Vegetable state monitoring system based on big data

Abstract

The invention relates to the technical field of vegetable supply management, in particular to a vegetable state monitoring system based on big data, which is provided with an order matching module, an information association module, a feature analysis module and a strategy management module. The information association module determines whether the storage area is an airflow fluctuation characteristic area, and the characteristic analysis module determines the storage risk expression category of the to-be-delivered vegetables. A strategy management module selects a distribution mode of vegetables corresponding to orders and a storage adjustment mode of remaining vegetables corresponding to non-orders in each airflow fluctuation characteristic region according to different storage risk expression categories, so that storage risk quantification of airflow unstable regions is realized; the distribution mode of the vegetables is adaptively configured, the storage mode of the vegetables with the storage state affected is adjusted in time, the data representativeness of vegetable state monitoring is improved, and vegetable loss is effectively reduced.

Description

Technical Field

[0001] The present invention relates to the technical field of vegetable supply management, and in particular to a vegetable status monitoring system based on big data. Background Art

[0002] As technologies such as the Internet of Things and big data are integrated into all aspects of life, comprehensive evaluation and calculation based on real-time data has become a widely used technical means. Vegetables are important economic crops, and people pay more and more attention to the quality of vegetables and the timeliness of their delivery. Traditional vegetable storage methods lack effective monitoring of the vegetable storage environment and cannot intuitively evaluate the quality of vegetables. It is necessary to understand the storage environment data parameters of vegetables to ensure the scientific nature of vegetable distribution. Establishing an effective monitoring system and a scientific strategy for vegetable distribution configuration are important means to ensure the high quality of vegetables.

[0003] For example, the Chinese patent publication number: CN111539672A, the invention discloses a method for monitoring big data of cold chain transportation of vegetables, importing the cold chain data of processed vegetables into the cold chain database and retrieving the associated cold chain data, scanning and obtaining the processed vegetable identification from the cold chain transportation device to map to the cold chain role and receiving the route selection and transportation reporting information of the cold chain transportation device in the cold chain database, determining the priority of the cold chain data to generate priority cold chain data and send the priority cold chain data to the cold chain transportation device, and after the cold chain transportation arrives at the transfer node, triggering the data processing end of the transfer node to generate local data record information and verify it. The invention uses the method of monitoring the cold chain data of vegetables in a distributed big data monitoring report to improve the intelligence of the logistics management system.

[0004] The prior art still has the following problems:

[0005] The existing technology does not take into account the impact of an unstable airflow storage environment on the quality of stacked stored vegetables. The existing technology cannot quantify storage risks in areas with unstable airflow that are representative of data, and cannot adaptively configure the distribution method of vegetables and timely adjust the storage method for vegetables whose storage status is affected. Summary of the invention

[0006] To this end, the present invention provides a vegetable status monitoring system based on big data to overcome the problems that the prior art cannot quantify storage risks in unstable airflow areas with data representativeness, cannot adaptively configure vegetable distribution methods, and cannot timely adjust the storage methods of vegetables whose storage status is affected.

[0007] To achieve the above object, the present invention provides a vegetable status monitoring system based on big data, comprising:

[0008] An order matching module, used for receiving an order sent by a user and determining the storage area where the vegetables corresponding to the order are located based on the pre-built association between each to-be-shipped vegetable and the storage area;

[0009] Wherein, the order includes the weight of the vegetables purchased and the location information of the destination to which the vegetables are delivered;

[0010] An information association module, which is connected to the order matching module, is used to obtain the air flow rate corresponding to the storage area where the vegetables corresponding to the order are located, and determine whether the storage area is an air flow fluctuation characteristic area based on the fluctuation degree of the air flow rate within a preset time period;

[0011] A characteristic analysis module, which is connected to the information association module and is used to determine the storage risk performance category of the vegetables to be shipped out according to the ratio of the total volume of the vegetables to be shipped out in the airflow fluctuation characteristic area to the spatial volume of the airflow fluctuation characteristic area;

[0012] A strategy management module, which is connected to the feature analysis module and the order matching module, is used to select a treatment method for the vegetables to be shipped out of each airflow fluctuation feature area according to the storage risk performance category, including:

[0013] Determine a priority delivery order based on the vegetable purchase weight corresponding to the order in each airflow fluctuation characteristic area, and the location information of the vegetable delivery destination of the priority delivery order is the first location of the delivery route;

[0014] Alternatively, the vegetables corresponding to the order in the air flow fluctuation characteristic area are delivered separately, and based on the purchase weight of the vegetables corresponding to the order and the weight of the remaining vegetables corresponding to the non-order, it is determined whether to store the remaining vegetables corresponding to the non-order in a dispersed manner.

[0015] Furthermore, the information association module is also connected to air flow rate detection units distributed in each storage area, and the air flow rate detection units are used to collect air flow rates in the corresponding storage area.

[0016] Furthermore, the information association module is also used to determine the degree of fluctuation of the air flow rate within a preset time period;

[0017] In response to the order matching module receiving an order sent by a user, the information association module obtains the air flow rate corresponding to the storage area where the vegetables corresponding to the order are located, determines the maximum air flow rate and the minimum air flow rate within a preset time period, and determines the difference between the maximum air flow rate and the minimum air flow rate as the air flow rate fluctuation value.

[0018] Further, the information association module is used to determine whether the storage area is an airflow fluctuation characteristic area according to a comparison result between the air flow velocity fluctuation value and a preset air flow velocity fluctuation value threshold;

[0019] If the air flow rate fluctuation value is greater than the air flow rate fluctuation value threshold, the information association module determines that the storage area is an air flow fluctuation characteristic area.

[0020] Furthermore, the characteristic analysis module is used to compare the ratio of the total volume of the vegetables to be shipped out of the airflow fluctuation characteristic area to the spatial volume of the airflow fluctuation characteristic area with a preset ratio reference value;

[0021] If the ratio is less than the ratio reference value, the characteristic analysis module determines that the vegetables to be shipped out are of a weakly dominant storage risk category;

[0022] If the ratio is greater than or equal to the ratio reference value, the characteristic analysis module determines that the vegetables to be shipped out are of a category with a strong dominant storage risk.

[0023] Furthermore, the strategy management module is used to select a treatment method for vegetables to be shipped out of the warehouse in each air flow fluctuation characteristic area according to different storage risk performance categories;

[0024] If the vegetables to be shipped out are of the category with weakly explicit storage risk, the processing method selected by the strategy management module is to determine the priority delivery order based on the vegetable purchase weight corresponding to the orders in each airflow fluctuation characteristic area, and the location information of the destination of the vegetables in the priority delivery order is the first location of the delivery route;

[0025] If the vegetables to be shipped out belong to a category with strong and obvious storage risk, the processing method selected by the strategy management module is to separately deliver the vegetables corresponding to the order in the airflow fluctuation characteristic area, and to determine whether to disperse the remaining vegetables corresponding to the non-order according to the purchase weight of the vegetables corresponding to the order and the weight of the remaining vegetables corresponding to the non-order.

[0026] Furthermore, the strategy management module is also used to determine the maximum vegetable purchase weight according to the vegetable purchase weights corresponding to the orders in each airflow fluctuation characteristic area, and determine the order corresponding to the maximum vegetable purchase weight as the priority delivery order.

[0027] Furthermore, the policy management module is also used to calculate the weight ratio of the purchased weight of vegetables corresponding to the order and the weight of the remaining vegetables corresponding to the non-order.

[0028] Furthermore, the policy management module is further used to compare the weight ratio with a preset weight ratio threshold;

[0029] If the weight ratio is less than the weight ratio threshold, the policy management module determines that the remaining vegetables not corresponding to the order need to be stored in a dispersed manner.

[0030] Furthermore, the dispersed storage is to disperse and stack the remaining vegetables not corresponding to the order according to a preset stacking height.

[0031] Compared with the prior art, the beneficial effect of the present invention lies in that, by setting an order matching module, an information association module, a feature analysis module and a strategy management module, the present invention determines the storage area where the vegetables corresponding to the order are located through the order matching module, determines whether the storage area is an airflow fluctuation characteristic area according to the fluctuation degree of the air flow rate in the storage area through the information association module, determines the storage risk performance category of the vegetables to be shipped out through the feature analysis module, and selects the delivery method of the vegetables corresponding to the orders in each airflow fluctuation characteristic area and the storage adjustment method of the remaining vegetables not corresponding to the order through the strategy management module, thereby realizing the quantification of storage risks in the unstable airflow area, adaptively configuring the delivery method of the vegetables, and timely adjusting the storage method of the vegetables whose storage status is affected, thereby improving the data representativeness of the vegetable status monitoring and effectively reducing the vegetable loss.

[0032] In particular, the present invention obtains the air flow rate corresponding to the storage area through the information association module. Those skilled in the art can understand that the air flow in the vegetable storage area is affected by factors such as the distribution of air outlets, the difference in air volume at the air outlets, and the obstruction of cargo accumulation. There are differences in the air flow rates corresponding to different storage areas, and different air flow rates have different degrees of influence on the quality of vegetables. The present invention realizes the analysis of the vegetable storage environment by obtaining the air flow rate, thereby improving the data representativeness of vegetable status monitoring.

[0033] In particular, the present invention determines whether a storage area is an airflow fluctuation characteristic area based on the fluctuation degree of air flow rate within a preset time period through an information association module. Those skilled in the art can understand that the greater the degree of air flow fluctuation in the vegetable storage area, the more it characterizes the current airflow change in the vegetable storage area, and there is obvious airflow convection phenomenon. Obvious airflow convection is not conducive to constant temperature and humidity in the area. Unstable temperature and humidity will affect the quality of vegetables. The present invention screens out areas with obvious airflow fluctuations, realizes the analysis of the storage environment of vegetables in different areas, and improves the data representativeness of vegetable status monitoring.

[0034] In particular, the present invention determines the storage risk performance category of vegetables to be shipped out through a feature analysis module. In an actual vegetable storage environment, the longer the vegetables to be shipped out are stored in an unstable temperature and humidity area with obvious airflow fluctuations, the more easily the vegetable quality will be affected. Similarly, the greater the total volume of vegetable accumulation in the current storage area, the worse the air fluidity at the bottom and inside of the vegetables, the greater the squeezing pressure on the bottom of the vegetables, and the more easily the freshness of the vegetables will be affected. The present invention determines the storage risk performance category of vegetables to be shipped out by calculating the ratio of the total volume of vegetables to be shipped out in the airflow fluctuation characteristic area to the spatial volume of the airflow fluctuation characteristic area. The larger the ratio, the greater the impact on the storage state of the vegetables, thereby realizing the quantification of storage risks in airflow unstable areas and improving the data representativeness of vegetable status monitoring.

[0035] In particular, under the condition that the status assessment of the impact of the storage environment on the quality of vegetables is a weakly dominant performance category of storage risk, the present invention can prioritize the delivery of orders with the largest vegetable purchase weight through the strategy management module, so as to avoid the temperature fluctuations in the delivery and unloading process from affecting the quality of vegetables in the order with the largest purchase weight. Furthermore, it realizes the adaptive configuration of the delivery method of vegetables and the timely adjustment of the storage method of vegetables whose storage status is affected, thereby improving the representativeness of the data of vegetable status monitoring and effectively reducing vegetable loss.

[0036] In particular, under the condition that the status assessment of the impact of the storage environment on the quality of vegetables is a category with strong dominant storage risk, the present invention can separately deliver vegetables in the category of strong dominant storage risk through the strategy management module, so that the temperature remains constant during the delivery process, thereby preventing the quality of vegetables with strong storage risk from being affected again during delivery, and by calculating the ratio of the purchased weight of vegetables corresponding to the order and the weight of the remaining vegetables corresponding to non-orders, the number of remaining vegetables corresponding to non-orders can be determined. When the number is higher than the preset value, the remaining vegetables are dispersed and stacked according to a preset stacking height to avoid the remaining vegetables being in an unstable temperature and humidity area with obvious air flow fluctuations. The freshness of the vegetables is affected by the poor air flow at the bottom and inside of the vegetables and the squeezing force on the bottom of the vegetables. Furthermore, the adaptive configuration of the delivery method of the vegetables and the timely adjustment of the storage method for the vegetables affected by the storage status are achieved, thereby improving the representativeness of the data of vegetable status monitoring and effectively reducing vegetable loss. BRIEF DESCRIPTION OF THE DRAWINGS

[0037] Figure 1 This is a system block diagram of a vegetable status monitoring system based on big data according to an embodiment of the present invention;

[0038] Figure 2 A logic flow chart of the information association module according to an embodiment of the present invention for determining whether a storage area is an area with airflow fluctuation characteristics;

[0039] Figure 3 A logic flow chart of the characteristic analysis module of an embodiment of the present invention for determining the storage risk performance category of vegetables to be shipped out;

[0040] Figure 4 A logical flow chart for selecting a processing method for vegetables to be shipped out of the warehouse in each airflow fluctuation characteristic area by the strategy management module of an embodiment of the present invention. DETAILED DESCRIPTION

[0041] In order to make the objects and advantages of the present invention more clearly understood, the present invention is further described below in conjunction with embodiments; it should be understood that the specific embodiments described herein are only used to explain the present invention and are not used to limit the present invention.

[0042] The preferred embodiments of the present invention are described below with reference to the accompanying drawings. It should be understood by those skilled in the art that these embodiments are only used to explain the technical principles of the present invention and are not intended to limit the protection scope of the present invention.

[0043] It should be noted that, in the description of the present invention, terms such as "upper", "lower", "inside" and "outside" indicating directions or positional relationships are based on the directions or positional relationships shown in the drawings. This is only for the convenience of description and does not indicate or imply that the device or element must have a specific orientation, be constructed and operated in a specific orientation. Therefore, it cannot be understood as a limitation on the present invention.

[0044] In addition, it should be noted that in the description of the present invention, unless otherwise clearly specified and limited, the term "connection" should be understood in a broad sense, for example, it can be a fixed connection, a detachable connection, or an integral connection; it can be a mechanical connection or an electrical connection; it can be a direct connection or an indirect connection through an intermediate medium, or it can be the internal communication of two components. For those skilled in the art, the specific meanings of the above terms in the present invention can be understood according to specific circumstances.

[0045] See also Figure 1 As shown, it is a system block diagram of a vegetable status monitoring system based on big data according to an embodiment of the present invention. A vegetable status monitoring system based on big data according to the present invention comprises:

[0046] An order matching module, used for receiving an order sent by a user and determining the storage area where the vegetables corresponding to the order are located based on the pre-built association between each to-be-shipped vegetable and the storage area;

[0047] Wherein, the order includes the weight of the vegetables purchased and the location information of the destination to which the vegetables are delivered;

[0048] An information association module, which is connected to the order matching module, is used to obtain the air flow rate corresponding to the storage area where the vegetables corresponding to the order are located, and determine whether the storage area is an air flow fluctuation characteristic area based on the fluctuation degree of the air flow rate within a preset time period;

[0049] A characteristic analysis module, which is connected to the information association module and is used to determine the storage risk performance category of the vegetables to be shipped out according to the ratio of the total volume of the vegetables to be shipped out in the airflow fluctuation characteristic area to the spatial volume of the airflow fluctuation characteristic area;

[0050] A strategy management module, which is connected to the feature analysis module and the order matching module, is used to select a treatment method for the vegetables to be shipped out of each airflow fluctuation feature area according to the storage risk performance category, including:

[0051] Determine a priority delivery order based on the vegetable purchase weight corresponding to the order in each airflow fluctuation characteristic area, and the location information of the vegetable delivery destination of the priority delivery order is the first location of the delivery route;

[0052] Alternatively, the vegetables corresponding to the order in the air flow fluctuation characteristic area are delivered separately, and based on the purchase weight of the vegetables corresponding to the order and the weight of the remaining vegetables corresponding to the non-order, it is determined whether to store the remaining vegetables corresponding to the non-order in a dispersed manner.

[0053] Specifically, the vegetables to be shipped out of the airflow fluctuation characteristic area include vegetables corresponding to the order and remaining vegetables not corresponding to the order.

[0054] Specifically, the present invention does not limit the specific structure of the order matching module. It can be a processor that pre-stores the association between each vegetable to be shipped and the storage area, pre-divides a number of storage areas, determines the vegetables to be shipped in each storage area, and stores the association between the vegetables to be shipped and the storage area in the order matching module. This is a prior art and will not be repeated here.

[0055] Specifically, the name information of the vegetables to be shipped ordered by the user in the order and the location information of the delivery destination are common order-included information. For example, the name information of the vegetables to be shipped may be celery, tomatoes, etc., and the location information of the delivery destination may be a street location, house number, etc. based on map positioning. It is an existing technology to locate the delivery destination of the order based on the street information and house number information contained in the order, which will not be repeated here.

[0056] Specifically, the present invention does not limit the specific structure of the information association module. Preferably, it can include a data receiver for receiving the air flow rate sent by the air flow rate detection unit of each storage area and a data processor for calculating the fluctuation degree of the air flow rate within a preset time period based on the received air flow rate. This is a prior art and will not be repeated here.

[0057] Specifically, the present invention does not limit the specific structure of the feature analysis module. Preferably, it can determine the storage time T of the vegetables to be shipped out by determining the time interval between the storage time of the vegetables to be shipped out and the current time, and determine the storage stacking height of the vegetables to be shipped out by acquiring the image information of the vegetable stacking in the current storage area. Among them, determining the storage stacking height of the vegetables to be shipped out based on the image information of the vegetable stacking is widely used in monitoring image processing technology. This is a prior art and will not be repeated here.

[0058] Specifically, the present invention does not limit the specific structure of the policy management module, which can be composed of logic components. The logic components can be programmable logic components, microprocessors, processors used in computers, etc., which will not be described in detail here.

[0059] Specifically, the information association module is also connected to air flow rate detection units distributed in each storage area, and the air flow rate detection units are used to collect the air flow rate in the corresponding storage area.

[0060] In the embodiment of the present invention, the air flow rate detection unit may select an air flow rate sensor dedicated to indoor air flow environment assessment, which is a prior art and will not be described in detail here.

[0061] Specifically, the present invention obtains the air flow rate corresponding to the storage area through the information association module. Those skilled in the art can understand that the air flow in the vegetable storage area is affected by factors such as the distribution of air outlets, the difference in air volume of air outlets, and the obstruction of cargo accumulation. There are differences in the air flow rates corresponding to different storage areas, and different air flow rates have different degrees of influence on the quality of vegetables. The present invention realizes the analysis of the vegetable storage environment by obtaining the air flow rate, thereby improving the data representativeness of vegetable status monitoring.

[0062] Specifically, the information association module is also used to determine the degree of fluctuation of the air flow rate within a preset time period;

[0063] In response to the order matching module receiving an order sent by a user, the information association module obtains the air flow rate corresponding to the storage area where the vegetables corresponding to the order are located, determines the maximum air flow rate and the minimum air flow rate within a preset time period, and determines the difference between the maximum air flow rate and the minimum air flow rate as the air flow rate fluctuation value.

[0064] In the embodiment of the present invention, the preset duration is adjusted by those skilled in the art according to the amount of data demand. Preferably, the preset duration has a value range of [5,8], and the unit is min.

[0065] Specifically, see Figure 2 As shown, it is a logic flow chart of the information association module of an embodiment of the present invention for determining whether a storage area is an airflow fluctuation characteristic area, and the information association module is used to determine whether the storage area is an airflow fluctuation characteristic area according to the comparison result of the air flow velocity fluctuation value AF and the preset air flow velocity fluctuation value threshold AF0;

[0066] If the air velocity fluctuation value AF is less than or equal to the air velocity fluctuation value threshold AF0, the information association module determines that the storage area is not an air flow fluctuation characteristic area;

[0067] If the air flow rate fluctuation value AF is greater than the air flow rate fluctuation value threshold AF0, the information association module determines that the storage area is an air flow fluctuation characteristic area.

[0068] In the embodiment of the present invention, the value range of the air flow rate fluctuation value threshold AF0 is [0.05.0.2], and the unit is m / s.

[0069] Specifically, the present invention determines whether a storage area is an airflow fluctuation characteristic area based on the fluctuation degree of air flow rate within a preset time period through an information association module. Those skilled in the art can understand that the greater the air flow fluctuation degree in the vegetable storage area, the more it characterizes the current airflow change in the vegetable storage area, and there is obvious airflow convection phenomenon. Obvious airflow convection is not conducive to constant temperature and humidity in the area. Unstable temperature and humidity will affect the quality of vegetables. The present invention screens out areas with obvious airflow fluctuations, realizes the analysis of the storage environment of vegetables in different areas, and improves the data representativeness of vegetable status monitoring.

[0070] Specifically, see Figure 3 As shown, it is a logic flow chart of determining the storage risk performance category of the vegetables to be shipped out by the characteristic analysis module of the embodiment of the present invention, wherein the characteristic analysis module is used to compare the ratio k of the total volume V1 of the vegetables to be shipped out in the airflow fluctuation characteristic area to the spatial volume V2 of the airflow fluctuation characteristic area with a preset ratio reference value k0, k=V1 / V2;

[0071] If the ratio k is less than the ratio reference value k0, the characteristic analysis module determines that the vegetables to be shipped out are of a weakly dominant storage risk category;

[0072] If the ratio k is greater than or equal to the ratio reference value k0, the characteristic analysis module determines that the vegetables to be shipped out are of a category with a strong dominant storage risk.

[0073] Specifically, the preset ratio reference value k0 is calculated in advance, and the average value of the ratio of the total volume of vegetables to be shipped out of several airflow fluctuation characteristic areas of the same size to the spatial volume of the airflow fluctuation characteristic area is calculated in advance, and the calculated average value of the ratio is determined as the preset ratio reference value k0.

[0074] In an embodiment of the present invention, the total volume of the vegetables can be determined by using three-dimensional scanning technology to scan the area where the vegetables are stored to generate a three-dimensional model. The three-dimensional model can be analyzed by software to quickly and accurately calculate the total volume of the vegetables. The spatial volume of the airflow fluctuation characteristic area can be calculated by using an approximate shape method to obtain the length, width and height of the space where the airflow fluctuation characteristic area is located. The three-dimensional scanning technology and the approximate shape method are existing technologies and will not be described in detail here.

[0075] Specifically, the present invention determines the storage risk performance category of vegetables to be shipped out through a feature analysis module. In an actual vegetable storage environment, the longer the vegetables to be shipped out are stored in an unstable temperature and humidity area with obvious airflow fluctuations, the more easily the vegetable quality will be affected. Similarly, the greater the total volume of vegetable accumulation in the current storage area, the worse the air fluidity at the bottom and inside of the vegetables, the greater the squeezing pressure on the bottom of the vegetables, and the more easily the freshness of the vegetables will be affected. The present invention determines the storage risk performance category of vegetables to be shipped out by calculating the ratio of the total volume of vegetables to be shipped out in the airflow fluctuation characteristic area to the spatial volume of the airflow fluctuation characteristic area. The larger the ratio, the greater the impact on the storage state of the vegetables. This realizes the quantification of storage risks in airflow unstable areas and improves the data representativeness of vegetable status monitoring.

[0076] Specifically, see Figure 4 As shown, it is a logical flow chart of the distribution strategy of the vegetables to be shipped out corresponding to the order selected by the strategy management module in the embodiment of the present invention, and the strategy management module is used to select the processing method of the vegetables to be shipped out in each airflow fluctuation characteristic area according to different storage risk performance categories;

[0077] If the vegetables to be shipped out are of the category with weakly explicit storage risk, the processing method selected by the strategy management module is to determine the priority delivery order based on the vegetable purchase weight corresponding to the orders in each airflow fluctuation characteristic area, and the location information of the destination of the vegetables in the priority delivery order is the first location of the delivery route;

[0078] If the vegetables to be shipped out belong to a category with strong and obvious storage risk, the processing method selected by the strategy management module is to separately deliver the vegetables corresponding to the order in the airflow fluctuation characteristic area, and to determine whether to disperse the remaining vegetables corresponding to the non-order according to the purchase weight of the vegetables corresponding to the order and the weight of the remaining vegetables corresponding to the non-order.

[0079] Specifically, the strategy management module is also used to determine the maximum vegetable purchase weight according to the vegetable purchase weights corresponding to the orders in each airflow fluctuation characteristic area, and determine the order corresponding to the maximum vegetable purchase weight as the priority delivery order.

[0080] Embodiment 1:

[0081] After determining that the storage area where the vegetables corresponding to three orders (D1, D2, D3) are located is a category with weakly explicit storage risk, obtain the vegetable purchase weights corresponding to the three orders (D1, D2, D3) respectively. The vegetable purchase weight corresponding to D1 is 10kg, the vegetable purchase weight corresponding to D2 is 13kg, and the vegetable purchase weight corresponding to D3 is 8.5kg. Order D2 is then determined as a priority delivery order, and the location information of the destination where the vegetables of priority delivery order D2 are delivered is the first location of the delivery route. For example, the three orders D1, D2, and D3 are delivered by the same transport vehicle, and the first destination arrived at on the delivery route is the destination corresponding to order D2.

[0082] Embodiment 2:

[0083] After determining that the storage area where the vegetables corresponding to three orders (D1, D2, D3) are located is a category with weakly explicit storage risk, obtain the vegetable purchase weights corresponding to the three orders (D1, D2, D3) respectively. The vegetable purchase weight corresponding to D1 is 30kg, the vegetable purchase weight corresponding to D2 is 20kg, and the vegetable purchase weight corresponding to D3 is 10kg. Order D1 is determined as the priority delivery order. When the three orders D1, D2, and D3 are delivered by the same transport vehicle, the first destination arrived at the delivery route is the destination corresponding to order D1.

[0084] Specifically, under the condition that the status assessment of the impact of the storage environment on the quality of vegetables is a weakly dominant storage risk category, the present invention can prioritize the delivery of orders with the largest vegetable purchase weight through the strategy management module, so as to avoid the temperature fluctuations in the delivery and unloading process from affecting the quality of vegetables in the order with the largest purchase weight. Furthermore, it realizes the adaptive configuration of the delivery method of vegetables and the timely adjustment of the storage method of vegetables affected by the storage status, thereby improving the representativeness of the data of vegetable status monitoring and effectively reducing vegetable losses.

[0085] Specifically, the policy management module is also used to calculate the weight ratio p of the vegetable purchase weight G1 corresponding to the order and the weight G2 of the remaining vegetables corresponding to the non-order, p=G1 / G2.

[0086] In an embodiment of the present invention, the vegetable purchase weight G1 corresponding to the order is the information included in the order, and the weight G2 of the remaining vegetables not corresponding to the order can be obtained according to the vegetable storage in-and-out warehouse management system. The existing vegetable storage in-and-out warehouse management system has clear records of the total storage weight of vegetables and the in-and-out weight. The weight of the remaining vegetables not corresponding to the order can be obtained by subtracting the vegetable purchase weight corresponding to the order from the total weight of the vegetables in the storage area. This is the prior art and will not be repeated here.

[0087] Specifically, the policy management module is further used to compare the weight ratio p with a preset weight ratio threshold p0;

[0088] If the weight ratio p is less than the weight ratio threshold p0, the policy management module determines that the remaining vegetables not corresponding to the order need to be stored in a dispersed manner.

[0089] Specifically, the preset weight ratio threshold value p0 is obtained by those skilled in the art based on preliminary tests. Preferably, the value range of the weight ratio threshold value p0 is [0.2, 0.5].

[0090] Specifically, the dispersed storage is to disperse and stack the remaining vegetables not corresponding to the order according to a preset stacking height.

[0091] Specifically, the preset stacking height h1 can be set according to the original stacking height H. Preferably, h1=0.25×H.

[0092] Specifically, the present invention can separately deliver vegetables in the category of strong storage risk manifestation under the condition that the status assessment of the impact of the storage environment on the quality of vegetables is a category with strong storage risk manifestation through the strategy management module, so that the temperature remains constant during the delivery process, thereby preventing the quality of vegetables with strong storage risk from being affected again during delivery. In addition, by calculating the ratio of the purchased weight of vegetables corresponding to the order and the weight of the remaining vegetables corresponding to non-orders, the number of remaining vegetables corresponding to non-orders can be determined. When the number is higher than the preset value, the remaining vegetables are dispersed and stacked according to a preset stacking height to avoid the remaining vegetables being in an unstable temperature and humidity area with obvious air flow fluctuations. The freshness of the vegetables is affected by the poor air flow at the bottom and inside of the vegetables and the squeezing force on the bottom of the vegetables. Furthermore, the delivery method of vegetables can be adaptively configured and the storage method of vegetables affected by the storage status can be adjusted in time, thereby improving the representativeness of data for vegetable status monitoring and effectively reducing vegetable loss.

[0093] So far, the technical solutions of the present invention have been described in conjunction with the preferred embodiments shown in the accompanying drawings. However, it is easy for those skilled in the art to understand that the protection scope of the present invention is obviously not limited to these specific embodiments. Without departing from the principle of the present invention, those skilled in the art can make equivalent changes or substitutions to the relevant technical features, and the technical solutions after these changes or substitutions will fall within the protection scope of the present invention.

[0094] The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. For those skilled in the art, the present invention may have various modifications and variations. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present invention shall be included in the protection scope of the present invention.

Claims

1. A vegetable status monitoring system based on big data, characterized in that: include: An order matching module, used for receiving an order sent by a user and determining the storage area where the vegetables corresponding to the order are located based on the pre-built association between each to-be-shipped vegetable and the storage area; Wherein, the order includes the weight of the vegetables purchased and the location information of the destination to which the vegetables are delivered; An information association module, which is connected to the order matching module, is used to obtain the air flow rate corresponding to the storage area where the vegetables corresponding to the order are located, and determine whether the storage area is an air flow fluctuation characteristic area based on the fluctuation degree of the air flow rate within a preset time period; A characteristic analysis module, which is connected to the information association module and is used to determine the storage risk performance category of the vegetables to be shipped out according to the ratio of the total volume of the vegetables to be shipped out in the airflow fluctuation characteristic area to the spatial volume of the airflow fluctuation characteristic area; A strategy management module, which is connected to the feature analysis module and the order matching module, is used to select a treatment method for the vegetables to be shipped out of each airflow fluctuation feature area according to the storage risk performance category, including: Determine a priority delivery order based on the vegetable purchase weight corresponding to the order in each airflow fluctuation characteristic area, and the location information of the vegetable delivery destination of the priority delivery order is the first location of the delivery route; Alternatively, the vegetables corresponding to the order in the air flow fluctuation characteristic area are delivered separately, and based on the purchased weight of the vegetables corresponding to the order and the weight of the remaining vegetables corresponding to non-orders, it is determined whether to store the remaining vegetables corresponding to the non-orders in a dispersed manner.

2. The vegetable status monitoring system based on big data according to claim 1 is characterized in that: The information association module is also connected to air flow rate detection units distributed in each storage area, and the air flow rate detection units are used to collect the air flow rate in the corresponding storage area.

3. The vegetable status monitoring system based on big data according to claim 1 is characterized in that: The information association module is also used to determine the degree of fluctuation of the air flow rate within a preset time period; In response to the order matching module receiving an order sent by a user, the information association module obtains the air flow rate corresponding to the storage area where the vegetables corresponding to the order are located, determines the maximum air flow rate and the minimum air flow rate within a preset time period, and determines the difference between the maximum air flow rate and the minimum air flow rate as the air flow rate fluctuation value.

4. The vegetable status monitoring system based on big data according to claim 3 is characterized in that: The information association module is used to determine whether the storage area is an air flow fluctuation characteristic area according to a comparison result between the air flow velocity fluctuation value and a preset air flow velocity fluctuation value threshold; If the air flow rate fluctuation value is greater than the air flow rate fluctuation value threshold, the information association module determines that the storage area is an air flow fluctuation characteristic area.

5. The vegetable status monitoring system based on big data according to claim 4 is characterized in that: The characteristic analysis module is used to compare the ratio of the total volume of the vegetables to be shipped out of the airflow fluctuation characteristic area to the spatial volume of the airflow fluctuation characteristic area with a preset ratio reference value; If the ratio is less than the ratio reference value, the characteristic analysis module determines that the vegetables to be shipped out are of a weakly dominant storage risk category; If the ratio is greater than or equal to the ratio reference value, the characteristic analysis module determines that the vegetables to be shipped out are of a category with strong dominant storage risk.

6. The vegetable status monitoring system based on big data according to claim 5 is characterized in that: The strategy management module is used to select a treatment method for vegetables to be shipped out of the warehouse in each air flow fluctuation characteristic area according to different storage risk performance categories; If the vegetables to be shipped out are of the category with weakly explicit storage risk, the processing method selected by the strategy management module is to determine the priority delivery order based on the vegetable purchase weight corresponding to the orders in each airflow fluctuation characteristic area, and the location information of the destination of the vegetables in the priority delivery order is the first location of the delivery route; If the vegetables to be shipped out belong to a category with strong and obvious storage risk, the processing method selected by the strategy management module is to separately deliver the vegetables corresponding to the order in the airflow fluctuation characteristic area, and to determine whether to disperse the remaining vegetables corresponding to the non-order according to the purchase weight of the vegetables corresponding to the order and the weight of the remaining vegetables corresponding to the non-order.

7. The vegetable status monitoring system based on big data according to claim 6 is characterized in that: The strategy management module is also used to determine the maximum vegetable purchase weight according to the vegetable purchase weights corresponding to the orders in each airflow fluctuation characteristic area, and determine the order corresponding to the maximum vegetable purchase weight as the priority delivery order.

8. The vegetable status monitoring system based on big data according to claim 6 is characterized in that: The policy management module is also used to calculate the weight ratio of the purchased weight of vegetables corresponding to the order and the weight of the remaining vegetables corresponding to the non-order.

9. The vegetable status monitoring system based on big data according to claim 8 is characterized in that: The policy management module is further used to compare the weight ratio with a preset weight ratio threshold; If the weight ratio is less than the weight ratio threshold, the policy management module determines that the remaining vegetables not corresponding to the order need to be stored in a dispersed manner.

10. The vegetable status monitoring system based on big data according to claim 9 is characterized in that: The dispersed storage is to disperse and stack the remaining vegetables not corresponding to the order according to a preset stacking height.

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