CN111815249A - Distribution management method - Google Patents
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- 238000007726 management method Methods 0.000 title claims abstract description 26
- 235000013305 food Nutrition 0.000 claims abstract description 198
- 244000005700 microbiome Species 0.000 claims abstract description 38
- 230000006866 deterioration Effects 0.000 claims abstract description 16
- 241000588986 Alcaligenes Species 0.000 claims description 9
- 241000186660 Lactobacillus Species 0.000 claims description 8
- 238000001514 detection method Methods 0.000 claims description 7
- 241000194033 Enterococcus Species 0.000 claims description 5
- 241000192132 Leuconostoc Species 0.000 claims description 5
- 241000607734 Yersinia <bacteria> Species 0.000 claims description 5
- 229940039696 lactobacillus Drugs 0.000 claims description 5
- 241000193468 Clostridium perfringens Species 0.000 claims description 4
- 230000001932 seasonal effect Effects 0.000 claims description 2
- 238000000275 quality assurance Methods 0.000 claims 5
- 238000000034 method Methods 0.000 description 13
- 238000004321 preservation Methods 0.000 description 13
- 238000004806 packaging method and process Methods 0.000 description 11
- 230000035945 sensitivity Effects 0.000 description 4
- 241000193830 Bacillus <bacterium> Species 0.000 description 3
- 238000009413 insulation Methods 0.000 description 3
- 244000199866 Lactobacillus casei Species 0.000 description 2
- 235000013958 Lactobacillus casei Nutrition 0.000 description 2
- 229940017800 lactobacillus casei Drugs 0.000 description 2
- 239000000463 material Substances 0.000 description 2
- 230000002503 metabolic effect Effects 0.000 description 2
- 238000005057 refrigeration Methods 0.000 description 2
- 238000007789 sealing Methods 0.000 description 2
- 241000272525 Anas platyrhynchos Species 0.000 description 1
- 241000272814 Anser sp. Species 0.000 description 1
- 241000894006 Bacteria Species 0.000 description 1
- 241000193403 Clostridium Species 0.000 description 1
- 241000287828 Gallus gallus Species 0.000 description 1
- 241000881860 Paenibacillus mucilaginosus Species 0.000 description 1
- 239000007633 bacillus mucilaginosus Substances 0.000 description 1
- 235000015278 beef Nutrition 0.000 description 1
- 230000009286 beneficial effect Effects 0.000 description 1
- 230000000694 effects Effects 0.000 description 1
- 230000007613 environmental effect Effects 0.000 description 1
- 239000013505 freshwater Substances 0.000 description 1
- 239000007788 liquid Substances 0.000 description 1
- 238000012986 modification Methods 0.000 description 1
- 230000004048 modification Effects 0.000 description 1
- 235000015277 pork Nutrition 0.000 description 1
- 238000005070 sampling Methods 0.000 description 1
- 235000014102 seafood Nutrition 0.000 description 1
- 230000008961 swelling Effects 0.000 description 1
- 239000002699 waste material Substances 0.000 description 1
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Abstract
The invention relates to a distribution management method, which comprises the following steps: detecting the number M of microorganisms of the fresh food to be distributed; secondly, inquiring the critical number N of microorganisms when the fresh food to be distributed is rotten and deteriorated according to historical data; thirdly, if M is more than or equal to N, destroying the fresh food; if M is less than N, calculating the quality guarantee distribution time of the fresh food according to a deterioration experience formula, and storing the quality guarantee distribution time into an actual intelligent routing code; and fourthly, receiving a target intelligent routing code in a customer order, wherein the target intelligent routing code stores customer expected delivery time, comparing the target intelligent routing code with an actual intelligent routing code, counting the difference value between the customer expected delivery time and quality guarantee delivery time, preferentially delivering the fresh food to customers with the customer expected delivery time later than the quality guarantee delivery time, and ensuring that all the actual delivery time and the time when the customers receive the fresh food are earlier than the quality guarantee delivery time.
Description
Technical Field
The invention relates to the technical field of electronic commerce, in particular to a distribution management method.
Background
The rapid development of cold-chain logistics makes it possible for people to buy fresh food through the mobile internet. In the prior art, express companies such as Jingdong, Shunfeng and the like can provide remote distribution service of fresh food, so that a great number of consumers can purchase the required fresh food at home without going out, and great convenience is brought to the daily life of people.
However, one challenge facing current cold chain logistics is how to avoid spoilage of fresh food products during transport from a freezer to a customer-specified location. In the prior art, the fresh food is usually sealed by an ice bag and an insulation can, so that the temperature rise of the fresh food is avoided to the maximum extent, and the fresh food is delivered to an address specified by a client from a refrigeration house as soon as possible.
Because the actual fresh food distribution is not controllable to the weather condition, especially the outdoor temperature during distribution is not artificially controlled, the distribution company does not have sufficient and complete knowledge about the quality of the transported fresh food, and it is unclear under which transport condition the transported fresh food can keep fresh quality, and it is unclear when the transported fresh food reaches the rotten and deteriorated critical value, which causes the situation that the goods are swelled and deteriorated when being delivered to the hands of purchasers, greatly influences the credit of the e-commerce company selling the fresh food, and greatly strikes the willingness of consumers to select network channels to purchase the fresh food.
In addition, the distribution mode of the fresh food in the prior art is relatively rough, and fine management is not realized. One of the problems of the conventional distribution method is that different fresh foods are not considered to have different temperature sensitivity, if different fresh foods are delivered to the same courier for distribution, some fresh foods may be close to the temperature limit of spoilage and some fresh foods may not reach the temperature critical value of spoilage and deterioration far away in the distribution process under the same environmental temperature. In order to avoid the deterioration of the fresh food close to the temperature limit of the spoilage, before the fresh food is sent to the address designated by the customer, the technical means adopted by the prior art is to adopt as high as possible sealed heat-preservation packages and as many ice bags as possible for all the fresh food distributed in the same batch, which is actually a great waste for the fresh food far reaching the temperature critical value of the spoilage, is not only environmentally friendly, but also increases the operation cost of the commodity supplier meaninglessly.
Disclosure of Invention
The invention aims to provide a distribution management method, and aims to solve the technical problems of how to make a distribution management strategy according to an objective rule of the spoilage of fresh food, maximally preventing the spoilage of the fresh food before the fresh food reaches a consumer, and how to distribute the fresh food with different temperature sensitivity degrees in the same batch.
In order to achieve the above object, the present invention provides a delivery management method, including the steps of:
firstly, detecting the number M of microorganisms causing putrefaction in fresh food to be distributed;
secondly, inquiring the critical quantity N of microorganisms causing spoilage when the fresh food to be distributed is subjected to spoilage according to historical data, wherein the historical data is stored in a historical database;
thirdly, if M is larger than or equal to N, the probability of the raw and fresh food undergoing putrefaction and deterioration during detection is higher than the expectation, and the raw and fresh food with the probability of undergoing putrefaction and deterioration higher than the expectation needs to be destroyed; if M is less than N, calculating the quality guarantee distribution time P of the fresh food according to the number of the microorganisms causing spoilage detected in the first step and the critical number of the microorganisms inquired in the second step and a deterioration experience formula, and storing the quality guarantee distribution time P into an actual intelligent routing code of the fresh food;
and fourthly, receiving a target intelligent routing code in a customer order, wherein the target intelligent routing code stores customer expected delivery time, comparing the target intelligent routing code with an actual intelligent routing code, counting the difference between the customer expected delivery time and the quality guarantee delivery time P, preferentially delivering the fresh food to the customers with the customer expected delivery time later than the quality guarantee delivery time P, and ensuring that all the actual delivery time and the time when the customers receive the fresh food are earlier than the quality guarantee delivery time.
Preferably, the Soleries real-time photoelectric microorganism rapid detection system is adopted to detect the number of microorganisms causing putrefaction in the fresh food to be distributed.
Further preferably, the microorganisms detected in the fresh food comprise Alcaligenes mucosae, Lactobacilli, Leuconostoc, Lactobacillus, Clostridium perfringens type A, Yersinia or enterococcus.
If a plurality of customer orders with the delivery time expected by the customer later than the quality guarantee delivery time P exist, the plurality of customer orders with the delivery time expected by the customer later than the quality guarantee delivery time P are sequenced according to the delivery time expected by the customer, and the fresh food is preferentially delivered to the customer with the delivery time expected by the customer being close to the quality guarantee delivery time.
Further preferably, if the customer expects a plurality of customer orders with delivery time later than the quality guarantee delivery time P, the plurality of customer orders with delivery time later than the quality guarantee delivery time P are ordered according to the delivery addresses in the customer orders, and the fresh food is preferentially delivered to the customer with the delivery address close to the warehouse where the fresh food is located.
Preferably, the shelf-life distribution time is calculated in a different season using the seasonal maximum air temperature of the previous year instead of the annual maximum air temperature.
More preferably, the shelf-stable distribution time is calculated for different months using the month maximum air temperature of the previous year instead of the previous year maximum air temperature.
Further preferably, each time fresh food is dispensed, a sample is reserved from the dispensed fresh food, the sample is packaged in the same package and the same dispensing condition as the dispensed fresh food and is dispensed to the same dispensing address as the dispensed fresh food, only the dispensed fresh food is delivered to a customer after being delivered to the dispensing address, meanwhile, the sample is detected immediately, the actual number of microorganisms in the sample is obtained, the highest value of the actual number of microorganisms in the sample in the last year is counted, and the highest value is used for replacing the critical number of microorganisms causing spoilage when the fresh food to be dispensed is subjected to spoilage to calculate the quality guarantee dispensing time.
Further preferably, the delivery management method of the present invention further includes:
summarizing order data: summarizing the types and the weights of the fresh food in all online orders received on the same day into a fresh food order data table;
calculating the expected delivery time Δ t: firstly, calculating the logarithmic mean temperature difference delta T of the fresh food in a fresh food order data table according to a logarithmic mean temperature difference formula;
then calculating the expected distribution time delta t of the fresh food in the order data table of the fresh food according to a distribution time formula;
calculating the heat exchange coefficient K of the fresh food according to a heat exchange coefficient formula;
sorting the calculated expected delivery times Δ t: selecting fresh foods with delta t more than or equal to N as a class, and loading the fresh foods classified as the class into the same transport delivery vehicle; where N is a numerical value greater than 0 specified by the user.
Preferably, after the fresh food meeting the requirement that delta t is larger than or equal to N is loaded into the same transport delivery vehicle, the fresh food which is not classified is loaded into another transport delivery vehicle, and the other transport delivery vehicle is responsible for distribution.
Advantageous effects
Compared with the prior art, the invention has the beneficial effects that: the distribution management method predicts the time of the spoilage of the fresh food according to the detection of the quality data of the fresh food, and formulates a distribution management strategy according to the predicted time, thereby ensuring that the fresh food is not spoiled before reaching the hands of consumers, and ensuring good quality of the transported fresh food when being sent to the hands of the consumers as far as possible.
In addition, the distribution management method provided by the invention also sorts the calculated expected distribution time delta t, selects the fresh foods with the delta t being more than or equal to N as one type, and loads the fresh foods with the one type into the same transport and delivery vehicle, so that the fresh foods with different temperature sensitivity degrees are prevented from being distributed in the same batch.
Drawings
The accompanying drawings are included to provide a further understanding of the invention and are incorporated in and constitute a part of this specification, illustrate embodiments of the invention and together with the description serve to explain the principles of the invention and not to limit the invention.
Fig. 1 is a flow chart of a distribution management method according to the present invention.
Detailed Description
The present invention is described in more detail below to facilitate an understanding of the present invention.
As shown in fig. 1, the distribution management method of the present invention includes the following steps:
firstly, detecting the number M of microorganisms causing putrefaction in fresh food to be distributed;
secondly, inquiring the critical quantity N of microorganisms causing spoilage when the fresh food to be distributed is subjected to spoilage according to historical data, wherein the historical data is stored in a historical database;
thirdly, if M is larger than or equal to N, the probability of the raw and fresh food undergoing putrefaction and deterioration during detection is higher than the expectation, and the raw and fresh food with the probability of undergoing putrefaction and deterioration higher than the expectation needs to be destroyed; if M is less than N, calculating the quality guarantee distribution time P of the fresh food according to the number of the microorganisms causing spoilage detected in the first step and the critical number of the microorganisms inquired in the second step and a deterioration experience formula, and storing the quality guarantee distribution time P into an actual intelligent routing code of the fresh food;
wherein, the deterioration empirical formula is as follows:
wherein, PJTo detect the number of spoilage-causing microorganisms in the fresh food to be dispensed;
t is the highest temperature in the last year of the area where the fresh food to be dispensed is located, and the unit is;
Tminthe temperature of the fresh food without metabolic activity is detected, and the unit is;
b is a microorganism species correction coefficient;
and fourthly, receiving a target intelligent routing code in a customer order, wherein the target intelligent routing code stores customer expected delivery time, comparing the target intelligent routing code with an actual intelligent routing code, counting the difference between the customer expected delivery time and the quality guarantee delivery time P, preferentially delivering the fresh food to the customers with the customer expected delivery time later than the quality guarantee delivery time P, and ensuring that all the actual delivery time and the time when the customers receive the fresh food are earlier than the quality guarantee delivery time.
The predetermined value in the above scheme may be specified by the supplier, for example, the guarantee delivery time P is 1 month, 5 days and 0 point, if the first customer expects the delivery time to be 1 month and 5 days, the second customer expects the delivery time to be 1 month and 1 day, and the third customer expects the delivery time to be 1 month and 8 days, in the prior art, the delivery is generally arranged according to the sequence of the delivery times expected by the customers, that is, the delivery is generally arranged for the second customer first, then the delivery is arranged for the first customer, and finally the delivery is arranged for the third customer. This leads to the problem that shelf-life delivery time is easily missed when delivering to customers, i.e. delivery is easily scheduled for customers after 0 o' clock of 1 month and 5 days, which easily leads to the problem that the fresh food is already burdened and deteriorated when being delivered to the customers.
The distribution management method is different from the traditional distribution sequence, the distribution is preferentially arranged for the third client, and the actual distribution time of the first client, the second client and the third client and the time of receiving the fresh food by the clients are all guaranteed to be earlier than the quality guarantee distribution time. Therefore, the swelling and deterioration of the fresh food when the fresh food is delivered to the hands of the client are avoided to the maximum extent.
It will of course be appreciated by those skilled in the art that the customer's expectation of delivery time being later than the shelf-life delivery time P as described in the present invention is not, and may not be, indefinitely later. The specific amount of the fresh food can be set by a supplier or an express company according to actual needs, for example, the fresh food can be effectively prevented from being swelled and deteriorated when being sent to a client within 48 hours at night. But the specific time is not used as a limitation to the scope of the present invention.
For the customers whose delivery time is expected to be earlier than the quality guarantee delivery time P, the customers can deliver the fresh food according to the normal sequence, and meanwhile, the actual delivery time and the time when the customers receive the fresh food are both guaranteed to be earlier than the quality guarantee delivery time.
Preferably, the fast detecting system of Soleries real-time photoelectric microorganisms at the front of the industry, developed by the American Neogen company, is used for detecting the number of microorganisms causing putrefaction in the fresh food to be distributed.
Further preferably, the microorganisms detected in the fresh food comprise Alcaligenes mucosae, Lactobacilli, Leuconostoc, Lactobacillus, Clostridium perfringens type A, Yersinia or enterococcus.
If a plurality of customer orders with the delivery time expected by the customer later than the quality guarantee delivery time P exist, the plurality of customer orders with the delivery time expected by the customer later than the quality guarantee delivery time P are sequenced according to the delivery time expected by the customer, and the fresh food is preferentially delivered to the customer with the delivery time expected by the customer being close to the quality guarantee delivery time.
Further preferably, if the customer expects a plurality of customer orders with delivery time later than the quality guarantee delivery time P, the plurality of customer orders with delivery time later than the quality guarantee delivery time P are ordered according to the delivery addresses in the customer orders, and the fresh food is preferentially delivered to the customer with the delivery address close to the warehouse where the fresh food is located.
For regions with clear seasons, due to the fact that actual temperature fluctuation in different seasons is large, for example, the highest temperature in winter may be lower than 10 ℃, the highest temperature in summer may be higher than 35 ℃, if the quality guarantee delivery time P is calculated by using the highest temperature in the last year in any season, the obtained result deviates more than the actual situation, and the quality guarantee delivery time P is abnormally shortened. In order to make the calculation result more accurate, it is preferable to calculate the shelf-stable distribution time P using the highest temperature of the season of the previous year instead of the highest temperature of the previous year in a different season.
More preferably, the shelf-life distribution time P is calculated for different months using the month maximum air temperature of the previous year instead of the previous year maximum air temperature.
Since some customers do not receive fresh food immediately and some customers cannot receive the fresh food to be distributed at the first time (for example, not at home), it is generally desirable that the fresh food delivered to the customers have the longest possible storage time to avoid the rapid deterioration of the fresh food due to the above reasons. If the shelf-life dispensing time P is calculated using the critical number N of microorganisms causing spoilage when spoilage of fresh food to be dispensed occurs, the remaining margin may not be large enough for the shelf life.
In order to more accurately reserve the allowance of the preservation time, when the fresh food is distributed, a sample is reserved from the distributed fresh food, the same package and the same distribution conditions as the distributed fresh food are adopted for the sample, the sample is distributed to the same distribution address as the distributed fresh food, the distributed fresh food is delivered to a customer after being delivered to the distribution address, meanwhile, the sample is immediately detected, the actual number of microorganisms in the sample is obtained, the highest value of the actual number of the microorganisms in the sample in the previous year is counted, and the highest value is adopted to replace the critical number N of the microorganisms causing the spoilage when the fresh food to be distributed is spoiled to calculate the quality guarantee period P.
To better illustrate the above embodiment, in a preferred embodiment, it is assumed that the time P when the number of Alcaligenes mucosae in the fresh food to be dispensed is detectedJ1 month, 1 day, 0 point; the highest temperature T of the area of the fresh food to be distributed in the last year is 38 ℃; detecting the temperature T of the fresh food at which the Alcaligenes mucosae has no metabolic activityminAt-8 ℃, the number M of the bacillus alcaligenes causing spoilage to be distributed in the fresh food to be distributed is 10cfu/g, the critical number N of the bacillus alcaligenes causing spoilage to be distributed when the fresh food to be distributed is subject to spoilage is inquired according to historical data to be 100000 cfu/g, and for the bacillus alcaligenes, b is 4.9 multiplied by 105The above numerical values are substituted into the deterioration empirical formula, and the quality guarantee delivery time P is calculated to be PJ+47.9, i.e. about 1 month, 3 days 0. The microorganism species correction factor b is obtained experimentally, and the meaning of the specific numerical value thereof is used for adjusting the dimensional unit, that is, for adjusting the calculation result to the unit of hourTime, thus directly at the moment PJAnd adding the calculated time value in hours to obtain the quality guarantee distribution time P.
As a preferable value, for Alcaligenes mucosae, b is 4.9X 105For mutton of Lactobacillus casei, Leuconostoc, Lactobacillus or Clostridium, b is 6.5 × 105For the type A bacterium, b is 8.9X 105For Yersinia or enterococcus, b is 10.6X 105。
It should be noted that, in the actual operation process, all of bacillus mucilaginosus, lactobacillus casei, leuconostoc, lactobacillus, clostridium perfringens type a, yersinia or enterococcus do not need to be detected, and only one of the microorganisms needs to be selected for detection. However, it is fully understood by those skilled in the art that protocols for detecting two or more microorganisms are also encompassed within the scope of the present invention.
In addition, although the above calculation process takes the units of M and N as cfu/g, it should be understood by those skilled in the art that the units of M and N may be cfu/ml for liquid sampling.
Further preferably, the delivery management method of the present invention further includes:
summarizing order data: summarizing the types and the weights of the fresh food in all online orders received on the same day into a fresh food order data table;
calculating the expected delivery time Δ t: firstly, calculating the logarithmic mean temperature difference delta T of the fresh food in the fresh food order data table according to a logarithmic mean temperature difference formula, wherein the logarithmic mean temperature difference formula is as follows:
wherein, Th1The outdoor maximum ambient temperature on the day of distribution;
Th2the lowest outdoor ambient temperature on the day of distribution;
T1for the life of a living beingThe temperature of the fresh food in the fresh food order data sheet when the fresh food leaves the refrigeration house;
T2the temperature critical value of the raw and fresh food in the raw and fresh food order data sheet is rotten and deteriorated;
then, the expected distribution time Δ t of the fresh food in the fresh food order data table is calculated according to the following distribution time formula:
c is the constant pressure specific heat capacity of the fresh food in the order data table of the fresh food;
m is the quality of the fresh food in the order data sheet of the fresh food;
k is the heat exchange coefficient of the fresh food in the order data sheet of the fresh food;
f, the surface area of the fresh food in the order data sheet of the fresh food;
the heat exchange coefficient K of the fresh food is calculated according to the following heat exchange coefficient formula:
wherein R is an empirical constant, for pork, R is 0.7, for beef or mutton, R is 0.5, for chicken, duck or goose, R is 0.2, for seafood or freshwater aquatic product, R is 0.07;
rho is the density of the fresh food in the fresh food order data sheet;
sorting the calculated expected delivery times Δ t: selecting fresh foods with delta t more than or equal to N as a class, and loading the fresh foods classified as the class into the same transport delivery vehicle; where N is a numerical value greater than 0 specified by the user.
Preferably, after the fresh food meeting the requirement that delta t is larger than or equal to N is loaded into the same transport delivery vehicle, the fresh food which is not classified is loaded into another transport delivery vehicle, and the other transport delivery vehicle is responsible for distribution.
Further preferably, the fresh food distribution method of the present invention further comprises:
and for the fresh food meeting the requirement that delta t is more than or equal to N, sorting according to the expected delivery time delta t, selecting the fresh food with the minimum expected delivery time delta t as a basis, and carrying out heat preservation packaging and ice bag type selection.
As the fresh food with the minimum expected distribution time delta t cannot be corrupted in the distribution process, and other fresh foods meeting the requirement that delta t is more than or equal to N cannot be corrupted in the distribution process, the fresh food with the minimum expected distribution time delta t is taken as the basis, and the fresh food with the delta t more than or equal to N can be subjected to heat preservation packaging and ice bag type selection, so that the fresh-keeping requirement of all the fresh foods meeting the requirement that delta t is more than or equal to N can be met. The actual requirement of the thermal insulation packaging specification for the fresh food with the minimum expected delivery time deltat is correspondingly lower in practice, and the requirement on the number of the ice bags is also minimum, so that the cost in terms of the delivery of consumables can be reduced on the whole. Compared with the prior art, the method has the advantages that all fresh foods distributed in the same batch are completely packaged in the sealing heat preservation mode as high as possible and in the ice bags as many as possible, and compared with the prior art, the distribution consumable material cost can be saved by more than 80% by selecting the fresh foods with the minimum expected distribution time delta t as the basis for heat preservation packaging and ice bag type selection.
Further preferably, the fresh food distribution method of the present invention further comprises:
and for fresh food which does not meet the condition that delta t is more than or equal to N, carrying out heat preservation packaging and ice bag type selection according to the actual expected delivery time delta t of the fresh food.
The method is characterized in that the raw and fresh food does not meet the condition that delta t is not more than N, which indicates that the raw and fresh food is easy to rot and deteriorate, and the raw and fresh food is not easy to store, so that the heat-preservation packaging and the ice bag type selection are carried out according to the actual expected distribution time delta t, and after the raw and fresh food is filled into the ice bag and the heat-preservation packaging is completed, the raw and fresh food is guaranteed not to rot and deteriorate before the distribution is completed and the raw and fresh food is actually delivered to customers.
According to the scheme, the calculated expected delivery time delta t is sequenced, the fresh foods with the delta t being larger than or equal to N are selected to be classified into one type, and the classified fresh foods are loaded into the same transport and delivery vehicle, so that the fresh foods with different temperature sensitivity degrees are prevented from being delivered in the same batch.
And for the fresh food meeting the requirement that delta t is more than or equal to N, sorting according to the expected delivery time delta t, selecting the fresh food with the minimum expected delivery time delta t as a basis, and carrying out heat preservation packaging and ice bag type selection.
As the fresh food with the minimum expected distribution time delta t cannot be corrupted in the distribution process, and other fresh foods meeting the requirement that delta t is more than or equal to N cannot be corrupted in the distribution process, the fresh food with the minimum expected distribution time delta t is taken as the basis, and the fresh food with the delta t more than or equal to N can be subjected to heat preservation packaging and ice bag type selection, so that the fresh-keeping requirement of all the fresh foods meeting the requirement that delta t is more than or equal to N can be met. The actual requirement of the thermal insulation packaging specification for the fresh food with the minimum expected delivery time deltat is correspondingly lower in practice, and the requirement on the number of the ice bags is also minimum, so that the cost in terms of the delivery of consumables can be reduced on the whole. Compared with the prior art, the method has the advantages that all fresh foods distributed in the same batch are completely packaged in the sealing heat preservation mode as high as possible and in the ice bags as many as possible, and compared with the prior art, the distribution consumable material cost can be saved by more than 80% by selecting the fresh foods with the minimum expected distribution time delta t as the basis for heat preservation packaging and ice bag type selection.
The foregoing describes preferred embodiments of the present invention, but is not intended to limit the invention thereto. Modifications and variations of the embodiments disclosed herein may be made by those skilled in the art without departing from the scope and spirit of the invention.
Claims (10)
1. A distribution management method is characterized by comprising the following steps:
firstly, detecting the number M of microorganisms causing putrefaction in fresh food to be distributed;
secondly, inquiring the critical quantity N of microorganisms causing spoilage when the fresh food to be distributed is subjected to spoilage according to historical data, wherein the historical data is stored in a historical database;
thirdly, if M is larger than or equal to N, the probability of the raw and fresh food undergoing putrefaction and deterioration during detection is higher than the expectation, and the raw and fresh food with the probability of undergoing putrefaction and deterioration higher than the expectation needs to be destroyed; if M is less than N, calculating the quality guarantee distribution time of the fresh food according to the number of the microorganisms causing spoilage detected in the first step and the critical number of the microorganisms inquired in the second step and a deterioration experience formula, and storing the quality guarantee distribution time into an actual intelligent routing code of the fresh food;
and fourthly, receiving a target intelligent routing code in a customer order, wherein the target intelligent routing code stores customer expected delivery time, comparing the target intelligent routing code with an actual intelligent routing code, counting the difference value between the customer expected delivery time and quality guarantee delivery time, preferentially delivering the fresh food to customers with the customer expected delivery time later than the quality guarantee delivery time, and ensuring that all the actual delivery time and the time when the customers receive the fresh food are earlier than the quality guarantee delivery time.
2. The distribution management method according to claim 1, wherein the number of spoilage-causing microorganisms in the fresh food to be distributed is detected using a Soleries real-time photoelectric rapid microorganism detection system.
3. The distribution management method according to claim 1, wherein the detected microorganisms in the fresh food include Alcaligenes mucosae, Lactobacilli, Leuconostoc, Lactobacillus, Clostridium perfringens type A, Yersinia, or enterococcus.
4. The delivery management method of claim 1, wherein if there are a plurality of customer orders whose customer expected delivery times are later than the quality assurance delivery times, the plurality of customer orders whose customer expected delivery times are later than the quality assurance delivery times are ordered according to the customer expected delivery times, and the fresh food is preferentially delivered to the customers whose customer expected delivery times are closer to the quality assurance delivery times.
5. The delivery management method according to claim 4, wherein if there are a plurality of customer orders whose delivery times are expected by the customer to be later than the quality assurance delivery time, the plurality of customer orders whose delivery times are expected by the customer to be later than the quality assurance delivery time are sorted according to delivery addresses in the customer orders, and the fresh food is preferentially delivered to the customer whose delivery address is close to the warehouse where the fresh food is located.
6. The distribution management method according to claim 1, wherein the quality guarantee distribution time is calculated using a seasonal maximum air temperature of a previous year instead of a previous annual maximum air temperature in a different season.
7. The distribution management method according to claim 1, wherein the shelf-guaranteed distribution time is calculated using a month maximum air temperature of a previous year instead of a previous year maximum air temperature for a different month.
8. The distribution management method according to claim 1, wherein the sample is left from the distributed fresh food every time the fresh food is distributed, the sample is distributed to the same distribution address as the distributed fresh food using the same package and the same distribution conditions as the distributed fresh food, only the distributed fresh food is delivered to the customer after the sample is delivered to the distribution address, the sample is immediately detected to obtain the actual number of microorganisms in the sample, the highest value of the actual number of microorganisms in the sample in the previous year is counted, and the shelf life distribution time is calculated by replacing the critical number of microorganisms causing spoilage when the fresh food to be distributed undergoes spoilage.
9. The delivery management method of claim 1, further comprising:
summarizing order data: summarizing the types and the weights of the fresh food in all online orders received on the same day into a fresh food order data table;
calculating the expected delivery time Δ t: firstly, calculating the logarithmic mean temperature difference delta T of the fresh food in a fresh food order data table according to a logarithmic mean temperature difference formula;
then calculating the expected distribution time delta t of the fresh food in the order data table of the fresh food according to a distribution time formula;
calculating the heat exchange coefficient K of the fresh food according to a heat exchange coefficient formula;
sorting the calculated expected delivery times Δ t: selecting fresh foods with delta t more than or equal to N as a class, and loading the fresh foods classified as the class into the same transport delivery vehicle; where N is a numerical value greater than 0 specified by the user.
10. The delivery management method of claim 9, wherein the fresh food satisfying Δ t ≧ N is loaded into the same delivery vehicle, and the fresh food not classified is loaded into another delivery vehicle, and the other delivery vehicle takes charge of delivery.
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