A multi-cloud management and migration system based on modular architecture

Abstract

The present application relates to the field of e-commerce technology, and in particular to a multi-cloud management and migration system based on a modular architecture, including: a product information collection unit, which collects private cloud product reallocation products and private cloud product order attribute indicators; a product information parsing unit, which calculates the average transportation cycle of private cloud product orders, the average occurrence rate of multiple order returns, and the percentage of the number of abnormal purchase orders; a product information storage unit, which stores the private cloud product reallocation products and the private cloud product order attribute indicators output by the product information parsing unit; a migration unit, which corrects the reallocation product preservation condition value and the number of product migration objects to the number of associated order migrations. The present invention can effectively perform timely redistribution after returns and returns for order processing within the cloud platform architecture, fully ensuring the freshness of the products sold.

Description

Technical Field

[0001] The present application relates to the field of e-commerce technology, and in particular to a multi-cloud management and migration system based on a modular architecture. Background Art

[0002] Cloud e-commerce refers to e-commerce platform services based on cloud computing business model applications. On the cloud platform, all e-commerce suppliers, agents, planning service providers, producers, industry associations, management agencies, industry media, legal structures, etc. are concentrated in the cloud and integrated into a resource pool. Each resource is displayed and interacted with each other, and communication and intentions are reached on demand, thereby reducing costs and improving efficiency.

[0003] However, in cloud platform orders, order cancellations or returns often occur. When this happens, the redistribution of the returned or returned products is often difficult to handle, especially for products with high time requirements such as aquatic products. Migrating the returned or returned products to other orders is an urgent problem that needs to be solved. Summary of the invention

[0004] To achieve the above objectives, this application provides the following technical solutions:

[0005] According to a first aspect of the present invention, the present invention claims protection for a multi-cloud management and migration system based on a modular architecture, comprising:

[0006] A product information collection unit, used to collect private cloud product reallocation products and private cloud product order attribute indicators;

[0007] A product information parsing unit, which is associated with the product information collecting unit, and is used to calculate and process the private cloud product order attribute indicators to obtain an average transportation cycle of private cloud product orders, an average occurrence rate of order returns in multiple cycles, and a percentage of the number of abnormal purchase orders;

[0008] A product information storage unit, which is sequentially associated with the product information collection unit and the product information analysis unit, and is used to sequentially store the private cloud product reallocation product and the private cloud product order attribute index output by the product information analysis unit;

[0009] A migration unit is sequentially associated with the product information collection unit, the product information parsing unit and the product information storage unit, and is used to correct the reallocated product preservation condition value to the first associated preservation condition value based on the number of reallocated products, and to correct the reallocated product preservation condition value to the second associated preservation condition value based on the average transportation period of private cloud product orders, and to correct the number of product migration objects to the associated order migration number based on the average occurrence rate of order returns in multiple periods.

[0010] Furthermore, the migration unit determines whether the product quality is within a preset threshold based on the number of reallocated products using three types of confirmation strategies, wherein:

[0011] The first type of confirmation strategy is that the migration unit confirms that the product quality is within a preset threshold under the first order quantity condition;

[0012] The second type of confirmation strategy is that the migration unit confirms that the product quality is less than a preset threshold under the second order number condition, preliminarily confirms that the private cloud product order stocking time is less than the preset threshold, summarizes the percentage of the number of orders that are greater than regular purchase orders, and reconfirms whether the private cloud product order stocking time is less than the preset threshold based on the percentage of the number of orders that are greater than regular purchase orders;

[0013] The third type of confirmation strategy is that the migration unit confirms that the product quality is less than a preset threshold under the condition of the third order number and adjusts the storage condition value of the reallocated product to the first associated storage condition value based on the difference between the number of reallocated products and the preset first allocated order number;

[0014] The first order quantity condition is that the number of reallocated products is not greater than the first order quantity; the second order quantity condition is that the number of reallocated products is greater than the first order quantity and not greater than the second order quantity; the third order quantity condition is that the number of reallocated products is greater than the second order quantity;

[0015] The first order quantity is smaller than the second order quantity.

[0016] Furthermore, the migration unit determines three types of correction strategies for the storage condition values ​​of the reallocated products based on the difference between the number of reallocated products and the preset first allocated order number under the third order number condition, wherein:

[0017] The first type of correction strategy is that the migration unit corrects the reallocated product storage condition value to a preset reallocated product storage condition value when the first order quantity is subtracted;

[0018] The second type of correction strategy is that the migration unit adjusts the reallocated product storage condition value to the first reallocated product storage condition value by presetting the first condition value correction coefficient when the second order number is subtracted;

[0019] The third type of correction strategy is that the migration unit adjusts the reallocated product preservation condition value to the second reallocated product preservation condition value by presetting the second condition value correction coefficient when the third order number is subtracted;

[0020] The first order number subtraction condition is that the difference between the number of reallocated products and the preset first allocation order number is not greater than the first order number difference; the second order number condition is that the difference between the number of reallocated products and the preset first allocation order number is greater than the first order number difference and not greater than the second order number difference; the third order number condition is that the difference between the number of reallocated products and the preset first allocation order number is greater than the second order number difference;

[0021] Among them, the first order quantity difference is smaller than the second order quantity difference, and the preset first condition value correction coefficient is smaller than the preset second condition value correction coefficient.

[0022] Furthermore, the migration unit determines whether the product quality is less than a preset threshold based on the percentage of the number of orders greater than the regular purchase orders calculated in summary under the second order number condition. The three types of reconfirmation strategies include:

[0023] The first type of reconfirmation strategy is that the migration unit reconfirms that the product quality is within a preset threshold under a first percentage condition;

[0024] The second type of reconfirmation strategy is that the migration unit reconfirms that the product quality is less than a preset threshold under a second percentage condition, and corrects the regular purchase order to an associated regular purchase order by calculating the difference between the order number percentage of the abnormal purchase order and the preset first number percentage;

[0025] The third type of reconfirmation strategy is that the migration unit confirms that there is a device abnormality in the private cloud product order module under a third percentage condition and issues a maintenance notice for the private cloud product order module;

[0026] The first percentage condition is that the percentage of the number of abnormal purchase orders is not greater than the preset first percentage; the second percentage condition is that the percentage of the number of abnormal purchase orders is greater than the preset first percentage and not greater than the preset second percentage; the third percentage condition is that the percentage of the number of abnormal purchase orders is greater than the preset second percentage;

[0027] The preset first number percentage is smaller than the preset second number percentage.

[0028] Furthermore, the calculation formula for the percentage S of the number of orders of the abnormal purchase orders is:

[0029] S=Ra / Rb

[0030] Where Ra is the number of orders that are larger than regular purchase orders, and Rb is the total number of orders.

[0031] Furthermore, the migration unit determines three types of correction strategies for regular purchase orders based on the difference between the order number percentage of abnormal purchase orders and the preset first number percentage under the second percentage condition, wherein:

[0032] The first type of purchase order correction strategy is that the migration unit corrects the regular purchase order to a preset regular purchase order when the first percentage is subtracted;

[0033] The second type of purchase order correction strategy is that the migration unit reduces the regular purchase order to the first regular purchase order by presetting a second purchase order correction coefficient when the second percentage is subtracted;

[0034] The third type of purchase order correction strategy is that the migration unit reduces the regular purchase order to a second regular purchase order by presetting the first purchase order correction coefficient when the third percentage is subtracted;

[0035] Wherein, the first percentage subtraction situation is that the difference between the order number percentage of abnormal purchase orders and the preset first number percentage is not greater than the preset first number percentage difference; the second percentage subtraction situation is that the difference between the order number percentage of abnormal purchase orders and the preset first number percentage is greater than the preset first number percentage difference and not greater than the preset second number percentage difference; the third percentage subtraction situation is that the difference between the order number percentage of abnormal purchase orders and the preset first number percentage is greater than the preset second number percentage difference;

[0036] The preset first number percentage difference is smaller than the preset second number percentage difference, and the preset first purchase order correction coefficient is smaller than the preset second purchase order correction coefficient.

[0037] Furthermore, after completing a correction of the reallocated product preservation condition value, the migration unit calculates the average transportation cycle of the private cloud product order based on the multiple private cloud product order transportation cycles associated with the multiple private cloud product orders, and determines whether the product metabolic waste is within the preset threshold based on the average transportation cycle of the private cloud product order. Three confirmation strategies, wherein:

[0038] The first metabolic confirmation strategy is that the migration unit confirms that the product metabolic waste is within a preset threshold under the first transport cycle conditions;

[0039] The second metabolism confirmation strategy is that the migration unit confirms that the product metabolic waste is less than a preset threshold under the second transportation cycle condition, and calculates the difference between the average transportation cycle of the private cloud product order and the preset first average transportation cycle to correct the reallocated product preservation condition value to the second associated reallocated product preservation condition value;

[0040] The third metabolic confirmation strategy is that the migration unit preliminarily confirms that the stored product does not meet the nutritional requirements under the third transportation cycle conditions, and calls the average occurrence rate of order returns in multiple cycles in the historical product information to further confirm whether the stored product meets the nutritional requirements;

[0041] Among them, the first transportation cycle condition is that the average transportation cycle of private cloud product orders is not greater than the preset first average transportation cycle; the second transportation cycle condition is that the average transportation cycle of private cloud product orders is greater than the preset first average transportation cycle and not greater than the preset second average transportation cycle; the third transportation cycle condition is that the average transportation cycle of private cloud product orders is greater than the preset second average transportation cycle;

[0042] The preset first average transportation period is smaller than the preset second average transportation period.

[0043] Furthermore, the migration unit determines three types of re-correction strategies for reallocating product preservation condition values ​​based on the difference between the average transportation cycle of private cloud product orders and the preset first average transportation cycle under the second transportation cycle condition, wherein:

[0044] The first type of re-correction strategy is that the migration unit corrects the reallocated product preservation condition value to the preset reallocated product preservation condition value when the first transportation period is subtracted;

[0045] The second type of re-correction strategy is that the migration unit re-corrects the reallocated product preservation condition value to a third reallocated product preservation condition value by presetting a second condition value re-correction coefficient when the second transportation period is subtracted;

[0046] The third type of re-correction strategy is that the migration unit re-corrects the reallocated product preservation condition value to the fourth reallocated product preservation condition value by re-correcting the preset first condition value by re-correcting the coefficient when the third transportation period is subtracted;

[0047] Among them, the subtraction of the first transportation cycle is that the difference between the average transportation cycle of private cloud product orders and the preset first average transportation cycle is not greater than the preset first average transportation cycle difference; the subtraction of the second transportation cycle is that the difference between the average transportation cycle of private cloud product orders and the preset first average transportation cycle is greater than the preset first average transportation cycle difference and is not greater than the preset second average transportation cycle difference; the subtraction of the third transportation cycle is that the difference between the average transportation cycle of private cloud product orders and the preset first average transportation cycle is greater than the preset second average transportation cycle difference;

[0048] The preset first average transportation cycle difference is smaller than the preset second average transportation cycle difference, and the preset first condition value re-correction coefficient is smaller than the preset second condition value re-correction coefficient.

[0049] Furthermore, the migration unit determines two types of reconfirmation strategies for determining whether the preserved product meets the nutritional requirements based on the average occurrence rate of order returns in multiple cycles under the third transportation cycle condition, wherein:

[0050] The reconfirmation strategy for the first category of products is that the migration unit reconfirms that the preserved product meets the nutritional requirements under the preset first abnormal occurrence rate condition;

[0051] The second category product reconfirmation strategy is that the migration unit reconfirms that the stored product does not meet the nutritional requirements under the preset second abnormality occurrence rate condition, and corrects the number of product migration objects to the associated number by calculating the difference between the average occurrence rate of order returns and the preset average occurrence rate of abnormalities;

[0052] Among them, the preset first abnormal occurrence rate condition is that the average occurrence rate of order returns is not greater than the preset average occurrence rate of abnormalities; the preset second abnormal occurrence rate condition is that the average occurrence rate of order returns is greater than the preset average occurrence rate of abnormalities.

[0053] Furthermore, the migration unit determines three types of correction strategies for the number of product migration objects based on the difference between the average occurrence rate of order returns and the preset average occurrence rate of abnormalities under the condition of a preset second abnormal occurrence rate, wherein:

[0054] The first type of number correction strategy is that the migration unit corrects the number of product migration objects to a preset number of product migration objects when the preset first abnormal occurrence rate is subtracted;

[0055] The second type of number correction strategy is that the migration unit corrects the number of product migration objects to the first number by using a preset first migration type number correction coefficient when the preset second abnormal occurrence rate is subtracted;

[0056] The third type of number correction strategy is that the migration unit corrects the number of product migration objects to the second number by using a preset second migration type number correction coefficient when the preset third abnormal occurrence rate is subtracted;

[0057] Among them, the subtraction of the preset first abnormal occurrence rate is that the difference between the average occurrence rate of order returns and the preset average occurrence rate of abnormalities is not greater than the preset first abnormal average occurrence rate difference; the subtraction of the preset second abnormal occurrence rate is that the difference between the average occurrence rate of order returns and the preset average occurrence rate of abnormalities is greater than the preset first abnormal average occurrence rate difference and is not greater than the preset second abnormal average occurrence rate difference; the subtraction of the preset third abnormal occurrence rate is that the difference between the average occurrence rate of order returns and the preset average occurrence rate of abnormalities is greater than the preset second abnormal average occurrence rate difference;

[0058] The preset first abnormal average occurrence rate difference is smaller than the preset second abnormal average occurrence rate difference, and the preset first migration type number correction coefficient is smaller than the preset second migration type number correction coefficient.

[0059] The present application relates to the field of e-commerce technology, and in particular to a multi-cloud management and migration system based on a modular architecture, including: a product information collection unit, which collects private cloud product reallocation products and private cloud product order attribute indicators; a product information parsing unit, which calculates the average transportation cycle of private cloud product orders, the average occurrence rate of multiple order returns, and the percentage of the number of abnormal purchase orders; a product information storage unit, which stores the private cloud product reallocation products and the private cloud product order attribute indicators output by the product information parsing unit; a migration unit, which corrects the reallocation product preservation condition value and the number of product migration objects to the number of associated order migrations. The present invention can effectively perform timely redistribution after returns and returns for order processing within the cloud platform architecture, fully ensuring the freshness of the products sold. BRIEF DESCRIPTION OF THE DRAWINGS

[0060] Figure 1 A structural diagram of a multi-cloud management and migration system based on a modular architecture as claimed in an embodiment of the present application;

[0061] Figure 2 A workflow diagram of a multi-cloud management and migration system based on a modular architecture as claimed in an embodiment of the present application. DETAILED DESCRIPTION

[0062] The following will be combined with the drawings in the embodiments of the present application to clearly and completely describe the technical solutions in the embodiments of the present application. Obviously, the described embodiments are only part of the embodiments of the present application, not all of the embodiments. Based on the embodiments in the present application, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of this application.

[0063] The terms "first", "second" and "third" in this application are only used for descriptive purposes and cannot be understood as indicating or implying relative importance or implicitly indicating the number of indicated technical features. Therefore, the features defined as "first", "second" and "third" can explicitly or implicitly include at least one of the features. In the description of this application, the meaning of "multiple" is at least two, such as two, three, etc., unless otherwise clearly and specifically defined. All directional indications (such as up, down, left, right, front, back...) in the embodiments of this application are only used to explain the relative position relationship, movement, etc. between the components under a certain specific posture (as shown in the accompanying drawings). If the specific posture changes, the directional indication also changes accordingly. In addition, the terms "including" and "having" and any of their variations are intended to cover non-exclusive inclusions. For example, a process, method, system, product or device that includes a series of steps or units is not limited to the listed steps or units, but optionally also includes steps or units that are not listed, or optionally also includes other steps or units inherent to these processes, methods, products or devices.

[0064] Reference to "embodiments" herein means that a particular feature, structure, or characteristic described in conjunction with the embodiments may be included in at least one embodiment of the present application. The appearance of the phrase in various locations in the specification does not necessarily refer to the same embodiment, nor is it an independent or alternative embodiment that is mutually exclusive with other embodiments. It is explicitly and implicitly understood by those skilled in the art that the embodiments described herein may be combined with other embodiments.

[0065] See also Figure 1 , Figure 2 As shown, they are respectively an overall structural block diagram and a workflow diagram of a multi-cloud management and migration system based on a modular architecture according to an embodiment of the present invention; a multi-cloud management and migration system based on a modular architecture according to an embodiment of the present invention comprises:

[0066] A product information collection unit, used to collect private cloud product reallocation products and private cloud product order attribute indicators;

[0067] A product information parsing unit, which is associated with the product information collecting unit, and is used to calculate and process the private cloud product order attribute indicators to obtain an average transportation cycle of private cloud product orders, an average occurrence rate of order returns in multiple cycles, and a percentage of the number of abnormal purchase orders;

[0068] A product information storage unit, which is sequentially associated with the product information collection unit and the product information analysis unit, and is used to sequentially store the private cloud product reallocation product and the private cloud product order attribute index output by the product information analysis unit;

[0069] A migration unit is sequentially associated with the product information collection unit, the product information parsing unit and the product information storage unit, and is used to correct the reallocated product preservation condition value to the first associated preservation condition value based on the number of reallocated products, and to correct the reallocated product preservation condition value to the second associated preservation condition value based on the average transportation period of private cloud product orders, and to correct the number of product migration objects to the associated order migration number based on the average occurrence rate of order returns in multiple periods.

[0070] Specifically, the product information collection unit includes: a private cloud product reallocation product collection module and a private cloud product order attribute indicator collection module, wherein the private cloud product reallocation product collection module is used to collect specific text and video information of private cloud product orders, and the private cloud product order attribute indicator collection module is used to collect multiple transportation cycles of multiple private cloud product orders, the number of order returns in multiple cycles, the number of abnormal purchase orders and the total number of orders.

[0071] Specifically, the product information parsing unit includes a screening module and a calculation module associated with the screening module, wherein the screening module is used to screen the private cloud product order attribute indicators, and the calculation module is used to calculate parameters such as the average transportation cycle of private cloud product orders, the average occurrence rate of order returns in multiple cycles, and the percentage of abnormal purchase orders.

[0072] The device of the present invention is provided with a product information collection unit, a product information analysis unit, a product information storage unit and a migration unit, and by setting a plurality of preset confirmation parameters and correction parameters, the reallocated product preservation condition value is corrected to the first associated preservation condition value based on the number of reallocated products, thereby reducing the impact of product quality degradation caused by insufficient reallocated product preservation condition values ​​reflected by excessive number of reallocated products; the reallocated product preservation condition value is corrected again to the second associated preservation condition value based on the average transportation cycle of private cloud product orders, thereby reducing the impact on product quality while also reducing the impact on the order efficiency of the private cloud product order process due to excessive correction of the reallocated product preservation condition value; the number of key feature types of migrated product structures is corrected to the number of associated field types based on the average abnormal occurrence rate of multiple cycles, thereby reducing the impact of insufficient preserved products for private cloud product orders reflected by the average abnormal occurrence rate, thereby achieving improved stability in private cloud operation.

[0073] Wherein, in this embodiment, the product is a marine product, such as fish, shrimp, crab, shellfish;

[0074] Take oysters as an example; the average shell length is 10.67±2.76cm, the shell width is 6.44±0.88cm, and the average wet weight is 153.10±20.59g. The unwashed oysters were directly put into a polyvinyl chloride box, and a 500g ice bag was placed in the box. After 1~2h of waterless transportation, they arrived at the laboratory. When they arrived at the laboratory, they were unpacked and checked. The unpacking temperature was 27±2.72℃, and the overall survival rate of oysters was >95%. The oysters were rinsed with a high-pressure seawater gun, and the oysters that were obviously dead were selected again. The remaining oysters were then placed in a 15℃ pool for temporary storage.

[0075] The preservation condition value at least includes:

[0076] Purification method before transportation Oyster purification seawater temperature and ozone concentration: The purified seawater temperature was set to 20℃, 15℃ and 10℃, and the ozone concentration was set to 0.1~0.2mg / L (only 20℃) and 0.3~0.4mg / L, which was divided into 4 seawater temperature-ozone concentration groups. Three parallels were set for each group, and 30 oysters were selected for purification in the culture ponds with corresponding temperature and ozone concentration. The survival status of oysters was observed at 0min, 10min, 30min, 1h and 2h after the start of purification, and 4 oysters were randomly selected at each time point. Use a sterile brush to brush the microorganisms on the surface of the oysters, and then rinse with 20mL of sterile PBS solution to obtain the bacterial solution; dissect the whole oyster organs and put them into a sterile culture dish, weigh them and homogenize them to obtain the oyster organ homogenate. The microbial samples of the surface and whole organs of the four oyster shells were mixed and stored in a 4℃ refrigerator, and the microbial colony culture was completed within 1 hour. Each mixed sample was set up with 3 parallels to detect the bacterial colony count. The determination method was based on GB4789.2-2022 Determination of total colony count in food microbiology inspection. The oyster purified seawater temperature and ozone concentration were determined based on the sterilization effect.

[0077] Weight loss rate before and after transportation: The purified oysters were placed in 25℃, 20℃, 15℃ and 10℃ seawater for 2h. Five parallels were set for each temperature, and 30 oysters were placed in each parallel culture pond. After 2h, the waterless survival transportation was simulated according to the conventional transportation packaging method. The specific method is: first put the oysters and ice bags (2×500g) into an aluminum foil insulation bag, and then put them into a polyvinyl chloride insulation box (36cm×21cm×24cm, wall thickness 5cm) for sealing. The insulation box was placed in a constant temperature box, set the temperature to 30℃, and simulated waterless survival transportation for 48h to calculate the oyster survival rate. The oysters were weighed before and after packaging and transportation as m0 and m, and the oyster weight loss rate was calculated. In addition, the oysters that were still alive were cultured in 15℃ seawater, and the rehydration survival rate was calculated after 24h. According to the indicators such as the survival rate, weight loss rate and rehydration survival rate of oysters after different transition temperature treatments, the optimal transition temperature before oyster transportation was determined. The calculation formula for the weight loss rate (Y) of oysters before and after simulated transportation is:

[0078] ;

[0079] m is the weight before transportation, m0 is the weight after transportation;

[0080] The migration unit determines whether the product quality is within a preset threshold based on the number of reallocated products using three types of confirmation strategies, wherein:

[0081] The first type of confirmation strategy is that the migration unit confirms that the product quality is within a preset threshold under the first order quantity condition;

[0082] The second type of confirmation strategy is that the migration unit confirms that the product quality is less than a preset threshold under the second order number condition, preliminarily confirms that the private cloud product order stocking time is less than the preset threshold, summarizes the percentage of the number of orders that are greater than regular purchase orders, and reconfirms whether the private cloud product order stocking time is less than the preset threshold based on the percentage of the number of orders that are greater than regular purchase orders;

[0083] The third type of confirmation strategy is that the migration unit confirms that the product quality is less than a preset threshold under the condition of the third order number and adjusts the storage condition value of the reallocated product to the first associated storage condition value based on the difference between the number of reallocated products and the preset first allocated order number;

[0084] The first order quantity condition is that the number of reallocated products is not greater than the first order quantity; the second order quantity condition is that the number of reallocated products is greater than the first order quantity and not greater than the second order quantity; the third order quantity condition is that the number of reallocated products is greater than the second order quantity;

[0085] The first order quantity is smaller than the second order quantity.

[0086] Specifically, the number of reallocated products is recorded as Q, the number of first orders is recorded as Q1, and the number of second orders is recorded as Q2, where Q1<Q2, and the difference between the number of reallocated products and the preset number of first allocated orders is recorded as △Q, and △Q=Q-Q1 is set.

[0087] Among them, in this embodiment, the indicators of product quality include at least

[0088] Texture and color difference: The texture of the soft part outside the gonad and the adductor muscle of the oyster was analyzed using the TMS-PRO texture analyzer. The surface moisture of the sample was absorbed with absorbent paper. In TPA mode, a cylindrical probe with a diameter of 8 mm was used to vertically squeeze the sample. The probe descended at a uniform speed of 30 mm / s and the initial force was 0.3 N. After the deformation of the sample reached 60%, the probe was moved up. Two different sites were measured for each sample, and the average values ​​of the adhesiveness (N) and hardness (N) of the two points were calculated as the final value of the sample. A handheld colorimeter was used to perform colorimetric analysis on the surface of the soft part outside the gonad of the oyster sample. After the instrument was calibrated, the brightness difference (ΔL * ), redness difference (Δa * ) and yellowness difference (Δb * ), calculate the total color difference (ΔE * ):

[0089] ;

[0090] Antioxidant enzyme activity: All viscera and closed muscle tissues of oysters were dissected, and the surface moisture of oyster meat was wiped clean with absorbent paper. Samples of oyster gills, closed muscles, visceral masses and mantle were taken respectively, and placed in 2mL centrifuge tubes. After quick freezing with liquid nitrogen, they were stored in a -80℃ refrigerator. When measuring, the samples were accurately weighed using a balance and placed in a 1.5ml homogenate tube, and double distilled water was added according to mass (g): volume (mL) = 1:9. The samples were centrifuged at 4000 rpm for 2 minutes under ice bath conditions, and the supernatant was taken for use. The protein content of the supernatant, superoxide dismutase (SOD), catalase (CAT), and total antioxidant capacity (T-AOC) were tested for each index according to the instructions of the corresponding name kits, and the test results were expressed as the average value of each index.

[0091] Furthermore, the device of the present invention reduces the impact of the low private cloud product order processing metabolism reflected by the number of reallocated products and the reallocated product preservation condition value required for allocating orders on the stability of private cloud product orders by setting the first order number and the second order number, and adopts three types of confirmation strategies based on the number of reallocated products to determine whether the product quality is within a preset threshold, thereby further achieving the improvement of the stability of private cloud operation.

[0092] The migration unit determines three types of correction strategies for the storage condition value of the reallocated product based on the difference between the number of reallocated products and the preset first allocation order number under the third order number condition, wherein:

[0093] The first type of correction strategy is that the migration unit corrects the reallocated product storage condition value to a preset reallocated product storage condition value when the first order quantity is subtracted;

[0094] The second type of correction strategy is that the migration unit adjusts the reallocated product storage condition value to the first reallocated product storage condition value by presetting the first condition value correction coefficient when the second order number is subtracted;

[0095] The third type of correction strategy is that the migration unit adjusts the reallocated product preservation condition value to the second reallocated product preservation condition value by presetting the second condition value correction coefficient when the third order number is subtracted;

[0096] The first order number subtraction condition is that the difference between the number of reallocated products and the preset first allocation order number is not greater than the first order number difference; the second order number condition is that the difference between the number of reallocated products and the preset first allocation order number is greater than the first order number difference and not greater than the second order number difference; the third order number condition is that the difference between the number of reallocated products and the preset first allocation order number is greater than the second order number difference;

[0097] Among them, the first order quantity difference is smaller than the second order quantity difference, and the preset first condition value correction coefficient is smaller than the preset second condition value correction coefficient.

[0098] Furthermore, the device of the present invention determines three types of correction strategies for the preservation condition values ​​of the reallocated products based on the difference between the number of reallocated products and the preset first allocated order number by setting the first order number difference, the second order number difference, the preset first condition value correction coefficient and the preset second condition value correction coefficient, thereby reducing the impact of untimely correction of the preservation condition values ​​of the reallocated products on the stability of private cloud product orders, and further achieving improved stability in the operation of the private cloud.

[0099] The migration unit determines whether the product quality is less than a preset threshold value based on the percentage of the number of orders greater than the regular purchase orders calculated in summary under the second order number condition.

[0100] The first type of reconfirmation strategy is that the migration unit reconfirms that the product quality is within a preset threshold under a first percentage condition;

[0101] The second type of reconfirmation strategy is that the migration unit reconfirms that the product quality is less than a preset threshold under a second percentage condition, and corrects the regular purchase order to an associated regular purchase order by calculating the difference between the order number percentage of the abnormal purchase order and the preset first number percentage;

[0102] The third type of reconfirmation strategy is that the migration unit confirms that there is a device abnormality in the private cloud product order module under a third percentage condition and issues a maintenance notice for the private cloud product order module;

[0103] The first percentage condition is that the percentage of the number of abnormal purchase orders is not greater than the preset first percentage; the second percentage condition is that the percentage of the number of abnormal purchase orders is greater than the preset first percentage and not greater than the preset second percentage; the third percentage condition is that the percentage of the number of abnormal purchase orders is greater than the preset second percentage;

[0104] The preset first number percentage is smaller than the preset second number percentage.

[0105] Specifically, the percentage of the number of abnormal purchase orders is recorded as S, the preset first number percentage is recorded as S1, and the preset second number percentage is recorded as S2, wherein S1<S2, the difference between the number of abnormal purchase orders and the preset first number percentage is recorded as △S, and △S=S-S1 is set.

[0106] Furthermore, the device of the present invention reduces the impact of the percentage of order numbers greater than regular purchase orders on the stability of the private cloud product order process by setting a preset first number percentage and a preset second number percentage, and by using three types of reconfirmation strategies to determine whether the product quality is less than a preset threshold based on the aggregated calculated percentage of order numbers greater than regular purchase orders, thereby further improving the stability of private cloud operation.

[0107] The calculation formula for the percentage S of the number of abnormal purchase orders is:

[0108] S=Ra / Rb;

[0109] Where Ra is the number of orders that are larger than regular purchase orders, and Rb is the total number of orders.

[0110] Furthermore, the device of the present invention improves the ability to accurately confirm the stability of the private cloud product order process by setting a calculation formula for the percentage of the number of abnormal purchase orders, thereby further improving the stability of private cloud operation.

[0111] The migration unit determines three types of correction strategies for regular purchase orders based on the difference between the order number percentage of abnormal purchase orders and the preset first number percentage under the second percentage condition, wherein:

[0112] The first type of purchase order correction strategy is that the migration unit corrects the regular purchase order to a preset regular purchase order when the first percentage is subtracted;

[0113] The second type of purchase order correction strategy is that the migration unit reduces the regular purchase order to the first regular purchase order by presetting a second purchase order correction coefficient when the second percentage is subtracted;

[0114] The third type of purchase order correction strategy is that the migration unit reduces the regular purchase order to a second regular purchase order by presetting the first purchase order correction coefficient when the third percentage is subtracted;

[0115] Wherein, the first percentage subtraction situation is that the difference between the order number percentage of abnormal purchase orders and the preset first number percentage is not greater than the preset first number percentage difference; the second percentage subtraction situation is that the difference between the order number percentage of abnormal purchase orders and the preset first number percentage is greater than the preset first number percentage difference and not greater than the preset second number percentage difference; the third percentage subtraction situation is that the difference between the order number percentage of abnormal purchase orders and the preset first number percentage is greater than the preset second number percentage difference;

[0116] The preset first number percentage difference is smaller than the preset second number percentage difference, and the preset first purchase order correction coefficient is smaller than the preset second purchase order correction coefficient.

[0117] Furthermore, the device of the present invention sets a preset first number percentage difference, a preset second number percentage difference, a preset first purchase order correction coefficient, and a preset second purchase order correction coefficient, and determines three types of correction strategies for regular purchase orders based on the difference between the order number percentage of abnormal purchase orders and the preset first number percentage, thereby reducing the impact of inaccurate corrections to regular purchase orders on preservation efficiency and further achieving improved stability in private cloud operation.

[0118] After completing a correction of the reallocated product preservation condition value, the migration unit calculates the average transportation cycle of the private cloud product order based on the multiple private cloud product order transportation cycles associated with the multiple private cloud product orders, and determines whether the product metabolic waste is within the preset threshold based on the average transportation cycle of the private cloud product order. Three confirmation strategies, wherein:

[0119] The first metabolic confirmation strategy is that the migration unit confirms that the product metabolic waste is within a preset threshold under the first transport cycle conditions;

[0120] The second metabolism confirmation strategy is that the migration unit confirms that the product metabolic waste is less than a preset threshold under the second transportation cycle condition, and calculates the difference between the average transportation cycle of the private cloud product order and the preset first average transportation cycle to correct the reallocated product preservation condition value to the second associated reallocated product preservation condition value;

[0121] The third metabolic confirmation strategy is that the migration unit preliminarily confirms that the stored product does not meet the nutritional requirements under the third transportation cycle conditions, and calls the average occurrence rate of order returns in multiple cycles in the historical product information to further confirm whether the stored product meets the nutritional requirements;

[0122] Among them, the first transportation cycle condition is that the average transportation cycle of private cloud product orders is not greater than the preset first average transportation cycle; the second transportation cycle condition is that the average transportation cycle of private cloud product orders is greater than the preset first average transportation cycle and not greater than the preset second average transportation cycle; the third transportation cycle condition is that the average transportation cycle of private cloud product orders is greater than the preset second average transportation cycle;

[0123] The preset first average transportation period is smaller than the preset second average transportation period.

[0124] Specifically, the average transportation cycle of private cloud product orders is recorded as U, the preset first average transportation cycle is recorded as U1, and the preset second average transportation cycle is recorded as U2, where U1<U2, and the difference between the average transportation cycle of private cloud product orders and the preset first average transportation cycle is recorded as △U, and △U=U-U1 is set.

[0125] In this embodiment, the nutritional requirements include at least:

[0126] Glycogen: It is the most direct and effective energy storage substance in the oyster body. When subjected to stress such as temperature and lack of feed, the body maintains a relatively stable blood sugar concentration by breaking down glycogen. Therefore, the body's glycogen content will decrease over time during the water-free transportation process. Glycogen is also one of the main flavor substances that affect the fatness of oyster meat. Its content directly affects the flavor and nutritional quality of oysters, so it is often used as an important criterion for judging the quality of oyster meat.

[0127] Amino acids: The richness of amino acids in muscle determines the quality and nutritional value of muscle. It is the most important nutritional index for evaluating muscle quality. The content of free amino acids is also an important factor affecting the flavor of crustaceans. Different treatment methods have different effects on the change of total amino acids in oysters. The difference of total amino acids in oysters is manifested in different tissues. The total amino acid content in the closed muscle of oysters is relatively low, while the total amino acid content in the gills and mantle is higher. The amino acid content in different tissues of Chinese mitten crab (Eriocheir sinensis) is different under the same time conditions, indicating that the amino acid content in different tissues of organisms responds differently to the same external conditions. At the same time, whether it is the gills, closed muscles or mantle of oysters, the amino acid content of oysters under different treatment methods did not change significantly (P>0.05); in the study of the effect of chlorella instead of fish meal on the growth performance and muscle quality of Penaeus vannamei and the nutrition improvement strategy, it was found that there was no significant difference in the total amino acid content of shrimp muscle in different treatment groups, but the content of some amino acids changed significantly, which shows that the amino acid composition in oysters is relatively stable, and the water-free live-preservation treatment method before transportation has little effect on the total amino acid content of oysters, which has certain feasibility.

[0128] In order to evaluate the effect of the new waterless live transportation method on the transportation of oysters, the texture, quality, oxidase reaction and nutritional value of oysters were analyzed.

[0129] The new transportation method will not have a significant effect on the texture and color difference of the oyster closing muscle and soft tissue. The changes in the muscle fibers of the oyster closing muscle under the new waterless transportation method and the traditional transportation method are similar. Compared with before transportation, the gaps between the closing muscle fibers increased and a small amount of fiber breakage occurred after transportation. The study of oxidative stress kinases found that compared with the traditional transportation method, the CAT and T-AOC of oysters under the new waterless transportation pre-treatment method were relatively high, and temporary storage treatment had a significant positive effect on alleviating oxidative stress during oyster transportation. By detecting the glycogen and total amino acid content in different tissues of oysters under different transportation methods, it was found that there was no significant effect on the total amino acids of each tissue of each experimental oyster. Compared with the traditional transportation method, the new transportation method has less effect on glycogen.

[0130] Under the new waterless live transportation method, oysters can maintain high vitality and nutritional quality, indicating that the new transportation method has certain feasibility.

[0131] Furthermore, the device of the present invention reduces the impact of the average transportation cycle of private cloud product orders reflecting that the product metabolic waste does not meet the nutritional requirements on the preservation efficiency by setting a preset first average transportation cycle and a preset second average transportation cycle, and adopts three confirmation strategies based on the average transportation cycle of private cloud product orders to determine whether the product metabolic waste is within the preset threshold, thereby further improving the operation stability of the private cloud.

[0132] The migration unit determines three types of re-correction strategies for reallocating product preservation condition values ​​based on the difference between the average transportation cycle of private cloud product orders and the preset first average transportation cycle under the second transportation cycle condition, wherein:

[0133] The first type of re-correction strategy is that the migration unit corrects the reallocated product preservation condition value to the preset reallocated product preservation condition value when the first transportation period is subtracted;

[0134] The second type of re-correction strategy is that the migration unit re-corrects the reallocated product preservation condition value to a third reallocated product preservation condition value by presetting a second condition value re-correction coefficient when the second transportation period is subtracted;

[0135] The third type of re-correction strategy is that the migration unit re-corrects the reallocated product preservation condition value to the fourth reallocated product preservation condition value by re-correcting the preset first condition value by re-correcting the coefficient when the third transportation period is subtracted;

[0136] Among them, the subtraction of the first transportation cycle is that the difference between the average transportation cycle of private cloud product orders and the preset first average transportation cycle is not greater than the preset first average transportation cycle difference; the subtraction of the second transportation cycle is that the difference between the average transportation cycle of private cloud product orders and the preset first average transportation cycle is greater than the preset first average transportation cycle difference and is not greater than the preset second average transportation cycle difference; the subtraction of the third transportation cycle is that the difference between the average transportation cycle of private cloud product orders and the preset first average transportation cycle is greater than the preset second average transportation cycle difference;

[0137] The preset first average transportation cycle difference is smaller than the preset second average transportation cycle difference, and the preset first condition value re-correction coefficient is smaller than the preset second condition value re-correction coefficient.

[0138] Furthermore, the device of the present invention sets a preset first average transportation cycle difference, a preset second average transportation cycle difference, a preset first condition value re-correction coefficient, and a preset second condition value re-correction coefficient, and determines three types of re-correction strategies for reallocating product preservation condition values ​​based on the difference between the average transportation cycle of private cloud product orders and the preset first average transportation cycle under the second transportation cycle condition, thereby reducing the impact of product metabolic waste on preservation efficiency and further improving the stability of private cloud operation.

[0139] Among them, in this embodiment, the metabolism-related indicators include at least: the quantitative values ​​of metabolites such as lipids or lipid-like substances, organic acids and their derivatives, nucleotides, organic heterocyclic compounds, organic oxygen compounds, benzene compounds, phenylacetone and polyketones, organic nitrogen compounds, alkaloids and their derivatives, and hydrocarbons.

[0140] The migration unit determines two types of reconfirmation strategies for determining whether the preserved product meets the nutritional requirements based on the average occurrence rate of order returns in multiple cycles under the third transportation cycle condition, wherein:

[0141] The reconfirmation strategy for the first category of products is that the migration unit reconfirms that the preserved product meets the nutritional requirements under the preset first abnormal occurrence rate condition;

[0142] The second category product reconfirmation strategy is that the migration unit reconfirms that the stored product does not meet the nutritional requirements under the preset second abnormality occurrence rate condition, and corrects the number of product migration objects to the associated number by calculating the difference between the average occurrence rate of order returns and the preset average occurrence rate of abnormalities;

[0143] Among them, the preset first abnormal occurrence rate condition is that the average occurrence rate of order returns is not greater than the preset average occurrence rate of abnormalities; the preset second abnormal occurrence rate condition is that the average occurrence rate of order returns is greater than the preset average occurrence rate of abnormalities.

[0144] Specifically, the average occurrence rate of order returns in multiple cycles is recorded as R, the preset average occurrence rate of exceptions is recorded as R0, the difference between the average occurrence rate of order returns and the preset average occurrence rate of exceptions is recorded as △R, and △R=R-R0 is set.

[0145] Furthermore, the device of the present invention sets a preset average abnormal occurrence rate, and determines two types of reconfirmation strategies based on the average occurrence rate of order returns in multiple cycles under the third transportation cycle condition to determine whether the preserved products meet the nutritional requirements, thereby reducing the impact of the order return occurrence rate on the preserved products and further improving the stability of private cloud operation.

[0146] The migration unit determines three types of correction strategies for the number of product migration objects based on the difference between the average occurrence rate of order returns and the preset average occurrence rate of abnormalities under the preset second abnormal occurrence rate condition, wherein:

[0147] The first type of number correction strategy is that the migration unit corrects the number of product migration objects to a preset number of product migration objects when the preset first abnormal occurrence rate is subtracted;

[0148] The second type of number correction strategy is that the migration unit corrects the number of product migration objects to the first number by using a preset first migration type number correction coefficient when the preset second abnormal occurrence rate is subtracted;

[0149] The third type of number correction strategy is that the migration unit corrects the number of product migration objects to the second number by using a preset second migration type number correction coefficient when the preset third abnormal occurrence rate is subtracted;

[0150] Among them, the subtraction of the preset first abnormal occurrence rate is that the difference between the average occurrence rate of order returns and the preset average occurrence rate of abnormalities is not greater than the preset first abnormal average occurrence rate difference; the subtraction of the preset second abnormal occurrence rate is that the difference between the average occurrence rate of order returns and the preset average occurrence rate of abnormalities is greater than the preset first abnormal average occurrence rate difference and is not greater than the preset second abnormal average occurrence rate difference; the subtraction of the preset third abnormal occurrence rate is that the difference between the average occurrence rate of order returns and the preset average occurrence rate of abnormalities is greater than the preset second abnormal average occurrence rate difference;

[0151] The preset first abnormal average occurrence rate difference is smaller than the preset second abnormal average occurrence rate difference, and the preset first migration type number correction coefficient is smaller than the preset second migration type number correction coefficient.

[0152] Specifically, the preset first abnormal average occurrence rate difference is recorded as △R1, the preset second abnormal average occurrence rate difference is recorded as △R2, the preset first migration type number correction coefficient is recorded as γ1, the preset second migration type number correction coefficient is recorded as γ2, and the preset product migration object number is recorded as P0, wherein △R1<△R2,

[0153] 1<γ1<γ2, the corrected number of product migration objects is recorded as P', and P'=P0×(γ1+γ2) / 2 is set.

[0154] Furthermore, the device of the present invention determines three types of correction strategies for the number of product migration objects based on the difference between the average occurrence rate of order returns and the preset average occurrence rate of abnormalities under the condition of the preset second abnormal occurrence rate by setting a preset first abnormal average occurrence rate difference, a preset second abnormal average occurrence rate difference, a preset first migration type number correction coefficient, and a preset second migration type number correction coefficient, thereby reducing the impact of inaccurate correction of the number of product migration objects on the preserved products and further improving the stability of private cloud operation.

[0155] Furthermore, the device of the present invention determines three types of correction strategies for the preservation condition values ​​of the reallocated products based on the difference between the number of reallocated products and the preset first allocated order number by setting the first order number difference, the second order number difference, the preset first condition value correction coefficient and the preset second condition value correction coefficient, thereby reducing the impact of untimely correction of the preservation condition values ​​of the reallocated products on the stability of private cloud product orders, and further achieving improved stability in the operation of the private cloud.

[0156] Furthermore, the device of the present invention determines three types of correction strategies for the preservation condition values ​​of the reallocated products based on the difference between the number of reallocated products and the preset first allocated order number by setting the first order number difference, the second order number difference, the preset first condition value correction coefficient and the preset second condition value correction coefficient, thereby reducing the impact of untimely correction of the preservation condition values ​​of the reallocated products on the stability of private cloud product orders, and further achieving improved stability in the operation of the private cloud.

[0157] In the several embodiments provided in the present application, it should be understood that the disclosed systems, devices and methods can be implemented through other strategies. For example, the device embodiments described above are only schematic. For example, the division of units is only a logical function division. There may be other division strategies in actual implementation, such as multiple units or modules can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be an indirect coupling or communication connection through some interfaces, devices or units, which can be electrical, mechanical or other forms.

[0158] In addition, each functional unit in each embodiment of the present application can be integrated into one parsing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit. The above integrated unit can be implemented in the form of hardware or in the form of software functional units. The above is only an implementation method of the present application, and does not limit the patent scope of the present application. Any equivalent structure or equivalent process transformation made using the contents of the specification and drawings of this application, or directly or indirectly used in other related technical fields, is also included in the patent protection scope of the present application.

[0159] The specific implementation methods of the invention are described in detail above, but they are only examples, and the present application is not limited to the specific implementation methods described above. For those skilled in the art, any equivalent modification or substitution of the invention is also within the scope of the present application, and therefore, the equalization, modification, and improvement made without departing from the spirit and principle of the present application should be included in the scope of the present application.

Claims

1. A multi-cloud management and migration system based on a modular architecture, characterized in that: include: A product information collection unit, used to collect private cloud product reallocation products and private cloud product order attribute indicators; A product information parsing unit, which is associated with the product information collecting unit, and is used to calculate and process the private cloud product order attribute indicators to obtain an average transportation cycle of private cloud product orders, an average occurrence rate of order returns in multiple cycles, and a percentage of the number of abnormal purchase orders; A product information storage unit, which is sequentially associated with the product information collection unit and the product information analysis unit, and is used to sequentially store the private cloud product reallocation product and the private cloud product order attribute index output by the product information analysis unit; a migration unit, which is sequentially associated with the product information collection unit, the product information parsing unit and the product information storage unit, and is used to correct the reallocated product storage condition value to the first associated storage condition value based on the number of reallocated products, and to correct the reallocated product storage condition value to the second associated storage condition value based on the average transportation cycle of private cloud product orders, and to correct the number of product migration objects to the number of associated order migrations based on the average occurrence rate of order returns in multiple cycles; The migration unit determines whether the product quality is within a preset threshold based on the number of reallocated products using three types of confirmation strategies, wherein: The first type of confirmation strategy is that the migration unit confirms that the product quality is within a preset threshold under the first order quantity condition; The second type of confirmation strategy is that the migration unit confirms that the product quality is less than a preset threshold under the second order number condition, preliminarily confirms that the private cloud product order stocking time is less than the preset threshold, summarizes the percentage of the number of orders that are greater than regular purchase orders, and reconfirms whether the private cloud product order stocking time is less than the preset threshold based on the percentage of the number of orders that are greater than regular purchase orders; The third type of confirmation strategy is that the migration unit confirms that the product quality is less than a preset threshold under the condition of the third order number and adjusts the storage condition value of the reallocated product to the first associated storage condition value based on the difference between the number of reallocated products and the preset first allocated order number; The first order quantity condition is that the number of reallocated products is not greater than the first order quantity; the second order quantity condition is that the number of reallocated products is greater than the first order quantity and not greater than the second order quantity; the third order quantity condition is that the number of reallocated products is greater than the second order quantity; The first order quantity is smaller than the second order quantity.

2. The multi-cloud management and migration system based on modular architecture according to claim 1, characterized in that: The migration unit determines three types of correction strategies for the storage condition value of the reallocated product based on the difference between the number of reallocated products and the preset first allocation order number under the third order number condition, wherein: The first type of correction strategy is that the migration unit corrects the reallocated product storage condition value to a preset reallocated product storage condition value when the first order quantity is subtracted; The second type of correction strategy is that the migration unit adjusts the reallocated product storage condition value to the first reallocated product storage condition value by presetting the first condition value correction coefficient when the second order number is subtracted; The third type of correction strategy is that the migration unit adjusts the reallocated product preservation condition value to the second reallocated product preservation condition value by presetting the second condition value correction coefficient when the third order number is subtracted; The first order number subtraction condition is that the difference between the number of reallocated products and the preset first allocation order number is not greater than the first order number difference; the second order number condition is that the difference between the number of reallocated products and the preset first allocation order number is greater than the first order number difference and not greater than the second order number difference; the third order number condition is that the difference between the number of reallocated products and the preset first allocation order number is greater than the second order number difference; Among them, the first order quantity difference is smaller than the second order quantity difference, and the preset first condition value correction coefficient is smaller than the preset second condition value correction coefficient.

3. The multi-cloud management and migration system based on modular architecture according to claim 2, characterized in that: The migration unit determines whether the product quality is less than a preset threshold value based on the percentage of the number of orders greater than the regular purchase orders calculated in summary under the second order number condition. The first type of reconfirmation strategy is that the migration unit reconfirms that the product quality is within a preset threshold under a first percentage condition; The second type of reconfirmation strategy is that the migration unit reconfirms that the product quality is less than a preset threshold under a second percentage condition, and corrects the regular purchase order to an associated regular purchase order by calculating the difference between the order number percentage of the abnormal purchase order and the preset first number percentage; The third type of reconfirmation strategy is that the migration unit confirms that there is a device abnormality in the private cloud product order module under a third percentage condition and issues a maintenance notice for the private cloud product order module; The first percentage condition is that the percentage of the number of abnormal purchase orders is not greater than the preset first percentage; the second percentage condition is that the percentage of the number of abnormal purchase orders is greater than the preset first percentage and not greater than the preset second percentage; the third percentage condition is that the percentage of the number of abnormal purchase orders is greater than the preset second percentage; The preset first number percentage is smaller than the preset second number percentage.

4. The multi-cloud management and migration system based on modular architecture according to claim 3, characterized in that: The calculation formula for the percentage S of the number of abnormal purchase orders is: S=Ra / Rb Where Ra is the number of orders that are larger than regular purchase orders, and Rb is the total number of orders.

5. The multi-cloud management and migration system based on modular architecture according to claim 4, characterized in that: The migration unit determines three types of correction strategies for regular purchase orders based on the difference between the order number percentage of abnormal purchase orders and the preset first number percentage under the second percentage condition, wherein: The first type of purchase order correction strategy is that the migration unit corrects the regular purchase order to a preset regular purchase order when the first percentage is subtracted; The second type of purchase order correction strategy is that the migration unit reduces the regular purchase order to the first regular purchase order by presetting a second purchase order correction coefficient when the second percentage is subtracted; The third type of purchase order correction strategy is that the migration unit reduces the regular purchase order to a second regular purchase order by presetting the first purchase order correction coefficient when the third percentage is subtracted; Wherein, the first percentage subtraction situation is that the difference between the order number percentage of abnormal purchase orders and the preset first number percentage is not greater than the preset first number percentage difference; the second percentage subtraction situation is that the difference between the order number percentage of abnormal purchase orders and the preset first number percentage is greater than the preset first number percentage difference and not greater than the preset second number percentage difference; the third percentage subtraction situation is that the difference between the order number percentage of abnormal purchase orders and the preset first number percentage is greater than the preset second number percentage difference; The preset first number percentage difference is smaller than the preset second number percentage difference, and the preset first purchase order correction coefficient is smaller than the preset second purchase order correction coefficient.

6. The multi-cloud management and migration system based on modular architecture according to claim 5, characterized in that: After completing a correction of the reallocated product preservation condition value, the migration unit calculates the average transportation cycle of the private cloud product order based on the multiple private cloud product order transportation cycles associated with the multiple private cloud product orders, and determines whether the product metabolic waste is within the preset threshold based on the average transportation cycle of the private cloud product order. Three confirmation strategies, wherein: The first metabolic confirmation strategy is that the migration unit confirms that the product metabolic waste is within a preset threshold under the first transport cycle conditions; The second metabolism confirmation strategy is that the migration unit confirms that the product metabolic waste is less than a preset threshold under the second transportation cycle condition, and calculates the difference between the average transportation cycle of the private cloud product order and the preset first average transportation cycle to correct the reallocated product preservation condition value to the second associated reallocated product preservation condition value; The third metabolic confirmation strategy is that the migration unit preliminarily confirms that the stored product does not meet the nutritional requirements under the third transportation cycle conditions, and calls the average occurrence rate of order returns in multiple cycles in the historical product information to further confirm whether the stored product meets the nutritional requirements; Among them, the first transportation cycle condition is that the average transportation cycle of private cloud product orders is not greater than the preset first average transportation cycle; the second transportation cycle condition is that the average transportation cycle of private cloud product orders is greater than the preset first average transportation cycle and not greater than the preset second average transportation cycle; the third transportation cycle condition is that the average transportation cycle of private cloud product orders is greater than the preset second average transportation cycle; The preset first average transportation period is smaller than the preset second average transportation period.

7. The multi-cloud management and migration system based on modular architecture according to claim 6, characterized in that: The migration unit determines three types of re-correction strategies for reallocating product preservation condition values ​​based on the difference between the average transportation cycle of private cloud product orders and the preset first average transportation cycle under the second transportation cycle condition, wherein: The first type of re-correction strategy is that the migration unit corrects the reallocated product preservation condition value to the preset reallocated product preservation condition value when the first transportation period is subtracted; The second type of re-correction strategy is that the migration unit re-corrects the reallocated product preservation condition value to a third reallocated product preservation condition value by presetting a second condition value re-correction coefficient when the second transportation period is subtracted; The third type of re-correction strategy is that the migration unit re-corrects the reallocated product preservation condition value to the fourth reallocated product preservation condition value by re-correcting the preset first condition value by re-correcting the coefficient when the third transportation period is subtracted; Among them, the subtraction of the first transportation cycle is that the difference between the average transportation cycle of private cloud product orders and the preset first average transportation cycle is not greater than the preset first average transportation cycle difference; the subtraction of the second transportation cycle is that the difference between the average transportation cycle of private cloud product orders and the preset first average transportation cycle is greater than the preset first average transportation cycle difference and is not greater than the preset second average transportation cycle difference; the subtraction of the third transportation cycle is that the difference between the average transportation cycle of private cloud product orders and the preset first average transportation cycle is greater than the preset second average transportation cycle difference; The preset first average transportation cycle difference is smaller than the preset second average transportation cycle difference, and the preset first condition value re-correction coefficient is smaller than the preset second condition value re-correction coefficient.

8. The multi-cloud management and migration system based on modular architecture according to claim 7, characterized in that: The migration unit determines two types of reconfirmation strategies for whether the preserved product meets the nutritional requirements based on the average occurrence rate of order returns in multiple cycles under the third transportation cycle condition, wherein: The reconfirmation strategy for the first category of products is that the migration unit reconfirms that the preserved product meets the nutritional requirements under the preset first abnormal occurrence rate condition; The second category product reconfirmation strategy is that the migration unit reconfirms that the stored product does not meet the nutritional requirements under the preset second abnormality occurrence rate condition, and corrects the number of product migration objects to the associated number by calculating the difference between the average occurrence rate of order returns and the preset average occurrence rate of abnormalities; Among them, the preset first abnormal occurrence rate condition is that the average occurrence rate of order returns is not greater than the preset average occurrence rate of abnormalities; the preset second abnormal occurrence rate condition is that the average occurrence rate of order returns is greater than the preset average occurrence rate of abnormalities.

9. The multi-cloud management and migration system based on modular architecture according to claim 8, characterized in that: The migration unit determines three types of correction strategies for the number of product migration objects based on the difference between the average occurrence rate of order returns and the preset average occurrence rate of abnormalities under the preset second abnormal occurrence rate condition, wherein: The first type of number correction strategy is that the migration unit corrects the number of product migration objects to a preset number of product migration objects when the preset first abnormal occurrence rate is subtracted; The second type of number correction strategy is that the migration unit corrects the number of product migration objects to the first number by using a preset first migration type number correction coefficient when the preset second abnormal occurrence rate is subtracted; The third type of number correction strategy is that the migration unit corrects the number of product migration objects to the second number by using a preset second migration type number correction coefficient when the preset third abnormal occurrence rate is subtracted; Among them, the subtraction of the preset first abnormal occurrence rate is that the difference between the average occurrence rate of order returns and the preset average occurrence rate of abnormalities is not greater than the preset first abnormal average occurrence rate difference; the subtraction of the preset second abnormal occurrence rate is that the difference between the average occurrence rate of order returns and the preset average occurrence rate of abnormalities is greater than the preset first abnormal average occurrence rate difference and is not greater than the preset second abnormal average occurrence rate difference; the subtraction of the preset third abnormal occurrence rate is that the difference between the average occurrence rate of order returns and the preset average occurrence rate of abnormalities is greater than the preset second abnormal average occurrence rate difference; The preset first abnormal average occurrence rate difference is smaller than the preset second abnormal average occurrence rate difference, and the preset first migration type number correction coefficient is smaller than the preset second migration type number correction coefficient.

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