A vegetable status monitoring system based on big data

Abstract

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. The present invention sets an order matching module, an information association module, a feature analysis module and a strategy management module. The order matching module is used to determine the storage area where vegetables corresponding to an order are located. The information association module is used to determine whether the storage area is an airflow fluctuation characteristic area. The feature analysis module is used to determine the storage risk performance category of vegetables to be shipped out. The strategy management module is used to select the distribution method of vegetables corresponding to orders in each airflow fluctuation characteristic area and the storage adjustment method of remaining vegetables not corresponding to the order according to different storage risk performance categories. The storage risk quantification of the airflow unstable area, the adaptive configuration of the vegetable distribution method and the timely adjustment of the storage method of the vegetables whose storage status is affected are realized, thereby improving the data representativeness of the vegetable status monitoring and effectively reducing the vegetable loss.

Description

Technical Field

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

[0002] With the integration of technologies such as the Internet of Things and big data into all aspects of life, comprehensive evaluation calculations based on real-time data have become 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 distribution. 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 data parameters of the vegetable storage environment to ensure the scientific nature of vegetable distribution. Establishing an effective monitoring system and configuring scientific strategies for vegetable distribution are important means to ensure high-quality vegetables.

[0003] For example, Chinese Patent Publication No.: CN111539672A. This invention discloses a big data monitoring method for cold chain transportation of vegetables, which imports the cold chain data of processed vegetables into a cold chain database and retrieves the associated cold chain data, scans the processed vegetable identification from the cold chain transportation device to map to the cold chain role and receives the route selection and transportation reporting information of the cold chain transportation device in the cold chain database, determines the priority of the cold chain data to generate priority cold chain data and sends the priority cold chain data to the cold chain transportation device. After the cold chain transportation reaches the transfer node, it triggers the data processing end of the transfer node to generate local data record information and perform verification. This invention is a method for monitoring and reporting data monitoring results of the cold chain data of vegetables in a distributed big data manner and improving the intelligence level of the logistics management system.

[0004] The following problems still exist in the prior art:

[0005] The prior art does not consider the impact of the storage environment with unstable air flow on the quality of stacked vegetables. The prior art cannot quantify the storage risks in the areas with unstable air flow that are representative of data, and cannot adaptively configure the distribution methods of vegetables and timely adjust the storage methods of vegetables whose storage status is affected. Summary of the Invention

[0006] Therefore, the present invention provides a vegetable status monitoring system based on big data to overcome the problems that the prior art cannot quantify the storage risks in the areas with unstable air flow that are representative of data, and cannot adaptively configure the distribution methods of vegetables and 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, including:

[0008] An order matching module, configured to receive an order sent by a user and determine a storage area where the vegetables corresponding to the order are located based on the pre-established association relationship between each vegetable to be shipped out and the storage area;

[0009] Wherein, the order includes the purchase weight of the vegetables and the location information of the destination where the vegetables are to be delivered;

[0010] An information association module, which is connected to the order matching module, configured 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, configured 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 air flow fluctuation characteristic area to the space volume of the air flow fluctuation characteristic area;

[0012] A strategy management module, which is connected to the characteristic analysis module and the order matching module, configured to select a processing method for the vegetables to be shipped out in each air flow fluctuation characteristic area according to the storage risk performance category, including,

[0013] Determine a priority delivery order based on the purchase weight of the vegetables corresponding to the order in each air flow fluctuation characteristic area, and the location information of the destination where the vegetables of the priority delivery order are to be delivered is the first location of the delivery route;

[0014] Or, separately deliver the vegetables corresponding to the order in the air flow fluctuation characteristic area, and determine whether to disperse and store 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.

[0015] Further, 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 configured to collect the air flow rate in the corresponding storage area.

[0016] Further, the information association module is also configured to determine the fluctuation degree 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 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 it is necessary to disperse and store the remaining vegetables corresponding to the non-order.

[0030] Furthermore, the dispersed storage is to disperse and stack the remaining vegetables corresponding to the non-order according to a preset stacking height.

[0031] Compared with the prior art, the beneficial effects of the present invention are as follows. By setting an order matching module, an information association module, a feature analysis module, and a policy management module, the storage area where the vegetables corresponding to the order are located is determined through the order matching module. Whether the storage area is an airflow fluctuation characteristic area is determined through the information association module according to the fluctuation degree of the air velocity in the storage area. The storage risk performance category of the vegetables to be shipped out is determined through the feature analysis module. The distribution method of the vegetables corresponding to the order in each airflow fluctuation characteristic area and the storage adjustment method for the remaining vegetables corresponding to the non-order are selected through the policy management module. Furthermore, the quantification of the storage risk in the airflow unstable area is realized, and the distribution method of the vegetables is adaptively configured, and the storage method of the vegetables affected by the storage state is timely adjusted, improving the data representativeness of the vegetable state monitoring and effectively reducing the vegetable loss.

[0032] In particular, the present invention obtains the air velocity corresponding to the storage area through the information association module. Those skilled in the art can understand that the air airflow 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 blockage of goods stacking. There are differences in the air velocities corresponding to different storage areas, and different air velocities 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 velocity, improving the data representativeness of the vegetable state monitoring.

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

[0034] In particular, the present invention determines the storage risk performance category of the vegetables to be shipped out through the feature analysis module. In the actual vegetable storage environment, the longer the storage duration of the vegetables to be shipped out in the area with unstable temperature and humidity where the air flow fluctuates significantly, the more easily the vegetable quality is affected. Similarly, the larger the total volume of the vegetables stacked in the current storage area, the poorer the air fluidity at the bottom and inside of the vegetables, and the greater the extrusion force on the bottom of the vegetables, resulting in the more easily the freshness of the vegetables is affected. The present invention determines the storage risk performance category of the vegetables to be shipped out by calculating the ratio of the total volume of the vegetables to be shipped out in the air flow fluctuation characteristic area to the space volume of the air flow fluctuation characteristic area. The larger the ratio, the greater the impact on the storage state of the vegetables, realizing the quantification of the storage risk in the area with unstable air flow and improving the data representativeness of vegetable state monitoring.

[0035] In particular, when the state evaluation of the impact of the storage environment on the vegetable quality by the strategy management module is the category of weak dominant manifestation of storage risk, the order with the largest vegetable purchase weight can be preferentially delivered, avoiding the impact of temperature fluctuations during the delivery and unloading process on the vegetables in the order with the largest vegetable purchase weight. Furthermore, it realizes the adaptive configuration of the vegetable delivery method and the timely adjustment of the storage method for the vegetables affected by the storage state, improves the data representativeness of vegetable state monitoring, and effectively reduces vegetable losses.

[0036] In particular, when the state evaluation of the impact of the storage environment on the vegetable quality by the strategy management module is the category of strong dominant manifestation of storage risk, the vegetables in the category of strong dominant manifestation of storage risk can be delivered separately, keeping the temperature constant during the delivery process to avoid the quality of the vegetables with strong storage risk from being affected again during the delivery. Moreover, by calculating the ratio of the vegetable purchase weight corresponding to the order to the weight of the remaining vegetables not corresponding to the order, the quantity of the remaining vegetables not corresponding to the order can be determined. When the quantity is higher than the preset value, the remaining vegetables are scattered and stacked according to the preset stacking height, avoiding the impact on the freshness of the vegetables caused by the poor air fluidity at the bottom and inside of the vegetables and the extrusion force on the bottom of the vegetables in the area with unstable temperature and humidity where the air flow fluctuates significantly. Furthermore, it realizes the adaptive configuration of the vegetable delivery method and the timely adjustment of the storage method for the vegetables affected by the storage state, improves the data representativeness of vegetable state monitoring, and effectively reduces vegetable losses. BRIEF DESCRIPTION OF THE DRAWINGS

[0037] Figure 1 It is the system block diagram of the vegetable state monitoring system based on big data according to the embodiment of the present invention;

[0038] Figure 2 It is the logical flow chart of the information association module in the embodiment of the present invention for determining whether the storage area is an air flow fluctuation characteristic area;

[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 velocity 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 velocity within a preset time period;

[0049] A feature analysis module, which is connected to the information association module, 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 air flow fluctuation characteristic area to the space volume of the air flow 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 the processing method for the vegetables to be shipped out in each air flow fluctuation characteristic area according to the storage risk performance category, including,

[0051] Determine the priority delivery order based on the purchase weight of the vegetables corresponding to the order in each air flow fluctuation characteristic area, and the location information of the destination where the vegetables of the priority delivery order are delivered is the first location of the delivery route;

[0052] Or, separately deliver the vegetables corresponding to the order in the air flow fluctuation characteristic area, and determine whether to disperse the storage of 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.

[0053] Specifically, the vegetables to be shipped out in the air flow fluctuation characteristic area include the vegetables corresponding to the order and the remaining vegetables corresponding to the non-order.

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

[0055] Specifically, the name information of the vegetables to be shipped out ordered by the user in the order and the location information of the destination are common information included in the order. For example, the name information of the vegetables to be shipped out can be celery, tomatoes, etc., and the location information of the destination can be the street location and house number based on map positioning. Locating the delivery destination of the order according to the street information and house number information included in the order is the prior art and will not be elaborated 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] 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.

[0069] 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;

[0070] 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;

[0071] 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.

[0072] Specifically, the preset ratio reference value k0 is obtained by pre-calculation. The average value of the ratio of the total volume of the vegetables to be shipped out in a plurality of airflow fluctuation characteristic regions of the same size to the spatial volume of the airflow fluctuation characteristic region is pre-calculated, and the calculated average value of the ratio is determined as the preset ratio reference value k0.

[0073] In the embodiment of the present invention, the total volume of the vegetables can be calculated quickly and accurately by using three-dimensional scanning technology to scan the area where the vegetables are stored to generate a three-dimensional model and analyzing the three-dimensional model through software. The spatial volume of the airflow fluctuation characteristic region can be obtained by the approximate shape method to obtain the length, width, and height of the space where the airflow fluctuation characteristic region is located, so as to calculate the spatial volume of the airflow fluctuation characteristic region. The three-dimensional scanning technology and the approximate shape method are prior arts and will not be elaborated here.

[0074] Specifically, the present invention determines the storage risk performance category of the vegetables to be shipped out through the feature analysis module. In the actual vegetable storage environment, the longer the storage time of the vegetables to be shipped out in the temperature and humidity unstable region with obvious airflow fluctuations, the more easily the vegetable quality is affected. Similarly, the more the total volume of the vegetables stacked in the current storage area, the worse the air fluidity at the bottom and inside of the vegetables, and the greater the extrusion force on the bottom of the vegetables, resulting in the more easily the freshness of the vegetables is affected. The present invention determines the storage risk performance category of the vegetables to be shipped out by calculating the ratio of the total volume of the vegetables to be shipped out in the airflow fluctuation characteristic region to the spatial volume of the airflow fluctuation characteristic region. The larger the ratio, the greater the impact on the storage state of the vegetables, realizing the quantification of the storage risk in the airflow unstable region and improving the data representativeness of the vegetable state monitoring.

[0075] Specifically, please refer to Figure 4 As shown, it is a logic flowchart 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. The strategy management module is used to select the processing method for the vegetables to be shipped out in each airflow fluctuation characteristic region according to different storage risk performance categories;

[0076] If the vegetables to be shipped out are in the weak dominant storage risk performance category, the processing method selected by the strategy management module is to determine the priority distribution order based on the vegetable purchase weight corresponding to the order in each airflow fluctuation characteristic region, and the location information of the vegetables in the priority distribution order delivered to the destination is the first location of the distribution route;

[0077] If the vegetables to be shipped out are in the strong dominant storage risk performance category, the processing method selected by the strategy management module is to separately distribute the vegetables corresponding to the order in the airflow fluctuation characteristic region, and determine whether to disperse the storage of the remaining vegetables corresponding to the non-order according to the vegetable purchase weight corresponding to the order and the weight of the remaining vegetables corresponding to the non-order.

[0078] Specifically, the policy management module is further configured to determine the maximum value of the vegetable purchase weight according to the vegetable purchase weights corresponding to the orders in each airflow fluctuation characteristic region, and determine the order corresponding to the maximum value of the vegetable purchase weight as the priority delivery order.

[0079] Example 1:

[0080] When it is determined that the storage areas where the vegetables corresponding to 3 orders (D1, D2, D3) are located are of the weak dominant manifestation category of storage risk, the vegetable purchase weights corresponding to these 3 orders (D1, D2, D3) are respectively obtained. The vegetable purchase weight corresponding to D1 is 10 kg, the vegetable purchase weight corresponding to D2 is 13 kg, and the vegetable purchase weight corresponding to D3 is 8.5 kg. Then, order D2 is determined as the priority delivery order, and the location information of the destination where the vegetables of the priority delivery order D2 are delivered is the first location of the delivery route. For example, when the 3 orders D1, D2, D3 are delivered by the same transport vehicle, the first destination reached by the delivery route is the destination corresponding to order D2.

[0081] Example 2:

[0082] When it is determined that the storage areas where the vegetables corresponding to 3 orders (D1, D2, D3) are located are of the weak dominant manifestation category of storage risk, the vegetable purchase weights corresponding to these 3 orders (D1, D2, D3) are respectively obtained. The vegetable purchase weight corresponding to D1 is 30 kg, the vegetable purchase weight corresponding to D2 is 20 kg, and the vegetable purchase weight corresponding to D3 is 10 kg. Then, order D1 is determined as the priority delivery order. When the 3 orders D1, D2, D3 are delivered by the same transport vehicle, the first destination reached by the delivery route is the destination corresponding to order D1.

[0083] Specifically, when the state evaluation of the influence of the storage environment on the vegetable quality by the policy management module is of the weak dominant manifestation category of storage risk, the order with the largest vegetable purchase weight can be preferentially delivered, avoiding the quality impact on the vegetables of the order with the largest vegetable purchase weight caused by the temperature fluctuation during the delivery and unloading process. Furthermore, it realizes the adaptive configuration of the vegetable delivery method and the timely adjustment of the storage method for the vegetables affected by the storage state, improves the data representativeness of the vegetable state monitoring, and effectively reduces the vegetable loss.

[0084] Specifically, the policy management module is further configured to calculate the weight ratio p of the vegetable purchase weight G1 corresponding to the order to the weight G2 of the remaining vegetables corresponding to the non - order, p = G1 / G2.

[0085] In the embodiment of the present invention, the vegetable purchase weight G1 corresponding to the order is information included in the order. The weight G2 of the remaining vegetables not corresponding to the order can be obtained according to the incoming and outgoing warehouse management system for vegetable storage. The existing incoming and outgoing warehouse management system for vegetable storage has clear records of the total weight of vegetable storage and the weight of incoming and outgoing. 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 vegetables in the storage area. This is the prior art and will not be elaborated here.

[0086] Specifically, the policy management module is further configured to compare the weight ratio p with a preset weight ratio threshold p0.

[0087] If the weight ratio p is less than the weight ratio threshold p0, the policy management module determines that it is necessary to disperse and store the remaining vegetables not corresponding to the order.

[0088] Specifically, the preset weight ratio threshold p0 is obtained by those skilled in the art based on pre-tests. Preferably, the value range of the weight ratio threshold p0 is [0.2, 0.5].

[0089] Specifically, the dispersed storage is to disperse and stack the remaining vegetables not corresponding to the order according to a preset stacking height.

[0090] Specifically, the preset stacking height h1 can be set according to the original stacking height H. Preferably, h1 = 0.25×H.

[0091] Specifically, when the state evaluation of the influence of the storage environment on the quality of vegetables by the policy management module of the present invention is a strong dominant manifestation category of storage risk, the vegetables in the strong dominant manifestation category of storage risk can be separately distributed, so that the temperature remains constant during the distribution process, avoiding the quality of vegetables with strong storage risk from being affected again during the distribution. Moreover, by calculating the ratio of the vegetable purchase weight corresponding to the order to the weight of the remaining vegetables not corresponding to the order, the quantity of the remaining vegetables not corresponding to the order can be determined. When the quantity is higher than the preset value, the remaining vegetables are dispersed and stacked according to the preset stacking height, avoiding the freshness of the remaining vegetables from being affected due to poor air fluidity at the bottom and inside of the vegetables and the extrusion force on the bottom of the vegetables in the area with obvious air flow fluctuations and unstable temperature and humidity. Furthermore, the distribution method of vegetables is adaptively configured and the storage method of vegetables with affected storage status is timely adjusted, improving the data representativeness of vegetable status monitoring and effectively reducing vegetable losses.

[0092] So far, the technical solution of the present invention has 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.

[0093] The above are only the preferred embodiments of the present invention and are not used to limit the present invention. For those skilled in the art, the present invention can have various changes and modifications. Any modification, equivalent substitution, 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; 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; 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; The preset duration value range is [5,8], and the unit is min; 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 velocity fluctuation value is greater than the air flow velocity fluctuation value threshold, the information association module determines that the storage area is an air flow fluctuation characteristic area; The value range of the air velocity fluctuation threshold is [0.05, 0.2], and the unit is m / s; 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; 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 storage risk expression; 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; The strategy management module determines the maximum value of vegetable purchase weight according to the vegetable purchase weight corresponding to the orders in each airflow fluctuation characteristic area, and determines the order corresponding to the maximum value of vegetable purchase weight as the priority delivery order, and the priority delivery order is the order with the greatest impact of temperature fluctuation during delivery and unloading; 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 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.

3. The vegetable status monitoring system based on big data according to claim 1 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.

4. The vegetable status monitoring system based on big data according to claim 3 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.

5. The vegetable status monitoring system based on big data according to claim 4 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.

Images