CN114662997B - Cold chain transport capacity demand prediction method and cold chain transport capacity distribution method - Google Patents
Cold chain transport capacity demand prediction method and cold chain transport capacity distribution method Download PDFInfo
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Abstract
The invention discloses a cold chain transport capacity demand prediction method and a cold chain transport capacity distribution method, which comprise the following steps: setting a plurality of refrigeration temperature intervals according to the cold chain transportation capacity, and then corresponding to each cold chain cargo; screening cold chain goods with the same refrigeration temperature interval to generate a training sample set, and processing the training sample set by using a self-service method to generate a plurality of training sample subsets; generating a plurality of decision trees by using a single training sample subset so as to judge the sales volume of various cold chain cargos; all the generated decision trees form a decision forest, so that the sales volume of each cold chain cargo in the training sample set is judged; and generating a decision forest corresponding to each training sample set. The cold storage temperature of each cold chain transport vehicle is changed, so that the transportation capacity requirement corresponding to each cold storage temperature interval is considered, the energy consumption waste of cold storage transportation is reduced as much as possible, and the cold chain transportation efficiency is improved to the maximum extent under the condition of limited transportation capacity.
Description
Technical Field
The invention relates to a cold chain transport capacity demand prediction method and a cold chain transport capacity distribution method, and belongs to the field of cold chain transport capacity prediction.
Background
Cold chain transportation is a transportation mode with a higher threshold, and the cost is relatively high, so that the transportation capacity is difficult to obviously improve in a short time. On the other hand, with the improvement of the living standard of the consumers, the market demand corresponding to the cold chain transportation is gradually increased, which causes the contradiction of the supply relationship of the cold chain transportation, and the consumers not only need to pay more transportation premium, but also are difficult to further increase the consumption experience.
Disclosure of Invention
The present invention provides a method for predicting cold chain capacity demand and a method for allocating cold chain capacity, which overcome the disadvantages of the prior art.
The technical scheme adopted by the invention is as follows:
a cold chain capacity demand forecasting method comprises the following steps:
step S1: setting a plurality of refrigeration temperature intervals according to the cold chain transportation capacity, and then corresponding to each cold chain cargo;
step S2: screening cold chain goods with the same refrigeration temperature interval to generate a training sample set, and processing the training sample set by using a self-service method to generate a plurality of training sample subsets;
step S3: generating a plurality of decision trees by using a single training sample subset so as to judge the sales volume of various cold chain cargos;
step S4, repeating the step S3 on each training sample subset to enable all generated decision trees to form a decision forest, and accordingly judging the sales volume of each cold chain cargo in the training sample set;
step S5: repeating the steps S2-S4 to generate a decision forest corresponding to each training sample set;
step S6: and respectively inputting the test sets into a decision forest to obtain the total transport capacity requirement corresponding to each refrigeration temperature interval in the target delivery cycle.
The invention has the beneficial effects that:
1. the cold chain transport vehicle has the function of refrigerated transport, the lowest refrigeration temperatures which can be reached by different cold chain transport vehicles are generally different, the lowest refrigeration temperature which can be reached can be determined by counting the existing cold chain transport vehicles by a transport department, and a refrigeration temperature interval is set by taking the lowest refrigeration temperature as a lower limit, so that the existing cold chain transport vehicles do not need to be replaced, and the extra vehicle replacement or transformation cost is avoided from being paid by the transport department;
2. the cold chain goods correspond to the refrigeration temperature intervals, so that a basis for predicting the transport capacity required by different refrigeration temperature intervals is provided;
3. the traditional cold chain transportation is often used for vehicles with different refrigeration capacities in an unordered mode, so that vehicles with lower refrigeration temperatures can be abused, cold chain goods with the same refrigeration temperature range are separately screened and the transportation capacity demand is predicted, the reasonable configuration of cold chain transportation vehicles can be met, unnecessary energy consumption is reduced, and the cold chain transportation efficiency is improved;
4. cold chain goods in the same training sample set contribute transport capacity requirements to the same refrigeration temperature interval, so that the transport capacity requirements of different cold chain goods in the same training sample set have certain relevance under most conditions, and the decision tree can predict the sales volume of specific cold chain goods in the training sample subset by means of the relevance;
5. the decision forest ensures the accuracy of predicting the sales volume of each cold chain cargo finally, and the total transport capacity demand is obtained by summing the sales volume of each cold chain cargo, so that the transport capacity demand corresponding to a single refrigeration temperature interval is predicted more accurately;
6. through the freight capacity demand that obtains each cold-stored temperature interval and correspond, and then change every cold chain haulage vehicle's cold-stored temperature to compromise the freight capacity demand that each cold-stored temperature interval corresponds, it is extravagant to reduce the energy consumption of cold-stored transportation as far as possible, furthest promotes cold chain transportation efficiency under limited freight capacity condition.
The decision tree generated in step S3 of the present invention corresponds to only the cold chain goods in the subset of training samples.
The characteristic values of the cold chain goods comprise historical sales and cross attributes of each delivery cycle, different cold chain goods in the training sample set are associated through the cross attributes, and the decision tree generated in the step S3 corresponds to each cold chain goods in the training sample set respectively.
In step S3, the generated decision tree is tested by using a test set, and the decision tree with the accuracy lower than the threshold is deleted.
In step S3, the decision tree is generated by the C4.5 algorithm to prune the decision tree before testing with the test set.
In step S6, the test set is completed before being input into the decision forest to ensure that each refrigeration temperature interval has at least one characteristic value corresponding to the cold chain cargo.
The window size of the refrigeration temperature interval of the present invention is inversely related to the center value.
A cold chain capacity allocation method, comprising the steps of:
step S1': implementing a cold chain transport capacity demand prediction method;
step S2': the refrigeration temperature of the existing distribution vehicle is adjusted to change the actual transportation capacity corresponding to different refrigeration temperature intervals.
In the target delivery cycle, the actual transport capacity and the total transport capacity requirement ratio corresponding to the refrigeration temperature interval are priorities corresponding to the refrigeration temperature interval, and the central value of the refrigeration temperature interval is positively correlated to the priority.
Other features and advantages of the present invention will be disclosed in more detail in the following detailed description of the invention and the accompanying drawings.
[ description of the drawings ]
Fig. 1 is a schematic flow chart of a cold chain transportation capacity demand prediction method according to embodiment 1 of the present invention.
[ detailed description ] embodiments
The technical solutions of the embodiments of the present invention are explained and illustrated below with reference to the drawings of the embodiments of the present invention, but the following embodiments are only preferred embodiments of the present invention, and not all embodiments. Based on the embodiments in the implementation, other embodiments obtained by those skilled in the art without any creative effort belong to the protection scope of the present invention.
In the following description, the appearances of the indicating orientation or positional relationship such as the terms "inner", "outer", "upper", "lower", "left", "right", etc. are only for convenience in describing the embodiments and for simplicity in description, and do not indicate or imply that the device or element being referred to must have a particular orientation, be constructed and operated in a particular orientation, and are not to be construed as limiting the present invention.
Example 1:
referring to fig. 1, the present embodiment provides a method for predicting a cold chain capacity demand, including the following steps:
step S1: setting a plurality of refrigeration temperature intervals according to the cold chain transportation capacity, and then corresponding to each cold chain cargo;
the number and specification of cold chain transportation vehicles owned by the cold chain transportation department are generally fixed, and the refrigeration capacity of the cold chain transportation vehicles is generally fixed on the basis of not performing secondary refitting or replacement on the cold chain transportation vehicles, for example, in the embodiment, the lowest refrigeration temperature of all the cold chain transportation vehicles is-20 ℃, the lower limit temperature of a refrigeration temperature interval is considered to be-20 ℃, and the upper limit temperature of a general refrigeration temperature interval is 0 ℃, on the basis, the embodiment is exemplified in the simplest case, for example, the number of the refrigeration temperature intervals is set to be two, the two refrigeration temperature intervals are respectively [ -20 ℃, T ℃) and [ T ℃, 0 ℃), the maximum transportation amount of each cold chain transportation vehicle is the same, if the lowest refrigeration temperatures of all the cold chain transportation vehicles are counted, the median of the lowest refrigeration temperatures is-15 ℃, then T may be-15, although in other embodiments it may be directly averaged for-20 ℃ and 0 ℃ such that T is-10; the setting process of the refrigeration temperature interval is completely based on the existing cold chain transportation capacity of a cold chain transportation department, and the existing cold chain transportation vehicle does not need to be replaced and modified, so that the vehicle replacement or modification cost is avoided from being additionally paid;
the method comprises the steps of subdividing the existing transport capacity level by setting different refrigeration temperature intervals, determining ideal refrigeration storage temperature according to the cargo attribute of each type of cold chain cargo, determining the specific transport capacity level occupied by the cold chain cargo, and matching the ideal refrigeration storage temperature of the cold chain cargo with the specific transport capacity level, so that on one hand, the storage effect of the cold chain cargo in the actual cold chain transportation process can be ensured, and on the other hand, unnecessary waste of energy consumption caused by transportation of the cold chain cargo at an excessively low temperature is avoided, unnecessary overuse of cold chain transportation vehicles capable of generating low refrigeration temperatures is reduced, the transportation efficiency is improved, and reasonable configuration of subsequent transport capacity is ensured;
in addition, the ideal refrigeration storage temperature of the cold-chain goods is matched with the specific transport capacity level, so that a supply-demand corresponding relation is established between the transport capacity requirement (namely the sales volume) of the cold-chain goods and the actual transport capacity of the specific transport capacity level, and a basis is provided for judging the size of the gap of the actual transport capacity;
it should be noted that, in general, the lower the ideal refrigeration storage temperature of the cold chain cargo, the narrower the suitable refrigeration temperature window, and in order to match this characteristic, the division of the refrigeration temperature interval needs to meet the criterion that the window size of the refrigeration temperature interval is inversely related to the central value of the refrigeration temperature interval;
step S2: screening cold chain goods with the same refrigeration temperature interval to generate a training sample set, and processing the training sample set by using a self-service method to generate a plurality of training sample subsets;
typically, the characteristic value of the cold-chain goods in the training sample subset comprises at least the historical sales amount of each shipping cycle;
step S3: generating a plurality of decision trees by using a single training sample subset so as to judge the sales volume of various cold chain cargos;
because the same training sample set of cold chain goods occupies the transportation capacity corresponding to the same refrigeration temperature interval, certain relevance exists among different cold chain goods in most cases, and therefore even if part of cold chain goods is absent in the training sample set obtained by a self-help method, the transportation capacity requirement corresponding to the missing cold chain goods can be predicted and estimated to a certain extent by using the historical sales volumes of a plurality of delivery cycles of other cold chain goods through a decision tree;
for example, if the training sample set contains n cold-chain cargos, at most n decision trees can be generated according to a single training sample subset, so as to respectively judge the sales of the n cold-chain cargos in the training sample set in a specific delivery cycle;
preferably, the characteristic values of the cold-chain goods further include a cross attribute, different cold-chain goods in the training sample set are associated by the cross attribute, and as is common, the cross attribute may be a place of production, for example, two cold-chain goods in the same training sample set have the same place of production, so that the historical sales volumes of the two cold-chain goods often have a negative correlation (competitive sales) or a positive correlation (cooperative sales), and correspondingly, the transportation needs of the two cold-chain goods often have a positive correlation or a negative correlation, so that the two cold-chain goods have a strong correlation, on one hand, due to the cross attribute, when the two cold-chain goods simultaneously exist in the training sample subset, the advantage of the correlation between the corresponding characteristic values of the two cold-chain goods can be fully played, the prediction and judgment accuracy of the generated decision tree for the transportation needs of the two cold-chain goods is improved, and if one of the two cold-chain goods is missing in one of the training sample subset and the other cold-chain goods is included, on the basis that the decision tree generated based on the training sample subset can also obtain more accurate estimation judgment aiming at the transport capacity requirement of the missing cold-chain goods, the same number of estimation results of each cold-chain goods in the same training sample set can be ensured to be obtained through the decision tree, and thus the transport capacity requirement judgment reference of each cold-chain goods is ensured to be approximately the same;
because the capacity requirement is usually a continuous variable, the C4.5 algorithm is usually adopted in the generation process of the decision tree, and meanwhile, the fluctuation of the historical sales amount to a certain degree is considered, and the regularity is relatively low, so that pruning can be carried out in the process of generating the decision tree by adopting the C4.5 algorithm, and the overfitting condition in the subsequent test process is reduced;
step S4, repeating the step S3 on each training sample subset to enable all generated decision trees to form a decision forest, and accordingly judging the sales volume of each cold chain cargo in the training sample set;
the decision trees generated by different training sample subsets do not necessarily have the same judgment result aiming at the transport capacity requirement of the same kind of cold-chain goods, and the decision forest judges the transport capacity requirement of the same kind of cold-chain goods by enabling each training sample subset to generate the decision tree, so that the accuracy of the judgment result can be effectively improved;
it should be noted that the judgment accuracy of the decision forest depends on the number of decision trees contained in the decision forest, and in general, the judgment accuracy is higher when the number of decision trees contained in the decision forest is larger, but it has been described in the foregoing that "there is a certain correlation between different cold-chain goods in most cases", and therefore there is certainly no correlation between different cold-chain goods in some cases, and therefore there is necessarily a case that a part of cold-chain goods is missing in a certain training sample subset, and the generated decision tree estimates the capacity demand of the missing cold-chain goods by using the characteristic value of the cold-chain goods contained in the decision tree, which results in a lower accuracy, and therefore, the judgment accuracy may be reduced when the number of decision trees contained in the decision forest is larger in this embodiment;
based on this, in step S3, the generated decision tree needs to be tested by using a test set, and the decision tree with the accuracy lower than the threshold is deleted, so that the scale of the finally obtained decision forest is reduced, and the judgment and prediction accuracy of the decision forest can be improved on the basis of improving the judgment speed of the decision forest;
step S5: repeating the steps S2-S4 to generate a decision forest corresponding to each training sample set;
step S6: respectively inputting the test set (or the data set to be tested) into a decision forest, namely obtaining the transport capacity requirement of each cold chain cargo corresponding to each refrigeration temperature interval, and summing the transport capacity requirements of each cold chain cargo corresponding to the same refrigeration temperature interval, namely obtaining the total transport capacity requirement corresponding to a target delivery cycle of a single refrigeration temperature interval;
it is worth noting that, because the finally retained decision tree generation process may only adopt the characteristic values of part of cold-chain goods, even if the type of the cold-chain goods in the test set (or the data set to be tested) is damaged, the high accuracy can be maintained at a high rate, and meanwhile, the cold-chain transport capacity demand prediction method can estimate the transport capacity demand of the cold-chain goods which are lost in the test set, so that the cold-chain transport capacity demand prediction method has good tolerance for problems such as untimely data statistics in the actual use process;
certainly, in order to ensure that the transportation capacity requirement corresponding to each refrigeration temperature interval can be estimated, the test set is completed before being input into the decision forest, so that each refrigeration temperature interval is ensured to have at least one characteristic value corresponding to cold chain goods.
Example 2:
the embodiment provides a cold chain capacity distribution method, which comprises the following steps:
step S1': the cold chain capacity demand prediction method in example 1 was implemented;
step S2': the refrigeration temperature of the existing distribution vehicle is adjusted to change the actual transportation capacity corresponding to different refrigeration temperature intervals.
The present embodiment is illustrated in a simpler manner, for example, the refrigerating temperature of the transportation vehicle corresponding to the actual transportation capacity of 0.2 unit is increased from [ -20 ℃, -10 ℃) to [ -10 ℃, -0 ℃ ], the maximum transportation capacity of 1.2 unit at [ -20 ℃, -10 ℃) by setting the transportation demand corresponding to the step S1 [ -20 ℃, -10 ℃ ] to 1 unit, the actual transportation capacity of 2 unit within the range of [ -20 [ -10 ℃, -0 ℃ ], and the refrigerating temperature of the transportation vehicle corresponding to the actual transportation capacity of 0.2 unit is increased from [ -20 ℃, -10 ℃) to [ -10 ℃, the temperature is 0 ℃, the energy consumption is reduced, and meanwhile, a cold chain transport vehicle is not required to be additionally arranged, so that the transport cost is reduced on the basis of ensuring the transport efficiency.
The above example is under the precondition that the total transportation capacity is relatively sufficient, but if the maximum transportation capacity is 2 units, and the transportation capacity requirements corresponding to the [ -20 ℃, -10 ℃) and [ -10 ℃, 0 ℃) are 1.2 units, the refrigeration temperature interval with a lower central value, namely [ -20 ℃, -10 ℃) needs to be preferentially met, because the demand amount of the cold-chain goods with harsh refrigeration temperature is lower than that of other cold-chain goods, the market demand fault tolerance is lower, the transportation timeliness requirement is higher, the price cost is generally higher, and therefore the transportation requirement for preferentially meeting the cold-chain goods with lower refrigeration temperature is more cost-effective than the whole cold-chain transportation market;
in this embodiment, the higher the priority, i.e. the higher the ratio between the actual transport capacity and the total transport capacity requirement, i.e. the smaller the transport capacity gap corresponding to the refrigeration temperature interval.
Example 3:
the difference between this embodiment and embodiment 1 is that the decision tree generated in step S3 only corresponds to the cold-chain cargo in the training sample subset, that is, if part of the cold-chain cargo is missing in the training sample subset, the decision tree for estimating the capacity demand of the missing part of the cold-chain cargo is not generated, so as to reduce the scale of the final decision forest and increase the operation speed of the decision forest.
While the invention has been described with reference to specific embodiments thereof, it will be understood by those skilled in the art that the invention is not limited thereto, and may be embodied in many different forms without departing from the spirit and scope of the invention as set forth in the following claims. Any modification which does not depart from the functional and structural principles of the present invention is intended to be included within the scope of the claims.
Claims (9)
1. A cold chain capacity demand prediction method is characterized by comprising the following steps:
step S1: setting a plurality of refrigeration temperature intervals according to the cold chain transportation capacity, and then corresponding to each cold chain cargo;
step S2: screening cold chain goods with the same refrigeration temperature interval to generate a training sample set, and processing the training sample set by using a self-service method to generate a plurality of training sample subsets;
step S3: generating a plurality of decision trees by using a single training sample subset so as to judge the sales volume of various cold chain cargos;
step S4, repeating the step S3 on each training sample subset to enable all generated decision trees to form a decision forest, and accordingly judging the sales volume of each cold chain cargo in the training sample set;
step S5: repeating the steps S2-S4 to generate a decision forest corresponding to each training sample set;
step S6: and respectively inputting the test sets into a decision forest to obtain the total transport capacity requirement corresponding to each refrigeration temperature interval in the target delivery cycle.
2. The cold-chain capacity demand forecasting method according to claim 1, wherein the decision tree generated in step S3 corresponds to only cold-chain goods in the training sample subset.
3. The method for predicting cold-chain capacity demand according to claim 1, wherein the characteristic values of the cold-chain cargos comprise historical sales volume of each shipping cycle and cross attributes, different cold-chain cargos in the training sample set are associated through the cross attributes, and the decision tree generated in step S3 corresponds to each cold-chain cargo in the training sample set.
4. The method for predicting demand for cold link capacity according to claim 3, wherein in step S3, the generated decision tree is tested by using a test set, and the decision tree with accuracy lower than a threshold is deleted.
5. The method for predicting cold link capacity demand according to claim 4, wherein the decision tree in step S3 is generated by C4.5 algorithm to prune the decision tree before testing with the test set.
6. A cold chain capacity demand forecasting method according to claim 3, wherein in step S6, the test set is supplemented before being input into the decision forest to ensure that each refrigeration temperature interval has at least one characteristic value corresponding to the cold chain cargo.
7. The cold chain capacity demand forecasting method of claim 1, wherein a window size of the refrigerating temperature interval is inversely related to the central value.
8. A cold chain capacity distribution method is characterized by comprising the following steps:
step S1': implementing a cold chain capacity demand prediction method as claimed in any one of claims 1 to 7;
step S2': the refrigeration temperature of the existing distribution vehicle is adjusted to change the actual transportation capacity corresponding to different refrigeration temperature intervals.
9. The cold chain capacity allocation method according to claim 8, wherein in the target shipping cycle, the ratio of the actual transport capacity to the total transport capacity requirement corresponding to the refrigerating temperature interval is a priority corresponding to the refrigerating temperature interval, and the central value of the refrigerating temperature interval is positively correlated to the priority.
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