Cold-chain logistics energy consumption optimization method and system based on digital twinning

Abstract

The invention discloses a cold-chain logistics energy consumption optimization method and system based on digital twinning, relates to the technical field of cold-chain logistics, and provides the following scheme that the method comprises the steps that an environment data distribution field in a cold-chain carrier and space coordinates of goods units are obtained and synchronized to a virtual body instantiated based on an existing digital twinning platform, and the space coordinates of the goods units are obtained; local environment information vectors are extracted from the environment data distribution field according to the space coordinates of all the goods units, and the equivalent heat exchange flux value of the surface of each goods unit is calculated through a heat balance equation in combination with the physical parameters of all the goods units. According to the method, the digital twin virtual body is constructed, the continuous environment field is synthesized, and the metamorphic reaction rate field is iteratively calculated in combination with the thermal penetration-metamorphic dynamics coupling model, so that the defects that a traditional monitoring system depends on scattered temperature and humidity point data, lacks the three-dimensional environment field modeling capability and cannot quantitatively predict the metamorphic rate are overcome; the full-dimension continuous reconstruction of the environment field in the cold chain carrier and the dynamic quantitative prediction of the corruption rate of the goods are realized.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of cold chain logistics, more particularly, the present application relates to a cold chain logistics energy consumption optimization method and system based on digital twinning. BACKGROUND

[0002] As the core guarantee link of the circulation of temperature-sensitive goods such as fresh food and pharmaceutical products, the monitoring system of cold chain logistics generally adopts a data acquisition architecture based on discrete point type temperature and humidity sensors, combined with a fixed threshold alarm mechanism: a limited number of sensors deployed in the cold chain carrier periodically upload local temperature and humidity data to the central control unit, and when any sensor reading exceeds the preset threshold (such as temperature > 5℃ or humidity > 90%), an audible and visual alarm is triggered, and the refrigeration equipment starts the overall cooling program according to the preset temperature zone setting value (such as 0~4℃), and uniformly delivers cold air through the fixed air outlet.

[0003] However, the above architecture has defects: first, the monitoring system only relies on scattered temperature and humidity point data and static threshold alarm, and fails to build a three-dimensional continuous environment field, and lacks a deterioration dynamics model based on heat exchange flux and physiological characteristics, resulting in inability to quantitatively predict the deterioration rate field and miss the intervention opportunity; second, the refrigeration equipment adopts an overall cooling mode, which fails to identify the conduction effect of high-risk units and optimize the refrigeration path combined with the stacking topology, resulting in inefficient dissipation of cold air and lack of real-time feedback calibration capability; finally, the system is forced to perform remedial high-energy consumption refrigeration after the deterioration of the goods, and the irreversible damage caused by the intervention lag, therefore, the cold chain logistics energy consumption optimization method and system based on digital twinning are proposed to solve this problem. SUMMARY

[0004] To solve the above technical problems, the cold chain logistics energy consumption optimization method and system based on digital twinning are provided, which solve the problems raised in the background art.

[0005] In order to achieve the above purpose, the technical scheme of the present application is as follows:

[0006] In a first aspect, the present application provides a cold chain logistics energy consumption optimization method based on digital twinning, which comprises:

[0007] Obtaining the environmental data distribution field in the cold chain carrier and the spatial coordinates of the goods units, and synchronizing them to the virtual body instantiated based on the existing digital twinning platform, the goods units including physical parameters and physiological characteristic parameters;

[0008] According to the spatial coordinates of each goods unit, the local environmental information vector is extracted from the environmental data distribution field, and the equivalent heat exchange flux value of the surface of each goods unit is calculated through the heat balance equation combined with the physical parameters of each goods unit;

[0009] Based on the equivalent heat exchange flux and physiological characteristic parameters of the surface of each cargo unit, the spoilage reaction rate field is iteratively calculated in the virtual body by combining the heat penetration-spoilage kinetics coupling model;

[0010] According to the preset spoilage interval, the risk level of the cargo unit in the spoilage reaction rate field is identified, and each high-risk unit and the secondary risk unit in the neighborhood thereof are aggregated into a refrigeration cluster;

[0011] For each refrigeration cluster, the minimum thermal resistance path of the unit in the refrigeration cluster is solved by combining the environmental data distribution field in the cold chain carrier and the stacking topology of the cargo unit.

[0012] The best refrigeration wind direction angle is generated based on the reference vector of the path endpoint of the minimum thermal resistance path, and the refrigeration instruction set containing the target coordinates, refrigeration intensity and duration is output and sent to the refrigeration equipment of the cold chain carrier.

[0013] In the second aspect, the present application provides a cold chain logistics energy consumption optimization system based on digital twinning, which is used to implement the cold chain logistics energy consumption optimization method based on digital twinning described in any of the above aspects, comprising:

[0014] An environmental data and cargo coordinate acquisition module is used to obtain the environmental data distribution field in the cold chain carrier and the spatial coordinates of the cargo unit, and synchronously to the virtual body instantiated based on the existing digital twinning platform;

[0015] A heat exchange flux calculation module is used to extract the local environmental information vector from the environmental data distribution field according to the spatial coordinates of each cargo unit, combine the physical parameters of each cargo unit, and calculate the equivalent heat exchange flux value on its surface through the heat balance equation;

[0016] A spoilage reaction rate field construction module is used to iteratively calculate the spoilage reaction rate field in the virtual body based on the equivalent heat exchange flux and physiological characteristic parameters of the surface of each cargo unit, combined with the heat penetration-spoilage kinetics coupling model;

[0017] A risk identification and refrigeration cluster aggregation module is used to identify the risk level of the cargo unit in the spoilage reaction rate field according to the preset spoilage interval, and aggregate each high-risk unit and the secondary risk unit in the neighborhood thereof into a refrigeration cluster.

[0018] A refrigeration optimization and instruction generation module is used to solve the minimum thermal resistance path of the unit in the refrigeration cluster by combining the environmental data distribution field in the cold chain carrier and the stacking topology of the cargo unit for each refrigeration cluster, generate the best refrigeration wind direction angle based on the reference vector of the path endpoint of the minimum thermal resistance path, and output the refrigeration instruction set containing the target coordinates, refrigeration intensity and duration and send it to the refrigeration equipment of the cold chain carrier.

[0019] In a third aspect, the present application provides a computer device, comprising a memory and a processor, wherein the memory stores a code, and the processor is configured to acquire the code and execute the feedback adjustment method based on somatosensory interaction.

[0020] In a fourth aspect, the present application provides a computer readable storage medium, which stores a computer program, and the computer program is executed by a processor to implement the feedback adjustment method based on somatosensory interaction.

[0021] Compared with the prior art, the present application has the following beneficial effects:

[0022] The present application solves the defects of traditional monitoring systems, such as relying on scattered temperature and humidity point data, lacking three-dimensional environmental field modeling capability, and being unable to quantitatively predict the metamorphic rate, by constructing a digital twin virtual body and combining a heat penetration-metamorphic dynamics coupling model to iteratively calculate the metamorphic reaction rate field, and realizes full-dimensional continuous reconstruction of the environmental field in the cold chain carrier and dynamic quantitative prediction of the spoilage rate of goods.

[0023] The present application solves the problem of invalid dissipation of cold air caused by the traditional overall cooling mode ignoring the conduction effect of high-risk units and not combining with the stacking topology optimization path, by identifying high-risk unit aggregation refrigeration clusters and solving the minimum thermal resistance path to generate the best refrigeration wind direction angle instruction set, and realizes targeted refrigeration for high-risk areas and dynamic optimization of the cold air path.

[0024] The present application solves the intervention lag problem caused by the static threshold alarm being unable to respond to the change in biological activity and lacking dynamic calibration capability, by embedding a biological heat feedback unit in the metamorphic dynamics model and calibrating real-time parameters based on the virtual value of the spoilage characteristics, and realizes adaptive feedback regulation of the spoilage process and online correction of the refrigeration strategy. BRIEF DESCRIPTION OF DRAWINGS

[0025] The disclosure of the present application is described with reference to the accompanying drawings. It should be understood that the drawings are only for illustrative purposes, and are not intended to limit the scope of protection of the present application. Among them:

[0026] Figure 1 The flowchart of the cold chain logistics energy consumption optimization method based on digital twinning proposed by the present application;

[0027] Figure 2 The method flowchart of the iterative calculation in the present application;

[0028] Figure 3 The structure block diagram of the cold chain logistics energy consumption optimization system based on digital twinning proposed by the present application. DETAILED DESCRIPTION

[0029] It is easy to understand that those skilled in the art can propose various structures and implementation manners which can be replaced with each other without changing the spirit of the present application. Therefore, the following specific embodiments and drawings are only exemplary descriptions of the technical solutions of the present application, and should not be regarded as the whole or as a limitation or restriction on the technical solutions of the present application.

[0030] In the prior art, the cold chain logistics monitoring system generally uses discrete point type temperature and humidity sensors combined with fixed threshold alarm mechanism. The sensor periodically uploads local temperature and humidity data, and triggers an alarm when the reading exceeds the preset threshold. The refrigeration equipment starts the overall cooling program. This architecture has problems such as lack of three-dimensional environmental field, insufficient deterioration rate prediction ability, and unoptimized refrigeration path, which leads to invalid dissipation of cold air and intensifies the risk of cargo loss.

[0031] To solve the above problems, research has found that the prior art has failed to establish a three-dimensional continuous environmental field and dynamic correlation with the goods unit, resulting in a calculation deviation of heat exchange flux. Through analysis of the deterioration mechanism of goods in cold chain logistics, it is recognized that the deterioration rate is nonlinearly coupled with local heat exchange flux and physiological characteristics. For the problem of refrigeration path optimization, a dynamic clustering method based on stacked topology and thermal resistance path is proposed, thereby forming a solution: constructing a virtual body to synchronize with the physical environment data, establishing a heat exchange flux calculation model, combining a deterioration dynamics model to iteratively predict risk areas, and finally generating directional refrigeration instructions.

[0032] Referring to Figures 1-2 The present application proposes a cold chain logistics energy consumption optimization method based on digital twinning, comprising:

[0033] Obtain the environmental data distribution field in the cold chain carrier and the spatial coordinates of the goods unit, and synchronize them to the virtual body instantiated based on the existing digital twinning platform. The goods unit includes physical parameters and physiological characteristic parameters;

[0034] According to the spatial coordinates of each goods unit, extract the local environmental information vector from the environmental data distribution field, combine the physical parameters of each goods unit, and calculate the equivalent heat exchange flux value of its surface through the heat balance equation;

[0035] Specifically, the process of obtaining the equivalent heat exchange flux value is as follows:

[0036] S1. Local environmental information vector extraction:

[0037] Locate the three-dimensional spatial coordinates of the goods unit in the virtual body;

[0038] Take the three-dimensional spatial coordinates as the center, and the radius as Establish a spherical sampling region, where, is the radius, is the goods type correction coefficient, is the minimum circumscribed sphere diameter of the product unit, is the correction term;

[0039] Exemplarily, the product unit k of fresh fruits and vegetables can take 1.5, the product unit k of frozen food can take 1.2, the product unit k of pharmaceutical preparations can take 2.0, and the formula of the correction term is: wherein, is the correction term, is the maximum wind speed;

[0040] The radial basis function (RBF) interpolation algorithm is used to calculate the temperature gradient , humidity gradient and air flow velocity vector in the spherical sampling area to construct a local environment information vector wherein, is the surface normal vector of the product unit;

[0041] S2. Extraction of physical parameters:

[0042] The thermal conductivity , equivalent diameter of the product unit , surface emissivity , boundary thickness , specific heat capacity and density of the product unit in the virtual body are extracted;

[0043] S3. Calculation of the equivalent heat exchange flux value by the heat balance equation:

[0044] ;

[0045] In the formula, is the equivalent heat exchange flux value, is the local air flow rate, is , is , is the actual surface temperature of the product unit, is , is the Stefan-Boltzmann constant, is the nearest neighbor product unit surface distance, k= is the convection enhancement coefficient, is the air thermal conductivity, is the Nusselt number;

[0046] Based on the equivalent heat exchange flux and physiological characteristic parameters of each product unit surface, the heat penetration-degeneration kinetics coupled model is used to iteratively calculate the degeneration reaction rate field in the virtual body.

[0047] Based on a preset degradation range, the risk level of the goods unit in the degradation reaction rate field is identified, and each high-risk unit and its neighboring secondary risk units are aggregated into a refrigeration cluster; for example, high-risk units > Secondary risk units are in Low-risk unit < The neighborhood radius of the advanced risk unit = 50cm × compensation coefficient (the compensation coefficient is 1.3 when the porosity is <25% and 0.7 when it is >40%).

[0048] For each refrigeration cluster, the minimum thermal resistance path of the unit within the refrigeration cluster is solved by combining the distribution field of the environmental data inside the cold chain vehicle with the stacking topology of the cargo units.

[0049] The optimal cooling wind direction angle is generated based on the endpoint of the path with the minimum thermal resistance. The cooling command set containing the target coordinates, cooling intensity and duration is output and sent to the cooling equipment of the cold chain vehicle.

[0050] In an optional embodiment, the process of synchronizing to the virtual entity instantiated based on an existing digital twin platform follows dynamic synchronization engine rules, specifically including:

[0051] Real-time acquisition of three-dimensional discrete point cloud data inside cold chain vehicles; use radial basis function interpolation algorithm to convert discrete point cloud data into continuous temperature field, humidity field and airflow field; synthesize the three into environmental data distribution field.

[0052] The temperature and humidity fields in the environmental data distribution field are updated at a preset frequency. When the displacement of the spatial coordinates of the goods unit exceeds the preset movement threshold, the spatial coordinates are resynchronized to the digital twin.

[0053] For example, the preset frequency can be set to 5-10 frames per second, depending on the airflow speed inside the cold chain vehicle. When the airflow speed is 0.5-1m / s (low speed range), a base frequency of 5-7 frames per second is used, and when it is increased to 1.5-2m / s (high speed range), it is increased to 8-10 frames per second. The preset movement threshold can be set to 15%-20% of the diameter of the outer ball of the cargo unit to prevent invalid resynchronization triggered by slight vibration.

[0054] It should be noted that when the cold chain carrier is in the non-stable running stage such as the bumpy road section and the large centrifugal force, the re-synchronization judgment condition is switched to: when the displacement of the space coordinates of the goods unit is detected for 3 times continuously and exceeds the preset moving threshold, and the local temperature gradient is greater than 2℃ / min, the re-synchronization of the space coordinates to the digital twin is performed; through the double verification mechanism of displacement and temperature gradient, the real goods unit displacement event is accurately identified in the vibration interference scene, the invalid re-synchronization caused by the vehicle bumping is avoided, the system calculation power waste is reduced, and the stability of the virtual body is improved;

[0055] When the space coordinates are updated, the local environment information vector and the physical parameters are associated with the goods unit again;

[0056] Through the above technical solutions, the three-dimensional continuous modeling of the internal environment field of the cold chain carrier is realized, and the blind area of the traditional discrete sensor monitoring is eliminated; through the dynamic synchronization mechanism, the real-time matching of the goods position and the environment data is ensured, the local heat exchange calculation error caused by the goods displacement is avoided, and the state consistency of the virtual body and the actual physical carrier is maintained through the re-synchronization operation, thereby providing accurate input for subsequent quality deterioration rate field calculation.

[0057] In an optional embodiment, the heat penetration-quality deterioration dynamics coupling model includes a heat penetration calculation unit, a quality deterioration dynamics unit, a biological heat feedback unit, and a rate field generation unit: the heat penetration calculation unit receives the equivalent heat exchange flux and the physiological characteristic parameters, extracts the tissue heat parameters in the physiological characteristic parameters, and solves the temperature space-time gradient inside the goods unit together with the equivalent heat exchange flux; the quality deterioration dynamics unit maps the temperature space-time gradient into the corruption reaction intensity, and the mapping weight is dynamically adjusted by the enzyme activity in the physiological characteristic parameters; the biological heat feedback unit converts the corruption reaction intensity into a biological heat increment, and reversely superimposes it to the equivalent heat exchange flux input of the next iteration; the rate field generation unit corrects the corruption reaction intensity according to the space coordinates and the physical parameters of the goods unit, and outputs the quality deterioration reaction rate field;

[0058] Specifically, the heat penetration calculation unit is:

[0059]

[0060] In the formula, is the temperature space-time gradient inside the goods unit, is the finite volume method, is the equivalent heat exchange flux, is the thermal conductivity, is the specific heat capacity, is the density;

[0061] The quality deterioration dynamics unit is:

[0062] ;

[0063] In the formula, The intensity of the putrefaction reaction, , Parameters are specified for the product type. For enzyme activity, for, A value of 0.8-1.2 is acceptable; for example, berries. , ,meat , Pharmaceutical preparations , ;

[0064] The bio-thermal feedback unit is:

[0065] ;

[0066] In the formula, For biological heat increment, This represents the equivalent heat exchange flux value for this iteration cycle;

[0067] The velocity field generating unit is:

[0068] ;

[0069] In the formula, For the rate field of the deterioration reaction, This is a correction factor for the number of stacking layers. The total height of the stack. For the spatial coordinates of the goods, As a benchmark factor for the rate of corruption; for example, , The layer number of the goods is given, and the spoilage rate benchmark factor is obtained from physiological characteristic parameters, which can be the ratio of moisture content to respiration intensity. The total stacking height is obtained from the goods stacking topology parameters.

[0070] Through the above technical solution, this application achieves dynamic and accurate prediction of the spoilage reaction rate of cold chain goods. By coupling heat conduction and spoilage kinetics, it can accurately identify the accelerated spoilage effect in local high-temperature regions. The introduction of a biothermal feedback mechanism effectively avoids the temperature field calculation bias caused by neglecting metabolic heat in traditional methods, and the spatial correction function of the rate field generation unit improves the accuracy of spoilage rate prediction in stacked environments.

[0071] In an optional embodiment, iteratively calculating the metamorphic reaction rate field specifically includes:

[0072] The temperature field gradient distribution in the local environmental information vector is extracted, and the temperature change rate on the surface of the goods unit is calculated. If the temperature change rate exceeds the preset change rate threshold, the deterioration reaction rate field of the goods unit is re-iterated. The preset change rate threshold can be 2℃ / min. The critical value for ice crystal destruction of conventional goods cells, such as berries like strawberries, is 1.8℃ / min, and poultry is 2.2℃ / min.

[0073] When a re-iteration is triggered, the equivalent heat exchange flux value of the surface of the cargo unit is recalculated, and the environmental distribution data field and the spatial coordinates of the cargo unit are updated.

[0074] The predicted rate of deterioration reaction output by the current iteration is compared with the historical measured spoilage rate of the same batch of goods during transportation. If the deviation between the two exceeds the preset deviation ratio, the tissue thermal parameter in the physiological characteristic parameters is adjusted according to the direction and magnitude of the deviation. The historical measured spoilage rate is extracted from the three most recent transportation records of the same type of goods on the same cold chain vehicle. If it is a new type of goods, the preset value of the standard database (such as the FDA shelf life prediction database) is used.

[0075] It should be noted that the initial value of the preset deviation ratio is set at 12%. If the standard deviation of the historical spoilage rate of the same batch of goods is greater than 5%, it will be tightened to 10%. When the deviation between the two exceeds the preset deviation ratio, the correction coefficient is selected according to the direction of the deviation: when the deviation is positive (predicted > measured), the coefficient is reduced by 0.5% per unit of deviation, and when the deviation is negative, the coefficient is increased by 0.8% per unit of deviation.

[0076] Specifically, in the virtual volume, when the temperature change rate on the surface of a cargo unit exceeds a preset change rate threshold, it indicates that there is a deviation between the current environmental data and the initial model. At this time, the re-iteration process is automatically triggered to re-collect the spatial coordinate data of the cargo unit, update the temperature and humidity parameters in the environmental distribution field, and recalculate the equivalent heat exchange flux based on the corrected data. After each iteration, the predicted value of the deterioration reaction rate is compared with the measured data of the same type of cargo in the historical transportation process. For example, when the predicted value is 15% higher than the measured value, it is determined that the tissue thermal parameters in the model are overestimated and the thermal conductivity coefficient needs to be reduced proportionally. By dynamically adjusting the parameters, the output result of the heat penetration-deterioration kinetic coupling model gradually approaches the real putrefaction state.

[0077] To facilitate understanding of the above embodiments, the following will illustrate two specific application scenarios of the above embodiments:

[0078] Example Scenario 1: In the transportation of berries in a refrigerated truck, the predicted rate of spoilage reaction for a certain cargo unit is... The historical measured spoilage rate of the same batch of goods is [value missing]. The deviation is ;

[0079] S1.1: Determined as a positive deviation, the tissue thermal parameters are adjusted downwards according to the rules. The original values ​​were: After adjustment, it is: ;

[0080] S1.2: Based on the adjusted tissue thermal parameters, the equivalent heat exchange flux is recalculated, and the predicted value of the metamorphic reaction rate is reduced to [value missing] after iteration. The deviation was reduced to +5%;

[0081] Example Scenario 2: During the transportation of berries in a refrigerated truck, a turn caused the cargo unit to shift by 8cm, exceeding the preset movement threshold of 6cm. The temperature field gradient in the local environmental information vector is 0.5℃ / m (<2℃ / m).

[0082] S2.1: When the cold chain vehicle is in an unstable operating phase such as a bumpy road or significant centrifugation, the system switches the resynchronization judgment condition and does not trigger re-iteration calculation, because the low temperature gradient indicates that the thermal environment is stable at this time. The purpose is to reduce the number of invalid resynchronizations and re-iterations during transportation and save computing power.

[0083] Through the above technical solution, this application can update the model parameters in a timely manner in response to sudden changes in the local environment, avoid the distortion of the deterioration rate prediction caused by the error in the calculation of heat exchange flux, and improve the calculation accuracy of the deterioration reaction rate field through closed-loop calibration of historical data and real-time prediction values, thereby providing a reliable data foundation for refrigeration cluster identification and path optimization, thereby reducing ineffective refrigeration energy consumption and suppressing the risk of goods spoilage.

[0084] In an optional embodiment, solving for the minimum thermal resistance path of the unit within the cooling cluster specifically includes:

[0085] The packaging layer thickness and thermal conductivity are extracted from the physical parameters, and the local airflow velocity and its direction vector within the cooling cluster are obtained from the environmental distribution field.

[0086] The wind direction angle is calculated by the spatial geometric relationship between the surface normal vector of the cargo unit and the airflow direction vector; the wind direction angle ranges from 0 to 180 degrees.

[0087] A formula for calculating the heat flow resistance factor is constructed, and the heat flow resistance factor value between goods units is calculated using this formula.

[0088] A three-dimensional node network is constructed in a virtual volume using all the goods units within the refrigeration cluster. The node coordinates are the geometric center of the unit, and the edge weights are the thermal flow resistance factor values.

[0089] Starting from the nozzle position of the refrigeration equipment closest to the refrigeration cluster within the cold chain vehicle, and ending at the high-risk unit node within the refrigeration cluster, an improved Dijkstra algorithm is used to search for the path with the minimum cumulative heat flow resistance factor value, denoted as the minimum thermal resistance path. The improved Dijkstra algorithm employs a pruning strategy: after calculating the cumulative heat flow resistance factor value of each path, the average value is calculated. When the cumulative heat flow resistance factor value of a subsequent path exceeds 1.8 times the average value during the search process, the search is terminated and the path is removed.

[0090] The formula for calculating the heat flow resistance factor is:

[0091] ;

[0092] In the formula, This is the heat flow resistance factor value. For packaging layer thickness, Thermal conductivity, For local airflow velocity, To calculate the wind direction angle, This is the current product unit number;

[0093] Compared with existing technologies, traditional cold chain refrigeration equipment only starts overall cooling based on fixed temperature zone settings and fails to dynamically adjust the air supply path according to the stacking topology of goods and local thermal resistance, resulting in uneven distribution of cold air and high energy consumption. In contrast, this solution constructs a three-dimensional node network and improves the path search algorithm, and dynamically optimizes the refrigeration path by combining thermal resistance factor, so that cold air can be accurately delivered to high-risk areas along the path with the least thermal resistance.

[0094] Through the above technical solution, this application can dynamically calculate the optimal cooling path based on the physical characteristics and environmental parameters of the goods unit, reduce the dissipation of cold air on ineffective paths, thereby reducing the energy consumption of the cooling equipment, while improving the cooling efficiency of high-risk areas and avoiding the deterioration of goods due to local overheating.

[0095] In an optional embodiment, the generation of the minimum thermal resistance path must satisfy the following constraints, specifically including:

[0096] A baseline coefficient is set based on the physiological characteristic parameters of the product unit;

[0097] For example, physiological characteristic parameters also include water content W, respiratory intensity R, and cold sensitivity coefficient C: W>85%: baseline value=1.2;

[0098] 60%≤W≤85%: Base value = 1.0;

[0099] W < 60%: Base value = 0.8;

[0100] Among them, high-moisture products have a large heat capacity and require enhanced compensation; if they contain no moisture, the value is set to 1.0.

[0101] R>50mgCO2 / kg·h: Respiratory enhancement factor = 1+(R-50) / 100;

[0102] R≤50mgCO2 / kg·h: Respiratory enhancement factor = 1.0;

[0103] Cold damage threshold > 10℃: Cold sensitivity factor = 0.7;

[0104] Cold damage threshold ≤10℃: Cold sensitivity factor = 1.2;

[0105] The critical temperature value at which cold damage occurs to fresh produce and causes irreversible physiological damage can be found in national / industry standard databases (GB) or international standards (ISO platform).

[0106] Ultimately, the baseline coefficient = baseline value × respiratory enhancement factor × cold sensitivity factor;

[0107] Extract the stacking porosity and anisotropy from the stacking topology of the goods unit, combine the two and compare them with the preset stacking interval, and correct the benchmark coefficient based on the comparison results;

[0108] Specifically, the comparison rules between the stacked porosity P and anisotropy A and the preset stacking interval are as follows:

[0109] If P < 25% and A ≥ 0.6: Corrected benchmark coefficient = benchmark coefficient × 1.5 + number of layers × 0.1;

[0110] P < 25% and A < 0.6: Corrected benchmark coefficient = benchmark coefficient × 1.3;

[0111] 25%≤P≤40% and A≥0.6: Corrected benchmark coefficient = benchmark coefficient × 1.2 + number of layers × 0.05;

[0112] 25%≤P≤40% and A<0.6: Corrected benchmark coefficient = benchmark coefficient;

[0113] If P > 40% and A ≥ 0.6: Corrected benchmark coefficient = benchmark coefficient × 0.8 + number of layers × 0.03;

[0114] P>40% and A<0.6: Corrected benchmark coefficient = benchmark coefficient × 0.6;

[0115] Based on the standard deviation of the local airflow velocity within the cooling cluster, the modified baseline coefficient is corrected a second time through a preset mapping relationship to obtain the dynamic adjustment coefficient.

[0116] Specifically, when the standard deviation is ≤0.2m / s, the dynamic adjustment coefficient = the corrected baseline coefficient; when it is 0.2-0.35m / s, the dynamic adjustment coefficient = 1.2 × the corrected baseline coefficient; when it is >0.35m / s, the dynamic adjustment coefficient = 1.8 × the corrected baseline coefficient, based on the fitting of wind tunnel test data;

[0117] The product of the dynamic adjustment coefficient and the diameter of the circumscribed sphere of the product unit is used as the physical constraint value. The node connection edge of the three-dimensional node network is established only when the distance between the surfaces of two product units is less than the physical constraint value.

[0118] When there are multiple paths with cumulative thermal resistance factor values ​​within the preset floating threshold, the path with the largest total thickness of the product unit packaging layer is selected as the path with the minimum thermal resistance.

[0119] Through the above technical solution, this application can dynamically adjust the generation conditions of node connection edges according to the stacking state of goods, local airflow disturbance and physiological characteristic parameters, accurately screen effective heat conduction paths, avoid path selection deviation caused by changes in stacking structure or unstable airflow, and at the same time, when the path weights are similar, prioritize the path with thicker packaging layer to enhance the penetration efficiency of cooling airflow, reduce ineffective dissipation of cold energy, and achieve precise directional cooling.

[0120] In an optional embodiment, after the refrigeration command set is sent to the refrigeration equipment of the cold chain vehicle, a monitoring and feedback mechanism must also be met, specifically including:

[0121] The actual surface temperature of the high-risk unit within the cooling cluster is collected in real time and compared with the predicted surface temperature of the goods unit during the iterative calculation process; the preset deviation threshold can be 10%, which is tightened to 7% for high-value goods;

[0122] If the deviation rate between the two exceeds the preset deviation threshold, the virtual value of the spoilage characteristic is calculated based on the physiological characteristic parameters of the product unit and the historical measured spoilage rate value, and the enzyme activity in the heat penetration-deterioration kinetic coupling model is calibrated using it.

[0123] The refrigeration instruction set is regenerated based on the calibrated thermal penetration-metamorphic dynamics coupling model and overwrites the original refrigeration instruction set.

[0124] Compared with existing technologies, existing cold chain monitoring systems rely solely on fixed thresholds to trigger refrigeration operations, lacking the ability to monitor the actual refrigeration effect and dynamically calibrate the model. This results in a mismatch between refrigeration commands and actual needs. In contrast, this solution collects actual temperature values ​​in real time and provides closed-loop feedback to the prediction model, which can dynamically identify model prediction deviations and improve model accuracy through biometric parameter calibration, thereby avoiding over-refrigeration or under-refrigeration problems caused by model inaccuracies.

[0125] Through the above technical solution, this application realizes dynamic monitoring and model self-correction during the execution of refrigeration commands, effectively improves the prediction accuracy of deterioration reaction rate, ensures that refrigeration commands match the actual state of goods in real time, reduces ineffective refrigeration energy consumption caused by model prediction deviations, and reduces the risk of spoilage caused by local temperature anomalies in goods.

[0126] See Figure 3 As shown, this solution proposes a cold chain logistics energy consumption optimization system based on digital twins to implement the aforementioned cold chain logistics energy consumption optimization method based on digital twins, including:

[0127] The environmental data and cargo coordinate acquisition module is used to acquire the distribution field of environmental data and the spatial coordinates of cargo units within the cold chain vehicle, and synchronize them to the virtual entity instantiated based on the existing digital twin platform.

[0128] The heat exchange flux calculation module is used to extract local environmental information vectors from the environmental data distribution field based on the spatial coordinates of each cargo unit, and calculate the equivalent heat exchange flux value of its surface through the heat balance equation by combining the physical parameters of each cargo unit.

[0129] The metamorphic reaction rate field construction module is used to iteratively calculate the metamorphic reaction rate field in a virtual volume based on the equivalent heat exchange flux and physiological characteristic parameters of the surface of each cargo unit, combined with the heat penetration-metamorphic dynamics coupling model.

[0130] The risk identification and cooling cluster aggregation module is used to identify the risk level of goods units in the deterioration reaction rate field according to the preset deterioration range, and aggregate each high-risk unit and its neighboring secondary risk units into a cooling cluster.

[0131] The refrigeration optimization and instruction generation module is used to solve the minimum thermal resistance path of each unit in the refrigeration cluster by combining the distribution field of the environmental data inside the cold chain vehicle and the stacking topology of the goods units. The module generates the optimal refrigeration wind direction angle with the endpoint of the minimum thermal resistance path as the reference vector, outputs a set of refrigeration instructions including target coordinates, refrigeration intensity and duration, and sends it to the refrigeration equipment of the cold chain vehicle.

[0132] In another embodiment, a computer device is provided, including a memory and a processor, wherein the memory stores a computer program, and the processor executes the computer program to implement the steps in the above embodiments.

[0133] In one embodiment, a computer-readable storage medium is provided storing a computer program that, when executed by a processor, implements the steps described above.

[0134] In one embodiment, a computer program product or computer program is provided, the computer program product or computer program including computer instructions stored in a computer-readable storage medium. A processor of a computer device reads the computer instructions from the computer-readable storage medium, and executes the computer instructions, causing the computer device to perform the steps described above.

[0135] The technical scope of this invention is not limited to the content described above. Those skilled in the art can make various modifications and variations to the above embodiments without departing from the technical concept of this invention, and all such modifications and variations should fall within the protection scope of this invention.

Claims

1. A digital-twin-based method for energy consumption optimization of cold-chain logistics, characterized in that, The method comprises: acquiring the environmental data distribution field and the spatial coordinates of the cargo units in the cold chain vehicle, and synchronizing to the virtual body instantiated based on the existing digital twin platform, the cargo units containing physical parameters and physiological characteristic parameters; extracting the local environmental information vector from the environmental data distribution field according to the spatial coordinates of each cargo unit, combining the physical parameters of each cargo unit, and calculating the equivalent heat exchange flux value of its surface through the heat balance equation; based on the equivalent heat exchange flux of the surface of each cargo unit and the physiological characteristic parameters, combining the heat penetration-degeneration kinetics coupling model to iteratively calculate the degeneration reaction rate field in the virtual body; according to the preset degeneration interval, identifying the risk level of the cargo unit in the degeneration reaction rate field, and aggregating each high-risk unit and the secondary-risk units in its neighborhood into a refrigeration cluster; for each refrigeration cluster, combining the environmental data distribution field and the cargo unit stacking topology in the cold chain vehicle to solve the minimum thermal resistance path of the units in the refrigeration cluster; taking the path endpoint of the minimum thermal resistance path as the reference vector to generate the optimal refrigeration wind direction angle, outputting the refrigeration instruction set containing the target coordinates, refrigeration intensity and duration, and sending it to the refrigeration equipment of the cold chain vehicle.

2. The method of claim 1, wherein, The process of synchronizing to the virtual body instantiated based on the existing digital twin platform follows the dynamic synchronization engine rules, specifically including: real-time acquisition of three-dimensional discrete point cloud data in the cold chain vehicle, conversion of the discrete point cloud data into continuous temperature field, humidity field and airflow field by using the radial basis function interpolation algorithm, and synthesis of the three into the environmental data distribution field; updating the temperature field and humidity field in the environmental data distribution field at a preset frequency, and when detecting that the displacement of the spatial coordinates of the cargo units exceeds the preset movement threshold, performing re-synchronization of the spatial coordinates to the digital twin body; when the spatial coordinates are updated, re-associating the local environmental information vector and the physical parameters for the cargo units.

3. The method of claim 1, wherein, The heat penetration-degeneration kinetics coupling model includes a heat penetration calculation unit, a degeneration kinetics unit, a biological heat feedback unit and a rate field generation unit: the heat penetration calculation unit receives the equivalent heat exchange flux and the physiological characteristic parameters, extracts the tissue heat parameters in the physiological characteristic parameters and the equivalent heat exchange flux to jointly solve the temperature spatiotemporal gradient inside the cargo unit; the degeneration kinetics unit maps the temperature spatiotemporal gradient into the corruption reaction intensity, and the mapping weight is dynamically adjusted by the enzyme activity in the physiological characteristic parameters; the biological heat feedback unit converts the corruption reaction intensity into a biological heat increment, which is reversely superimposed into the equivalent heat exchange flux input of the next iteration; the rate field generation unit corrects the corruption reaction intensity according to the spatial coordinates and the physical parameters of the cargo unit, and outputs the degeneration reaction rate field.

4. The method of claim 3, wherein, The iterative calculation of the degeneration reaction rate field specifically includes: extracting the temperature field gradient distribution in the local environmental information vector, calculating the temperature change rate of the surface of the cargo unit, and if the temperature change rate exceeds the preset change rate threshold, triggering the reiteration of the degeneration reaction rate field of the cargo unit; when reiteration is triggered, the equivalent heat exchange flux value of the surface of the cargo unit is recalculated, and the environmental distribution data field and the spatial coordinates of the cargo unit are updated; The spoiled reaction rate prediction value output by the current iteration is compared with the historical measured spoilage rate value of the same batch of product units in transportation. If the deviation exceeds the preset deviation ratio, the tissue thermal parameter in the physiological characteristic parameter is adjusted according to the exceeding direction and amplitude.

5. The method of claim 1, wherein, The solving the minimum thermal resistance path of the unit in the refrigeration cluster specifically comprises: The packaging layer thickness and thermal conductivity are extracted from the physical parameters, and the local airflow velocity and its direction vector in the refrigeration cluster are obtained from the environment distribution field; The wind direction angle is calculated through the spatial geometric relationship between the surface normal vector of the product unit and the airflow direction vector; A heat flow resistance factor calculation formula is constructed, and the heat flow resistance factor value between the product units is calculated by using the formula; A three-dimensional node network is constructed in the virtual body for all product units in the refrigeration cluster, the node coordinates are the geometric centers of the units, and the edge weight values are the heat flow resistance factor values; The minimum cumulative heat flow resistance factor value path, recorded as the minimum thermal resistance path, is searched by using the improved Dijkstra algorithm from the refrigeration equipment nozzle position closest to the refrigeration cluster in the cold chain carrier to the high-risk unit node in the refrigeration cluster; The heat flow resistance factor calculation formula is: ； wherein, is a heat flow resistance factor value, is a package layer thickness, is a thermal conductivity, is a local air flow velocity, is a calculated wind direction angle, is a current item unit number.

6. The method of claim 5, wherein, The generation of the minimum thermal resistance path needs to meet the following constraint conditions, specifically comprising: A reference coefficient is set based on the physiological characteristic parameters of the product unit; The stacking porosity and anisotropy in the stacking topology of the product unit are extracted, combined with the preset stacking interval, and the reference coefficient is corrected according to the comparison result; The corrected reference coefficient is secondarily corrected through a preset mapping relationship according to the standard deviation of the local airflow velocity in the refrigeration cluster to obtain a dynamic adjustment coefficient; The product of the dynamic adjustment coefficient and the circumscribed sphere diameter of the product unit is taken as a physical constraint value, and the node connection edge of the three-dimensional node network is established only when the surface distance between two product units is less than the physical constraint value; When there are multiple paths with cumulative heat flow resistance factor values within a preset floating threshold, the path with the maximum total thickness of the product unit packaging layer is selected as the minimum thermal resistance path.

7. The method of claim 4, wherein, After the refrigeration instruction set is sent to the refrigeration equipment of the cold chain carrier, a monitoring and feedback mechanism needs to be met, specifically comprising: The actual surface temperature value of the high-risk unit in the refrigeration cluster is collected in real time, and it is compared with the temperature prediction value of the product unit surface in the iterative calculation process; If the deviation rate exceeds the preset deviation threshold, the spoilage characteristic virtual value is calculated based on the physiological characteristic parameters of the product unit and the historical measured spoilage rate value, and the enzyme activity in the heat penetration-spoilage dynamics coupling model is calibrated by using the spoilage characteristic virtual value; The refrigeration instruction set is regenerated based on the calibrated heat penetration-spoilage dynamics coupling model and covers the original refrigeration instruction set.

8. A cold-chain logistics energy consumption optimization system based on digital twinning, characterized in that, A cold chain logistics energy consumption optimization method based on digital twinning is implemented, comprising: An environment data and product coordinate collection module is used to obtain the environment data distribution field in the cold chain carrier and the spatial coordinates of the product units, and synchronously to the virtual body instantiated based on the existing digital twinning platform; a heat exchange flux calculation module configured to extract a local environmental information vector from the environmental data distribution field according to the spatial coordinates of each product unit, combine physical parameters of each product unit, and calculate an equivalent heat exchange flux value on the surface thereof through a heat balance equation; a deterioration reaction rate field construction module configured to iteratively calculate a deterioration reaction rate field in the virtual body based on the equivalent heat exchange flux on the surface of each product unit and physiological characteristic parameters, and combine a heat penetration-deterioration kinetics coupling model; a risk identification and refrigeration cluster aggregation module configured to identify risk levels of product units in the deterioration reaction rate field according to a preset deterioration interval, and aggregate each high-risk unit and secondary-risk units in the neighborhood thereof into a refrigeration cluster; a refrigeration optimization and instruction generation module configured to, for each refrigeration cluster, combine an environmental data distribution field and a product unit stacking topology in a cold chain vehicle, solve a minimum thermal resistance path of units in the refrigeration cluster, generate a best refrigeration wind direction angle with a path endpoint of the minimum thermal resistance path as a reference vector, and output a refrigeration instruction set containing target coordinates, refrigeration intensity, and duration and send the refrigeration instruction set to a refrigeration device of the cold chain vehicle. 9.A computer device, comprising a memory and a processor, wherein the memory stores a computer program, and the computer device is configured to perform the method according to any one of claims 1-8 when the computer program is executed by the processor. The processor executes the computer program to implement the method of any one of claims 1-7.

10. A computer-readable storage medium storing a computer program, the computer program comprising instructions that, when executed by a computer, cause the computer to perform the method of any one of claims 1 to 9. The computer program is executed by the processor to implement the method of any one of claims 1-7.

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