correlation module being configured to perform dynamic control operations based on the gas control valve opening instruction and the compressor frequency instruction, and to obtain a comprehensive evaluation index of spoilage bacteria and compressor power consumption data in real time; The processing module is used to perform instruction feedback analysis based on the comprehensive evaluation index of spoilage bacteria and the compressor power consumption data, and to construct a strategy to output a freshness-keeping control strategy.

[0011] The present invention also provides a food preservation device, comprising: Memory, used to store programs; The processor is used to execute the program to implement each step of any one of the above-mentioned food preservation methods.

[0012] The present invention also provides a storage medium storing computer instructions, wherein the computer instructions are used to enable a computer to execute any of the above methods.

[0013] The present invention provides a food preservation method, device, equipment and storage medium, which have the following beneficial effects: By collecting real-time data on food respiration and spoilage gas concentrations, and dynamically adjusting environmental parameters based on preservation thresholds, this approach overcomes the shortcomings of traditional fixed-threshold preservation methods, significantly improving food preservation effectiveness while avoiding energy waste caused by over-regulation. Closed-loop feedback analysis based on food bioactivity data and cold chain equipment operational data enables refined control of environmental parameters, ensuring consistent food quality while optimizing equipment energy efficiency. By monitoring comprehensive spoilage bacteria assessment indicators and compressor power consumption in real time, a dynamic control strategy is developed to reduce refrigeration system energy consumption while ensuring food spoilage rates meet standards, thereby improving the economics and sustainability of cold chain transportation. Dynamically adjusting controlled atmosphere and refrigeration strategies based on the respiration characteristics of different foods and the growth dynamics of spoilage bacteria enables more flexible preservation methods, adapting to the transportation needs of different fresh food types and enhancing the versatility and adaptability of the cold chain system. Automated data collection, analysis, and command generation reduce reliance on manual experience, enhance the intelligence of cold chain preservation, reduce operational errors, and improve the stability and reliability of the transportation process. BRIEF DESCRIPTION OF THE DRAWINGS

[0014] Figure 1 This is a flow chart of a food preservation method provided by the present invention; Figure 2 This is a structural diagram of a food preservation device provided by the present invention; Figure 3 This is a structural diagram of a food preservation device provided by the present invention.

[0015] The purpose, features and advantages of the present invention will be further described with reference to the accompanying drawings and in conjunction with the embodiments. DETAILED DESCRIPTION

[0016] In order to make the purpose, technical solutions and advantages of the present invention more clearly understood, the present invention will be further described in detail below with reference to 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 intended to limit the present invention.

[0017] The present invention will be further described below with reference to the accompanying drawings and specific embodiments.

[0018] Reference Figure 1 As shown, the present invention provides a food preservation method, comprising: Step S101: collecting the respiration intensity data and corruption gas concentration of the target food in the cold chain compartment, matching them with the preservation threshold, and generating food preservation information; Step S201: Collecting the real-time environmental parameters of the cold chain compartment, generating a fresh-keeping instruction based on the food fresh-keeping information and the real-time environmental parameters, and obtaining an air conditioning valve opening instruction and a compressor frequency instruction; Step S301: performing dynamic control operations based on the gas control valve opening instruction and the compressor frequency instruction, and obtaining the comprehensive evaluation index of spoilage bacteria and compressor power consumption data in real time; Step S401: Conduct command feedback analysis based on the comprehensive evaluation index of spoilage bacteria and the compressor power consumption data, construct a strategy, and output a freshness-keeping control strategy.

[0019] Based on the above steps, the detailed process is as follows: Step S101: Collection of respiration intensity data and spoilage gas concentrations of target foods (such as fresh fruits and vegetables) within cold chain vehicles forms the basis for food preservation control. Respiration intensity data is monitored in real time by non-invasive gas sensors, reflecting changes in carbon dioxide and oxygen concentrations produced by food metabolism, reflecting physiological activity. Spoilage gas concentrations are quantitatively analyzed using biosensors or gas detection technology to analyze the metabolic gases of specific spoilage bacteria (such as their respiratory gases, metabolite gases, and gases produced by volatile organic compounds). The collected data is matched against a pre-set preservation threshold database, which contains safe ranges for respiration intensity and spoilage bacteria concentrations for different foods at different preservation stages. This matching process incorporates a machine learning model to dynamically adjust thresholds based on historical data to ensure the accuracy of food preservation information. The resulting food preservation information includes the current food preservation status (e.g., fresh, critical, or spoilage risk) and the desired ideal environmental parameter ranges (e.g., oxygen and carbon dioxide ratios).

[0020] Step S201: Real-time environmental parameter collection covers temperature, humidity, gas composition (oxygen, carbon dioxide, ethylene, etc.), and airflow velocity. A distributed sensor network enables simultaneous monitoring at multiple locations within the vehicle. Target parameters from food preservation information and real-time environmental data are input into the control model, which uses a multi-objective optimization algorithm (such as NSGA-II) to balance food preservation requirements with energy efficiency. The gas control valve opening command calculates the deviation between the current gas composition and the target value and, combined with a PID control algorithm, dynamically adjusts the valve opening to precisely regulate the amount of nitrogen or carbon dioxide injected. The compressor frequency command uses fuzzy logic control to determine the frequency variation based on temperature deviation and the food's respiration heat load, avoiding frequent starts and stops. The generated instruction set is transmitted to the execution device via the communication module, and the instruction parameters and timestamp are recorded to provide a data foundation for subsequent feedback analysis.

[0021] Step S301: The command for the controlled atmosphere valve opening drives a servo motor to adjust the valve opening, changing the gas mixture ratio and suppressing the activity of spoilage bacteria. The compressor frequency command, via a frequency converter, adjusts the refrigeration system output to stabilize the temperature field within the compartment. During execution, a comprehensive spoilage bacteria assessment indicator is calculated by real-time monitoring of the decrease in biomarker concentrations, reflecting the effectiveness of the controlled atmosphere preservation system. Compressor power consumption data is collected by the energy metering module to assess energy efficiency. A sensor network simultaneously monitors changes in environmental parameters after control, verifying the effectiveness of command execution. Data is uploaded to edge computing nodes for short-term trend analysis. If the inhibition rate falls short of expectations or power consumption exceeds the limit, an early warning signal is triggered. Dynamic control operations achieve refined control through a high-frequency "collection-execution-feedback" cycle (e.g., once per minute), ensuring a real-time balance between food preservation and energy consumption.

[0022] Step S401: Comprehensive spoilage bacteria assessment indicators and compressor power consumption data are input into the feedback analysis module, and a reinforcement learning framework is used to construct a policy optimization model. When the inhibition rate meets the standard but the power consumption is too high, the model reduces the compressor frequency weight and prioritizes adjusting the gas conditioning ratio. When the inhibition rate is insufficient, the temperature control priority is increased. Historical instruction data and feedback results form a policy library, and high-frequency effective strategies are extracted through cluster analysis to generate a rule tree for rapid decision-making. The final output of the preservation control strategy consists of two categories: one is real-time adjustment rules (such as "prioritize valve opening and then cooling when carbon dioxide concentration exceeds the threshold"), and the other is long-term optimization suggestions (such as "reduce the compressor frequency by 5% for specific food combinations"). The strategy is updated to the on-board controller via OTA, forming a self-iterating intelligent preservation system, achieving a transition from single-time control to long-term optimization.

[0023] The present invention provides a food preservation method, which collects the respiration intensity and corruption gas concentration of food in real time, and dynamically adjusts the environmental parameters in combination with the preservation threshold, thereby overcoming the shortcomings of the traditional fixed threshold preservation method, significantly improving the food preservation effect, and avoiding the energy waste caused by excessive regulation. Based on the closed-loop feedback analysis of food bioactivity data and cold chain equipment operation data, the refined regulation of environmental parameters is achieved, ensuring the stability of food quality while optimizing the energy efficiency of the equipment. By real-time monitoring of the comprehensive evaluation index of corruption bacteria and the power consumption of the compressor, a dynamic control strategy is constructed to reduce the energy consumption of the refrigeration system while ensuring that the food corruption rate meets the standard, thereby improving the economy and sustainability of cold chain transportation. According to the respiration characteristics of different foods and the growth dynamics of corruption bacteria, the gas conditioning and refrigeration strategies are dynamically adjusted to make the preservation method more flexible, suitable for the transportation needs of different types of fresh food, and improve the versatility and adaptability of the cold chain system. Through automated data collection, analysis and instruction generation, the dependence on manual experience is reduced, the intelligence level of cold chain preservation is improved, operational errors are reduced, and the stability and reliability of the transportation process are improved.

[0024] In one embodiment, the respiration intensity data and corruption gas concentration of target food in the cold chain compartment are collected, and the preservation threshold is matched to generate food preservation information, including: Within the cold chain vehicle, target foods (such as fruits and leafy greens) are sampled at multiple locations. Gas sensors are used at each sampling point to measure the carbon dioxide release rate and ethylene concentration. A respiration fusion algorithm integrates the gas data from multiple locations, eliminating the effects of local environmental fluctuations and outputting respiration intensity data for the target food. Respiration intensity data is measured in units of carbon dioxide released per unit mass of food per unit time.

[0025] The number of sampling points is determined by the carriage volume and food stacking density, with at least three sampling points per cubic meter. Respiratory fusion calculations use a weighted average method, with sampling points closer to the center of the food receiving a higher weight.

[0026] Based on the respiration intensity data, the target food is calibrated for gas concentration to determine the putrefactive gas concentration. A gas sensor array (e.g., semiconductor sensors or electrochemical ammonia sensors) deployed around the food pile monitors the concentration of target putrefactive gases (e.g., biogenic amines and volatile sulfur compounds) in real time. The sensor array covers both food contact and exposed areas. The detection signal is directly converted to putrefactive gas concentration in micrograms per cubic meter using a pre-set putrefactive bacteria calibration curve (established based on metabolic experiments with standard strains).

[0027] The calibration curve associates the respiration intensity data with the metabolic rate of spoilage bacteria. For example, when the respiration intensity exceeds a threshold, the concentration of sulfide produced by the metabolism of spoilage bacteria increases exponentially, and the gas concentration calibration value is corrected through a dynamic regression model.

[0028] The respiratory intensity data and the corruption gas concentration are mapped to threshold intervals according to the preset respiratory intensity threshold range and corruption gas concentration threshold range to obtain the respiratory intensity deviation and corruption gas concentration deviation. The respiratory intensity threshold range and the corruption bacteria concentration threshold range are preset according to the food type. For example, the respiratory intensity threshold range of strawberries is A and B units, and the corruption gas concentration threshold range is C and D micrograms per cubic meter. The deviation is calculated as the percentage of the difference between the actual data and the threshold. If the data exceeds the upper limit of the threshold, the deviation is positive; if it is lower than the lower limit, it is negative. Among them, a respiratory intensity deviation exceeding +10% triggers a high metabolism alarm, and the deviation grade is defined as: ±5% is normal, ±(5%~20%) is a mild deviation, and more than ±20% is a severe deviation.

[0029] A two-dimensional deviation matrix is ​​constructed based on the respiration intensity deviation and the spoilage gas concentration deviation, and dynamic weighting is applied to generate a comprehensive freshness index. This dynamic weighting is adjusted based on the food type: respiration-sensitive foods (such as leafy greens) are weighted 70% for respiration intensity and 30% for spoilage bacteria; the opposite is true for microbially sensitive foods (such as meat). The comprehensive freshness index is calculated through weighted summation, ranging from 0 to 100, with higher values ​​indicating poorer freshness.

[0030] A weighted distribution table is built into the system and automatically matched by food code. The comprehensive freshness index is updated every 30 minutes, and an alert is triggered if it exceeds 60 three times in a row.

[0031] The comprehensive freshness index is evaluated based on a preset index grading table to generate food freshness information, including the level of controlled atmosphere (CA) requirement and the level of antibacterial requirement. The comprehensive freshness index is entered into the preset index grading table to match the CA requirement and antibacterial requirement levels: an index of 0-30 represents Level 1, requiring only basic CA; 31-60 represents Level 2, requiring elevated oxygen concentration to inhibit respiration; and 61 and above represent Level 3, requiring a combination of CA and antimicrobial treatment. Antibacterial requirement levels are categorized as low (deviation ±5%), medium (±5%-15%), and high (±15% and above).

[0032] Among them, the atmosphere control demand level corresponds to different gas ratio formulas (such as the oxygen concentration for secondary preservation is 5%~8%), and the antibacterial demand level triggers ultraviolet disinfection or ozone sterilization.

[0033] This embodiment uses multi-position sampling and respiratory fusion calculations to accurately obtain the overall respiratory intensity data of the target food in the cold chain compartment, avoid measurement errors caused by local environmental fluctuations, and significantly improve the reliability of respiratory metabolic activity assessment. Surface microbial swab sampling is combined with quantitative analysis technology to directly calibrate the concentration of corruption gases, making microbial contamination detection more accurate and reducing the lag of traditional culture methods. The deviation calculation based on the preset threshold interval mapping can intuitively reflect the degree of abnormality in the food preservation state, facilitating the rapid identification of high-risk samples. The two-dimensional deviation matrix is ​​combined with dynamic weight distribution to comprehensively consider the effects of respiratory intensity and microbial contamination, so that the comprehensive preservation index is more in line with the corruption characteristics of different foods and improves the targeted nature of the assessment.

[0034] In one embodiment, real-time environmental parameters of the cold chain compartment are collected, and preservation instructions are generated based on food preservation information and real-time environmental parameters to obtain gas control valve opening instructions and compressor frequency instructions, including: The multi-source data recognition module collects real-time environmental parameters using temperature, humidity, oxygen, and carbon dioxide concentration sensors deployed within the vehicle cabin. The sensors transmit raw signals at a fixed sampling interval to a data processing unit, which filters out noise and normalizes the raw signals to separate independent temperature, humidity, oxygen, and carbon dioxide concentration data. Temperature data represents the Celsius temperature of the air inside the cabin, humidity data represents the relative humidity in percentage, and oxygen and carbon dioxide concentration data represent gas compositions in volume percentages. These four processed data are stored in an environmental parameter database.

[0035] The parameter importance grading module uses cargo attribute records from the food preservation information database, which contain biological characteristic parameters such as cargo type, maturity, and respiration intensity. When the cargo is a fruit with respiratory climacteric, such as strawberries, the system classifies oxygen concentration data and carbon dioxide concentration data as first-level key parameters, temperature data as a second-level important parameter, and humidity data as a third-level auxiliary parameter. The grading rules are based on the weight of the impact of different environmental factors on the physiological activities of the cargo. Cargo with vigorous respiration is given a higher priority for gas concentration, while temperature-sensitive cargo has its temperature parameter level adjusted. The environmental parameter grading results generate an evaluation framework that includes the weight coefficients of each parameter.

[0036] The parameter combination assessment module uses a weighted fusion algorithm to process the environmental parameter classification results. Level 1 key parameters are assigned a weight of 0.4, level 2 important parameters a weight of 0.3, and level 3 auxiliary parameters a weight of 0.3. When temperature data exceeds the set threshold, an abnormal state is flagged, and the temperature parameter weight is automatically increased to 0.5. Each parameter is normalized and multiplied by its corresponding weight. The sum of the results yields a comprehensive environmental assessment value ranging from 0 to 1. This value reflects the degree to which the cabin environment deviates from the ideal freshness-preserving state, with higher values ​​indicating more significant environmental anomalies.

[0037] The dynamic matching module maps the comprehensive environmental assessment value to the index dimensions of the gas control valve opening reference table and the compressor frequency reference table. The reference table divides the assessment value intervals into 0.1 intervals, and each interval corresponds to a preset valve body opening percentage and compressor operating frequency. When the comprehensive environmental assessment value is 0.63, the reference command within the 0.6-0.7 interval is matched, generating an initial gas control valve opening command of 58% and an initial compressor frequency command of 45Hz. The reference table data is derived from the optimized parameter set of historical preservation experiments, establishing a nonlinear correspondence between the assessment value and the equipment control quantity.

[0038] The respiratory compensation correction module analyzes the respiratory entropy parameter in the food preservation information, which represents the rate at which a unit mass of goods consumes oxygen and produces carbon dioxide per unit time. For example, when the respiratory entropy of strawberries reaches 5.2 ml / kg·h, a 12% opening compensation is added to the initial gas control valve opening instruction to accelerate gas exchange. The spoilage bacteria inhibition correction module detects the predicted total bacterial count on the surface of the goods. When the predicted value exceeds 1×10 4 When the CFU / g is reached, an 8Hz frequency increment is added to the initial compressor frequency command to enhance the cooling intensity and inhibit the proliferation of microorganisms. The modified opening command and the modified frequency command form the intermediate control variable.

[0039] The command consistency coordination module verifies the physical feasibility of the revised command. When the revised opening command reaches the maximum opening limit of the gas control valve, the adjustment range of the revised frequency command is simultaneously reduced to maintain the dynamic balance between the gas circulation and the refrigeration system. This coordination process uses a constraint satisfaction algorithm to ensure that the final output of the gas control valve opening command does not exceed the rated operating range of the equipment and that the compressor frequency command meets the motor's safe operating specifications. The gas control valve opening command and the compressor frequency command are transmitted to the execution module to complete the closed-loop control.

[0040] This embodiment ensures the measurement accuracy and data independence of various environmental parameters through the independent collection and processing of temperature, humidity, and gas concentration by a multi-source data identification module, eliminating cross-interference caused by multi-sensor signal coupling. A parameter importance grading mechanism based on the biological characteristics of goods dynamically adjusts the control priority of different environmental factors, implements differentiated preservation strategies for goods that require respiration or are susceptible to spoilage, and improves the targeted nature of environmental regulation. The parameter combination evaluation module introduces a weighted adaptive mechanism that automatically strengthens the control weights of key factors when parameters are abnormal, enhancing the system's responsiveness to sudden environmental fluctuations and maintaining the dynamic accuracy of the evaluation results.

[0041] In one embodiment, dynamic control operations are performed based on the gas control valve opening instruction and the compressor frequency instruction, and the comprehensive evaluation index of spoilage bacteria and compressor power consumption data are obtained in real time, including: The core goal of regulating the opening of a gas control valve is to precisely adjust the valve opening according to control commands to control the gas exchange rate within the cold chain compartment. This process involves multiple steps, including command reception, actuator driving, feedback detection, and closed-loop control.

[0042] The control system receives a command signal for the air control valve's opening from a host computer or an automatic control algorithm. This command typically expresses the target valve position as a percentage or opening value. Upon receiving this command, an actuator (such as an electric valve or stepper motor) begins to drive the mechanical components of the air control valve to adjust the valve's opening and closing. During this adjustment process, a position sensor (such as a potentiometer or encoder) on the valve continuously monitors the actual valve opening and provides real-time feedback to the control system.

[0043] The control system compares the actual valve opening data fed back with the target opening command. If there is a deviation, a control algorithm (such as PID control) calculates a correction and adjusts the actuator's action to gradually bring the valve closer to the target opening. This process may involve multiple fine-tuning cycles until the actual valve opening stabilizes near the set value. Ultimately, the actual gas control valve opening data is recorded and transmitted to the monitoring system for subsequent dynamic monitoring of gas composition.

[0044] The purpose of compressor frequency regulation is to optimize the refrigeration system's cooling capacity by adjusting the compressor's operating frequency to maintain the target temperature within the cold chain vehicle. This process involves command calculation, variable frequency drive, operating status monitoring, and dynamic adjustment.

[0045] The control system calculates the required operating frequency command for the compressor based on the vehicle's set temperature and current temperature distribution data. This command is sent to the inverter, which adjusts the power frequency input to the compressor motor to change the compressor's speed. This change in compressor speed directly affects the refrigerant circulation rate, thereby regulating the refrigeration system's cooling efficiency.

[0046] During operation, a current sensor or speed sensor monitors the compressor's actual operating frequency in real time and feeds this data back to the control system. If the actual frequency differs from the commanded frequency, the control system analyzes the cause of the deviation (such as load changes or voltage fluctuations) and adjusts the inverter output to stabilize the compressor at the target frequency. Ultimately, the actual compressor frequency data is recorded and used for subsequent temperature monitoring and power consumption calculations.

[0047] Dynamic gas composition monitoring is the real-time monitoring of the gas environment within cold chain vehicles to ensure it meets food preservation requirements. This process involves sensor placement, data acquisition, signal processing, and anomaly detection.

[0048] Multiple gas sensors (such as oxygen and carbon dioxide sensors) are positioned throughout the vehicle cabin to comprehensively monitor gas distribution. These sensors continuously collect gas concentration data within the cabin and transmit analog signals to signal conditioning circuits. The signal conditioning circuits filter, amplify, and perform analog-to-digital conversion on the raw data to improve its accuracy and stability.

[0049] The control system further analyzes the processed digital signals, calculating the average concentration or spatial distribution of each gas. If a gas concentration exceeds a preset range (e.g., oxygen is too high or carbon dioxide is too low), the system triggers an alarm and may adjust the gas control valve opening to optimize the gas environment. Real-time gas composition data is stored and used for subsequent analysis of spoilage bacteria inhibition effectiveness.

[0050] The goal of dynamic temperature distribution monitoring is to ensure temperature uniformity within cold chain compartments and prevent local overheating or overcooling that can affect food preservation. This process involves multi-point temperature collection, data fusion, and dynamic adjustment.

[0051] Multiple temperature sensors (such as thermocouples or digital temperature sensors) are deployed throughout the vehicle cabin to collect real-time temperature data from different areas. The collected analog signals are then converted to digital signals through signal conditioning circuits (such as filtering and amplification) for processing by the control system.

[0052] The control system integrates and analyzes multiple temperature data sources to generate a temperature distribution map within the vehicle cabin. If a localized temperature anomaly is detected (e.g., a high temperature near the cabin wall), the system adjusts the compressor operating frequency or optimizes airflow distribution to improve temperature uniformity. Real-time temperature data is recorded and used to evaluate cooling performance and optimize energy consumption.

[0053] The purpose of spoilage bacteria inhibition analysis is to evaluate the current gas environment's ability to inhibit food spoilage bacteria and optimize preservation strategies based on the results. This process involves calculating microbial growth models, evaluating inhibition rates, and dynamically adjusting them.

[0054] The control system calculates the current environment's effectiveness against spoilage bacteria based on real-time gas composition data (such as oxygen and carbon dioxide concentrations) and a pre-defined microbial growth model. This model compares microbial growth rates under different gas concentrations to assess the activity of spoilage bacteria. If the inhibition is less than expected (e.g., excessive oxygen concentration leading to spoilage bacteria activation), the system can adjust the controlled atmosphere strategy, such as lowering the oxygen concentration or increasing the carbon dioxide concentration, to enhance preservation. This comprehensive spoilage bacteria assessment index is recorded and used to evaluate the overall performance of the preservation system.

[0055] The purpose of compressor power consumption calculation is to monitor the energy consumption of the refrigeration system in real time and provide data support for energy-saving optimization. This process involves power measurement, energy consumption estimation, and operation strategy adjustment.

[0056] The control system estimates the compressor's real-time power consumption based on its actual operating frequency and the compressor's power-frequency characteristic curve. If the compressor is equipped with an energy meter, it can directly measure the input power; otherwise, the system indirectly calculates energy consumption using a frequency-power relationship model.

[0057] Power consumption data can be used to evaluate system energy efficiency and, if necessary, adjust operating strategies (such as optimizing start-stop cycles or adjusting target temperatures) to reduce energy consumption. Accumulated power consumption data can be used for long-term energy consumption analysis and cost calculations.

[0058] This embodiment ensures that the gas exchange volume accurately matches the preservation requirements through closed-loop control of the gas control valve opening, combined with real-time feedback and dynamic adjustment, to avoid fluctuations in the gas environment caused by valve opening deviations, thereby improving the preservation effect. The compressor frequency is adjusted based on the dynamic monitoring data of temperature distribution to match the refrigeration capacity with actual needs, reduce ineffective energy consumption, avoid local temperature anomalies, improve refrigeration uniformity and extend equipment life. By collaboratively monitoring gas components through multiple sensors, the gas control strategy is dynamically adjusted to effectively inhibit the growth of spoilage bacteria, extend the food shelf life, and reduce the risk of food spoilage due to gas concentration imbalance. The compressor power consumption is calculated in real time and the operating strategy is optimized in combination with temperature requirements to reduce energy waste while ensuring the preservation effect, thereby improving the overall energy efficiency of the cold chain system. Integrate gas, temperature and power consumption data to achieve multi-parameter collaborative control, reduce the need for manual intervention, and make the preservation process more automated and reliable.

[0059] In one embodiment, command feedback analysis is performed based on the comprehensive evaluation index of spoilage bacteria and compressor power consumption data, and a strategy is constructed to output a freshness control strategy, including: Dynamic Trend Analysis of Spoilage Bacteria Comprehensive Assessment Indicators and Grading of Inhibition Effectiveness: Dynamic trend analysis of the comprehensive assessment indicators for spoilage bacteria first requires real-time continuous monitoring data of these indicators during cold chain transportation. This data is typically acquired from sensors or detection equipment at regular intervals (e.g., every 5 minutes). After acquiring the data, the system calculates the temporal trend of the comprehensive assessment indicators for spoilage bacteria and extracts their slope. This slope reflects the rate of increase or decrease in the inhibition rate and is a key indicator for determining the effectiveness of current antibacterial measures.

[0060] When extracting the slope of change, the system uses time series analysis to compare the inhibition rate values ​​at different time points and calculate the strength of the linear change trend. If the slope is large and positive, it indicates that the inhibition rate is rapidly increasing and the antibacterial effect is significant; if the slope is negative and the absolute value is large, it indicates that the inhibition rate continues to decline and the antibacterial measures may be ineffective. Based on the numerical range of the slope, the system divides the inhibition effect into different levels, such as level I (excellent), level II (medium), and level III (poor). The grading rules need to be pre-defined in combination with the actual application scenario. For example, when the slope exceeds a certain threshold, it is judged as a high effect level, otherwise it is a low level.

[0061] Once the spoilage bacteria inhibition level is generated, it will serve as an important basis for subsequent strategy adjustments. For example, if the inhibition level is Level III, it indicates that the current environment is insufficiently controlling spoilage bacteria and requires immediate optimization of the gas conditioning parameters or compressor operating status.

[0062] Compressor power consumption data: Power consumption fluctuation feature extraction and stability assessment: Processing compressor power consumption data requires obtaining real-time or historical power consumption data from the energy consumption monitoring system and performing segmented analysis based on time windows. For example, within a 15-minute time window, the fluctuations in compressor power consumption are analyzed. The core goal of power consumption fluctuation feature extraction is to identify abnormal power consumption fluctuations or stability issues, such as frequent power peaks or persistent inefficient operation.

[0063] The system quantifies fluctuations by calculating the standard deviation and peak-to-valley difference of power consumption data. The standard deviation reflects the degree of dispersion in the power consumption data; a larger standard deviation indicates greater fluctuations. The peak-to-valley difference reflects the difference between the maximum and minimum power consumption within the same time window; a larger difference indicates more unstable compressor load fluctuations. Combining these two indicators, the system evaluates the stability of compressor energy consumption and generates a stability index (such as high stability, medium stability, or low stability). For example, if the power consumption standard deviation within a certain time window is small and the peak-to-valley difference is below a threshold, it is judged as high stability; otherwise, it is classified as low stability.

[0064] The compressor power consumption stability index not only reflects the current operating status of the equipment, but also indirectly reveals problems such as equipment aging or abnormal load, providing energy consumption constraints for subsequent strategy optimization.

[0065] Joint Analysis of Spoilage Bacteria Inhibition Level and Compressor Power Consumption Stability: The goal of this joint analysis is to establish a correlation between spoilage bacteria inhibition effectiveness and compressor energy consumption. The system combines inhibition levels (e.g., I / II / III) with stability indicators (e.g., high / medium / low stability) to create multi-dimensional correlation scenarios. For example, if the inhibition level is III and the stability is low, the current strategy has serious issues with both bacteria inhibition and energy consumption control. If the inhibition level is I and the stability is high, the system is in an ideal state.

[0066] Using preset rules or machine learning models, the system categorizes different scenarios and defines their optimization priorities. For example, a combination of "high inhibition level + low and stable energy consumption" might require maintaining the current strategy, while a combination of "low inhibition level + high and fluctuating energy consumption" might prioritize adjusting the gas control valve opening. The combined analysis results generate power consumption inhibition association types, such as "high efficiency, high consumption" and "low efficiency, low consumption," to guide targeted optimization of subsequent strategies.

[0067] Historical command retrospective analysis of gas control valve opening commands and compressor frequency commands: This analysis involves retrieving the execution records of gas control valve opening commands and compressor frequency commands over a period of time from the control system and correlating and comparing them with the corresponding antibacterial levels and energy consumption stability indicators. For example, this analysis can examine whether an increase in gas control valve opening leads to a simultaneous increase in the comprehensive evaluation index for spoilage bacteria, or whether power consumption fluctuations increase after adjusting the compressor frequency.

[0068] By comparing the execution results of historical commands, inefficient or conflicting command combinations can be identified. For example, a compressor frequency increase may temporarily improve antibacterial effectiveness but significantly reduce power consumption stability. Based on this, the system calculates the optimization space for the commands, such as determining a reasonable compensation range for the air control valve opening (e.g., the opening needs to be increased by 5%-10%) or a step size limit for compressor frequency adjustment (e.g., a single frequency adjustment should not exceed 2Hz).

[0069] The generation of instruction optimization parameters needs to comprehensively consider the statistical laws of historical data and the priority of current correlation relationships to ensure that the adjustment plan can not only improve the antibacterial effect but also avoid causing new energy consumption problems.

[0070] Dynamic Optimization of the Gas Control Valve Opening Adjustment Range and Compressor Frequency Adjustment Step: Based on command optimization parameters, the system dynamically calculates the adjustment range of the gas control valve opening command. For example, if historical data shows that a 5% increase in opening significantly improves the antibacterial level, the system will set the current opening command adjustment range to ±5% of the base value. At the same time, physical limitations of the equipment (such as the maximum opening of a gas control valve of 90°) must be considered to avoid over-limit operation. Optimization of the compressor frequency command focuses on fine-tuning the adjustment step size. For example, if excessively large frequency steps can easily lead to power consumption fluctuations, the system will reduce the original step size (such as 5Hz) to a smaller step size (such as 2Hz) and adjust the frequency in stages to balance antibacterial requirements with energy consumption stability. The frequency adjustment step size must be set in conjunction with the compressor's minimum adjustment accuracy and stability threshold to ensure that the amplitude of each adjustment is controllable.

[0071] Multi-objective control strategy combination and strategy screening: After determining the gas control valve opening adjustment range and compressor frequency adjustment step size, the system needs to generate multiple possible control strategy combinations. For example: an aggressive antibacterial strategy: prioritizes antibacterial effectiveness with maximum opening adjustment and a higher frequency step size; a balanced strategy: takes an intermediate value between opening and frequency adjustment, balancing antibacterial effect and energy consumption; and an energy-saving strategy: maintains the current antibacterial level with the minimum adjustment range, prioritizing reducing power consumption fluctuations.

[0072] When screening strategies, each strategy is simulated and evaluated based on pre-set priority rules (e.g., prioritizing antibacterial effect over energy consumption), predicting changes in antibacterial levels and stability indicators after implementation. Strategies that could cause equipment to exceed limits or deteriorate stability are also excluded. For example, if a strategy improves antibacterial levels but causes frequent compressor starts and stops, it is marked as a high-risk option. Ultimately, the system selects the strategy with the highest overall score as the output freshness control strategy.

[0073] This embodiment dynamically analyzes the changing trends of the comprehensive evaluation indicators of spoilage bacteria and generates inhibition levels, which can perceive the control effect of the preservation environment on microorganisms in real time, thereby avoiding the risk of food spoilage due to delayed antibacterial effects, and reducing energy waste caused by excessive inhibition. Combined with the extraction of compressor power consumption fluctuation characteristics and stability evaluation, it can accurately identify inefficient or abnormal conditions in equipment operation, ensure the balance between energy consumption optimization and equipment life, and reduce the overall energy consumption cost in cold chain transportation. By jointly analyzing the inhibition level and power consumption stability index and establishing a multi-dimensional correlation relationship, it can dynamically adjust the gas control valve and compressor instructions for different scenarios, and achieve dual-objective coordinated optimization of energy consumption and antibacterial while ensuring the preservation effect.

[0074] In one embodiment, a combined analysis is performed based on the corruption bacteria inhibition level and the compressor power consumption stability index to obtain a power consumption inhibition correlation relationship, including: The steps for generating the quantitative value of spoilage bacteria inhibition are as follows: (1) the microbial detection unit collects the total number of colonies on the surface of the goods in real time and uses the fluorescence detection method to obtain accurate CFU / g data; (2) Preset rules for the classification of spoilage bacteria inhibition levels: Level I corresponds to a total colony count of ≤1×10 4 CFU / g, level II is 1×10 4 ~5×10 4 CFU / g, level III>5×10 4 CFU / g; (3) The quantization encoder uses a piecewise linear conversion algorithm to map the original detection value to the range of 0-100, where level I corresponds to 0-40, level II corresponds to 41-70, and level III corresponds to 71-100; (4) Smoothing the boundary values, using an S-shaped curve transition within the range of ±5% of the critical point, to avoid sudden changes in the quantitative value. The output of this step provides standardized input for subsequent collaborative analysis, and the quantitative value range directly affects the resolution of subsequent curve construction.

[0075] The power consumption stability value generation step converts the compressor power fluctuation characteristics into a quantifiable indicator. The specific implementation steps are: (1) The power sensor collects the three-phase current and voltage data of the compressor at a sampling frequency of 1 Hz and calculates the instantaneous power value; (2) The sliding window statistics unit sets a 10-minute time window, and the standard deviation σ is calculated for 600 power samples within the window. (3) The indicator value converter uses an inverse linear mapping function, setting the maximum output value to 100 when σ≤5W, deducting 20 points for every 5W increase in σ, and returning to zero when σ≥25W. (4) A filtering mechanism is set for sudden power spikes, and abnormal points exceeding three times the average power value are automatically eliminated and the standard deviation is recalculated. The power consumption stability value generated in this step reflects the smooth operation of the equipment, and its numerical accuracy directly affects the morphological accuracy of the subsequent coordinated change curve.

[0076] The specific steps for generating the power consumption co-variation curve are as follows: (1) the data alignment module matches the corruption bacteria inhibition quantization value with the power consumption stability value of the same period according to the timestamp to form a two-dimensional coordinate point set; (2) the cubic spline interpolation algorithm sets the natural spline condition with the second-order derivative of the boundary equal to zero, and determines the spline coefficient by solving the tridiagonal matrix equation system; (3) the Mahalanobis distance method is used for outlier detection, and data points that deviate from the main distribution area by more than 2.5σ are eliminated; (4) the Savitzky-Golay filter is used for curve smoothing, with the window width set to 11 data points and the polynomial order to 3. The generated curve contains the continuous functional relationship between the quantization value and the stability value, and its mathematical expression serves as the basic input for subsequent segmentation processing.

[0077] The steps for generating the power consumption suppression segmentation relationship table are as follows: (1) The curvature calculation module samples along the curve with a 0.5% quantization value step size and calculates the discrete curvature value using differential geometry formulas; (2) The dynamic threshold setter automatically adjusts the segmentation threshold according to the curvature distribution histogram, and the first 20% high curvature area is set as the mutation segment; (3) The segmentation merging algorithm merges adjacent similar slope intervals, and continuous segments with a slope difference of less than 10% are merged into the same interval; (4) The table generator records the start and end quantization values, average curvature, maximum slope, and stability extreme value of each segment. The segmentation relationship table establishes a classification of control modes under different spoilage bacteria inhibition intensities, providing a structural framework for mapping relationship generation.

[0078] The implementation steps of the power consumption stability mapping relationship are as follows: (1) The box plot analyzer calculates the 25%, 50%, and 75% quantiles of the stability values ​​within each segmented interval; (2) The fluctuation range calculator determines the upper and lower limits of the stability of each spoilage bacteria inhibition quantification value interval, and uses the Tukey method to identify abnormal fluctuation boundaries; (3) The mapping rule engine sets the interval as a high fluctuation risk area when the interquartile range exceeds 1.5 times the global median; (4) The relationship table optimization module applies data smoothing technology to perform gradient transition processing on the boundary values ​​of adjacent intervals. The generated mapping relationship table establishes a statistical association between the inhibition level and power consumption stability, providing data support for threshold setting.

[0079] The optimal control strategy is determined by the step of generating parameters for power consumption priority matching. The specific implementation steps are as follows: (1) The weighted least squares fitter assigns different weights to data points of different suppression levels, and the weight coefficient of level III data is set to 3 times that of level I; (2) The goodness of fit evaluation module calculates the R² value, and when R² is less than 0.9, it triggers local polynomial regression refitting; (3) The threshold extractor selects the stability threshold from the change point of the fitting curve derivative to ensure that the threshold point is in the inflection point area of ​​the curve; (4) The confidence interval calculator uses the Bootstrap method to repeat sampling 1000 times to determine the 95% confidence range of the threshold parameter. Prioritizing matching parameters achieves the optimal balance of the control strategy, and its parameter quality directly affects the control efficiency of the final correlation relationship.

[0080] The power consumption suppression relationship construction step integrates the multi-dimensional analysis results to form an executable knowledge base. The specific implementation steps are: (1) The entity relationship modeler defines the foreign key constraints between curve parameters, segment tables, and matching parameters; (2) The data fusion module applies Kalman filtering technology to eliminate time series deviations between different data sources; (3) The index builder creates a B+ tree index based on the spoilage bacteria inhibition quantification value, supporting millisecond-level data retrieval; (4) The integrity check unit generates a SHA-256 hash value to ensure consistency during data storage and transmission. The resulting power consumption suppression relationship forms the decision-making core of the closed-loop control system, and its structural design ensures fast response and high reliability during real-time control.

[0081] This embodiment uses a spoilage bacteria inhibition quantification value generation mechanism to convert the degree of microbial contamination into a standardized numerical code, establishing a unified assessment benchmark and improving the quantitative comparability of different cargo spoilage states. The power consumption stability value generation process utilizes dynamic filtering and inverse mapping techniques to accurately characterize the fluctuation characteristics of compressor operation, providing a highly sensitive indicator for equipment status monitoring. The power consumption inhibition synergistic variation curve construction method reveals the correlation between microbial inhibition requirements and equipment energy consumption, breaking through the limitations of traditional single-dimensional control strategies and enabling multi-parameter coupling analysis. A power consumption inhibition segmentation relationship table identifies key transition intervals for environmental control, divides optimized control domains for different spoilage levels, and enhances the system's adaptability to complex operating conditions. The power consumption inhibition stability mapping relationship establishes a quantitative correspondence between microbial inhibition intensity and equipment fluctuation tolerance, providing a dynamic constraint boundary for the control strategy and balancing preservation effectiveness with energy consumption costs. The priority matching parameter generation mechanism utilizes weighted regression and threshold optimization techniques to ensure the prioritized allocation of control resources under high spoilage risk conditions, improving the system's response efficiency under emergency conditions.

[0082] Reference Figure 2 As shown, the present invention further provides a food preservation device, which is applied to any of the above-mentioned food preservation methods, comprising: The acquisition module is used to collect the respiration intensity data and corruption gas concentration of the target food in the cold chain compartment, match the preservation threshold, and generate food preservation information; Analysis module: The analysis module is used to collect real-time environmental parameters of the cold chain compartment, generate preservation instructions based on food preservation information and real-time environmental parameters, and obtain gas control valve opening instructions and compressor frequency instructions; A correlation module is used to perform dynamic control operations based on the gas control valve opening instruction and the compressor frequency instruction, and obtain the comprehensive evaluation index of spoilage bacteria and the compressor power consumption data in real time; The processing module is used to perform instruction feedback analysis based on the comprehensive evaluation indicators of spoilage bacteria and the compressor power consumption data, and to construct strategies and output freshness control strategies.

[0083] Reference Figure 3 As shown, the present invention also provides a food preservation device, comprising: Memory, used to store programs; The processor is used to execute the program to implement each step of any one of the above-mentioned food preservation methods.

[0084] In this embodiment, the processor and memory may be connected via a bus or other means. The memory may include volatile memory, such as random access memory; the memory may also include non-volatile memory, such as read-only memory, flash memory, hard disk, or solid-state drive. The processor may be a general-purpose processor, such as a central processing unit, a digital signal processor, an application-specific integrated circuit, or one or more integrated circuits configured to implement the embodiments of the present invention.

[0085] The present invention also provides a storage medium storing computer instructions, wherein the computer instructions are used to enable a computer to execute any of the above methods.

[0086] It should be noted that, those skilled in the art will clearly understand that, for the sake of convenience and brevity of description, the specific working processes of the above-described system and each module can refer to the corresponding processes in the aforementioned method embodiments and will not be repeated here.

[0087] The above description is only a preferred embodiment of the present invention and does not limit the patent scope of the present invention. Any equivalent structure or equivalent process transformation made by using the contents of the present invention description and drawings, or directly or indirectly applied in other related technical fields, are also included in the patent protection scope of the present invention.

Claims

1. A food preservation method, characterized in that: include: Collect the respiration intensity data and corruption gas concentration of target food in the cold chain compartment, match them with the preservation threshold, and generate food preservation information; Collecting real-time environmental parameters of the cold chain compartment, generating a preservation instruction based on the food preservation information and the real-time environmental parameters, and obtaining an air conditioning valve opening instruction and a compressor frequency instruction; Performing dynamic control operations based on the gas control valve opening instruction and the compressor frequency instruction, and obtaining a comprehensive evaluation index of spoilage bacteria and compressor power consumption data in real time; According to the comprehensive evaluation index of spoilage bacteria and the power consumption data of the compressor, command feedback analysis is performed, and a strategy is constructed to output a freshness-keeping control strategy.

2. The food preservation method according to claim 1, characterized in that: The method of collecting the respiration intensity data and corruption gas concentration of the target food in the cold chain compartment and matching the preservation threshold to generate food preservation information includes: Sampling the target food at multiple locations to obtain carbon dioxide release rate and ethylene concentration, and performing respiratory fusion calculation to obtain respiratory intensity data; Calibrate the gas concentration of the target food according to the respiration intensity data to obtain the corruption gas concentration; Performing threshold interval mapping on the respiratory intensity data and the corrupt gas concentration according to a preset respiratory intensity threshold range and a corrupt gas concentration threshold range to obtain a respiratory intensity deviation and a corrupt gas concentration deviation; A two-dimensional deviation matrix is ​​constructed according to the respiratory intensity deviation and the corruption gas concentration deviation, and dynamic weight distribution is performed to obtain a comprehensive freshness preservation index; The comprehensive freshness-keeping index is evaluated according to a preset index grading table to generate food freshness-keeping information including a modified atmosphere requirement level and an antibacterial requirement level.

3. The food preservation method according to claim 1, characterized in that: The collecting of the real-time environmental parameters of the cold chain compartment, generating a preservation instruction according to the food preservation information and the real-time environmental parameters, and obtaining an air conditioning valve opening instruction and a compressor frequency instruction, includes: Performing multi-source data recognition on the real-time environmental parameters to obtain temperature data, humidity data, oxygen concentration data, and carbon dioxide concentration data; performing parameter importance grading on the temperature data, the humidity data, the oxygen concentration data, and the carbon dioxide concentration data according to the food preservation information, and generating an environmental parameter grading result; Performing parameter combination evaluation on the temperature data, the humidity data, the oxygen concentration data, and the carbon dioxide concentration data based on the environmental parameter classification result to obtain a comprehensive environmental evaluation value; Dynamically matching a preset gas control valve opening reference table and a compressor frequency reference table based on the comprehensive environmental assessment value to generate an initial gas control valve opening instruction and an initial compressor frequency instruction; According to the food preservation information, breathing compensation correction and spoilage bacteria inhibition correction are performed on the initial gas control valve opening instruction and the initial compressor frequency instruction respectively to obtain the gas control valve opening instruction and the compressor frequency instruction.

4. The food preservation method according to claim 1, characterized in that: The dynamic control operation is performed based on the gas control valve opening instruction and the compressor frequency instruction, and the comprehensive evaluation index of spoilage bacteria and compressor power consumption data are obtained in real time, including: Performing air-control valve opening adjustment processing on the air-control valve opening instruction to obtain actual air-control valve opening data; performing compressor frequency adjustment processing on the compressor frequency instruction to obtain actual compressor frequency data; Dynamically monitor the gas composition of the cold chain compartment according to the actual gas control valve opening data to obtain real-time gas composition data; Dynamically monitor the temperature distribution of the cold chain compartment according to the actual compressor frequency data to obtain real-time temperature distribution data; Analyzing the growth inhibition effect of spoilage bacteria in the cold chain compartment based on the real-time gas composition data to obtain a comprehensive evaluation index of the spoilage bacteria; The operating power consumption of the compressor of the cold chain compartment is calculated based on the actual compressor frequency data to obtain the compressor power consumption data.

5. The food preservation method according to claim 1, characterized in that: The command feedback analysis is performed based on the comprehensive evaluation index of the spoilage bacteria and the compressor power consumption data, and a strategy is constructed to output a freshness-keeping control strategy, including: Performing a dynamic trend analysis on the comprehensive evaluation index of spoilage bacteria, extracting a change slope of the comprehensive evaluation index of spoilage bacteria, and grading the inhibition effect according to the change slope of the comprehensive evaluation index of spoilage bacteria to generate a spoilage bacteria inhibition grade; Extracting power consumption fluctuation characteristics from the compressor power consumption data, and performing power consumption stability evaluation to generate a compressor power consumption stability index; A correlation relationship between power consumption inhibition is obtained by performing a joint analysis based on the spoilage bacteria inhibition level and the compressor power consumption stability index; Performing a historical instruction backtracking analysis on the gas control valve opening instruction and the compressor frequency instruction according to the power consumption suppression correlation relationship, and performing instruction optimization space calculation to generate instruction optimization parameters; Calculating the dynamic adjustment range of the gas control valve opening instruction according to the instruction optimization parameter to generate the gas control valve opening adjustment interval, and performing frequency step optimization on the compressor frequency instruction to obtain the compressor frequency adjustment step length; A multi-objective control strategy combination is performed based on the gas control valve opening adjustment interval and the compressor frequency adjustment step, and strategy screening is performed to output the freshness preservation control strategy.

6. The food preservation method according to claim 1, characterized in that: The power consumption inhibition correlation relationship is obtained by performing a joint analysis based on the spoilage bacteria inhibition level and the compressor power consumption stability index, including: performing level quantization encoding on the spoilage bacteria inhibition level to generate a spoilage bacteria inhibition quantization value; Constructing a synergistic change trend curve based on the spoilage bacteria inhibition quantification value and the compressor power consumption stability index to obtain a synergistic change curve for power consumption inhibition; Segmenting the power consumption suppression coordinated change curve into change intervals to obtain a power consumption suppression segmentation relationship table; Calculate the power consumption stability fluctuation range corresponding to different suppression levels according to the power consumption suppression segment relationship table, and generate a power consumption suppression stability mapping relationship; Determining a power consumption stability threshold corresponding to a suppression level according to the power consumption stability suppression mapping relationship, and performing priority trend fitting to obtain a power consumption suppression priority matching parameter; The power consumption suppression correlation relationship is obtained by performing association construction based on the power consumption suppression coordinated change curve, the power consumption suppression segment relationship table and the power consumption suppression priority matching parameter.

7. A food preservation device, characterized in that: The food preservation method according to any one of claims 1 to 6 comprises: A collection module is used to collect the respiration intensity data and corruption gas concentration of the target food in the cold chain compartment, match the preservation threshold value, and generate food preservation information; An analysis module, the analysis module is used to collect real-time environmental parameters of the cold chain compartment, generate a preservation instruction based on the food preservation information and the real-time environmental parameters, and obtain an air conditioning valve opening instruction and a compressor frequency instruction; a correlation module, the correlation module being configured to perform dynamic control operations based on the gas control valve opening instruction and the compressor frequency instruction, and to obtain a comprehensive evaluation index of spoilage bacteria and compressor power consumption data in real time; The processing module is used to perform instruction feedback analysis based on the comprehensive evaluation index of spoilage bacteria and the compressor power consumption data, and to construct a strategy to output a freshness-keeping control strategy.

8. A food preservation device, characterized in that: include: Memory, used to store programs; A processor is used to execute the program to implement each step of a food preservation method as described in any one of claims 1 to 6.

9. A storage medium, characterized in that: Computer instructions are stored, and the computer instructions are used to make a computer execute the method according to any one of claims 1 to 6.

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