CN117808385A - Logistics transportation management method and system based on Internet of things - Google Patents

Logistics transportation management method and system based on Internet of things Download PDF

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CN117808385A
CN117808385A CN202410226039.9A CN202410226039A CN117808385A CN 117808385 A CN117808385 A CN 117808385A CN 202410226039 A CN202410226039 A CN 202410226039A CN 117808385 A CN117808385 A CN 117808385A
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logistics
capacity
season
period
shortage
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CN117808385B (en
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林秀强
陈龙
董占龙
石悦
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Zhidan Yunbao Fujian Technology Co ltd
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Abstract

The invention belongs to the technical field of logistics transportation management, and particularly discloses a logistics transportation management method and system based on the Internet of things.

Description

Logistics transportation management method and system based on Internet of things
Technical Field
The invention belongs to the technical field of logistics transportation management, and particularly relates to a logistics transportation management method and system based on the Internet of things.
Background
The logistics transportation management means that transportation activities in the logistics process are reasonably organized and coordinated through effective planning, implementation and control means so as to achieve the aims of reducing cost, improving efficiency and the like. The logistics transportation management mainly relates to transportation route planning, capacity distribution management, transportation cost management and the like, wherein the capacity distribution management is a key link in logistics transportation management due to the fact that the capacity distribution management is directly related to timely delivery, cost benefit and customer satisfaction of goods.
In the prior art, the following defects exist when carrying out capacity allocation management: first, with the increase of economy and the expansion of market, the demand of logistics will increase correspondingly, and the capacity will be short when the capacity expansion is not performed in time, so it is especially necessary to judge whether the capacity of logistics companies is short in time. However, in the prior art, seasonal fluctuation of logistics demands is ignored when judging whether the capacity is short, so that the judgment process does not divide the seasonal period, the accuracy of the judgment result is reduced to a certain extent, an effective seasonal policy cannot be formulated, the customer demands cannot be met easily in the peak period, delivery delay or unstable service is caused, the waste of resources and cost is possibly caused in the low peak period, and the flexibility and adaptability guarantee of logistics operation are not facilitated.
Secondly, most of the prior art directly selects the capacity expansion when judging that the logistics company has the capacity shortage, and the reasons for causing the capacity shortage are not only the capacity shortage, but also the capacity shortage possibly caused by improper distribution, so that the capacity shortage treatment can only adopt a superficial solution, has no pertinence and cannot solve the actual problem, and the capacity shortage can be repeatedly caused, thereby being unfavorable for fundamentally solving the capacity shortage.
Disclosure of Invention
Therefore, an object of the embodiments of the present application is to provide a method and a system for logistics transportation management based on the internet of things, which effectively solve the problems mentioned in the background art.
The aim of the invention can be achieved by the following technical scheme: the first aspect of the invention provides a logistics transportation management method based on the Internet of things, which comprises the following steps: s1, locating the location of the capacity resource of the logistics company, determining season distribution months corresponding to the location of the capacity resource, and selecting a plurality of historical years, so as to obtain each season period corresponding to each historical year based on the season distribution months corresponding to the location of the capacity resource.
S2, analyzing the utilization rate of the existing transport capacity resources in the season time periods corresponding to the historical years, counting the number of the commodity flow orders in the season time periods corresponding to the historical years, and further extracting the commodity flow order receiving time and the commodity flow order receiving time from the commodity flow orders, so that the order receiving time corresponding to the commodity flow orders is obtained.
S3, judging whether the logistics company has the capacity shortage or not based on the utilization rate of the existing capacity resources in the season time periods corresponding to the historical years and the order receiving time length corresponding to the logistics orders.
S4, identifying a shortage season period when judging that the capacity is shortage, analyzing a capacity shortage reason corresponding to the shortage season period, executing S5 when the analyzed capacity shortage reason is insufficient in capacity resources, and executing S6 when the analyzed capacity shortage reason is improper in capacity distribution.
S5, counting the occupation ratio of the short-cut season time period, comparing the occupation ratio with the high occupation ratio of the preset value, if the occupation ratio of the short-cut season time period is larger than or equal to the high occupation ratio, expanding the capacity resources, otherwise, additionally renting the capacity resources in the short-cut season time period.
And S6, identifying the capacity distribution optimizing direction of the logistics company in the shortage season time period, and accordingly carrying out capacity distribution optimizing.
According to one implementation manner of the first aspect of the present invention, the seasonal distribution month is a continuous month corresponding to each season, wherein the implementation manner of determining the seasonal distribution month corresponding to the location where the capacity resource is located is as follows: comparing the location of the capacity resource with the distribution areas corresponding to various climate types in the map, comparing the climate types corresponding to the location of the capacity resource, and matching the seasonal distribution months corresponding to the various climate types in the cloud reference library, thereby obtaining the seasonal distribution months corresponding to the location of the capacity resource.
According to one implementation manner of the first aspect of the present invention, the existing capacity resource utilization analysis process is as follows: the number of the existing logistics vehicles of the logistics company is counted, and the logistics vehicles are numbered according to a set sequence.
Acquiring the used time length of each logistics vehicle in the period of each season corresponding to each historical year, and further importing a formulaAnd obtaining the occupancy rate of each logistics vehicle in each season period corresponding to each historical year.
The occupancy rate of each logistics vehicle in each historical year corresponding to each season period is expressed by an expressionCalculating the utilization rate of the current capacity resources of the logistics company in the time period corresponding to each season of each historical year, wherein +.>Denoted as +.>Occupancy of logistic vehicle, < >>Denoted as logistics vehicle number>，/>Expressed as the number of existing logistics vehicles of the logistics company, < >>Expressed as a natural constant.
According to one implementation manner of the first aspect of the present invention, the specific process of evaluating whether the capacity of the logistics company is short is as follows: (1) The same season period is transported in each history yearComparing the utilization rate of force resources, extracting the maximum utilization rate and the minimum utilization rate from the comparison result, and importing the utilization approach degree calculation formulaAnd obtaining the utilization approach degree of the existing transport capacity resources corresponding to each season period.
(2) Comparing the utilization approach degree of the existing transport capacity resources corresponding to each season period with the set ideal utilization approach degree, and passing through a modelAnalyzing and obtaining the utilization rate of the existing transportation capacity resource tendency corresponding to each season period>In the model->Expressed as season period number->Wherein->、/>、/>、/>Respectively expressed as spring time period, summer time period, autumn time period and winter time period, < + >>Expressed as the d-th seasonal period in +.>Utilization of existing capacity resources in historical years, < +.>Expressed as historical year number->，/>Expressed as the number of selected historical years, +.>、/>Expressed as maximum utilization, minimum utilization, respectively, of the same seasonal period in each historical year,/->Indicating the existing capacity resource utilization approach corresponding to the d-th season period,/for>Indicating the ideal utilization approach of the setting.
(3) Comparing the order receiving time length corresponding to each logistics order in each season time period corresponding to each historical year with the set effective order receiving time length, and calculating the order receiving time efficiency corresponding to each logistics order, wherein the order receiving time length is equal to the effective order receiving time length。
(4) And carrying out average calculation on the order receiving time efficiency corresponding to each logistics order in each season period corresponding to each historical year to obtain the average order receiving time efficiency of the logistics order in each season period corresponding to each historical year.
(5) Similarly, the (1) and (2) are referred to obtain the order taking time efficiency of the logistics order tendency corresponding to each season period：
In->Indicate->Season period is at->Average order taking time of logistics orders in historical year, < + >>Indicate->A time-dependent approach to order taking of the logistics orders corresponding to the seasonal period, wherein +.>，/>And indicating the set ideal logistics order taking time-effect approaching degree.
(6) Substituting the existing capacity resource trend utilization rate and logistics order trend order taking time efficiency corresponding to each season period into the expressionObtaining the capacity shortage index corresponding to each season period>In the formula->Expressed as a set constant, and +.>>1。
(7) Importing the capacity shortage index corresponding to each season period into a judgment modelObtaining the judging result of whether the logistics company has the capacity shortage +.>In the model->Indicating that the logistics company has the shortage of the capacity and +.>Indicating that the logistics company has no capacity shortage, < - > about>Represented as a pre-configured threshold.
According to one implementation manner of the first aspect of the present invention, the identifying process of the shortage season period is: comparing the capacity shortage index corresponding to each seasonal period with a preset threshold value, and selecting the seasonal period with the capacity shortage index being greater than or equal to the preset threshold value as the shortage seasonal period.
According to one implementation manner of the first aspect of the present invention, the resolving process of the cause of the capacity shortage is as follows: and extracting the transport start point, the transport end point, the actual transport route, the actual delivery time, the expected delivery time and the reporting information of the abnormal event from the transport records of the logistics orders in each historical year in the period of the short season.
Planning a preferred transportation route based on a transportation start point and a transportation end point of the logistics order, and comparing the length of the preferred transportation route with the length of an actual transportation route, calculating a transportation inefficiency index of the logistics order, wherein
。
And comparing the transportation inefficiency index of the logistics order with the set allowable transportation inefficiency index, and if the transportation inefficiency index of the certain logistics order is larger than the allowable transportation inefficiency index, marking the logistics order as a first abnormal logistics order.
Comparing the actual delivery time of the physical distribution order with the expected delivery time, calculating a delivery delay index of the physical distribution order, wherein
Comparing the delivery delay index of the logistics order with the set allowable delivery delay index, if the delivery delay index of a certain logistics order is larger than the allowable delivery delay index, identifying whether an abnormal event exists in the abnormal event report information of the logistics order, and if the abnormal event does not exist, marking the logistics order as a second abnormal logistics order.
Counting the number of first abnormal logistics orders and the number of second abnormal logistics orders existing in each historical year in the short-cut season period, calculating a logistics transportation abnormality index corresponding to the short-cut season period, comparing the logistics transportation abnormality index with the set acceptable abnormality index, and analyzing the reasons of the short-cut season period to be improper in transportation capacity distribution if the logistics transportation abnormality index corresponding to the short-cut season period is larger than the acceptable abnormality index, otherwise analyzing the reasons of the short-cut season period to be insufficient in transportation capacity.
According to one implementation manner of the first aspect of the present invention, the calculation expression of the abnormal logistics transportation index corresponding to the shortage season periodIn the formula->Logistics transportation abnormality index expressed as correspondence to the period of the shortage season,/->、/>Respectively indicated as the shortage season period is in +.>First abnormal number of physical distribution orders, second abnormal number of physical distribution orders, ∈1, existing in the historical year>Indicated as the period of the shortage season is at +.>The number of logistics orders in the historical year,、/>respectively expressed as weight factors corresponding to the first abnormal material flow order and the second abnormal material flow order, and +.>。
According to one implementation manner of the first aspect of the present invention, the calculation expression of the ratio of the shortage season period is that。
According to one possible manner of the first aspect of the present invention, the identifying the distribution optimization direction of capacity of the logistics company during the season of the shortage is as follows: dividing the first abnormal logistics order quantity and the second abnormal logistics order quantity of the shortage season period in each historical year by the logistics order quantity of the corresponding historical year respectively to obtain a first abnormal occupation ratio and a second abnormal occupation ratio of the shortage season period in each historical year.
Comparing the first abnormal ratio and the second abnormal ratio of the short-cut season period in each historical year, and utilizing an algorithmObtaining the misdistribution cause of the capacity of the shortage season period in each history year +.>In algorithm +.>Indicated as misexecution of the transportation route,/-)>Indicating that the transport duration is not properly controlled,、/>expressed as a first abnormal ratio, a second abnormal ratio, ">Effective duty cycle expressed as set is close to progress, +.>Representing and.
Comparing the reasons of improper distribution of the capacity in the short-cut season period in each historical year, counting the occurrence frequency of the reasons of improper distribution of the capacity, and further taking the reason of improper distribution of the capacity corresponding to the maximum occurrence frequency as the capacity distribution optimization direction of the logistics company in the short-cut season period.
The second aspect of the invention provides a logistics transportation management system based on the internet of things, which comprises the following modules: the seasonal period acquisition module is used for positioning the location of the capacity resource of the logistics company, determining seasonal distribution months corresponding to the location of the capacity resource, and selecting a plurality of historical years, so as to obtain each seasonal period corresponding to each historical year based on the seasonal distribution months corresponding to the location of the capacity resource.
And the capacity resource utilization rate analysis module is used for analyzing the utilization rate of the existing capacity resources in the season periods corresponding to the historical years.
The logistics order receiving time length obtaining module is used for counting the number of the logistics orders in the time period of each season corresponding to each historical year, extracting the genre order time and the logistics order receiving time from each logistics order, and obtaining the order receiving time length corresponding to each logistics order.
And the cloud reference library is used for storing season distribution months corresponding to various climate types.
And the capacity shortage judging module is used for judging whether the logistics company has the capacity shortage or not based on the utilization rate of the existing capacity resources in the season time periods corresponding to the historical years and the order receiving time length corresponding to the logistics orders.
And the capacity shortage reason analysis module is used for identifying the shortage season time period when judging that the capacity shortage exists and analyzing the capacity shortage reason corresponding to the shortage season time period.
And the capacity shortage processing module is used for counting the occupation ratio of the shortage season time period when the analyzed capacity shortage source is insufficient in capacity resources, comparing the occupation ratio with the high occupation ratio of the preset value, and if the occupation ratio of the shortage season time period is larger than or equal to the high occupation ratio, expanding the capacity resources, otherwise, additionally renting the capacity resources in the shortage season time period.
And the capacity allocation mismatching processing module is used for identifying the capacity allocation optimization direction in the season shortage period when the analyzed capacity shortage reason is that the capacity allocation is mismatching, and accordingly carrying out capacity allocation optimization.
By combining all the technical schemes, the invention has the following positive effects: 1. according to the method, the location of the capacity resources of the logistics company is positioned, so that each season period is obtained, and further, the capacity resource utilization rate and the logistics order taking time length are analyzed in each season period, so that whether the logistics company has the capacity shortage or not is judged, the capacity shortage judgment under the consideration of seasonal fluctuation of logistics demands is realized, the judgment process is more reasonable, the accuracy of the judgment result can be improved to a certain extent, the method is beneficial to formulating an effective seasonal strategy, the logistics demands are met to the greatest extent, delivery delay and transportation resource waste are avoided, and the flexibility and adaptability of logistics operation are guaranteed.
2. According to the method, when the logistics company is judged to have the capacity shortage, the capacity shortage reason is analyzed, and then the targeted treatment is carried out according to the capacity shortage reason, so that the treatment effect can be improved, the actual problem is effectively solved, the repeated occurrence of the capacity shortage is avoided, and the treatment efficiency is improved.
Drawings
The invention will be further described with reference to the accompanying drawings, in which embodiments do not constitute any limitation of the invention, and other drawings can be obtained by one of ordinary skill in the art without inventive effort from the following drawings.
FIG. 1 is a flow chart of the steps of the method of the present invention.
Fig. 2 is a schematic diagram of system module connection according to the present invention.
Detailed Description
The following description of the embodiments of the present invention will be made clearly and completely with reference to the accompanying drawings, in which it is apparent that the embodiments described are only some embodiments of the present invention, but not all embodiments. All other embodiments, which can be made by those skilled in the art based on the embodiments of the invention without making any inventive effort, are intended to be within the scope of the invention.
Example 1
Referring to fig. 1, a first aspect of the present invention provides a logistics transportation management method based on the internet of things, including the following steps: s1, locating the position of the capacity resource of the logistics company, determining season distribution months corresponding to the position of the capacity resource, and selecting a plurality of historical years, so as to obtain each season period corresponding to each historical year based on the season distribution months corresponding to the position of the capacity resource, wherein the season period comprises spring period, summer period, autumn period and winter period.
The above embodiment is applied, the season distribution month is a continuous month corresponding to each season, wherein the season distribution month implementation method for determining the location of the capacity resource is as follows: comparing the location of the capacity resource with the distribution areas corresponding to various climate types in the map, comparing the climate types corresponding to the location of the capacity resource, and matching the seasonal distribution months corresponding to the various climate types in the cloud reference library, thereby obtaining the seasonal distribution months corresponding to the location of the capacity resource.
As an example of the above-mentioned scheme, the climate types include tropical rain forest climate, tropical grassland climate, tropical monsoon climate, subtropical monsoon climate, etc., and the climate distribution caused by the difference of the climate characteristics corresponding to the different climate types is different, which makes the season distribution month different for the different climate types, for example, the subtropical monsoon climate corresponds to the spring for 3 months to 5 months (solar calendar), the summer for 6 months to 8 months (solar calendar), the autumn for 9 months to 11 months (solar calendar), and the winter for 12 months to 2 months (solar calendar).
In the example of the subtropical monsoon climate described above, the spring period, summer period, autumn period and winter period are 3 months to 5 months (solar calendar), 6 months to 8 months (solar calendar), 9 months to 11 months (solar calendar), 12 months to 2 months of the year (solar calendar), respectively.
S2, analyzing the utilization rate of the existing transport capacity resources in the season time period corresponding to each historical year, counting the number of the commodity flow orders in the season time period corresponding to each historical year, and further extracting the commodity flow order receiving time and the commodity flow order receiving time from each commodity flow order, so that the order receiving time corresponding to each commodity flow order is obtained, wherein the order receiving time = the commodity flow order receiving time-the commodity flow order receiving time.
Preferably, the existing capacity resource utilization analysis process is as follows: the number of the existing logistics vehicles of the logistics company is counted, and the logistics vehicles are numbered according to a set sequence.
Acquiring the used time length of each logistics vehicle in the period of each season corresponding to each historical year, and further importing a formulaAnd obtaining the occupancy rate of each logistics vehicle in each season period corresponding to each historical year.
It should be added that the time length of each logistics vehicle used can be obtained from the use records of the logistics vehicles.
The occupancy rate of each logistics vehicle in each historical year corresponding to each season period is expressed by an expressionCalculating the utilization rate of the current capacity resources of the logistics company in the time period corresponding to each season of each historical year>In the formula->Denoted as +.>Occupancy of logistic vehicle, < >>Denoted as logistics vehicle number>，/>Expressed as the number of existing logistics vehicles of the logistics company, < >>Expressed as a natural constant, wherein the greater the occupancy of the logistics vehicle, the greater the utilization of the existing capacity resources.
S3, judging whether the logistics company has the capacity shortage or not based on the utilization rate of the existing capacity resources in the season time periods corresponding to the historical years and the order receiving time length corresponding to the logistics orders, wherein the specific judging process is as follows: (1) Comparing the utilization rate of the existing capacity resources in each historical year in the same season period, extracting the maximum utilization rate and the minimum utilization rate from the comparison, and importing the utilization approach degree calculation formulaAnd obtaining the utilization approach degree of the existing transport capacity resources corresponding to each season period.
(2) Comparing the existing capacity resource utilization approach degree corresponding to each season period with the set ideal utilization approach degree, wherein the ideal utilization approach degree can be set to be 0.8 by a modelAnalyzing and obtaining the utilization rate of the existing transportation capacity resource tendency corresponding to each season period>In the model->Expressed as season period number->Wherein->、/>、/>、/>Respectively expressed as spring time period, summer time period, autumn time period and winter time period, < + >>Expressed as the d-th seasonal period in +.>Utilization of existing capacity resources in historical years, < +.>Expressed as historical year number->，/>Expressed as the number of selected historical years, +.>、/>Expressed as maximum utilization, minimum utilization, respectively, of the same seasonal period in each historical year,/->Representation ofExisting capacity resource utilization approach corresponding to the d-th seasonal period, < >>Indicating the ideal utilization approach of the setting.
(3) Comparing the order receiving time length corresponding to each logistics order in each season time period corresponding to each historical year with the set effective order receiving time length, wherein the effective order receiving time length can be set to be 10min, and the order receiving time efficiency corresponding to each logistics order is calculated, and the method comprises the following steps ofWherein the shorter the order receiving time of the logistics order, the greater the order receiving time efficiency.
It should be noted that the order duration and the set effective order duration are unified.
(4) And carrying out average calculation on the order receiving time efficiency corresponding to each logistics order in each season period corresponding to each historical year to obtain the average order receiving time efficiency of the logistics order in each season period corresponding to each historical year.
(5) Similarly, the (1) and (2) are referred to obtain the order taking time efficiency of the logistics order tendency corresponding to each season period：
In->Indicate->Season period is at->Average order taking time of logistics orders in historical year, < + >>Indicate->A time-dependent approach to order taking of the logistics orders corresponding to the seasonal period, wherein +.>，/>And indicating the set ideal logistics order taking time-effect approaching degree.
(6) Substituting the existing capacity resource trend utilization rate and logistics order trend order taking time efficiency corresponding to each season period into the expressionObtaining the capacity shortage index corresponding to each season period>In the formula->Expressed as a set constant, and +.>>1, illustratively,/->Wherein the larger the utilization rate of the existing capacity resource trend, the smaller the order taking efficiency of the logistics order trend is, and the larger the capacity shortage index is.
According to the invention, when the capacity shortage index of a logistics company is analyzed, the analysis process can be more comprehensive by starting from two dimensions of capacity resource utilization and order receiving aging, so that analysis limitation is avoided, and the accuracy of an analysis result is improved to the greatest extent.
(7) Importing the capacity shortage index corresponding to each season period into a judgment modelObtaining the judging result of whether the logistics company has the capacity shortage +.>In the model->Indicating that the logistics company has the shortage of the capacity and +.>Indicating that the logistics company has no capacity shortage, < - > about>Represented as a pre-configured threshold, illustratively, < >>。
According to the method, the location of the capacity resources of the logistics company is positioned, so that each season period is obtained, and further, the capacity resource utilization rate and the logistics order taking time length are analyzed in each season period, so that whether the logistics company has the capacity shortage or not is judged, the capacity shortage judgment under the consideration of seasonal fluctuation of logistics demands is realized, the judgment process is more reasonable, the accuracy of the judgment result can be improved to a certain extent, the method is beneficial to formulating an effective seasonal strategy, the logistics demands are met to the greatest extent, delivery delay and transportation resource waste are avoided, and the flexibility and adaptability of logistics operation are guaranteed.
S4, identifying a shortage season period when judging that the capacity is shortage, analyzing a capacity shortage reason corresponding to the shortage season period, executing S5 when the analyzed capacity shortage reason is insufficient in capacity resources, and executing S6 when the analyzed capacity shortage reason is improper in capacity distribution.
In a specific embodiment, the identifying process of the shortage season period is: comparing the capacity shortage index corresponding to each seasonal period with a preset threshold value, and selecting the seasonal period with the capacity shortage index being greater than or equal to the preset threshold value as the shortage seasonal period.
Further, the analysis process of the reasons for the shortage of the transportation capacity is as follows: and extracting the transport start point, the transport end point, the actual transport route, the actual delivery time, the expected delivery time and the reporting information of the abnormal event from the transport records of the logistics orders in each historical year in the period of the short season.
It is added that the expected lead time is extractable from the shipping record. The expected lead time is typically recorded in shipping documents, which may include shipping slips, shipping notices, shipping manifests, and the like.
In examples of the above scenario, the abnormal event may include bad weather, traffic accident, vehicle malfunction, and the like.
Planning a preferred transportation route based on a transportation start point and a transportation end point of the logistics order, and comparing the length of the preferred transportation route with the length of an actual transportation route, calculating a transportation inefficiency index of the logistics order, wherein
。
When the actual transport route length is greater than the preferred transport route length, it is indicated that there may be detours in the actual transport route, and the greater the actual transport route length is, the greater the actual transport detours probability is, and the more inefficient transport is likely to occur.
The shipping inefficiency index of the logistics order is compared to the set shipping inefficiency index allowed, which may be set to 0.3, for example, and if the shipping inefficiency index of a certain logistics order is greater than the shipping inefficiency index allowed, the logistics order is marked as a first abnormal logistics order.
Comparing the actual delivery time of the physical distribution order with the expected delivery time, calculating a delivery delay index of the physical distribution order, whereinThe later the actual delivery time is than the expected delivery time, the larger the delivery delay index is.
Comparing the delivery delay index of the logistics order with the set allowable delivery delay index, wherein the allowable delivery delay index can be set to 0.2 by way of example, if the delivery delay index of a certain logistics order is larger than the allowable delivery delay index, identifying whether an abnormal event exists in the report information of the abnormal event of the logistics order, and if the abnormal event does not exist, indicating that the delivery delay of the logistics order is largely caused by subjective factors of transport distributors, marking the logistics order as a second abnormal logistics order.
Counting the number of first abnormal logistics orders and the number of second abnormal logistics orders existing in each historical year in the short-cut season period, so as to calculate the logistics transportation abnormal index corresponding to the short-cut season period, and specifically calculate the expressionIn the formula->Logistics transportation abnormality index expressed as correspondence to the period of the shortage season,/->、/>Respectively indicated as the shortage season period is in +.>First abnormal number of physical distribution orders, second abnormal number of physical distribution orders, ∈1, existing in the historical year>Indicated as the period of the shortage season is at +.>Logistics order quantity of historical year, +.>、/>Respectively expressed as weight factors corresponding to the first abnormal material flow order and the second abnormal material flow order, and +.>As an example, ∈ ->，。
And comparing the logistics transportation abnormality index corresponding to the shortage season period with the set acceptable abnormality index, wherein the acceptable abnormality index is set to 0.4, and if the logistics transportation abnormality index corresponding to the shortage season period is larger than the acceptable abnormality index, indicating that the capacity shortage in the shortage season period is caused by transportation detours or subjective delivery delay to a great extent, analyzing the capacity shortage cause to be the capacity shortage.
S5, counting the occupation ratio of the shortage season time periodAnd comparing with a preset high ratio, wherein the high ratio can be preset as +.>If the occupation ratio of the short-cut season time period is larger than or equal to the high occupation ratio, the capacity resource expansion is carried out, otherwise, the short-cut season time period has smaller coverage in all season time periods, if the capacity resource expansion is carried out, the capacity resource expansion can only have the utilization value in a specific time period, the value is not high, and the capacity resource is leased in the short-cut season time period.
And S6, identifying the capacity distribution optimizing direction of the logistics company in the shortage season time period, and accordingly carrying out capacity distribution optimizing.
In particular, the direction of capacity allocation optimization for identifying logistics companies during the season of the shortage is as follows: dividing the first abnormal logistics order quantity and the second abnormal logistics order quantity of the shortage season period in each historical year by the logistics order quantity of the corresponding historical year respectively to obtain a first abnormal occupation ratio and a second abnormal occupation ratio of the shortage season period in each historical year.
Comparing the first abnormal ratio and the second abnormal ratio of the short-cut season period in each historical year, and utilizing an algorithmObtaining the misdistribution cause of the capacity of the shortage season period in each history year +.>In algorithm +.>Indicated as misexecution of the transportation route,/-)>Indicating improper handling of the transport duration, +.>、Expressed as a first abnormal ratio, a second abnormal ratio, ">Effective duty ratio expressed as setting is close to progress, in particular +.>，/>Representing and.
In the algorithm described aboveExpressed as a first and a second duty ratio proximity, wherein the larger the first and the second duty ratio proximity is, the smaller the first and the second duty ratio proximity is, the larger the first abnormal duty ratio and the second abnormal duty ratio are, the larger the capacity allocation miscause corresponding to the maximum abnormal duty ratio is, and the selection is carried outThe reasons for the maldistribution of the transport capacity are more reasonable and reliable.
Comparing the reasons of improper distribution of the capacity in the short-cut season period in each historical year, counting the occurrence frequency of the reasons of improper distribution of the capacity, and further taking the reason of improper distribution of the capacity corresponding to the maximum occurrence frequency as the capacity distribution optimization direction of the logistics company in the short-cut season period.
According to the method, when the logistics company is judged to have the capacity shortage, the capacity shortage reason is analyzed, and then the targeted treatment is carried out according to the capacity shortage reason, so that the treatment effect can be improved, the actual problem is effectively solved, the repeated occurrence of the capacity shortage is avoided, and the treatment efficiency is improved.
Example 2
Referring to fig. 2, the invention provides a logistics transportation management system based on the internet of things, which comprises the following modules: the seasonal period acquisition module is connected with the cloud reference library and used for positioning the location of the capacity resources of the logistics company, determining seasonal distribution months corresponding to the location of the capacity resources, and selecting a plurality of historical years, so that each seasonal period corresponding to each historical year is obtained based on the seasonal distribution months corresponding to the location of the capacity resources.
And the capacity resource utilization rate analysis module is connected with the season period acquisition module and is used for analyzing the utilization rate of the existing capacity resources in the season periods corresponding to the historical years.
The logistics order receiving time length acquisition module is connected with the seasonal period acquisition module and is used for counting the number of the logistics orders in the seasonal period corresponding to each historical year, extracting the genre order time and the logistics order receiving time from each logistics order, and accordingly obtaining the order receiving time length corresponding to each logistics order.
And the cloud reference library is used for storing season distribution months corresponding to various climate types.
And the capacity shortage judging module is respectively connected with the capacity resource utilization rate analysis module and the logistics order receiving time length acquisition module and is used for judging whether the logistics company has capacity shortage or not based on the utilization rate of the existing capacity resources in the season time periods corresponding to the historical years and the order receiving time length corresponding to the logistics orders.
And the capacity shortage reason analysis module is connected with the capacity shortage judgment module and is used for identifying the shortage season time period when the judgment is in the shortage of the capacity and analyzing the capacity shortage reason corresponding to the shortage season time period.
And the capacity shortage processing module is connected with the capacity shortage reason analysis module and is used for counting the occupation ratio of the shortage season time period when the analyzed capacity shortage reason is the capacity shortage and comparing the occupation ratio with the high occupation ratio of the preset value, if the occupation ratio of the shortage season time period is larger than or equal to the high occupation ratio, carrying out capacity resource expansion, otherwise, additionally renting the capacity resource in the shortage season time period.
And the improper capacity allocation processing module is connected with the capacity shortage reason analysis module and is used for identifying the capacity allocation optimization direction in the shortage season period when the analyzed capacity shortage reason is that the capacity allocation is improper, so that the capacity allocation optimization is performed.
The foregoing is merely illustrative of the structures of this invention and various modifications, additions and substitutions for those skilled in the art of describing particular embodiments without departing from the structures of the invention or exceeding the scope of the invention as defined by the claims.

Claims (10)

1. The logistics transportation management method based on the Internet of things is characterized by comprising the following steps of:
s1, locating a capacity resource location of a logistics company, determining season distribution months corresponding to the capacity resource location, and selecting a plurality of historical years, so as to obtain season periods corresponding to the historical years based on the season distribution months corresponding to the capacity resource location;
s2, analyzing the utilization rate of the existing transport capacity resources in the season time period corresponding to each historical year, counting the number of the commodity flow orders in the season time period corresponding to each historical year, and further extracting the commodity flow order receiving time and the commodity flow order receiving time from each commodity flow order, so that the order receiving time corresponding to each commodity flow order is obtained;
s3, judging whether the logistics company has the capacity shortage or not based on the utilization rate of the existing capacity resources in the season time period corresponding to each historical year and the order receiving time length corresponding to each logistics order;
s4, identifying a shortage season period when judging that the capacity is shortage, analyzing a capacity shortage reason corresponding to the shortage season period, executing S5 when the analyzed capacity shortage reason is insufficient in capacity resources, and executing S6 when the analyzed capacity shortage reason is improper in capacity distribution;
s5, counting the occupation ratio of the short-cut season time period, comparing the occupation ratio with the high occupation ratio of the preset value, if the occupation ratio of the short-cut season time period is larger than or equal to the high occupation ratio, expanding the capacity resources, otherwise, additionally renting the capacity resources in the short-cut season time period;
and S6, identifying the capacity distribution optimizing direction of the logistics company in the shortage season time period, and accordingly carrying out capacity distribution optimizing.
2. The logistics transportation management method based on the internet of things as set forth in claim 1, wherein: the season distribution month is a continuous month corresponding to each season, wherein the season distribution month implementation mode corresponding to the location of the capacity resource is determined as follows:
comparing the location of the capacity resource with the distribution areas corresponding to various climate types in the map, comparing the climate types corresponding to the location of the capacity resource, and matching the seasonal distribution months corresponding to the various climate types in the cloud reference library, thereby obtaining the seasonal distribution months corresponding to the location of the capacity resource.
3. The logistics transportation management method based on the internet of things as set forth in claim 1, wherein: the utilization rate analysis process of the existing transport capacity resources is as follows:
counting the number of the existing logistics vehicles of a logistics company, and numbering the logistics vehicles according to a set sequence;
acquiring the used time length of each logistics vehicle in the period of each season corresponding to each historical year, and further importing a formulaObtaining the occupancy rate of each logistics vehicle in each season period corresponding to each historical year;
the occupancy rate of each logistics vehicle in each historical year corresponding to each season period is expressed by an expressionCalculating the utilization rate of the current capacity resources of the logistics company in the time period corresponding to each season of each historical year, wherein +.>Denoted as +.>Occupancy of logistic vehicle, < >>Denoted as logistics vehicle number>，/>Expressed as the number of existing logistics vehicles of the logistics company, < >>Expressed as a natural constant.
4. The logistics transportation management method based on the internet of things as set forth in claim 1, wherein: the specific process for evaluating whether the logistics company has the capacity shortage is as follows:
(1) Comparing the utilization rate of the existing capacity resources in each historical year in the same season period, extracting the maximum utilization rate and the minimum utilization rate from the comparison, and importing the utilization approach degree calculation formulaObtaining the utilization approach degree of the existing transport capacity resources corresponding to each season period;
(2) Comparing the utilization approach degree of the existing transport capacity resources corresponding to each season period with the set ideal utilization approach degree, and passing through a modelAnalyzing and obtaining the utilization rate of the existing transportation capacity resource tendency corresponding to each season period>In the model->Expressed as season period number->Wherein->、/>、/>、/>Respectively expressed as spring time period, summer time period, autumn time period and winter time period, < + >>Expressed as the d-th seasonal period in +.>Utilization of existing capacity resources in historical years, < +.>Expressed as historical year number->，/>Expressed as the number of selected historical years, +.>、/>Expressed as maximum utilization, minimum utilization, respectively, of the same seasonal period in each historical year,/->Indicating the existing capacity resource utilization approach corresponding to the d-th season period,/for>Representing the ideal utilization proximity of the setting;
(3) Comparing the order receiving time length corresponding to each logistics order in each season time period corresponding to each historical year with the set effective order receiving time length, and calculating the order receiving time efficiency corresponding to each logistics order, wherein the order receiving time length is equal to the effective order receiving time length；
(4) Average value calculation is carried out on the order receiving time efficiency corresponding to each logistics order in each season period corresponding to each historical year, so that the average order receiving time efficiency of the logistics order in each season period corresponding to each historical year is obtained;
(5) Similarly, the (1) and (2) are referred to obtain the order taking time efficiency of the logistics order tendency corresponding to each season period：
In the middle ofIndicate->Season period is at->Average order taking time of logistics orders in historical year, < + >>Indicate->A time-dependent approach to order taking of the logistics orders corresponding to the seasonal period, wherein +.>，/>The set ideal logistics order taking time-effect approach degree is represented;
(6) Substituting the existing capacity resource trend utilization rate and logistics order trend order taking time efficiency corresponding to each season period into the expressionObtaining the capacity shortage index corresponding to each season period>In the formula->Expressed as a set constant, and +.>>1；
(7) Importing the capacity shortage index corresponding to each season period into a judgment modelObtaining the judging result of whether the logistics company has the capacity shortage +.>In the model->Indicating that the logistics company has the shortage of the capacity and +.>Indicating that the logistics company has no capacity shortage, < - > about>Represented as a pre-configured threshold.
5. The logistics transportation management method based on the internet of things as set forth in claim 4, wherein: the identification process of the shortage season period is as follows: comparing the capacity shortage index corresponding to each seasonal period with a preset threshold value, and selecting the seasonal period with the capacity shortage index being greater than or equal to the preset threshold value as the shortage seasonal period.
6. The logistics transportation management method based on the internet of things as set forth in claim 1, wherein: the analysis process of the transport capacity shortage reasons is as follows:
extracting a transportation starting point, a transportation end point, an actual transportation route, an actual delivery time, an expected delivery time and reporting information of abnormal events from a transportation record of a logistics order in each historical year in the period of the short season;
planning a preferred transportation route based on a transportation start point and a transportation end point of the logistics order, and comparing the length of the preferred transportation route with the length of an actual transportation route, calculating a transportation inefficiency index of the logistics order, wherein；
Comparing the transportation inefficiency index of the logistics order with the set allowable transportation inefficiency index, and if the transportation inefficiency index of a certain logistics order is larger than the allowable transportation inefficiency index, marking the logistics order as a first abnormal logistics order;
comparing the actual delivery time of the physical distribution order with the expected delivery time, calculating a delivery delay index of the physical distribution order, wherein；
Comparing the delivery delay index of the logistics order with the set allowable delivery delay index, if the delivery delay index of a certain logistics order is larger than the allowable delivery delay index, identifying whether an abnormal event exists in the abnormal event report information of the logistics order, and if the abnormal event does not exist, marking the logistics order as a second abnormal logistics order;
counting the number of first abnormal logistics orders and the number of second abnormal logistics orders existing in each historical year in the short-cut season period, calculating a logistics transportation abnormality index corresponding to the short-cut season period, comparing the logistics transportation abnormality index with the set acceptable abnormality index, and analyzing the reasons of the short-cut season period to be improper in transportation capacity distribution if the logistics transportation abnormality index corresponding to the short-cut season period is larger than the acceptable abnormality index, otherwise analyzing the reasons of the short-cut season period to be insufficient in transportation capacity.
7. The logistics transportation management method based on the internet of things as set forth in claim 6, wherein: calculating expression of logistics transportation abnormality index corresponding to shortage season periodIn the formula->Indicated as different logistics transportation corresponding to the period of the shortage seasonFrequent index (I) of->、/>Respectively indicated as the shortage season period is in +.>First abnormal number of physical distribution orders, second abnormal number of physical distribution orders, ∈1, existing in the historical year>Indicated as the period of the shortage season is at +.>Logistics order quantity of historical year, +.>、/>Respectively expressed as weight factors corresponding to the first abnormal material flow order and the second abnormal material flow order, and +.>。
8. The logistics transportation management method based on the internet of things as set forth in claim 1, wherein: the calculation expression of the ratio of the short-cut season time period is。
9. The logistics transportation management method based on the internet of things as set forth in claim 6, wherein: the capacity distribution optimization direction of the identified logistics company in the shortage season period is as follows:
dividing the first abnormal logistics order quantity and the second abnormal logistics order quantity of the shortage season period in each historical year by the logistics order quantity of the corresponding historical year respectively to obtain a first abnormal occupation ratio and a second abnormal occupation ratio of the shortage season period in each historical year;
comparing the first abnormal ratio and the second abnormal ratio of the short-cut season period in each historical year, and utilizing an algorithmObtaining the misdistribution cause of the capacity of the shortage season period in each history year +.>In algorithm +.>Indicated as misexecution of the transportation route,/-)>Indicating that the transport duration is not properly controlled,、/>expressed as a first abnormal ratio, a second abnormal ratio, ">Effective duty cycle expressed as set is close to progress, +.>Representation and;
comparing the reasons of improper distribution of the capacity in the short-cut season period in each historical year, counting the occurrence frequency of the reasons of improper distribution of the capacity, and further taking the reason of improper distribution of the capacity corresponding to the maximum occurrence frequency as the capacity distribution optimization direction of the logistics company in the short-cut season period.
10. A logistics transportation management system based on the internet of things, which is used for implementing the logistics transportation management method based on the internet of things according to claims 1-9, and is characterized by comprising the following modules: the seasonal period acquisition module is used for positioning the location of the capacity resource of the logistics company, determining seasonal distribution months corresponding to the location of the capacity resource, and selecting a plurality of historical years, so as to obtain each seasonal period corresponding to each historical year based on the seasonal distribution months corresponding to the location of the capacity resource;
the capacity resource utilization rate analysis module is used for analyzing the utilization rate of the existing capacity resources in the season periods corresponding to the historical years;
the logistics order receiving time length acquisition module is used for counting the number of logistics orders in each historical year corresponding to each season period, extracting the genre order time and the logistics order receiving time from each logistics order, and obtaining the order receiving time length corresponding to each logistics order;
the cloud reference library is used for storing season distribution months corresponding to various climate types;
the capacity shortage judging module is used for judging whether the logistics company has capacity shortage or not based on the utilization rate of the existing capacity resources in the season time periods corresponding to the historical years and the order receiving time length corresponding to the logistics orders;
the capacity shortage reason analysis module is used for identifying the shortage season time period when judging that the capacity shortage exists and analyzing the capacity shortage reason corresponding to the shortage season time period;
the capacity shortage processing module is used for counting the occupation ratio of the shortage season time period when the analyzed capacity shortage source is insufficient in capacity resources, comparing the occupation ratio with the high occupation ratio of the preset value, if the occupation ratio of the shortage season time period is larger than or equal to the high occupation ratio, expanding the capacity resources, otherwise, additionally renting the capacity resources in the shortage season time period;
and the capacity allocation mismatching processing module is used for identifying the capacity allocation optimization direction in the season shortage period when the analyzed capacity shortage reason is that the capacity allocation is mismatching, and accordingly carrying out capacity allocation optimization.
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