Chicken freshness detection method based on Internet of Things

Abstract

The invention relates to the technical field of meat freshness detection, in particular to a chicken freshness detection method based on Internet of Things, which comprises the following steps: acquiring fresh chicken after cold processing, and detecting the surface temperature of the chicken to determine a pressing force value, a detection time window and delay time; applying a pressing force value to the chicken, keeping the pressing force value for a preset time, then relieving the pressure, and based on the detection time window, performing impedance measurement on a pressing area of the chicken to be detected to obtain a recovery rate value and a structural strength value; based on the recovery rate value and the structural strength value, determining the detected freshness of the chicken and outputting the detected freshness to the Internet of Things terminal; in cold-chain logistics, the temperature of the environment where chicken is located is obtained through a temperature sensor, the environment temperature deviation value and the corresponding deviation duration time are recorded, and a basic heat load is obtained; and updating the detected freshness according to the effective thermal load to obtain the current corrected freshness of the fresh chicken. According to the invention, accurate detection and dynamic correction of chicken freshness are realized.

Description

Technical Field

[0001] This invention relates to the field of meat freshness detection technology, and in particular to a method for detecting chicken freshness based on the Internet of Things. Background Technology

[0002] In the cold chain logistics of fresh chicken, while freshness testing equipment can be deployed at the front-end stages (such as the production end), terminal storage and sales locations generally lack large-scale testing capabilities due to cost and facility limitations, and can only conduct small-batch sampling tests. More importantly, the lack of a closed refrigerated environment and inefficient handling during loading and unloading processes leads to frequent exposure of products to high-temperature environments, creating a risk of cold chain disruption. At the same time, terminal refrigeration equipment often struggles to maintain a stable low-temperature environment due to insufficient temperature control accuracy, limited refrigeration capacity, and low automation. Under the current technological system, front-end testing data and the actual terminal storage environment are disconnected, making it impossible to establish a dynamic correlation, which significantly reduces the reference value of front-end freshness indicators in the terminal scenario.

[0003] The current technological bottleneck lies in the disconnect between front-end testing data and terminal cold chain monitoring. Uncontrollable factors such as chain breaks during loading and unloading and terminal temperature control failures accelerate the deterioration of chicken quality, but front-end testing results cannot reflect these dynamic changes in real time. The terminal stage lacks both continuous freshness monitoring methods for individual products and the ability to quantify the impact of temperature changes as changes in freshness. This results in a significant discrepancy between the freshness information obtained by consumers and the actual condition of the product. The entire cold chain quality traceability chain breaks at the terminal stage, rendering front-end testing data meaningless for practical guidance.

[0004] Chinese Patent Publication No. CN109900767A discloses a method for detecting the freshness of chicken using electrochemistry, comprising the following steps: (1) washing chicken samples of different freshness in deionized water, then absorbing the water, crushing the meat, weighing the sample, adding solvent for ultrasonic extraction, and filtering to obtain filtrate; (2) dripping the filtrate onto the surface of a glassy carbon electrode and drying it, immersing it in a buffer solution, forming a three-electrode system with a platinum electrode and a silver / silver chloride electrode, and using a linear voltammetric scanning method to collect the electrochemical response spectrum of the filtrate; (3) collecting the electrochemical response spectrum of chicken samples of unknown freshness according to the process in step (2); (4) processing the collected electrochemical response spectrum, extracting feature values, comparing the differences in feature values ​​between samples of unknown freshness and samples of known freshness, and determining the freshness of the chicken. The detection method of the present invention has the advantages of simple operation, high sensitivity, low instrument cost, fast speed and high accuracy. However, the method of detecting chicken freshness by electrochemistry has the following problems: the detection relies on destructive sampling experiments, the data obtained cannot correspond to the freshness of each chicken product, and it cannot be correlated with the continuous monitoring of the cold chain process, lacking correction for continuous changes in freshness. Summary of the Invention

[0005] Therefore, this invention provides an IoT-based method for detecting chicken freshness, overcoming the problems of inaccurate front-end detection data and disconnection from dynamic changes in the terminal cold chain environment in existing technologies, leading to inaccurate chicken freshness information. To achieve the above objective, this invention provides an IoT-based method for detecting chicken freshness, comprising: Step S1: Obtain the fresh chicken to be tested, detect the surface temperature of the chicken to be tested, and determine the pressing pressure value, detection time window and delay time based on the processing temperature difference between the surface temperature and the preset reference temperature. Step S2: Apply the pressure value to the chicken meat to be tested and hold it for a preset time, then release the pressure. Based on the detection time window, perform impedance measurement on the pressed area of ​​the chicken meat to be tested to obtain the recovery rate value and the structural strength value. The recovery rate value is the rate of change of the real part of the impedance determined by the first AC signal within a detection time window, and the structural strength value is the peak value of the imaginary part of the impedance determined by the second AC signal within a detection time window. Step S3: Based on the recovery rate value and the structural strength value, determine the freshness of the chicken meat to be tested and output it to the Internet of Things terminal; Step S4: In cold chain logistics, the ambient temperature of the chicken to be tested is obtained by a temperature sensor, and the ambient temperature deviation value relative to the ambient temperature threshold and the corresponding deviation duration are recorded. Step S5: Obtain the basic heat load based on the ambient temperature deviation value and the duration of the deviation, and correct the basic heat load based on the detection freshness to obtain the effective heat load; Step S6: Determine the current corrected freshness of the fresh chicken meat based on the effective heat load and the detected freshness. Step S7, based on the current corrected freshness, determine the freshness of the raw chicken and output the result, including outputting the current corrected freshness, or repeating steps S1 to S3 based on the detection result that the current corrected freshness is less than or equal to the freshness threshold, to obtain the current re-inspection freshness of the raw chicken and output it.

[0006] Further, step S2 includes: Step S21: Apply the pressure value to the chicken meat to be tested and maintain it for a preset time; Step S22: After releasing the pressure, start the timing. Take this moment as the start time of the detection time window. Use the first AC signal to measure the impedance of the pressed area of ​​the chicken to be tested. When the timing reaches the end time of the detection time window, determine the recovery rate value based on the rate of change of the real part of the impedance within the detection time window in the impedance measurement. Step S23: After waiting for the delay time, take that time as the start time of the detection time window, and use the second AC signal to perform impedance measurement on the pressing area of ​​the chicken to be tested. When the timing reaches the end time of the detection time window, determine the structural strength value based on the peak value of the imaginary part of the impedance in the impedance measurement.

[0007] Further, in step S3, the recovery rate value and the structural strength value are input into a preset freshness model to obtain the freshness of the chicken to be tested, wherein the freshness calculation model is determined based on the correlation between the biomechanical properties of chicken and freshness.

[0008] Further, step S4 includes: Step S41: Obtain the ambient temperature using a temperature sensor at a preset detection frequency; Step S42: When the duration of the ambient temperature being greater than the ambient temperature threshold is greater than or equal to the minimum recording duration, the temperature rise event is determined to be a valid temperature rise event. Step S43: Record the ambient temperature deviation value and the corresponding deviation duration in each effective temperature rise event.

[0009] Further, step S5 includes: Step S51: The basic heat load is obtained by multiplying the ambient temperature deviation value in the current effective temperature rise event by the corresponding deviation duration. Step S52: Determine the heat load correction coefficient based on the detected freshness; Step S53: Calculate the effective heat load based on the basic heat load and the heat load correction coefficient.

[0010] Furthermore, in step S42, the number of effective temperature rise events is recorded. When the number of effective temperature rise events is greater than or equal to the threshold number of temperature rise events, the detection frequency of the temperature sensor acquiring the ambient temperature is increased.

[0011] Furthermore, the detection freshness is positively correlated with the recovery rate value, and the detection freshness is positively correlated with the structural strength value.

[0012] Furthermore, in step S1, the delay time is positively correlated with the processing temperature difference.

[0013] Furthermore, in step S1, the pressing pressure value is negatively correlated with the processing temperature difference value.

[0014] Furthermore, in step S1, the detection time window is negatively correlated with the processing temperature difference.

[0015] Compared with the prior art, the beneficial effects of the present invention are that, by integrating temperature adaptive impedance detection and dynamic heat load correction mechanism, the present invention establishes a cold chain full-process freshness tracking model at the Internet of Things terminal, realizes real-time correlation between the initial detection data at the front end and the temperature change of the terminal environment, and ensures that the quality status of each link from production to consumption is quantifiable and traceable, overcoming the problem of inaccurate freshness data caused by the disconnect between traditional sampling data and logistics process.

[0016] Furthermore, this invention overcomes the deformation response deviation caused by initial state differences by dynamically setting the pressing pressure value, delay time, and detection window based on the surface temperature of the chicken after cold processing through a temperature-adaptive impedance detection parameter adjustment mechanism, thus ensuring the universality of biomechanical property measurement.

[0017] Furthermore, this invention employs a dual-time-separated impedance measurement strategy, measuring the rate of change of the real part of the impedance during the rapid recovery period after pressure relief and capturing the peak value of the imaginary part of the impedance during the structural stabilization period. This quantifies freshness from both fluid migration and solid structure dimensions, avoiding signal coupling interference.

[0018] Furthermore, this invention quantifies temperature rise events into effective heat loads through a heat load-driven nonlinear freshness correction model, and dynamically adjusts the correction coefficient based on detected freshness, objectively reflecting the accelerated correlation between temperature stress and muscle putrefaction.

[0019] Furthermore, this invention utilizes a frequency-adaptive monitoring mechanism for cold chain temperature change events to dynamically increase the sampling frequency based on the number of effective temperature rise events, thereby optimizing the energy efficiency of IoT terminals while ensuring data validity. Attached Figure Description

[0020] Figure 1 This is a flowchart of the IoT-based chicken freshness detection method of the present invention; Figure 2 This is a flowchart of step S2 of the chicken freshness detection method based on the Internet of Things of the present invention; Figure 3 This is a flowchart of step S4 in the IoT-based chicken freshness detection method of the present invention; Figure 4 This is a flowchart of step S5 of the Internet of Things-based chicken freshness detection method of the present invention. Detailed Implementation

[0021] To make the objectives and advantages of the present invention clearer, the present invention will be further described below with reference to embodiments; it should be understood that the specific embodiments described herein are merely for explaining the present invention and are not intended to limit the present invention.

[0022] Preferred embodiments of the present invention will now be described with reference to the accompanying drawings. Those skilled in the art should understand that these embodiments are merely illustrative of the technical principles of the present invention and are not intended to limit the scope of protection of the present invention.

[0023] It should be noted that in the description of this invention, the terms "upper", "lower", "left", "right", "inner", "outer", etc., which indicate directions or positional relationships, are based on the directions or positional relationships shown in the accompanying drawings. This is only for the convenience of description and is not intended to indicate or imply that the device or element must have a specific orientation, or be constructed and operated in a specific orientation. Therefore, it should not be construed as a limitation of this invention.

[0024] Furthermore, it should be noted that, in the description of this invention, unless otherwise explicitly specified and limited, the terms "installation," "connection," and "linking" should be interpreted broadly. For example, they can refer to a fixed connection, a detachable connection, or an integral connection; they can refer to a mechanical connection or an electrical connection; they can refer to a direct connection or an indirect connection through an intermediate medium; and they can refer to the internal connection of two components. Those skilled in the art can understand the specific meaning of the above terms in this invention according to the specific circumstances.

[0025] Please see Figure 1 The diagram shows a flowchart of the chicken freshness detection method based on the Internet of Things (IoT) of the present invention. The present invention provides a chicken freshness detection method based on the Internet of Things (IoT), comprising: Step S1: Obtain the fresh chicken to be tested, detect the surface temperature of the chicken to be tested, and determine the pressing pressure value, detection time window and delay time based on the processing temperature difference between the surface temperature and the preset reference temperature. In one specific embodiment, temperature detection uses an infrared temperature sensor, with the probe perpendicularly measuring 10mm to 15mm from the surface of the chicken, and a sampling frequency of 1Hz.

[0026] In a specific embodiment, the formula for calculating the processing temperature difference is as follows: , Wherein, △T is the processing temperature difference between the surface temperature and the preset reference temperature, in °C; T is the actual surface temperature of the chicken obtained by detection, in °C; T0 is the preset reference temperature, which is 2 °C. Those skilled in the art will understand that, according to the poultry meat cooling requirements in GB / T 40464 "Technical Requirements for Cooled Meat Processing", the center temperature of the poultry carcass should be maintained between 0 °C and 4 °C after cooling. Therefore, the preset reference temperature T0 is 2 °C.

[0027] Specifically, in step S1, the delay time is negatively correlated with the processing temperature difference.

[0028] Specifically, in step S1, the pressing pressure value is negatively correlated with the processing temperature difference value.

[0029] Specifically, in step S1, the detection time window is negatively correlated with the processing temperature difference.

[0030] In one specific embodiment, the formula for calculating the pressure value is as follows: , Wherein, F is the pressing force value, in Newtons (N); k1 is the supercooling pressing force correction factor, in N / ℃, with a value range of 0.5N / ℃ to 1.0N / ℃, preferably k1 is 0.5N / ℃; k2 is the superheating pressing force correction factor, in N / ℃, with a value range of 0.2N / ℃ to 0.5N / ℃, preferably k2 is 0.2N / ℃; F0 is the base pressure, in Newtons (N), preferably 2.0N, to meet the minimum contact pressure requirement.

[0031] Understandably, k1 and k2 in the formula are based on experimental calibration of the correlation between chicken stiffness and temperature. In the relatively overcooled scenario during cold processing, the low temperature intensifies myosin cross-linking and increases muscle stiffness, requiring increased pressure to trigger deformation. In the relatively overheated scenario during cold processing, the high temperature accelerates myofibril degradation and structural relaxation, requiring reduced pressure to avoid excessive compression.

[0032] Understandably, this step dynamically adjusts the pressing pressure value based on the temperature difference, thus solving the problem of inconsistent deformation response caused by differences in the initial state of chicken: low-temperature meat has high stiffness, requiring increased pressure to produce effective elastic deformation; high-temperature meat has a loose structure, and the basic pressure is sufficient to meet the measurement requirements, avoiding excessive pressing that could damage the tissue; at the same time, it provides a unified testing standard for measuring the freshness of chicken.

[0033] In a specific embodiment, the formula for calculating the delay time is as follows: , Where y is the delay time in seconds (s); y0 is the base delay time in seconds (s), ranging from 15s to 20s; and b is the delay slope coefficient in degrees Celsius (°C). -1 The value range is -0.4℃ -1 ~-0.3℃ -1 Preferably, b is -0.35℃ -1 .

[0034] Understandably, the delay time setting stems from the temperature-dependent mechanism of the chicken muscle fiber recovery process: when the chicken surface temperature is below the baseline value, the rate of calcium ion release from the sarcoplasmic reticulum slows down, and the dissociation rate of troponin and actin binding sites decreases, resulting in a prolonged state of muscle fiber contraction. In this case, a longer delay time needs to be set to allow the muscle structure to fully relax. Conversely, when the surface temperature is above the baseline value, the activity of the sarcoplasmic reticulum calcium pump is enhanced, accelerating the return of calcium ions to the sarcoplasmic reticulum and causing the myosin head to quickly detach from actin. The process of muscle fiber recovery to a relaxed state is significantly accelerated. In this case, the delay time needs to be shortened to avoid missing the optimal measurement window.

[0035] In a specific embodiment, the calculation formula for the detection time window is as follows: , Where u is the detection time window, in seconds (s); u0 is the basic detection time window, in seconds (s), with a value range of 6s to 7s, preferably 6s; m is the window adjustment coefficient, in degrees Celsius (°C). -1 The value range is 0.6℃. -1 ~0.8℃ -1 Preferably, m is 0.8℃ -1 .

[0036] Understandably, when the surface temperature of chicken meat is below the baseline value, the melting rate of ice crystals in the myofibril interstitium slows down, and the sarcoplasmic viscosity increases, resulting in a slow and gradual structural recovery in the pressure area. In this case, the detection window needs to be extended to fully capture the gradual trajectory of impedance parameters. Conversely, when the surface temperature is above the baseline value, myosin light chain kinase activity increases, and the elastic recovery of sarcomeres exhibits rapid saturation characteristics. In this case, the detection window needs to be shortened to focus on the abrupt change phase of impedance parameters. This invention, through adaptive adjustment of the impedance detection time window, ensures both the key data capture efficiency of the rapid recovery process and meets the complete feature extraction requirements of the slow recovery process. It ensures that the impedance measurement window always matches the actual time-varying law of muscle bioelectrical response, while avoiding the introduction of environmental noise due to an excessively long window.

[0037] Understandably, the aforementioned adaptive adjustment of impedance detection parameters is based on the relationship between the mechanical properties of poultry meat and temperature. The degree of rigor mortis in chicken after cold processing is significantly affected by historical temperature. Low temperatures exacerbate myofibril protein denaturation and cause sarcomere structure contraction, resulting in increased firmness and slower elastic recovery. High temperatures, on the other hand, accelerate ATP consumption, prompting the muscle to enter a rigor mortis release phase, leading to structural relaxation and faster recovery. This invention quantifies this state difference through the processing temperature difference and dynamically adjusts the pressing pressure, delay time, and detection time window accordingly. This ensures that subsequent impedance measurements accurately reflect the muscle's microstructure rather than initial state differences, thereby improving the universality and reliability of freshness indicators. This adaptive mechanism is particularly suitable for temperature fluctuation scenarios in cold chain logistics, overcoming the error problems caused by different sample pretreatment temperatures in traditional fixed-parameter detection methods.

[0038] Step S2: Apply the pressure value to the chicken meat to be tested and hold for a preset time, then release the pressure. Based on the testing time window, perform impedance measurement on the pressed area of ​​the chicken meat to be tested to obtain the recovery rate value and the structural strength value. The recovery rate value is the rate of change of the real part of the impedance determined by the first AC signal within a testing time window, and the structural strength value is the peak value of the imaginary part of the impedance determined by the second AC signal within a testing time window.

[0039] Specifically, step S2 includes: Step S21: Apply the pressure value to the chicken meat to be tested and maintain it for a preset time; In one specific embodiment, a food-grade stainless steel pressure head (8mm to 10mm in diameter) is driven by an electric push rod (stroke accuracy ±0.1mm) to apply a vertical pressure value, which is held for a fixed duration of 3 seconds, covering the main cycle of muscle stress relaxation.

[0040] Understandably, the 3-second holding time was determined based on muscle rheology experiments, which is sufficient for myofibrils to complete stress transfer, trigger elastic deformation, and avoid creep interference.

[0041] Step S22: After releasing the pressure, start the timing. Take this moment as the start time of the detection time window. Use the first AC signal to measure the impedance of the pressed area of ​​the chicken to be tested. When the timing reaches the end time of the detection time window, determine the recovery rate value based on the rate of change of the real part of the impedance within the detection time window in the impedance measurement. In one specific embodiment, timing is started after the pressure is released, and the first AC signal is specifically 10kHz in frequency and 0.1mA in current.

[0042] Impedance real part sampling is specifically performed as follows: the sampling interval is denoted as Δt, where Δt = 0.1s; the total number of samples is denoted as M. Specifically, , In one specific embodiment, the formula for calculating the recovery rate value is as follows: , Where R is the recovery rate value, in Ω / s; N is the total number of sampling points actually used, specifically N = ⌈0.5M⌉; i is the sampling sequence number, i = 1, 2, 3, ..., N; t i The time for the i-th sampling point is expressed in seconds (s). Specifically, t i =(i-1)×△t;R i For t i The real part of the impedance at time t, in ohms (Ω); μ t μ is the arithmetic mean of the time series, expressed in seconds (s). R This is the arithmetic mean of the real part of the impedance, expressed in ohms (Ω).

[0043] It is understandable that for the first 50% of the impedance real part data points within the time window (t... i R i Linear regression was performed to obtain the slope. The first 50% window (rapid recovery period) was approximately linear, while the latter 50% (plateau period) was significantly nonlinear. The least squares method only fitted the quasi-linear segment to avoid diluting the slope with plateau period data. Its biomechanical significance is that the slope quantifies the initial rate of extracellular fluid redistribution after muscle compression is relieved, directly reflecting the elastic recovery ability of muscle fibers.

[0044] Understandably, in the initial stage after stress relief, fresher chicken exhibits elastic recoil of the extracellular matrix, rapid reflux of intercellular fluid after pressure, and a fast rate of decrease in impedance. In contrast, less fresh chicken has more ruptured cell membranes, resulting in slower fluid migration and a significantly lower rate of change. This parameter is directly related to the water-holding capacity of chicken and is a core indicator for determining freshness.

[0045] Step S23: After waiting for the delay time, take that time as the start time of the detection time window, and use the second AC signal to perform impedance measurement on the pressing area of ​​the chicken to be tested. When the timing reaches the end time of the detection time window, determine the structural strength value based on the peak value of the imaginary part of the impedance in the impedance measurement.

[0046] In one specific embodiment, timing is started after waiting for the delay time, and the second AC signal is specifically 100kHz in frequency and 0.05mA in current.

[0047] In a specific embodiment, the formula for calculating the structural strength value is as follows: , Where X is the structural strength value, in ohms (Ω); X j For the first j The measured value of the imaginary part of the impedance at the sampling time, in ohms (Ω); j is the sampling number, j=1,2,...,M.

[0048] Understandably, this step quantifies the integrity of the muscle microstructure by capturing the peak value of cell membrane capacitance characteristics. Fresh chicken has intact cell membranes and high capacitive reactance values ​​under high-frequency current. Chicken with lower freshness has decreased capacitive reactance due to membrane lipid decomposition and ion leakage. This peak value corresponds to the moment when the muscle structure recovers to its optimal state, avoiding attenuation errors introduced by continuous measurement.

[0049] Understandably, after the pressure is released, the extracellular matrix of avian muscle cells undergoes rapid elastic recoil, and the intercellular fluid redistributes. At this time, measuring the rate of change of the real part of impedance using low-frequency current can accurately characterize the fluid migration rate and reflect the macroscopic elastic recovery ability of the muscle. Conversely, after a delayed period of muscle structural stabilization, the distribution of ions across the cell membrane reaches a dynamic equilibrium. At this point, using high-frequency current to penetrate the cell membrane and capturing the capacitive reactance peak can assess cell membrane integrity and myofiber structural strength. Both methods quantify freshness from the dimensions of fluid migration and solid structure, respectively, and avoid signal crosstalk through temporal separation, overcoming the inherent limitation of coupling between the real and imaginary parts in traditional single-segment impedance measurements.

[0050] Step S3: Based on the recovery rate value and the structural strength value, determine the freshness of the chicken meat to be tested and output it to the Internet of Things terminal; Specifically, in step S3, the recovery rate value and the structural strength value are input into a preset freshness model to obtain the freshness of the chicken to be tested, wherein the freshness calculation model is determined based on the correlation between the biomechanical properties of chicken and freshness.

[0051] Specifically, the detection freshness is positively correlated with the recovery rate value, and the detection freshness is positively correlated with the structural strength value.

[0052] In one specific embodiment, the freshness model is as follows: , Wherein, P represents the freshness of the sample, dimensionless, ranging from 0 to 100; R0 is the recovery rate benchmark value, preferably 10 Ω / s; X0 is the structural strength benchmark value, preferably 500 Ω; the recovery rate benchmark value R0 and the structural strength benchmark value X0 are calibrated based on several measured data of fresh chicken meat, which will not be elaborated here; α is the recovery rate weighting index, dimensionless, ranging from 0.6 to 0.7; β is the structural strength weighting index, dimensionless, ranging from 0.3 to 0.4; where α + β = 1, and α > β.

[0053] Understandably, this model reflects the synergistic effect of two parameters, where a decrease in either parameter significantly reduces freshness; the exponential weights, α > β, reflect the dominance of the recovery rate, with elastic recovery being more sensitive than structural strength; at the same time, the benchmark value normalizes the dimensional parameters and outputs a uniform scale (0-100).

[0054] Understandably, this step constructs a quantitative model of freshness that aligns with the spoilage mechanism of chicken by integrating the dual biomechanical responses of macroscopic elasticity and microstructure in muscle. Fresh chicken has an intact myofiber network, and after pressure, extracellular fluid rapidly returns, exhibiting a high recovery rate. Simultaneously, the cell membrane lipid bilayer structure is stable, displaying a high capacitive reactance peak under high-frequency current. As spoilage progresses, proteolytic activity leads to myofiber breakage, reducing elastic recovery capacity and decreasing the recovery rate; while membrane phospholipid degradation weakens cell dielectric properties, resulting in a simultaneous decrease in structural strength. Both characteristics represent the degree of spoilage from the perspectives of tissue hydrodynamics and solid-state electrochemistry, respectively, and are temporally correlated. The weighted geometric mean model, through exponential weighting, accurately captures the sensitivity advantage of the recovery rate in early spoilage (enzymatic hydrolysis precedes membrane collapse), while retaining the stability contribution of structural strength in deep spoilage stages. This overcomes the susceptibility of single parameters to environmental interference, providing an accurate freshness benchmark for cold chain logistics.

[0055] Step S4: In cold chain logistics, the ambient temperature of the chicken to be tested is obtained by a temperature sensor, and the ambient temperature deviation value relative to the ambient temperature threshold and the corresponding deviation duration are recorded. Specifically, step S4 includes: Step S41: Obtain the ambient temperature using a temperature sensor at a preset detection frequency; In one specific embodiment, the temperature sensor uses a UHF passive RFID temperature tag to obtain the ambient temperature, with a temperature measurement range of -40℃ to 85℃ (accuracy ±0.5℃), an initial detection frequency of 1 time / min, and is powered by the reader's radio frequency field (no battery required).

[0056] Taking a cold chain transport vehicle as an example, the installation method is to embed UHF passive RFID temperature tags into the inner wall of the chicken packaging box (compliant with ISO / IEC 18000-6C standard); the reader antenna is deployed on the top of the cold chain compartment (reading distance ≤1.5m).

[0057] Step S42: When the duration of the ambient temperature being greater than the ambient temperature threshold is greater than or equal to the minimum recording duration, the temperature rise event is determined to be a valid temperature rise event. Specifically, in step S42, the number of effective temperature rise events is recorded. When the number of effective temperature rise events is greater than or equal to the threshold of the number of temperature rise events, the detection frequency of the temperature sensor acquiring the ambient temperature is increased.

[0058] In one specific embodiment, the ambient temperature threshold T th =4℃, according to GB / T 40464-2021, during transportation, the temperature inside the compartment should be maintained between 0℃ and 4℃, and the core temperature of the cooled meat should not exceed 4℃.

[0059] In one specific embodiment, the minimum recording duration is denoted as t. min Preferably, t min =5 minutes to avoid interference from instantaneous fluctuations; the event count threshold is denoted as s, in units of times, and ranges from 3 to 5 times, preferably 3 times. When the number of effective temperature rise events is greater than or equal to the temperature rise event count threshold, the detection frequency of the temperature sensor to obtain the ambient temperature is increased, preferably from 1 time / min to 2 times / min.

[0060] Step S43: Record the ambient temperature deviation value and the corresponding deviation duration in each effective temperature rise event.

[0061] In one specific embodiment, the ambient temperature deviation value and the corresponding deviation duration in each effective temperature rise event are recorded; at the same time, the timestamp of the corresponding effective temperature rise event is also recorded to assist in the investigation of cold chain problems, which will not be elaborated further.

[0062] Understandably, this step uses a dynamic temperature monitoring mechanism to accurately capture quality risk windows in cold chain logistics. The core cause of chicken spoilage is the accelerated microbial proliferation and surge in enzyme activity due to temperature fluctuations. When the ambient temperature exceeds a threshold, the generation time of psychrophilic bacteria such as Pseudomonas can be shortened from twelve hours to about thirty minutes, while the activity of cathepsins increases exponentially. The criterion for determining an effective temperature rise event requires that the temperature exceedance last for more than five minutes, avoiding instantaneous interference such as opening and closing doors, and only counting the harmful periods that lead to microbial growth. The design of increasing the detection frequency based on the cumulative number of events is to provide early warning through sampling when a systemic temperature runaway trend is identified, thus gaining a critical time window for quality intervention. This mechanism quantifies the temperature hazard from both the duration and frequency dimensions, providing a high-confidence data foundation for subsequent freshness correction.

[0063] Step S5: Obtain the basic heat load based on the ambient temperature deviation value and the duration of the deviation, and correct the basic heat load based on the detection freshness to obtain the effective heat load; Specifically, step S5 includes: Step S51: The basic heat load is obtained by multiplying the ambient temperature deviation value in the current effective temperature rise event by the corresponding deviation duration. In a specific embodiment, the basic heat load is denoted as H0, with the unit being °C / min. It is the product of the ambient temperature deviation value and the corresponding deviation duration. The ambient temperature deviation value is obtained by subtracting the ambient temperature threshold from the ambient temperature, with the unit being degrees Celsius (°C). The deviation duration is the duration of the current effective temperature rise event, with the unit being minutes (min).

[0064] Step S52: Determine the heat load correction coefficient based on the detected freshness; In a specific embodiment, the formula for calculating the heat load correction factor is as follows: , Wherein, K is the heat load correction coefficient, which is dimensionless; f is the spoilage acceleration factor, which is dimensionless and ranges from 0.015 to 0.02, preferably f is 0.02.

[0065] Understandably, the range and optimal value of the spoilage acceleration factor f are determined based on the correlation experiment between initial freshness and temperature rise damage during the spoilage process of chicken. The correlation experiment involves controlling chicken samples with different initial freshness and continuously measuring the increase in volatile basic nitrogen (TVB-N) under the same temperature rise conditions for a preset time. The experiment shows that the lower the initial freshness, the greater the increase in spoilage rate caused by unit heat load, and its quantitative relationship conforms to the exponential decay law.

[0066] Step S53: Calculate the effective heat load based on the basic heat load and the heat load correction coefficient.

[0067] In a specific embodiment, the formula for calculating the effective heat load is as follows: , Where H represents the effective heat load, in °C / min.

[0068] Understandably, this step constructs a dynamic heat load assessment model based on freshness detection by quantifying the cumulative damage effect of temperature fluctuations on chicken quality. The baseline heat load reflects the physical intensity of the environmental temperature rise, its core being the product of the temperature deviation value and its duration. This product directly correlates with the metabolic activity intensity of the main spoilage bacteria in chicken, psychrophilic bacteria (including *Pseudomonas*, *Acinetobacter*, and *Moraxella*). The higher the temperature, the shorter their generation, while the duration determines their proliferation generation. The heat load correction coefficient characterizes the resistance of muscle tissue. High-freshness chicken, due to its intact cell membranes and abundant antioxidants, has a strong buffering capacity against temperature rises; while low-freshness meat, due to membrane lipid oxidation and protease activation, experiences significantly accelerated spoilage under the same temperature rise. The effective heat load dynamically combines the physical temperature rise intensity with biological resistance, quantifying the actual harmfulness of temperature rise events and making the quantification of chicken freshness more accurate.

[0069] Step S6: Determine the current corrected freshness of the fresh chicken meat based on the effective heat load and the detected freshness. In a specific embodiment, the formula for calculating the current corrected freshness is as follows: , Among them, P c The current corrected freshness is dimensionless and ranges from 0 to 100; s is the heat load damage coefficient, in units of (°C / min). -2 The value range is 0.001 (℃ / min). -2 ~0.0001 (℃ / min) -2 Preferably, s is 0.0005 (℃ / min). -2 .

[0070] It is understandable that the square of the effective heat load H reflects the nonlinear cumulative damage of heat load to freshness. When the effective heat load H increases, the rate of freshness decay accelerates. The heat load damage coefficient s is calibrated based on chicken colony growth experiments. Those skilled in the art can make adaptive adjustments to the selection of this parameter according to different types and parts of chicken, which will not be elaborated here.

[0071] Understandably, when the temperature rises in the cold chain environment, the metabolic activity of psychrophilic bacteria increases non-linearly with the accumulation of heat load. In the initial stage of temperature rise, colony growth is mainly linear expansion, and the decomposition of muscle tissue is relatively slow. As the heat load continues to increase, the microbial density increases exponentially, accelerating the disintegration of myofibril structure. At lower heat loads, freshness decays slowly; the higher the heat load, the higher the microbial metabolic activity, and the faster the rate of freshness decay. This can objectively reflect the correlation between temperature changes and chicken quality degradation, making the quantification of chicken freshness deterioration during the cold chain process more accurate.

[0072] Step S7, based on the current corrected freshness, determine the freshness of the raw chicken and output the result, including outputting the current corrected freshness, or repeating steps S1 to S3 based on the detection result that the current corrected freshness is less than or equal to the freshness threshold, to obtain the current re-inspection freshness of the raw chicken and output it.

[0073] In one specific embodiment, after the logistics transportation is completed, the current corrected freshness output in step S6 is read. When P c When P < 0, the re-inspection process is triggered. Steps S1 to S3 are repeated for the chicken product to obtain the re-inspection freshness P. f ; When P c When P > 0, output the result directly as the final freshness result; Wherein, P0 is the freshness threshold, with a value range of 25 to 30, preferably P0 is 30; The freshness threshold P0 range is defined according to GB / T 2707-2016 "Fresh (Frozen) Livestock and Poultry Products", which specifies the unsaleable state corresponding to volatile basic nitrogen > 15 mg / 100g. This will not be elaborated further.

[0074] Output freshness P f As the final freshness result, it overwrites the original current corrected freshness.

[0075] Understandably, when the calibrated freshness falls below the freshness threshold, there are two possibilities: actual spoilage, i.e., cold chain temperature changes cause quality deterioration beyond the safe range; and data distortion, i.e., temperature sensor malfunction or accumulated error in the heat load model. By re-performing physical testing, the current biomechanical state of the muscle can be directly obtained, eliminating the risk of data drift and avoiding misjudgments caused by sensor failure or environmental interference. This ensures that the quality judgment result is always based on the real-time biomechanical characteristics of the muscle, rather than solely relying on electronic data for calculation.

[0076] Specifically, step S7 also includes correcting the modified heat load damage coefficient s in steps S5 and S6 based on the difference between the re-inspected freshness and the corrected freshness, specifically as follows: , Among them, s new η is the corrected thermal load damage coefficient; η is the corrected intensity coefficient, dimensionless, with a value range of 0.2 to 0.5, preferably 0.3.

[0077] In implementation, the revised s new The non-volatile memory of the IoT terminal is written, and in the subsequent batch detection step S6, the updated s is used. new Calculate freshness.

[0078] Understandably, when the re-inspection freshness is significantly higher than the calibration value, it indicates that the original model has over-amplified the temperature rise damage effect, and the correction heat load damage coefficient needs to be reduced proportionally. Conversely, when the re-inspection value is much lower than the calibration value, it suggests that the model has underestimated heat load damage, and the correction heat load damage coefficient needs to be increased. The correction magnitude is controlled by quantifying the severity of the deviation and combining it with the correction intensity coefficient to control the adjustment step size, avoiding model instability caused by a single fluctuation. This design enables the heat load damage coefficient to have continuous evolution capabilities, gradually approaching the spoilage dynamics of actual cold chain scenarios.

[0079] The technical solution of the present invention has been described above with reference to the preferred embodiments shown in the accompanying drawings. However, it will be readily understood by those skilled in the art that the scope of protection of the present invention is obviously not limited to these specific embodiments. Without departing from the principles of the present invention, those skilled in the art can make equivalent changes or substitutions to the relevant technical features, and the technical solutions after these changes or substitutions will all fall within the scope of protection of the present invention.

Claims

1. A method for detecting the freshness of chicken based on the Internet of Things, characterized in that, include: Step S1: Obtain the fresh chicken to be tested, detect the surface temperature of the chicken to be tested, and determine the pressing pressure value, detection time window and delay time based on the processing temperature difference between the surface temperature and the preset reference temperature. Step S2: Apply the pressure value to the chicken meat to be tested and hold it for a preset time, then release the pressure. Based on the detection time window, perform impedance measurement on the pressed area of ​​the chicken meat to be tested to obtain the recovery rate value and the structural strength value. The recovery rate value is the rate of change of the real part of the impedance determined by the first AC signal within a detection time window, and the structural strength value is the peak value of the imaginary part of the impedance determined by the second AC signal within a detection time window. Step S3: Based on the recovery rate value and the structural strength value, determine the freshness of the chicken meat to be tested and output it to the Internet of Things terminal; Step S4: In cold chain logistics, the ambient temperature of the chicken to be tested is obtained by a temperature sensor, and the ambient temperature deviation value relative to the ambient temperature threshold and the corresponding deviation duration are recorded. Step S5: Obtain the basic heat load based on the ambient temperature deviation value and the duration of the deviation, and correct the basic heat load based on the detection freshness to obtain the effective heat load; Step S6: Determine the current corrected freshness of the fresh chicken meat based on the effective heat load and the detected freshness. Step S7, based on the current corrected freshness, determine the freshness of the raw chicken and output the result, including outputting the current corrected freshness, or repeating steps S1 to S3 based on the detection result that the current corrected freshness is less than or equal to the freshness threshold, to obtain the current re-inspection freshness of the raw chicken and output it.

2. The method for detecting chicken freshness based on the Internet of Things according to claim 1, characterized in that, Step S2 includes: Step S21: Apply the pressure value to the chicken meat to be tested and maintain it for a preset time; Step S22: After releasing the pressure, start the timing. Take this moment as the start time of the detection time window. Use the first AC signal to measure the impedance of the pressed area of ​​the chicken to be tested. When the timing reaches the end time of the detection time window, determine the recovery rate value based on the rate of change of the real part of the impedance within the detection time window in the impedance measurement. Step S23: After waiting for the delay time, take that time as the start time of the detection time window, and use the second AC signal to perform impedance measurement on the pressing area of ​​the chicken to be tested. When the timing reaches the end time of the detection time window, determine the structural strength value based on the peak value of the imaginary part of the impedance in the impedance measurement.

3. The method for detecting chicken freshness based on the Internet of Things according to claim 2, characterized in that, In step S3, the recovery rate value and the structural strength value are input into a preset freshness model to obtain the freshness of the chicken to be tested. The freshness calculation model is determined based on the correlation between the biomechanical properties of chicken and freshness.

4. The method for detecting chicken freshness based on the Internet of Things according to claim 1, characterized in that, Step S4 includes: Step S41: Obtain the ambient temperature using a temperature sensor at a preset detection frequency; Step S42: When the duration of the ambient temperature being greater than the ambient temperature threshold is greater than or equal to the minimum recording duration, the temperature rise event is determined to be a valid temperature rise event. Step S43: Record the ambient temperature deviation value and the corresponding deviation duration in each effective temperature rise event.

5. The method for detecting chicken freshness based on the Internet of Things according to claim 4, characterized in that, Step S5 includes: Step S51: The basic heat load is obtained by multiplying the ambient temperature deviation value in the current effective temperature rise event by the corresponding deviation duration. Step S52: Determine the heat load correction coefficient based on the detected freshness; Step S53: Calculate the effective heat load based on the basic heat load and the heat load correction coefficient.

6. The method for detecting chicken freshness based on the Internet of Things according to claim 4, characterized in that, In step S42, the number of effective temperature rise events is recorded. When the number of effective temperature rise events is greater than or equal to the threshold of the number of temperature rise events, the detection frequency of the temperature sensor acquiring the ambient temperature is increased.

7. The method for detecting chicken freshness based on the Internet of Things according to claim 3, characterized in that, The detection freshness is positively correlated with the recovery rate value, and the detection freshness is positively correlated with the structural strength value.

8. The method for detecting chicken freshness based on the Internet of Things according to claim 1, characterized in that, In step S1, the delay time is positively correlated with the processing temperature difference.

9. The method for detecting chicken freshness based on the Internet of Things according to claim 1, characterized in that, In step S1, the pressing pressure value is negatively correlated with the processing temperature difference value.

10. The method for detecting chicken freshness based on the Internet of Things according to claim 1, characterized in that, In step S1, the detection time window is negatively correlated with the processing temperature difference.

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