An intelligent management system for fresh meat cold chain transportation

Abstract

The application relates to the technical field of intelligent management of transportation, and particularly discloses a fresh meat cold-chain transportation intelligent management system, which comprises the following steps: firstly, collecting vehicle compartment temperature time series data, identifying temperature sudden rise and recovery time of each door opening event, and obtaining a temperature abnormal interval; then, extracting the maximum temperature value and the time length in the interval, taking the maximum temperature difference as the impact amplitude and the time length as the action time to construct a single heat shock representation; next, superimposing each representation in time series, and dynamically correcting the adjacent interval back-falling slope as the attenuation coefficient to generate a cumulative impact index; finally, mapping the cumulative index to a shelf life loss model, correcting the initial shelf life to obtain a real-time remaining shelf life, comparing the real-time remaining shelf life with a subsequent distribution node predicted arrival time, and outputting a distribution operation suggestion; the application realizes accurate quantification of the cumulative heat shock effect and dynamic evaluation of the goods quality, and effectively improves the intelligent management level of fresh meat cold-chain transportation.

Description

Technical Field

[0001] This invention relates to the field of intelligent transportation management technology, and in particular to an intelligent management system for cold chain transportation of fresh meat. Background Technology

[0002] With the rapid development of the cold chain logistics industry, intelligent cold chain management systems based on IoT sensing technology and data analysis have been gradually and widely applied. Chinese invention application CN114399243A discloses a method and device for monitoring temperature in cold chain transportation. This method calculates the influence coefficient of door opening time at each loading and unloading point using historical data, predicts the cargo temperature when the vehicle arrives at subsequent loading and unloading points, and issues an over-limit alarm. However, this method only uses door opening time as a single characteristic parameter and fails to perform cumulative analysis of the thermal effects of multiple door opening events, making it difficult to accurately reflect the comprehensive degree of thermal damage suffered by goods in transit.

[0003] Existing intelligent management systems for cold chain transportation of fresh meat typically monitor the temperature inside the truck compartment continuously and issue alarms when limits are exceeded. In the context of multi-point delivery in urban environments, during the cold chain transportation of fresh meat, each time a refrigerated truck opens its rear door for loading and unloading, a large influx of hot external air into the cold compartment causes the surface of the goods inside to experience transient thermal shocks exceeding safe temperature thresholds within a short period. As the delivery process progresses, the individual thermal shocks caused by multiple door openings accumulate over time, creating a comprehensive thermal load effect that causes irreversible damage to the quality of the fresh meat in transit—a phenomenon known as the cumulative thermal shock effect. The extent of this damage depends not only on the intensity of each individual thermal shock but also on the frequency of the thermal shocks, the time intervals between them, and the degree of temperature recovery of the goods between each shock.

[0004] However, existing intelligent management systems for cold chain transportation of fresh meat have long lacked effective quantitative control over the cumulative thermal shock effect, significantly impacting the quality control of fresh meat goods in transit. First, the sampling intervals of existing temperature monitoring systems are too large, making it difficult to capture transient temperature peaks caused by door opening events. This results in monitoring records masking the actual thermal damage process experienced by the goods, preventing managers from obtaining accurate quality risk information. Second, the lack of a data processing mechanism to cumulatively superimpose multiple door-opening thermal shocks prevents the integration of the intensity of each thermal shock with the degree of temperature recovery between adjacent shocks into a quantifiable comprehensive thermal damage index. This leads to significant uncertainty regarding the actual remaining shelf life of goods upon arrival at the distribution terminal, easily posing food safety risks. Furthermore, due to the lack of quantitative analysis capabilities for the cumulative thermal shock effect, existing systems cannot provide dynamic control suggestions for subsequent unloading operations, resulting in the continued occurrence of avoidable quality losses. Summary of the Invention

[0005] In order to overcome the above-mentioned defects of the prior art, embodiments of the present invention provide an intelligent management system for cold chain transportation of fresh meat to solve the problems mentioned in the background art.

[0006] To achieve the above objectives, the present invention provides the following technical solution: an intelligent management system for cold chain transportation of fresh meat, comprising an abnormal interval identification module, a single impact quantification module, a cumulative impact generation module, a shelf life correction module, and a transportation management decision module.

[0007] Anomaly interval identification module: used to collect temperature time series data in refrigerated truck compartments, and based on the temperature time series data, identify the start time of temperature surge and the end time of temperature recovery for each door opening event, and obtain the temperature anomaly feature interval for each door opening event;

[0008] Single-impact quantification module: used to obtain the maximum temperature and duration of the interval within the temperature anomaly characteristic range. The difference between the maximum temperature and the set temperature of the refrigerated truck is taken as the thermal shock amplitude, and the duration of the interval is taken as the thermal shock duration, thus constructing a single thermal shock characterization quantity.

[0009] Cumulative impact generation module: It is used to superimpose all the characterization quantities of single thermal shocks according to time sequence, and dynamically correct the superposition result by using the slope of the fall of adjacent temperature anomaly characteristic intervals as the attenuation coefficient to generate a cumulative thermal shock intensity index.

[0010] Shelf life correction module: used to map the cumulative thermal shock intensity index to the preset shelf life loss model, correct the initial shelf life of fresh meat products in transit, and generate the real-time remaining shelf life.

[0011] Transportation Management Decision Module: This module compares the real-time remaining shelf life with the estimated arrival times of subsequent delivery nodes one by one, and outputs delivery operation management suggestions for the corresponding delivery nodes based on the comparison results.

[0012] Preferably, the step of obtaining the temperature anomaly characteristic range includes:

[0013] S1. Continuously collect the temperature inside the refrigerated compartment at a preset sampling frequency to obtain temperature time series data;

[0014] S2. Perform local extreme point detection on the temperature time series data, obtain all local maxima and local minima in the temperature time series data, and arrange them in time order to obtain the extreme point sequence;

[0015] S3. Pair adjacent local minima and local maxima in the extreme point sequence, extract the time interval and temperature amplitude interval between each pair of adjacent extreme points, and combine the time interval and temperature amplitude interval to form the two-dimensional interval feature of the extreme point pair.

[0016] S4. In all two-dimensional spacing features, match extreme point pairs where the ratio of temperature amplitude spacing to time spacing is greater than a preset ratio. Record the time corresponding to the local minimum point in the successfully matched extreme point pair as the start time of the temperature surge and the corresponding local maximum point as the peak point of the temperature surge.

[0017] S5. Starting from the peak point of the temperature surge, obtain the adjacent local minimum point in the extreme point sequence, and record the time corresponding to the local minimum point as the temperature recovery termination time.

[0018] S6. The start time of the temperature surge and the end time of the temperature recovery corresponding to the same door opening event are combined to form a temperature anomaly feature interval. Steps S1 to S5 are executed sequentially for all door opening events in the temperature time series data to obtain the temperature anomaly feature interval of each door opening event.

[0019] Preferably, the step of constructing the characterization quantity for a single thermal shock includes:

[0020] Obtain the temperature anomaly characteristic range for each door opening event, extract the maximum temperature value within the temperature anomaly characteristic range, and calculate the difference between the maximum temperature value and the set temperature of the refrigerated truck to obtain the thermal shock amplitude corresponding to each door opening event.

[0021] The difference between the temperature recovery termination time and the temperature rise start time of the temperature anomaly characteristic interval is used to obtain the thermal shock time corresponding to each door opening event.

[0022] For each sampling point within the temperature anomaly characteristic range of each door opening event, the temperature value of the sampling point is subtracted from the set temperature of the refrigerated truck to obtain the instantaneous over-temperature value of the sampling point;

[0023] The instantaneous over-temperature values ​​of all sampling points within the temperature anomaly characteristic range are summed sequentially to obtain the cumulative over-temperature amount corresponding to each door opening event;

[0024] Substituting the thermal shock amplitude, thermal shock duration, and cumulative overheating amount corresponding to each door opening event into the formula for constructing the single thermal shock characterization quantity, we obtain the single thermal shock characterization quantity corresponding to each door opening event.

[0025] Preferably, the step of generating the cumulative thermal shock intensity index includes:

[0026] The single thermal shock characteristic quantity corresponding to the first door opening event is used as the initial cumulative thermal shock intensity index.

[0027] For the i-th door opening event, obtain the slope of the temperature anomaly characteristic range of the (i-1)-th door opening event, calculate the ratio of the absolute value of the slope to the thermal shock amplitude corresponding to the (i-1)-th door opening event, and obtain the slope amplitude ratio corresponding to the (i-1)-th door opening event.

[0028] Substitute the slope amplitude ratio corresponding to the (i-1)th door opening event into the preset attenuation coefficient calculation formula to obtain the attenuation coefficient corresponding to the i-th door opening event.

[0029] Add the cumulative thermal shock intensity index after the (i-1)th door opening event to the single thermal shock characterization quantity corresponding to the i-th door opening event to obtain the uncorrected cumulative thermal shock intensity index corresponding to the i-th door opening event.

[0030] Multiply the uncorrected cumulative thermal shock intensity index by the attenuation coefficient corresponding to the i-th door opening event to obtain the corrected cumulative thermal shock intensity index after the i-th door opening event.

[0031] For the second to the last door opening event, the modified cumulative thermal shock intensity index obtained each time is used as the basis for the next superposition. The slope amplitude ratio, attenuation coefficient, unmodified cumulative thermal shock intensity index and modified cumulative thermal shock intensity index are calculated in sequence. The final modified cumulative thermal shock intensity index is taken as the cumulative thermal shock intensity index.

[0032] Preferably, the step of obtaining the slope of the temperature anomaly characteristic interval of the (i-1)th door opening event includes:

[0033] Obtain the temperature value and time corresponding to the peak point of the temperature surge in the temperature anomaly characteristic range of the (i-1)th door opening event, and the temperature value and time corresponding to the termination time of the temperature recovery in the temperature anomaly characteristic range of the (i-1)th door opening event.

[0034] The difference between the temperature value corresponding to the end of the temperature recovery and the temperature value corresponding to the peak of the temperature rise is divided by the difference between the end of the temperature recovery and the peak of the temperature rise to obtain the fall slope corresponding to the i1th door opening event.

[0035] Preferably, the step of generating real-time remaining shelf life is as follows:

[0036] Obtain information on the types of fresh meat products in transit, and query the preset shelf life data table based on the type information to obtain the initial shelf life of that type of fresh meat product at the current refrigeration setting temperature.

[0037] Obtain the preset critical cumulative thermal shock intensity index corresponding to this type of fresh meat product.

[0038] The ratio of the cumulative thermal shock intensity index to the preset critical cumulative thermal shock intensity index is calculated to obtain the thermal shock loss ratio.

[0039] Substituting the initial shelf life to the thermal shock loss ratio into the preset shelf life loss model, the real-time remaining shelf life is obtained.

[0040] Preferably, the steps for obtaining the delivery operation management suggestions are as follows:

[0041] Obtain the estimated arrival time of each subsequent delivery node, and subtract the estimated arrival time from the start time of the transportation task to obtain the estimated transit time for each delivery node.

[0042] Compare the real-time remaining shelf life with the estimated transit time for each delivery node one by one;

[0043] If the remaining shelf life in real time is less than the estimated transit time for a certain delivery node, it is determined that the fresh meat goods in transit cannot safely reach the delivery node within the shelf life, and delivery operation control suggestions for that delivery node are output.

[0044] As described above, the intelligent management system for cold chain transportation of fresh meat provided by the present invention has at least the following beneficial effects:

[0045] This invention provides an intelligent management system for cold chain transportation of fresh meat. By frequently collecting time-series temperature data within the vehicle compartment, and based on local extreme point detection and two-dimensional spacing feature matching, it automatically identifies the temperature anomaly characteristic intervals corresponding to each door opening event. This effectively overcomes the problem of missed transient temperature peaks caused by excessively large sampling intervals in existing methods. It can completely capture the entire process of temperature surge and recovery triggered by each door opening event, significantly improving the accuracy of temperature monitoring data in reflecting the actual thermal damage process. By introducing three dimensions—thermal shock amplitude, thermal shock duration, and cumulative overheating—to jointly construct a single thermal shock characterization quantity, compared to existing methods that only assess thermal damage based on a single dimension such as temperature peak or duration, it can more accurately quantify the actual degree of thermal damage caused to fresh meat goods in transit by each door opening event.

[0046] By introducing the slope-to-amplitude ratio, calculated jointly based on the fall slope and the thermal shock amplitude, as an attenuation coefficient, the characterization quantities of each individual thermal shock are dynamically corrected and superimposed to generate a cumulative thermal shock intensity index. This fills the gap in the existing methods' ability to quantitatively analyze the cumulative effect of multiple door-opening thermal shocks, achieving an accurate characterization of the comprehensive thermal damage degree of goods in transit. By mapping the cumulative thermal shock intensity index to the shelf-life loss model to dynamically correct the initial shelf life, and comparing the resulting real-time remaining shelf life with the estimated arrival times of subsequent delivery nodes, a complete closed-loop transformation from data analysis results to transportation management decisions is achieved. This enables managers to obtain accurate quality risk warning information in a timely manner and take proactive intervention measures, effectively reducing avoidable quality losses and improving the intelligent management level of cold chain transportation of fresh meat. Attached Figure Description

[0047] The present invention will be further described with reference to the accompanying drawings, but the embodiments in the drawings do not constitute any limitation on the present invention. For those skilled in the art, other drawings can be obtained based on the following drawings without creative effort.

[0048] Figure 1 This is a schematic diagram of the structure of an intelligent management system for cold chain transportation of fresh meat according to the present invention. Detailed Implementation

[0049] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0050] Please see Figure 1 As shown, the present invention provides an intelligent management system for cold chain transportation of fresh meat, including an abnormal interval identification module, a single impact quantification module, a cumulative impact generation module, a shelf life correction module, and a transportation management decision module.

[0051] Anomaly interval identification module: used to collect temperature time series data in refrigerated truck compartments, and based on the temperature time series data, identify the start time of temperature surge and the end time of temperature recovery for each door opening event, and obtain the temperature anomaly feature interval for each door opening event;

[0052] The steps for obtaining the temperature anomaly characteristic range include:

[0053] S1. Continuously collect the temperature inside the refrigerated compartment at a preset sampling frequency to obtain temperature time series data;

[0054] S2. Perform local extreme point detection on the temperature time series data, obtain all local maxima and local minima in the temperature time series data, and arrange them in time order to obtain the extreme point sequence;

[0055] S3. Pair adjacent local minima and local maxima in the extreme point sequence, extract the time interval and temperature amplitude interval between each pair of adjacent extreme points, and combine the time interval and temperature amplitude interval to form the two-dimensional interval feature of the extreme point pair.

[0056] S4. In all two-dimensional spacing features, match extreme point pairs where the ratio of temperature amplitude spacing to time spacing is greater than a preset ratio. Record the time corresponding to the local minimum point in the successfully matched extreme point pair as the start time of the temperature surge and the corresponding local maximum point as the peak point of the temperature surge.

[0057] S5. Starting from the peak point of the temperature surge, obtain the adjacent local minimum point in the extreme point sequence, and record the time corresponding to the local minimum point as the temperature recovery termination time.

[0058] S6. The start time of the temperature surge and the end time of the temperature recovery corresponding to the same door opening event are combined to form a temperature anomaly feature interval. Steps S1 to S5 are executed sequentially for all door opening events in the temperature time series data to obtain the temperature anomaly feature interval of each door opening event.

[0059] In this embodiment, a temperature sensor installed on the inner wall of the refrigerated compartment is used to continuously collect the temperature inside the compartment at a preset sampling frequency. The collected temperature data is sorted according to the time of collection and integrated to obtain temperature time series data.

[0060] The preset sampling frequency is set according to the typical duration of the temperature surge caused by a single door opening event in a multi-point delivery scenario of a refrigerated truck in the city. It should ensure that a sufficient number of sampling points are included in the complete process of a single temperature surge and recovery so that subsequent steps can fully identify the temperature change pattern corresponding to the door opening event.

[0061] In one specific embodiment, the preset sampling frequency is set to collect data once every 30 seconds. In the scenario of multi-point delivery in the city, the entire process of temperature rise and recovery caused by a single door opening event usually lasts 3 to 8 minutes. This ensures that a sufficient number of sampling points (such as 6 to 16) are included in the single temperature rise and recovery process.

[0062] It should be noted that during multi-point delivery in urban areas, each time the rear door of a refrigerated truck is opened for loading and unloading, hot outside air rushes into the compartment, causing the temperature inside to rise rapidly in a short period. After the rear door is closed, the refrigeration unit operates, causing the temperature to gradually decrease. This creates a continuous extreme value structure in the temperature time series data, consisting of "local minimum → local maximum → local minimum". Based on this physical phenomenon, by identifying and matching the local extreme points in the temperature time series data, the temperature anomaly characteristic range corresponding to each door opening event can be located.

[0063] It should be noted that temperature time series data, as a data sequence that lasts for a relatively long period of time, has only one maximum and one minimum value in the global scope, but there are multiple local maxima and local minima in different time periods.

[0064] It should be noted that the time interval is the difference between the time corresponding to the local maximum and the time corresponding to the local minimum, and the temperature amplitude interval is the difference between the temperature value of the local maximum and the temperature value of the local minimum. For example, in a pair of extreme points, if the time of the local minimum is 10:00:00 and the temperature value is 2℃, and the time of the local maximum is 10:03:00 and the temperature value is 8℃, then the time interval of this pair of extreme points is 3 minutes, and the temperature amplitude interval is 6℃.

[0065] In this embodiment, the preset ratio is obtained based on the refrigerated compartment volume specifications, refrigeration unit power and historical transportation data statistical analysis, and is used to distinguish between the temperature surge caused by the door opening event and the temperature fluctuation caused by the normal start and stop of the refrigeration unit.

[0066] In one specific embodiment, for a commonly used 6.8-meter refrigerated truck, the preset ratio is set to 1.5℃ / minute. For example, if a local minimum point occurs at 10:00:00 with a temperature of 2℃, and a local maximum point occurs at 10:03:00 with a temperature of 8℃, then the time interval is 3 minutes, the temperature amplitude interval is 6℃, and the ratio of the temperature amplitude interval to the time interval is 2℃ / minute, which is greater than the preset ratio of 1.5℃ / minute. This is determined to be a door opening event, and the start time of the temperature surge is recorded as 10:00:00. If the local minimum point immediately following the temperature surge peak occurs at 10:08:00, then the end time of temperature recovery is recorded as 10:08. The temperature anomaly characteristic range corresponding to this door opening event is from 10:00:00 to 10:08:00, a total of 8 minutes.

[0067] Single-impact quantification module: used to obtain the maximum temperature and duration of the interval within the temperature anomaly characteristic range. The difference between the maximum temperature and the set temperature of the refrigerated truck is taken as the thermal shock amplitude, and the duration of the interval is taken as the thermal shock duration, thus constructing a single thermal shock characterization quantity.

[0068] The steps for constructing the characterization quantity of a single thermal shock include:

[0069] Obtain the temperature anomaly characteristic range for each door opening event, extract the maximum temperature value within the temperature anomaly characteristic range, and calculate the difference between the maximum temperature value and the set temperature of the refrigerated truck to obtain the thermal shock amplitude corresponding to each door opening event.

[0070] The difference between the temperature recovery termination time and the temperature rise start time of the temperature anomaly characteristic interval is used to obtain the thermal shock time corresponding to each door opening event.

[0071] For each sampling point within the temperature anomaly characteristic range of each door opening event, the temperature value of the sampling point is subtracted from the set temperature of the refrigerated truck to obtain the instantaneous over-temperature value of the sampling point;

[0072] The instantaneous over-temperature values ​​of all sampling points within the temperature anomaly characteristic range are summed sequentially to obtain the cumulative over-temperature amount corresponding to each door opening event;

[0073] Substituting the thermal shock amplitude, thermal shock duration, and cumulative overheating amount corresponding to each door opening event into the formula for constructing the single thermal shock characterization quantity, we obtain the single thermal shock characterization quantity corresponding to each door opening event.

[0074] In this embodiment, the formula for constructing the characterization of a single thermal shock is as follows:

[0075] ;

[0076] in, , ;

[0077] H i Let A be the characteristic quantity of a single thermal shock corresponding to the i-th door opening event. i Let T be the thermal shock amplitude corresponding to the i-th door opening event. i Let Q be the thermal shock time corresponding to the i-th door opening event. i Let i be the cumulative overtemperature value corresponding to the i-th door opening event, where i is the time sequence number of each door opening event. These are the weighting factors for the product of thermal shock amplitude and thermal shock duration, and the product of cumulative overtemperature and thermal shock duration, respectively. ; The maximum temperature value within the temperature anomaly characteristic range of the i-th door opening event. Setting the temperature for a refrigerated truck, i.e., the refrigeration setting temperature for the refrigerated truck. Let K be the temperature value of the k-th sampling point within the temperature anomaly characteristic interval of the i-th door opening event. The total number of sampling points within the temperature anomaly characteristic interval of the i-th door opening event, where k is the number of each sampling point.

[0078] In this embodiment, the formula for constructing the characterization of a single thermal shock consists of two terms: the first term The contribution of thermal damage reflects the expansion of the peak overheat amplitude with duration, characterizing the equivalent heat load sustained during the most severe temperature deviation from the safe range in a door-opening event; the second term It reflects the contribution of thermal damage caused by the cumulative heat load at each point throughout the entire process, expanding with the duration of the event, and characterizes the overall degree of thermal exposure actually borne by the goods during the entire opening process. The synergistic effect of these two aspects allows the single thermal shock characterization quantity to simultaneously cover both the "peak damage intensity" and the "process cumulative damage," enabling a more accurate reflection of the actual degree of thermal damage caused to fresh meat goods in transit by each opening event.

[0079] It should be noted that the weighting factor and The settings should be adjusted according to the temperature sensitivity of the meat varieties being transported; for meat varieties that are more sensitive to temperature peaks (such as poultry), the settings should be increased appropriately. For meat varieties more sensitive to cumulative heat exposure (such as beef), appropriately increase the amount of... .

[0080] In one specific embodiment, taking the transportation of cut pork as an example, It can be set to 0.4. It can be set to 0.6.

[0081] Taking the i-th door opening event as an example, the refrigerated truck's set temperature is 2℃. The temperature anomaly characteristic range of the i-th door opening event is from 10:00:00 to 10:08:00. Therefore, the thermal shock duration is 8 minutes. There are 12 sampling points within the temperature anomaly characteristic range, with temperature values ​​of 4, 6, 8, 10, 9, 7, 5, 4, 3, 2.5, 2, 2 (unit: ℃). Therefore, the maximum temperature within the temperature anomaly characteristic range of the i-th door opening event is 10℃, and the thermal shock amplitude A... i =10-2=8℃;

[0082] The instantaneous over-temperature values ​​at each sampling point are 2, 4, 6, 8, 7, 5, 3, 2, 1, 0.5, 0, 0 (unit: °C), so the cumulative over-temperature is 38.5 °C.

[0083] Substituting the values ​​into the formula, the single thermal shock characterization value is calculated to be 210.4℃·min. This means that the single thermal shock characterization value caused by the door opening event to the goods in transit is 210.4℃·min. The larger this value is, the more severe the thermal damage caused to the goods by the door opening event. This value will be used as the basis for calculating the cumulative thermal shock intensity index in the future.

[0084] Cumulative impact generation module: It is used to superimpose all the characterization quantities of single thermal shocks according to time sequence, and dynamically correct the superposition result by using the slope of the fall of adjacent temperature anomaly characteristic intervals as the attenuation coefficient to generate a cumulative thermal shock intensity index.

[0085] The step of generating the cumulative thermal shock intensity index includes:

[0086] The single thermal shock characteristic quantity corresponding to the first door opening event is used as the initial cumulative thermal shock intensity index.

[0087] For the i-th door opening event (i≥2), obtain the slope of the temperature anomaly characteristic range of the (i-1)-th door opening event, calculate the ratio of the absolute value of the slope to the thermal shock amplitude corresponding to the (i-1)-th door opening event, and obtain the slope amplitude ratio corresponding to the (i-1)-th door opening event.

[0088] Substitute the slope amplitude ratio corresponding to the (i-1)th door opening event into the preset attenuation coefficient calculation formula to obtain the attenuation coefficient corresponding to the i-th door opening event. The attenuation coefficient calculation formula introduces the preset reference slope amplitude ratio as a normalization benchmark.

[0089] Add the cumulative thermal shock intensity index after the (i-1)th door opening event to the single thermal shock characterization quantity corresponding to the i-th door opening event to obtain the uncorrected cumulative thermal shock intensity index corresponding to the i-th door opening event.

[0090] Multiply the uncorrected cumulative thermal shock intensity index by the attenuation coefficient corresponding to the i-th door opening event to obtain the corrected cumulative thermal shock intensity index after the i-th door opening event.

[0091] For the second to the last door opening event, the modified cumulative thermal shock intensity index obtained each time is used as the basis for the next superposition. The slope amplitude ratio, attenuation coefficient, unmodified cumulative thermal shock intensity index and modified cumulative thermal shock intensity index are calculated in sequence. The final modified cumulative thermal shock intensity index is taken as the cumulative thermal shock intensity index.

[0092] The step of obtaining the slope of the temperature anomaly characteristic interval of the (i-1)th door opening event includes:

[0093] For the i-th door opening event (i≥2);

[0094] Obtain the temperature value and time corresponding to the peak point of the temperature surge in the temperature anomaly characteristic range of the (i-1)th door opening event, and the temperature value and time corresponding to the termination time of the temperature recovery in the temperature anomaly characteristic range of the (i-1)th door opening event.

[0095] The difference between the temperature value corresponding to the end of the temperature recovery and the temperature value corresponding to the peak of the temperature surge is divided by the difference between the end of the temperature recovery and the peak of the temperature surge to obtain the fall slope corresponding to the (i-1)th door opening event.

[0096] In this embodiment, the temperature anomaly feature intervals corresponding to the (i-1)th door opening event and the ith door opening event are obtained and marked as adjacent temperature anomaly feature intervals. The adjacent temperature anomaly feature intervals are obtained, which are two adjacent door opening events, with one door opening event corresponding to one temperature anomaly feature interval.

[0097] The formula for calculating the attenuation coefficient is:

[0098] ,in, R is the attenuation coefficient corresponding to the i-th door opening event. i-1 R0 is the slope amplitude ratio corresponding to the (i-1)th door opening event, and R0 is the preset reference slope amplitude ratio. It is an exponential function;

[0099] The preset reference slope amplitude ratio R0 is determined based on the refrigeration unit power of the refrigerated truck and statistical analysis of historical transportation data. It is used to characterize the minimum slope amplitude ratio benchmark value required for sufficient repair of heat damage to fresh meat goods en route. In a specific embodiment, R0 can be set to 0.51 / min.

[0100] It should be specifically noted that the slope amplitude ratio corresponding to the (i-1)th door opening event is jointly determined by the absolute value of the slope of the temperature anomaly characteristic range of the (i-1)th door opening event and the thermal shock amplitude corresponding to the (i-1)th door opening event. It reflects the relative repair capability of the refrigeration system to the heat load caused by the (i-1)th thermal shock, that is, the temperature drop rate under unit thermal shock intensity.

[0101] The reason for substituting the slope amplitude ratio corresponding to the (i-1)th door opening event into the attenuation coefficient calculation formula to obtain the attenuation coefficient corresponding to the i-th door opening event is that, after the (i-1)th door opening event ends, the temperature inside the carriage undergoes a complete natural drop process, which reflects the actual degree of dissipation of the heat load of the (i-1)th thermal shock by the refrigeration system. When the i-th door opening event occurs, the dissipation process of the (i-1)th thermal shock has been completed, and its degree of dissipation determines the weight of the (i-1)th accumulated thermal damage on the i-th superposition correction. Therefore, the attenuation coefficient obtained by using the slope amplitude ratio of the (i-1)th event is applied to the i-th superposition correction, which corresponds completely to the actual thermal damage accumulation process in terms of timing logic.

[0102] The formula for calculating the uncorrected cumulative thermal shock intensity index is as follows:

[0103] (i≥2) where U i Let C be the uncorrected cumulative thermal shock intensity index corresponding to the i-th door opening event. i-1 H is the cumulative thermal shock intensity index after the (i-1)th door opening event. i Let be the characteristic quantity of a single thermal shock corresponding to the i-th door opening event;

[0104] The formula for calculating the cumulative thermal shock intensity index is as follows:

[0105] ;

[0106] Among them, C i This is the modified cumulative thermal shock intensity index after the i-th door opening event.

[0107] In this embodiment, each time the superposition correction operation is performed, the attenuation coefficient is calculated in real time by the fall slope and thermal shock amplitude of the (i-1)th door opening event, rather than using a fixed attenuation coefficient; different door opening events correspond to different slope amplitude ratios, thus obtaining different attenuation coefficients, so that each superposition correction dynamically corresponds to the actual temperature recovery behavior, achieving true dynamic correction superposition.

[0108] Shelf life correction module: used to map the cumulative thermal shock intensity index to the preset shelf life loss model, correct the initial shelf life of fresh meat products in transit, and generate the real-time remaining shelf life.

[0109] The steps for generating real-time remaining shelf life are as follows:

[0110] Obtain information on the types of fresh meat products in transit, and query the preset shelf life data table based on the type information to obtain the initial shelf life of that type of fresh meat product at the current refrigeration setting temperature.

[0111] Obtain the preset critical cumulative thermal shock intensity index corresponding to this type of fresh meat product.

[0112] The ratio of the cumulative thermal shock intensity index to the preset critical cumulative thermal shock intensity index is calculated to obtain the thermal shock loss ratio.

[0113] Substituting the initial shelf life to the thermal shock loss ratio into the preset shelf life loss model, the real-time remaining shelf life is obtained.

[0114] The expression for the shelf-life loss model is as follows: ,in, , L0 represents the real-time remaining shelf life, while L0 represents the initial shelf life. C represents the thermal shock loss ratio, and C represents the cumulative thermal shock intensity index. This is a preset critical cumulative thermal shock intensity index.

[0115] It should be specifically noted that the physical meaning of the shelf-life loss model is: thermal shock loss ratio It represents the proportion of the current cumulative thermal shock intensity index to the critical cumulative thermal shock intensity index, reflecting the relative degree of damage to goods during their shelf life; The remaining shelf life of goods is represented by the percentage of their shelf life remaining; the product of the two is multiplied by the initial shelf life to obtain the real-time remaining shelf life at the current moment.

[0116] When C=0 =0, =L0, no loss during shelf life;

[0117] when hour, =1, =0, the shelf life is just exhausted, which is completely consistent with the definition of the preset critical cumulative thermal shock intensity index.

[0118] In this embodiment, the preset shelf-life data table is a lookup table pre-established based on shelf-life experimental data of different types of fresh meat products under different refrigeration temperature conditions. The table records the initial shelf life of various types of fresh meat products at the corresponding refrigeration set temperature. The initial shelf life of different types of fresh meat products can be obtained through food microbiology experiments, or it can be established with reference to relevant data in existing national and industry food safety standards. In a specific embodiment, under a refrigeration set temperature of 2°C, the initial shelf life L0 of pork cuts is 72 hours.

[0119] The preset critical cumulative thermal shock intensity index refers to the cumulative thermal shock intensity index corresponding to the exact expiration of the shelf life of fresh meat products in transit, that is, when the cumulative thermal shock intensity index C reaches... At that time, the remaining shelf life in real time dropped to zero, and the quality of the goods reached the critical state for safe consumption. This data was obtained through statistical analysis of experimental data on the quality changes of the same type of fresh meat products under different cumulative thermal shock intensities. Specifically, it was determined by simulating multiple door-opening thermal shock tests and recording the cumulative thermal shock intensity index value corresponding to the point where the quality of the goods just reached the critical state for safe consumption. In a specific embodiment, the preset critical cumulative thermal shock intensity index for pork cuts is... Set to 1200℃ min.

[0120] Transportation Management Decision Module: This module compares the real-time remaining shelf life with the estimated arrival times of subsequent delivery nodes one by one, and outputs delivery operation management suggestions for the corresponding delivery nodes based on the comparison results.

[0121] The steps for obtaining the delivery operation management suggestions are as follows:

[0122] Obtain the estimated arrival time of each subsequent delivery node, and subtract the estimated arrival time from the start time of the transportation task to obtain the estimated transit time for each delivery node.

[0123] Compare the real-time remaining shelf life with the estimated transit time for each delivery node one by one;

[0124] If the remaining shelf life in real time is less than the estimated transit time for a certain delivery node, it is determined that the fresh meat goods in transit cannot safely reach the delivery node within the shelf life, and delivery operation control suggestions for that delivery node are output.

[0125] In this embodiment, the estimated transit time is obtained by subtracting the estimated arrival time of each delivery node from the start time of the transportation task, reflecting the total time required for the goods to travel from the transportation origin to each delivery node.

[0126] In this embodiment, the logic for comparing the real-time remaining shelf life with the estimated transit time is as follows:

[0127] If the real-time remaining shelf life is greater than or equal to the estimated transit time for a certain delivery node, it is determined that the goods can safely reach that node within the shelf life, and no control recommendations need to be issued.

[0128] If the remaining shelf life in real time is less than the estimated transit time for a certain delivery node, a quality risk is identified, and delivery operation control recommendations for that node are output.

[0129] In one specific implementation, for example, if the real-time remaining shelf life is 10 hours and the estimated transit time for the third delivery node is 12 hours, then it is determined that the goods cannot safely reach the third delivery node, and corresponding control recommendations are output. These delivery operation control recommendations may include adjusting the delivery sequence or shortening the dwell time at the current node.

[0130] In this embodiment, it should be specifically noted that the above formulas are all dimensionless calculations. The formulas are derived from software simulations using a large amount of collected data to obtain the most recent real-world results. The preset parameters in the formulas are set by those skilled in the art according to the actual situation.

[0131] In conclusion, the above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the protection scope of the present invention.

[0132] The above description is merely a specific embodiment of this application, but the scope of protection of this application is not limited thereto. Any variations or substitutions that can be easily conceived by those skilled in the art within the scope of the technology disclosed in this application should be included within the scope of protection of this application. Therefore, the scope of protection of this application should be determined by the scope of the claims.

Claims

1. An intelligent management system for cold chain transportation of fresh meat, characterized in that, include: Anomaly interval identification module: used to collect temperature time series data in refrigerated truck compartments, and based on the temperature time series data, identify the start time of temperature surge and the end time of temperature recovery for each door opening event, and obtain the temperature anomaly feature interval for each door opening event; Single-impact quantification module: used to obtain the maximum temperature and duration of the interval within the temperature anomaly characteristic range. The difference between the maximum temperature and the set temperature of the refrigerated truck is taken as the thermal shock amplitude, and the duration of the interval is taken as the thermal shock duration, thus constructing a single thermal shock characterization quantity. Cumulative impact generation module: It is used to superimpose all the characterization quantities of single thermal shocks according to time sequence, and dynamically correct the superposition result by using the slope of the fall of adjacent temperature anomaly characteristic intervals as the attenuation coefficient to generate a cumulative thermal shock intensity index. Shelf life correction module: used to map the cumulative thermal shock intensity index to the preset shelf life loss model, correct the initial shelf life of fresh meat products in transit, and generate the real-time remaining shelf life. Transportation Management Decision Module: This module compares the real-time remaining shelf life with the estimated arrival times of subsequent delivery nodes one by one, and outputs delivery operation management suggestions for the corresponding delivery nodes based on the comparison results.

2. The intelligent management system for cold chain transportation of fresh meat according to claim 1, characterized in that, The steps for obtaining the temperature anomaly characteristic range include: S1. Continuously collect the temperature inside the refrigerated compartment at a preset sampling frequency to obtain temperature time series data; S2. Perform local extreme point detection on the temperature time series data, obtain all local maxima and local minima in the temperature time series data, and arrange them in time order to obtain the extreme point sequence; S3. Pair adjacent local minima and local maxima in the extreme point sequence, extract the time interval and temperature amplitude interval between each pair of adjacent extreme points, and combine the time interval and temperature amplitude interval to form the two-dimensional interval feature of the extreme point pair. S4. In all two-dimensional spacing features, match extreme point pairs where the ratio of temperature amplitude spacing to time spacing is greater than a preset ratio. Record the time corresponding to the local minimum point in the successfully matched extreme point pair as the start time of the temperature surge and the corresponding local maximum point as the peak point of the temperature surge. S5. Starting from the peak point of the temperature surge, obtain the adjacent local minimum point in the extreme point sequence, and record the time corresponding to the local minimum point as the temperature recovery termination time. S6. The start time of the temperature surge and the end time of the temperature recovery corresponding to the same door opening event are combined to form a temperature anomaly feature interval. Steps S1 to S5 are executed sequentially for all door opening events in the temperature time series data to obtain the temperature anomaly feature interval of each door opening event.

3. The intelligent management system for cold chain transportation of fresh meat according to claim 1, characterized in that, The steps for constructing the characterization quantity of a single thermal shock include: Obtain the temperature anomaly characteristic range for each door opening event, extract the maximum temperature value within the temperature anomaly characteristic range, and calculate the difference between the maximum temperature value and the set temperature of the refrigerated truck to obtain the thermal shock amplitude corresponding to each door opening event. The difference between the temperature recovery termination time and the temperature rise start time of the temperature anomaly characteristic interval is used to obtain the thermal shock time corresponding to each door opening event. For each sampling point within the temperature anomaly characteristic range of each door opening event, the temperature value of the sampling point is subtracted from the set temperature of the refrigerated truck to obtain the instantaneous over-temperature value of the sampling point; The instantaneous over-temperature values ​​of all sampling points within the temperature anomaly characteristic range are summed sequentially to obtain the cumulative over-temperature amount corresponding to each door opening event; Substituting the thermal shock amplitude, thermal shock duration, and cumulative overheating amount corresponding to each door opening event into the formula for constructing the single thermal shock characterization quantity, we obtain the single thermal shock characterization quantity corresponding to each door opening event.

4. The intelligent management system for cold chain transportation of fresh meat according to claim 1, characterized in that, The step of generating the cumulative thermal shock intensity index includes: The single thermal shock characteristic quantity corresponding to the first door opening event is used as the initial cumulative thermal shock intensity index. For the i-th door opening event, obtain the slope of the temperature anomaly characteristic range of the (i-1)-th door opening event, calculate the ratio of the absolute value of the slope to the thermal shock amplitude corresponding to the (i-1)-th door opening event, and obtain the slope amplitude ratio corresponding to the (i-1)-th door opening event. Substitute the slope amplitude ratio corresponding to the (i-1)th door opening event into the preset attenuation coefficient calculation formula to obtain the attenuation coefficient corresponding to the i-th door opening event. Add the cumulative thermal shock intensity index after the (i-1)th door opening event to the single thermal shock characterization quantity corresponding to the i-th door opening event to obtain the uncorrected cumulative thermal shock intensity index corresponding to the i-th door opening event. Multiply the uncorrected cumulative thermal shock intensity index by the attenuation coefficient corresponding to the i-th door opening event to obtain the corrected cumulative thermal shock intensity index after the i-th door opening event. For the second to the last door opening event, the modified cumulative thermal shock intensity index obtained each time is used as the basis for the next superposition. The slope amplitude ratio, attenuation coefficient, unmodified cumulative thermal shock intensity index and modified cumulative thermal shock intensity index are calculated in sequence. The final modified cumulative thermal shock intensity index is taken as the cumulative thermal shock intensity index.

5. The intelligent management system for cold chain transportation of fresh meat according to claim 4, characterized in that, The step of obtaining the slope of the temperature anomaly characteristic interval of the (i-1)th door opening event includes: Obtain the temperature value and time corresponding to the peak point of the temperature surge in the temperature anomaly characteristic range of the (i-1)th door opening event, and the temperature value and time corresponding to the termination time of the temperature recovery in the temperature anomaly characteristic range of the (i-1)th door opening event. The difference between the temperature value corresponding to the end of the temperature recovery and the temperature value corresponding to the peak of the temperature surge is divided by the difference between the end of the temperature recovery and the peak of the temperature surge to obtain the fall slope corresponding to the (i-1)th door opening event.

6. The intelligent management system for cold chain transportation of fresh meat according to claim 1, characterized in that, The steps for generating real-time remaining shelf life are as follows: Obtain information on the types of fresh meat products in transit, and query the preset shelf life data table based on the type information to obtain the initial shelf life of that type of fresh meat product at the current refrigeration setting temperature. Obtain the preset critical cumulative thermal shock intensity index corresponding to this type of fresh meat product. The ratio of the cumulative thermal shock intensity index to the preset critical cumulative thermal shock intensity index is calculated to obtain the thermal shock loss ratio. Substituting the initial shelf life to the thermal shock loss ratio into the preset shelf life loss model, the real-time remaining shelf life is obtained.

7. The intelligent management system for cold chain transportation of fresh meat according to claim 1, characterized in that, The steps for obtaining the delivery operation management suggestions are as follows: Obtain the estimated arrival time of each subsequent delivery node, and subtract the estimated arrival time from the start time of the transportation task to obtain the estimated transit time for each delivery node. Compare the real-time remaining shelf life with the estimated transit time for each delivery node one by one; If the remaining shelf life in real time is less than the estimated transit time for a certain delivery node, it is determined that the fresh meat goods in transit cannot safely reach the delivery node within the shelf life, and delivery operation control suggestions for that delivery node are output.

Images