A multi-node linkage-based intelligent supply chain scheduling optimization method and system

Abstract

The application is suitable for the technical field of supply chain storage management and cold chain energy consumption optimization, and provides a smart supply chain scheduling optimization method and system based on multi-node linkage, which comprises the following steps: when it is determined that the fluctuation range of temperature or cooling intensity of the future storage area of the target goods in the target cold storage exceeds the preset standard range, the historical refrigeration data of the cold storage is acquired, and the initial power compensation range generated for the target goods is acquired. The application introduces the correlation between the packaging performance value and the delivery quality index, and proposes a fine regulation scheme for the energy consumption distribution problem of the target goods in the abnormal storage area of the cold storage. Although the prior art model may have considered the influence of the packaging performance, it is only processed in a rough way, and cannot distinguish the specific requirements for the power compensation range under different packaging conditions in detail, and cannot quantitatively and sequentially determine the adjustment of the power compensation range.

Description

Technical Field

[0001] This invention belongs to the field of supply chain storage management and cold chain energy consumption optimization technology, and particularly relates to a smart supply chain scheduling optimization method and system based on multi-node linkage. Background Technology

[0002] In the field of supply chain storage management, especially in cross-border e-commerce and cold chain warehousing scenarios, cold storage facilities serve as core nodes, undertaking the tasks of transshipment and preservation of goods. Existing technologies typically involve installing temperature and humidity sensors and environmental monitoring systems within the cold storage to detect environmental fluctuations in different areas. Based on the monitoring results, additional power compensation is allocated to goods in abnormal storage areas to reduce the impact of temperature or humidity fluctuations on product quality. While these methods can, to some extent, prevent product spoilage due to environmental instability, current energy consumption control primarily relies on setting a uniform compensation strategy based on the overall abnormal conditions of the cold storage area.

[0003] However, in practical applications, the packaging performance of the goods themselves plays a crucial role in resisting fluctuations in the cold storage environment. While existing technologies may consider packaging factors in some models, the processing is often rather coarse, merely using packaging type as a classification condition without delving into the quantification of the packaging's thermal and moisture insulation performance. Therefore, when placed in the same abnormal storage area, the system often assigns a uniform initial power compensation level to all goods. This approach ignores the differences in packaging performance among different goods, resulting in necessary energy input for goods with poor packaging performance, but overcompensation for goods with good packaging performance, leading to energy waste and insufficient control precision.

[0004] Therefore, the shortcomings of the existing technology are that it fails to establish a refined correlation between packaging performance values ​​and product quality results, and it is also unable to quantify the specific impact of packaging performance differences on the power compensation range. Summary of the Invention

[0005] The purpose of this invention is to provide a smart supply chain scheduling optimization method and system based on multi-node linkage, aiming to solve the problems mentioned in the background art.

[0006] This invention is implemented as follows: a smart supply chain scheduling optimization method based on multi-node linkage, the method comprising:

[0007] When the future storage area of ​​the target goods is determined to be an abnormal storage area in the target cold storage where the temperature or cooling intensity fluctuation exceeds the preset standard range, historical cold storage data of the cold storage and the initial power compensation range generated for the target goods are obtained.

[0008] Based on historical cold storage data, several historical samples with the same background information as the target goods, located in the same abnormal storage area, and using the same control conditions as the initial power compensation amplitude, but with different packaging performance values, were selected. The analysis was conducted to determine whether there was a specific pattern in the historical samples, that is, as the packaging performance value improved, the corresponding shipment quality index also improved.

[0009] If a specific pattern is identified, then the historical sample with the lowest packaging performance value that meets the standard requirements and meets the shipment quality index is selected as the reference sample.

[0010] The deviation of the packaging performance values ​​of the target product from those of the reference sample is quantified to generate a correction factor. The initial power compensation range is then corrected based on the correction factor to obtain the target power compensation range, and energy consumption regulation is implemented accordingly.

[0011] As a further limitation of the technical solution of the embodiments of the present invention, the same background information refers to the target goods being consistent with historical samples in terms of goods category, storage duration and packaging type.

[0012] As a further limitation of the technical solution of the present invention, the packaging performance value refers to a value that characterizes the heat insulation and moisture insulation performance of the packaging in a cold storage environment, obtained by weighted calculation, normalization processing or calculation by a preset evaluation model based on parameters such as thermal resistance, air tightness, moisture permeability, thickness and / or structural strength of the packaging material.

[0013] As a further limitation of the technical solution of this embodiment of the invention, the acquisition of the shipment quality index specifically includes:

[0014] The historical cold storage data is analyzed to obtain the quality inspection results, return rate statistics, and user feedback results of the historical goods after they have been refrigerated in the target cold storage. Based on the above results, quality characteristic information related to the cold storage environment is identified, and a shipment quality index related to the cold storage process is generated according to the preset evaluation rules.

[0015] As a further limitation of the technical solution of this invention embodiment, based on historical cold storage data, several historical samples with the same background information as the target goods, located in the same abnormal storage area, using the same control conditions as the initial power compensation amplitude, but with different packaging performance values ​​are selected. The step of analyzing whether there is a specific pattern in the historical samples, that is, as the packaging performance value increases, the corresponding shipment quality index also increases, includes:

[0016] Analyze historical cold storage data to select several historical samples that have the same background information as the target goods, are also located in abnormal storage areas, use the same control conditions as the initial power compensation amplitude, but have different packaging performance values.

[0017] The packaging performance values ​​and corresponding shipment quality indices of the aforementioned historical samples are sorted according to their numerical values.

[0018] Based on the ranking results, the trend of change between packaging performance value and shipment quality index is analyzed. If both show an overall continuous growth relationship, then a specific pattern is determined to exist in the aforementioned historical samples.

[0019] As a further limitation of the technical solution of this invention, the steps of quantifying the deviation of the packaging performance value of the target product from that of the reference sample to generate a correction factor, correcting the initial power compensation range based on the correction factor to obtain the target power compensation range, and implementing energy consumption control accordingly include:

[0020] Obtain and calculate the packaging performance values ​​of the target product and the reference sample;

[0021] Calculate the deviation of the packaging performance value of the target product from that of the reference sample, wherein the deviation is the percentage difference of the packaging performance value of the target product from that of the reference sample;

[0022] Based on the deviation range and the preset control coefficient, the initial power compensation range of the target goods is corrected to obtain the target power compensation range;

[0023] The target power compensation range is applied to the abnormal storage area of ​​the target cold storage to implement energy consumption control for the target goods.

[0024] A smart supply chain scheduling and optimization system based on multi-node linkage, the system comprising:

[0025] The data acquisition module is used to acquire historical refrigeration data of the cold storage and the initial power compensation range generated for the target goods when the future storage area of ​​the target goods is determined to be an abnormal storage area in the target cold storage where the fluctuation range of temperature or cooling intensity exceeds the preset standard range.

[0026] The sample screening module is used to screen out several historical samples that have the same background information as the target goods, are also located in abnormal storage areas, use the same control conditions as the initial power compensation amplitude, but have different packaging performance values, based on historical cold storage data. It also analyzes whether there is a specific pattern in the historical samples, that is, as the packaging performance value improves, the corresponding shipment quality index also improves.

[0027] The reference sample determination module is used to determine the historical sample with the lowest packaging performance value that meets the standard requirements and meets the shipment quality index in the historical sample if a specific pattern is determined to exist.

[0028] The parameter correction module is used to quantify the deviation of the packaging performance value of the target product from that of the reference sample to generate a correction factor, and to correct the initial power compensation amplitude based on the correction factor to obtain the target power compensation amplitude, and to implement energy consumption control accordingly.

[0029] As a further limitation of the technical solution of the embodiments of the present invention, the same background information refers to the target goods being consistent with historical samples in terms of goods category, storage duration and packaging type.

[0030] As a further limitation of the technical solution of the present invention, the packaging performance value refers to a value that characterizes the heat insulation and moisture insulation performance of the packaging in a cold storage environment, obtained by weighted calculation, normalization processing or calculation by a preset evaluation model based on parameters such as thermal resistance, air tightness, moisture permeability, thickness and / or structural strength of the packaging material.

[0031] As a further limitation of the technical solution of this invention embodiment, the acquisition of the shipment quality index specifically includes: parsing the historical cold storage data, obtaining the quality inspection results, return rate statistics and user feedback results of the historical goods corresponding to the historical samples after being refrigerated in the target cold storage; and identifying quality characteristic information related to the cold storage environment based on the above results, and generating a shipment quality index related to the cold storage process according to preset evaluation rules.

[0032] Compared with the prior art, the present invention has the following beneficial effects:

[0033] This invention proposes a refined control scheme for energy consumption allocation of target goods in abnormal cold storage areas by introducing the correlation between packaging performance values ​​and shipment quality index. While existing technical models may consider the impact of packaging performance, they only address this in a coarse manner and cannot precisely differentiate the specific power compensation requirements under different packaging conditions, nor can they quantify and determine the adjustment of the power compensation range sequentially. This invention, by screening historical cold storage data to establish a reference sample, uses the difference in packaging performance values ​​between the target goods and the reference sample as a correction factor to dynamically adjust the initial power compensation range, enabling energy consumption allocation to accurately match the actual needs of the goods.

[0034] This method not only ensures the quality and safety of goods with poor packaging performance, but also avoids excessive energy consumption for goods with good packaging performance, achieving dual optimization of energy efficiency and quality, significantly improving the intelligent management level of the cold chain supply chain, and has outstanding energy-saving and sustainable application value. Attached Figure Description

[0035] Figure 1 A flowchart of the method provided in the embodiments of the present invention;

[0036] Figure 2This is a flowchart illustrating the method provided in this embodiment of the invention, which analyzes historical refrigeration data to determine if a specific pattern exists.

[0037] Figure 3 This is a flowchart illustrating the process of correcting the initial power compensation amplitude based on a correction factor in the method provided in this embodiment of the invention.

[0038] Figure 4 The application architecture diagram of the system provided in the embodiments of the present invention. Detailed Implementation

[0039] To make the objectives, technical solutions, and advantages of this invention clearer, the invention will be further described in detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative and not intended to limit the invention.

[0040] Figure 1 A flowchart of the method provided by an embodiment of the present invention is shown.

[0041] Specifically, a smart supply chain scheduling optimization method based on multi-node linkage includes the following steps:

[0042] Step S100: When it is determined that the future storage area of ​​the target goods is an abnormal storage area in the target cold storage where the temperature or cooling intensity fluctuation exceeds the preset standard range, the historical refrigeration data of the cold storage and the initial power compensation range generated for the target goods are obtained.

[0043] In this embodiment of the invention, the target goods may include cold-chain cosmetics and some cold-chain food products commonly found in e-commerce and foreign trade. Examples include high-end cosmetics such as creams, serums, and face masks, as well as dairy products, chocolates, and frozen foods stored in cross-border e-commerce warehouses. These target goods share a common characteristic: they are highly sensitive to fluctuations in temperature and humidity. Unstable storage conditions can easily lead to quality degradation, spoilage, or even spoilage. Furthermore, they have high market value and a high risk of returns, making them particularly important in cross-border e-commerce and foreign trade supply chains.

[0044] The inconsistencies in temperature or cooling intensity fluctuations within the target cold storage facility are primarily due to its structural layout and operational characteristics. For instance, areas near the entrance or aisles experience more significant hot and cold air convection due to frequent door opening and closing, leading to larger temperature fluctuations. Areas near refrigeration fans or cold air outlets experience excessively high cooling intensity and drastic temperature differences. Conversely, corners of shelves and airflow blind spots suffer from poor cold air circulation and unstable temperatures. For example, a batch of creams and cosmetics stacked near the entrance of a cold storage facility may exhibit unstable emulsification even if the overall cold storage temperature meets requirements, due to frequent temperature fluctuations at the entrance.

[0045] Preset standard ranges are typically set based on the suitable refrigeration range for goods and national / industry standards. For example, vaccines require 2-8℃; if a temperature fluctuation in a certain area exceeds ±2℃, it can be defined as an abnormal storage area exceeding the preset standard range. Similarly, high-end cosmetics will also be marked as abnormal areas if humidity fluctuations exceed a set threshold (e.g., ±5%RH). Target goods stored in such abnormal areas often experience quality degradation, such as oil-water separation in cosmetic creams, reduced efficacy of pharmaceuticals, and deterioration in the taste of food. Due to this risk, existing technology models directly generate a higher initial power compensation level for goods in abnormal storage areas based on environmental monitoring data (temperature and humidity sensors), regional distribution characteristics, and product sensitivity parameters. This compensation level compensates for potential instability by increasing refrigeration power, strengthening fan operation, or extending the cooling cycle. Specifically, the initial power compensation level refers to the additional refrigeration energy pre-allocated by the system in existing cold chain energy consumption control models to offset environmental fluctuations in abnormal areas. This setting is already used in traditional cold storage control systems and intelligent warehouse energy consumption control.

[0046] Historical cold storage data primarily originates from the cold storage's temperature and humidity monitoring system, product tracking system, and outbound inspection records. Specifically, this includes: temperature and humidity curves of the target cold storage area, operating status data of cooling equipment; storage location and time records of different products during the cold storage process; corresponding quality inspection reports at the time of shipment, return rate statistics, and customer feedback. This data forms a historical cold storage dataset, which can be used for subsequent analysis of the target products' performance under different environmental conditions, thus providing data support for correcting the power compensation range.

[0047] Furthermore, the intelligent supply chain scheduling optimization method based on multi-node linkage also includes the following steps:

[0048] Step S200: Based on historical cold storage data, select several historical samples that have the same background information as the target goods, are also located in abnormal storage areas, use the same control conditions as the initial power compensation amplitude, but have different packaging performance values, and analyze whether there is a specific pattern in the historical samples, that is, as the packaging performance value improves, the corresponding shipment quality index also improves.

[0049] The same background information refers to the fact that the target product is consistent with the historical samples in terms of product category, storage duration, and packaging type.

[0050] The packaging performance value refers to a numerical value that characterizes the thermal and moisture insulation performance of packaging in a cold storage environment. This value is obtained by weighted calculation, normalization, or calculation using a preset evaluation model based on parameters such as thermal resistance, air tightness, moisture permeability, thickness, and / or structural strength of the packaging material.

[0051] The acquisition of the shipment quality index specifically includes:

[0052] The historical cold storage data is analyzed to obtain the quality inspection results, return rate statistics, and user feedback results of the historical goods after they have been refrigerated in the target cold storage. Based on the above results, quality characteristic information related to the cold storage environment is identified, and a shipment quality index related to the cold storage process is generated according to the preset evaluation rules.

[0053] Specifically, Figure 2 A flowchart is shown to illustrate whether a specific pattern exists based on historical cold storage data analysis.

[0054] Based on historical cold storage data, several historical samples with the same background information as the target goods, located in the same abnormal storage area, and using the same control conditions as the initial power compensation amplitude, but with different packaging performance values, were selected. The analysis then examined whether a specific pattern existed among these historical samples, i.e., whether the corresponding shipment quality index increased as the packaging performance value improved. Specifically, this included the following steps:

[0055] Step S201: Analyze historical cold storage data and select several historical samples that have the same background information as the target goods, are also located in abnormal storage areas, use the same control conditions as the initial power compensation amplitude, but have different packaging performance values.

[0056] Step S202: Sort the packaging performance values ​​and corresponding shipment quality indices of the historical samples according to their numerical values.

[0057] Step S203: Analyze the changing trend between packaging performance value and shipment quality index based on the ranking results. If both show an overall continuous growth relationship, then it is determined that there is a specific pattern in the aforementioned historical samples.

[0058] In this embodiment of the invention, to ensure the accuracy of the comparison results, the abnormal storage area is preferably the same area. This is because different locations within a cold storage facility may exhibit differences in airflow organization and temperature and humidity fluctuations. If the historical samples and the target product are not in the same area, even with identical background information, environmental differences may introduce biases, rendering the pattern analysis unreliable. Therefore, limiting the historical samples to the same abnormal storage area as the target product ensures high data comparability.

[0059] The product categories within the same background information should be further refined. Not only should the product types be consistent, but the formulas or main ingredients should also remain consistent. For cosmetic products, such as creams, significant differences in formula (e.g., different oil / water phase ratios) will result in varying sensitivities to cold storage environments. Therefore, it is necessary to explicitly limit the product categories and formulas to be largely consistent during the historical sample screening to ensure the validity of the analysis results. The packaging type can be bagged, canned, bottled, or boxed, etc. Different packaging types have significantly different insulation and moisture-proofing capabilities, making them important background information elements. Additionally, the stacking method and density of the goods in the cold storage should be kept largely consistent.

[0060] It is reasonable to adopt control conditions consistent with the initial power compensation range. Existing cold storage energy control systems typically allocate a uniform compensation range based on temperature fluctuations and product sensitivity when goods are identified as being in the same abnormal storage area. This approach ignores the actual buffering capacity resulting from differences in packaging performance. The core technical problem of this invention is to discover and utilize this difference: for goods with poor packaging performance, energy compensation is clearly necessary and appropriate; however, for goods with good packaging performance, their own thermal and moisture insulation capabilities can withstand some environmental fluctuations, and in this case, a uniform power compensation range would lead to energy waste. This invention improves the precision of energy consumption control by introducing packaging performance values ​​for correction.

[0061] The quantification of packaging performance values ​​has mature testing methods in existing technologies. Thermal resistance can be measured by the material's thermal conductivity and thickness; airtightness and moisture permeability have standardized experimental methods (such as oxygen permeability and moisture permeability tests); and thickness and structural strength can be obtained using conventional physical testing equipment. The so-called pre-defined evaluation model can be a linear model based on weighted calculations, a comprehensive score obtained through normalization, or even a predictive model trained on statistical regression or machine learning. Their common purpose is to transform multi-dimensional physical parameters into a single numerical value to facilitate comparison of the thermal and moisture insulation performance of different packaging in refrigerated environments. Such models are widely used in packaging engineering, food storage and transportation, and are therefore considered mature existing technologies.

[0062] The acquisition of the shipment quality index is also based on existing technology. By analyzing historical cold storage data, combined with quality inspection results at the post-cold storage stage (such as appearance, component stability, and sensory indicators), return rate statistics (such as the proportion of returns due to spoilage or deformation), and user feedback results (such as negative reviews and complaints), quality characteristics related to the cold storage process can be identified. This data is stored in e-commerce and foreign trade supply chains, and existing research has used such indicators for supply chain quality evaluation. Based on this, this invention generates a shipment quality index related to the cold storage process according to preset evaluation rules, making it a quantitative indicator for measuring cold storage quality results.

[0063] The significance of identifying a specific pattern lies in the fact that if a sustained overall upward relationship can be found between packaging performance values ​​and the shipment quality index in historical samples, it indicates that improvements in packaging performance can significantly improve the refrigerated quality of goods in abnormal storage areas. This pattern provides a scientific basis for subsequent adjustments, allowing power compensation to be dynamically optimized based on differences in packaging performance, rather than being a one-size-fits-all approach.

[0064] In practical implementation, to avoid interference from random data, the average slope of the two trends can be further limited to a certain deviation range. For example, the correlation coefficient between the packaging performance value curve and the shipment quality index curve after regression fitting can be required to reach a preset threshold, and the deviation of the average slope can not exceed a preset limit. This quantitative constraint helps ensure that the identified specific pattern is stable and statistically significant, thereby enhancing the reliability of the correction factor.

[0065] Furthermore, the intelligent supply chain scheduling optimization method based on multi-node linkage also includes the following steps:

[0066] Step S300: If a specific pattern is determined to exist, then the historical sample with the lowest packaging performance value that meets the standard requirements in the historical sample is identified as the reference sample.

[0067] In this embodiment of the invention, the standard requirements are typically based on industry standards, product quality testing specifications, and internal quality control requirements of the enterprise. For example, for cold chain cosmetics, national or industry standards regarding appearance, ingredient stability, and safety of use can be referenced. Alternatively, return rate thresholds and complaint rate indicators set by e-commerce platforms or cross-border supply chain companies can be used to set acceptable quality standards for shipments. In other words, standard requirements are the bottom line to ensure that the target goods maintain an acceptable quality level during the shipment process.

[0068] When a sample with the lowest packaging performance value that meets the standard requirements in the historical sample is identified as the reference sample, its significance lies in the fact that this sample has proven that, under the same abnormal storage area and consistent initial power compensation range, the lowest packaging performance value can still maintain the product quality meeting the standard requirements. This indicates that as long as the target product's packaging performance value is higher than that of the reference sample, it inherently possesses a stronger ability to withstand environmental fluctuations. If a uniform initial power compensation range generated by existing technology is still used in this case, it will lead to energy redundancy and waste for such target products with better packaging performance.

[0069] The reference sample provides a benchmark for adjusting the initial power compensation range, enabling the system to dynamically adjust the power compensation range based on the differences in packaging performance values ​​between the target product and the reference sample. This approach avoids the current technology model's "one-size-fits-all" allocation of energy consumption to all products located in abnormal storage areas, thus addressing and resolving the core technical issue raised earlier: the current technology does not adequately consider the impact of packaging performance differences on product quality maintenance, leading to unreasonable energy consumption allocation.

[0070] Furthermore, the intelligent supply chain scheduling optimization method based on multi-node linkage also includes the following steps:

[0071] Step S400: Quantify the deviation of the packaging performance value of the target product from that of the reference sample to generate a correction factor, and correct the initial power compensation range based on the correction factor to obtain the target power compensation range, and implement energy consumption control accordingly.

[0072] Specifically, Figure 3 A flowchart is shown to correct the initial power compensation magnitude based on the correction factor.

[0073] The process of quantifying the deviation of the packaging performance values ​​of the target product from those of the reference sample to generate a correction factor, and then correcting the initial power compensation range based on the correction factor to obtain the target power compensation range, and implementing energy consumption control accordingly, specifically includes the following steps:

[0074] Step S401: Obtain and calculate the packaging performance values ​​of the target product and the reference sample;

[0075] Step S402: Calculate the deviation of the packaging performance value of the target product from that of the reference sample, wherein the deviation is the percentage difference of the packaging performance value of the target product from that of the reference sample.

[0076] Step S403: Based on the deviation range and the preset control coefficient, the initial power compensation range of the target product is corrected to obtain the target power compensation range;

[0077] Step S404: Apply the target power compensation range to the abnormal storage area of ​​the target cold storage to implement energy consumption control for the target goods.

[0078] In this embodiment of the invention, the acquisition of the packaging performance values ​​of the target product and the reference sample in step S401 is still done in the same way as described above, that is, based on parameters such as thermal resistance, air tightness, moisture permeability, thickness and / or structural strength of the packaging material, the values ​​are obtained through weighted calculation, normalization processing or calculation by a preset evaluation model, so as to ensure that the obtained packaging performance values ​​are comparable and consistent.

[0079] In steps S402 and S403, the deviation range is used as a correction factor to adjust the initial power compensation range. This is significant because it allows for a more refined adjustment of the original one-size-fits-all energy allocation strategy based on the differences between the actual packaging performance of the target product and the reference sample. If the packaging performance of the target product is better than the reference sample, it has stronger resistance to environmental fluctuations in the cold storage area, and therefore does not require excessive energy consumption to maintain its quality. In this case, appropriately reducing the initial power compensation range through the correction factor can effectively reduce energy waste. Conversely, if the packaging performance of the target product is worse than the reference sample, the power compensation range needs to be increased to prevent quality deterioration. The preset control coefficient limits excessive fluctuations in the correction range, avoiding over-adjustment of energy compensation due to data anomalies or model fitting deviations, thus ensuring the safety and stability of system operation. This preset control coefficient can be derived from historical statistical experience, standard experimental results, or set by the system operator according to the energy consumption control strategy.

[0080] Defining the deviation as the percentage difference between the target product's packaging performance value and the reference sample offers the advantages of being intuitive, clearly quantifiable, and capable of considering comparisons under different product and packaging conditions. The percentage difference format ensures a consistent comparison scale across different units and benchmarks, making the correction factor more universally applicable. Of course, other forms can also be used, such as directly using absolute differences, employing difference coefficients obtained from regression models, or utilizing machine learning to predict the impact of packaging performance differences on shipment quality to construct the correction factor.

[0081] In step S404, the target power compensation range is applied to the abnormal storage area of ​​the target cold storage, which can be specifically implemented in the following ways: adjusting the operating time and air volume of the fans in this area, changing the start-stop frequency of the refrigeration unit, optimizing the opening of the cold air distribution valve, or dynamically adjusting the refrigerant supply in this area. Through these control measures, the temperature fluctuation range and cooling intensity of the abnormal storage area can be directly affected, ensuring the quality stability of the target goods.

[0082] For a specific example: Suppose the packaging performance value of a batch of target goods (bottled cream) is 85, and the packaging performance value of the reference sample is 70. After obtaining the values ​​of both in step S401, step S402 calculates the deviation, i.e., the percentage difference = (85-70) / 70×100% = 21.4%. In step S403, if the initial power compensation range is set to +30%, then under the condition that the preset control coefficient is 0.5, the correction factor = 21.4%×0.5 = 10.7%. Therefore, the corrected target power compensation range = 30%×(1−10.7%)≈26.8%. In step S404, the system applies the target power compensation range to the abnormal storage area of ​​the cold storage, for example, reducing the operating frequency of the fan in this area from 12 times per hour to about 10.7 times per hour, and shortening the extra operating time of the refrigeration unit from 30 minutes to about 26.8 minutes, thereby achieving an additional energy consumption reduction of about 10.7%. This ensures the quality stability of bottled creams under fluctuating environments while avoiding unnecessary energy waste.

[0083] The overall benefit of this solution lies in its pioneering introduction of a correlation between packaging performance values ​​and the shipment quality index. By quantifying differences in packaging performance, it corrects energy consumption allocation, shifting cold storage energy consumption control from past regional environmental compensation to refined management that considers the inherent attributes of the goods. This ensures sufficient energy compensation to maintain quality for goods with poor packaging performance, while preventing energy waste for goods with good packaging performance, significantly improving energy efficiency. In terms of application prospects, this solution is not only applicable to cold chain supply chain scenarios such as cross-border e-commerce bonded warehouses, but can also be extended to fresh food distribution centers, cross-border cold chain food warehousing, and cross-border transportation of cosmetics, providing technical support for energy consumption optimization and carbon emission reduction in cold chain logistics, while simultaneously improving product quality stability and user satisfaction.

[0084] Furthermore, Figure 4 An application architecture diagram of the system provided in an embodiment of the present invention is shown.

[0085] In another preferred embodiment of the present invention, a smart supply chain scheduling and optimization system based on multi-node linkage includes:

[0086] The data acquisition module 100 is used to acquire historical refrigeration data of the cold storage and the initial power compensation range generated for the target goods when the future storage area of ​​the target goods is determined to be an abnormal storage area in the target cold storage where the fluctuation range of temperature or cooling intensity exceeds the preset standard range.

[0087] Furthermore, the intelligent supply chain scheduling and optimization system based on multi-node linkage also includes:

[0088] The sample screening module 200 is used to screen out several historical samples based on historical cold storage data. These samples have the same background information as the target goods, are located in the same abnormal storage area, and are subject to the same control conditions as the initial power compensation amplitude, but have different packaging performance values. The module also analyzes whether there is a specific pattern in the historical samples, i.e., as the packaging performance value improves, the corresponding shipment quality index also improves.

[0089] The same background information refers to the fact that the target product is consistent with the historical samples in terms of product category, storage duration, and packaging type.

[0090] The packaging performance value refers to a numerical value that characterizes the thermal and moisture insulation performance of packaging in a cold storage environment. This value is obtained by weighted calculation, normalization, or calculation using a preset evaluation model based on parameters such as thermal resistance, air tightness, moisture permeability, thickness, and / or structural strength of the packaging material.

[0091] The acquisition of the shipment quality index specifically includes: parsing the historical cold storage data to obtain the quality inspection results, return rate statistics, and user feedback results of the historical goods corresponding to the historical samples after being refrigerated in the target cold storage; and based on the above results, identifying quality characteristic information related to the cold storage environment, and generating a shipment quality index related to the cold storage process according to preset evaluation rules.

[0092] Furthermore, the intelligent supply chain scheduling and optimization system based on multi-node linkage also includes:

[0093] The reference sample determination module 300 is used to determine, if a specific pattern is determined to exist, a historical sample in which the shipment quality index meets the standard requirements and the packaging performance value is the lowest, as a reference sample.

[0094] The parameter correction module 400 is used to quantify the deviation of the packaging performance value of the target product from that of the reference sample to generate a correction factor, and to correct the initial power compensation amplitude based on the correction factor to obtain the target power compensation amplitude, and to implement energy consumption control accordingly.

[0095] It should be understood that although the steps in the flowcharts of the various embodiments of the present invention are shown sequentially according to the arrows, these steps are not necessarily executed in the order indicated by the arrows. Unless explicitly stated herein, there is no strict order restriction on the execution of these steps, and they can be executed in other orders. Moreover, at least some steps in the various embodiments may include multiple sub-steps or multiple stages. These sub-steps or stages are not necessarily completed at the same time, but can be executed at different times. The execution order of these sub-steps or stages is not necessarily sequential, but can be performed alternately or in turn with other steps or at least a portion of the sub-steps or stages of other steps.

[0096] Those skilled in the art will understand that all or part of the processes in the methods of the above embodiments can be implemented by a computer program instructing related hardware. The program can be stored in a non-volatile computer-readable storage medium, and when executed, it can include the processes of the embodiments of the above methods. Any references to memory, storage, databases, or other media used in the embodiments provided in this application can include non-volatile and / or volatile memory. Non-volatile memory can include read-only memory (ROM), programmable ROM (PROM), electrically programmable ROM (EPROM), electrically erasable programmable ROM (EEPROM), or flash memory. Volatile memory can include random access memory (RAM) or external cache memory. By way of illustration and not limitation, RAM is available in various forms, such as static RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), dual data rate SDRAM (DDRSDRAM), enhanced SDRAM (ESDRAM), synchronous link DRAM (SLDRAM), Rambus direct RAM (RDRAM), direct memory bus dynamic RAM (DRDRAM), and memory bus dynamic RAM (RDRAM), etc.

[0097] The technical features of the above embodiments can be combined in any way. For the sake of brevity, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

[0098] The embodiments described above are merely illustrative of several implementations of the present invention, and while the descriptions are specific and detailed, they should not be construed as limiting the scope of the present invention. It should be noted that those skilled in the art can make various modifications and improvements without departing from the concept of the present invention, and these modifications and improvements all fall within the scope of protection of the present invention. Therefore, the scope of protection of this patent should be determined by the appended claims.

[0099] 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, and improvements made within the spirit and principles of the present invention should be included within the protection scope of the present invention.

Claims

1. A smart supply chain scheduling optimization method based on multi-node linkage, characterized in that, The method includes: When the future storage area of ​​the target goods is determined to be an abnormal storage area in the target cold storage where the temperature or cooling intensity fluctuation exceeds the preset standard range, historical cold storage data of the cold storage and the initial power compensation range generated for the target goods are obtained. Based on historical cold storage data, several historical samples with the same background information as the target goods, located in the same abnormal storage area, and using the same control conditions as the initial power compensation amplitude, but with different packaging performance values, were selected. The analysis was conducted to determine whether there was a specific pattern in the historical samples, that is, as the packaging performance value improved, the corresponding shipment quality index also improved. The steps include: Analyze historical cold storage data to select several historical samples that have the same background information as the target goods, are also located in abnormal storage areas, use the same control conditions as the initial power compensation amplitude, but have different packaging performance values. The packaging performance values ​​and corresponding shipment quality indices of the aforementioned historical samples are sorted according to their numerical values. Based on the ranking results, the trend of change between packaging performance value and shipment quality index is analyzed. If both show an overall continuous growth relationship, then it is determined that there is a specific pattern in the aforementioned historical samples. If a specific pattern is identified, then the historical sample with the lowest packaging performance value that meets the standard requirements and meets the shipment quality index is selected as the reference sample. The deviation of the packaging performance value of the target product from that of the reference sample is quantified to generate a correction factor. The initial power compensation range is then corrected based on the correction factor to obtain the target power compensation range, and energy consumption regulation is implemented accordingly. The steps include: Obtain and calculate the packaging performance values ​​of the target product and the reference sample; Calculate the deviation of the packaging performance value of the target product from that of the reference sample, wherein the deviation is the percentage difference of the packaging performance value of the target product from that of the reference sample; Based on the deviation range and the preset control coefficient, the initial power compensation range of the target goods is corrected to obtain the target power compensation range; The target power compensation range is applied to the abnormal storage area of ​​the target cold storage to implement energy consumption control for the target goods.

2. The intelligent supply chain scheduling optimization method based on multi-node linkage according to claim 1, characterized in that, The same background information refers to the fact that the target product is consistent with the historical samples in terms of product category, storage duration, and packaging type.

3. The intelligent supply chain scheduling optimization method based on multi-node linkage according to claim 2, characterized in that, The packaging performance value refers to a numerical value that characterizes the thermal and moisture insulation performance of packaging in a cold storage environment. This value is obtained by weighted calculation, normalization, or calculation using a preset evaluation model based on parameters such as thermal resistance, air tightness, moisture permeability, thickness, and / or structural strength of the packaging material.

4. The intelligent supply chain scheduling optimization method based on multi-node linkage according to claim 1, characterized in that, The acquisition of the shipment quality index specifically includes: The historical cold storage data is analyzed to obtain the quality inspection results, return rate statistics, and user feedback results of the historical goods after they have been refrigerated in the target cold storage. Based on the above results, quality characteristic information related to the cold storage environment is identified, and a shipment quality index related to the cold storage process is generated according to the preset evaluation rules.

5. A smart supply chain scheduling and optimization system based on multi-node linkage, characterized in that, The system includes: The data acquisition module is used to acquire historical refrigeration data of the cold storage and the initial power compensation range generated for the target goods when the future storage area of ​​the target goods is determined to be an abnormal storage area in the target cold storage where the fluctuation range of temperature or cooling intensity exceeds the preset standard range. The sample screening module is used to screen out several historical samples that have the same background information as the target goods, are also located in abnormal storage areas, use the same control conditions as the initial power compensation amplitude, but have different packaging performance values, based on historical cold storage data. It also analyzes whether there is a specific pattern in the historical samples, that is, as the packaging performance value improves, the corresponding shipment quality index also improves. Analyze historical cold storage data to select several historical samples that have the same background information as the target goods, are also located in abnormal storage areas, use the same control conditions as the initial power compensation amplitude, but have different packaging performance values. The packaging performance values ​​and corresponding shipment quality indices of the aforementioned historical samples are sorted according to their numerical values. Based on the ranking results, the trend of change between packaging performance value and shipment quality index is analyzed. If both show an overall continuous growth relationship, then it is determined that there is a specific pattern in the aforementioned historical samples. The reference sample determination module is used to determine the historical sample with the lowest packaging performance value that meets the standard requirements and meets the shipment quality index in the historical sample if a specific pattern is determined to exist. The parameter correction module is used to quantify the deviation of the packaging performance value of the target product from that of the reference sample to generate a correction factor, and to correct the initial power compensation amplitude based on the correction factor to obtain the target power compensation amplitude, and to implement energy consumption control accordingly. Obtain and calculate the packaging performance values ​​of the target product and the reference sample; Calculate the deviation of the packaging performance value of the target product from that of the reference sample, wherein the deviation is the percentage difference of the packaging performance value of the target product from that of the reference sample; Based on the deviation range and the preset control coefficient, the initial power compensation range of the target goods is corrected to obtain the target power compensation range; The target power compensation range is applied to the abnormal storage area of ​​the target cold storage to implement energy consumption control for the target goods.

6. The intelligent supply chain scheduling and optimization system based on multi-node linkage according to claim 5, characterized in that, The same background information refers to the fact that the target product is consistent with the historical samples in terms of product category, storage duration, and packaging type.

7. The intelligent supply chain scheduling and optimization system based on multi-node linkage according to claim 6, characterized in that, The packaging performance value refers to a numerical value that characterizes the thermal and moisture insulation performance of packaging in a cold storage environment. This value is obtained by weighted calculation, normalization, or calculation using a preset evaluation model based on parameters such as thermal resistance, air tightness, moisture permeability, thickness, and / or structural strength of the packaging material.

8. The intelligent supply chain scheduling and optimization system based on multi-node linkage according to claim 7, characterized in that, The acquisition of the shipment quality index specifically includes: parsing the historical cold storage data to obtain the quality inspection results, return rate statistics, and user feedback results of the historical goods corresponding to the historical samples after being refrigerated in the target cold storage; and based on the above results, identifying quality characteristic information related to the cold storage environment, and generating a shipment quality index related to the cold storage process according to preset evaluation rules.

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