CN113487081B - Path optimization method based on logistics tracking - Google Patents
Path optimization method based on logistics tracking Download PDFInfo
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Abstract
The invention discloses a path optimization method based on logistics tracking, which comprises the steps of classifying all articles in a first preset time to obtain a plurality of article sets, and generating an initial transportation path of each article set based on historical data of the logistics tracking; counting all the commodity flow of each logistics network point in each preset time interval in real time to obtain a predicted overload time interval exceeding the logistics upper limit threshold value of the logistics network point and predicted delay time in each predicted overload time interval; and if the accumulated estimated delay time of one type of the article sets in the process of the initial transportation path exceeds the average delay time of the current logistics, optimizing the initial transportation path of the article sets to obtain a final transportation path with the transportation time being less than the transportation time of the initial transportation path. The invention can balance the commodity flow of each logistics network, reduce the delay time, and ensure that partial possibly delayed commodities can be delivered in time, thereby reducing the situation of delaying the arrival of the commodities.
Description
Technical Field
The invention relates to the technical field of logistics, in particular to a path optimization method based on logistics tracking.
Background
The logistics tracking is originally a means for tracking the flow direction of internal articles by logistics enterprises, and the logistics enterprises open a query to a client to become a value-added service, which is usually also a free service. A sophisticated tracking system depends on the time of each transport, sort, transfer, dispatch, and may even be accurate to the exact time at each link.
However, the existing material tracking is more applied to tracking the flow direction of the object to inform the user of the logistics information of the object, but no more substantial application is made after the flow direction of the object is known to solve some existing logistics problems. The problem that when the overall pressure of logistics is high, people, vehicles, equipment and the like are temporarily added in the existing solutions, but even in this case, a lot of goods cannot be circulated in time, so that the goods are delayed to arrive is still existed.
Disclosure of Invention
The technical problem to be solved by the invention is as follows: a path optimization method based on logistics tracking is provided, and the situation that goods are delayed to arrive is reduced through logistics tracking.
In order to solve the technical problems, the invention adopts the technical scheme that:
a path optimization method based on logistics tracking comprises the following steps:
step S1, when each article is sent out, classifying all articles in a first preset time according to the current logistics network point and the end logistics network point to obtain a plurality of article sets, generating an initial transportation path of each article set based on historical data of logistics tracking to obtain all logistics network points passed by each article set from the initial logistics network point to the end logistics network point and the predicted time of reaching each logistics network point;
step S2, counting all commodity flow of each logistics network point in each preset time interval in real time to obtain a predicted overload time interval exceeding the logistics upper limit threshold of the logistics network point and predicted delay time in each predicted overload time interval, wherein the predicted time interval corresponds to departure time intervals of the logistics network points, and the predicted delay time is set for all commodities arriving at the logistics network points in the predicted overload time interval;
and step S3, if the accumulated estimated delay time of one type of the article sets in the process of the initial transportation path exceeds the average delay time of the current logistics, optimizing the initial transportation path of the article sets to obtain a final transportation path with the transportation time shorter than the transportation time of the initial transportation path.
The invention has the beneficial effects that: a path optimization method based on logistics tracking indicates that the flow direction of an article is the same when a current logistics branch point and an ending logistics branch point are the same, and the current logistics branch point and the ending logistics branch point are taken into consideration as a logistics set so as to reduce the subsequent calculation amount. Meanwhile, a plurality of first preset times are set in one departure time interval, and a total logistics set of the departure time interval is changed into a logistics set with less data volume, so that the flexibility of subsequent path optimization is facilitated. At the moment, an initial transport path of each type of article set is generated based on historical data of logistics tracking, all the commodity flow of each logistics node in each preset time interval is obtained, a predicted overload time interval exceeding a logistics upper limit threshold value of each logistics node and predicted delay time in each predicted overload time interval are obtained according to the commodity flow, the initial transport path of the article set is optimized according to the predicted delay time, a final transport path is obtained, the commodity flow of each logistics node is balanced, the delay time is shortened, partial articles which are possibly delayed can be timely delivered, and the condition that the articles are delayed to arrive is reduced.
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Fig. 1 is a schematic flow chart of a path optimization method based on logistics tracking according to an embodiment of the present invention;
Detailed Description
In order to explain technical contents, achieved objects, and effects of the present invention in detail, the following description is made with reference to the accompanying drawings in combination with the embodiments.
Referring to fig. 1, a method for path optimization based on logistics tracking includes:
step S1, when each article is sent out, classifying all articles in a first preset time according to the current logistics network point and the end logistics network point to obtain a plurality of article sets, generating an initial transportation path of each article set based on historical data of logistics tracking to obtain all logistics network points passed by each article set from the initial logistics network point to the end logistics network point and the predicted time of reaching each logistics network point;
step S2, counting all the commodity flow of each logistics network point in each preset time interval in real time to obtain a predicted overload time interval exceeding the logistics upper limit threshold of the logistics network point and a predicted delay time in each predicted overload time interval, wherein the predicted time interval corresponds to the departure time interval of the logistics network point, and the predicted delay time is set to exist for all the commodities arriving at the logistics network point in the predicted overload time interval;
and S3, if the estimated delay time accumulated in the process of the initial transportation path of one type of the item sets exceeds the average delay time of the current logistics, optimizing the initial transportation path of the item set to obtain a final transportation path with the transportation time less than the transportation time of the initial transportation path.
The historical data of the logistics tracking comprises the transportation time between each logistics point and the residence time of each logistics point, and the initial transportation path of each type of item set is automatically generated by adopting the existing path planning algorithm based on the historical data and the requirement of the shortest transportation time.
From the above description, the beneficial effects of the present invention are: when the current logistics branch point and the ending logistics branch point are the same, the flow direction of the articles is also the same, and the articles are taken into consideration as a logistics set at the moment so as to reduce the subsequent calculation amount. Meanwhile, a plurality of first preset times are set in one departure time interval, and a total logistics set of the departure time interval is changed into a logistics set with less data volume, so that the flexibility of subsequent path optimization is facilitated. At the moment, an initial transport path of each type of article set is generated based on historical data of logistics tracking, all the commodity flow of each logistics node in each preset time interval is obtained, a predicted overload time interval exceeding a logistics upper limit threshold value of each logistics node and predicted delay time in each predicted overload time interval are obtained according to the commodity flow, the initial transport path of the article set is optimized according to the predicted delay time, a final transport path is obtained, the commodity flow of each logistics node is balanced, the delay time is shortened, partial articles which are possibly delayed can be timely delivered, and the condition that the articles are delayed to arrive is reduced.
Further, the step S3 specifically includes:
step S31, if the estimated delay time accumulated in the process of the initial transportation path of one type of the item sets exceeds the average delay time of the current logistics, adding the type of the item sets into the item set to be optimized as a subset to be optimized, and sequencing the item sets to be optimized from large to small according to the estimated delay time;
step S32, generating all selectable transportation paths of all the item sets to be optimized based on historical data of logistics tracking, and sequentially enabling each subset to be optimized to execute step S33 according to the sequence, wherein the paths of two adjacent logistics nodes in the selectable transportation paths are logistics lines opened in advance by the two logistics nodes;
and step S33, sorting each selectable transportation path according to transportation time length according to all commodity flow of each current logistics network point in each preset time interval, taking the selectable transportation path with the shortest transportation time length as a final transportation path, and updating all commodity flow of each logistics network point in each preset time interval in real time according to the final transportation path.
From the above description, it can be known whether the path is feasible or not, but is not communicated between roads, and whether logistics lines are opened at two logistics points, that is, whether vehicles are used for transporting goods at the two logistics points or not. On the basis, the articles to be optimized are sequentially sorted from large to small according to the expected delay time, then the optimization is performed firstly, and the article flow rates of all logistics points are updated, so that too long delay time is avoided as far as possible, the delay time is averaged, when the logistics is busy, such as too many online purchases caused by certain festivals, online residents can generally understand the logistics slowly, and the too long delay time is still definitely not understood, so that the logistics experience of users can be improved by delay time averaging.
Further, the step S2 specifically includes:
counting all the commodity flow of each logistics network point in each preset time interval in real time, gradually calculating backwards from the current preset time interval whether the predicted commodity flow of each preset time interval exceeds the upper logistics limit threshold value of the logistics network point, when the accumulated material flow rate in the last preset time interval exceeds the material flow upper limit threshold value of the accumulated material flow rate, accumulating the exceeded part and the predicted material flow rate in the next preset time interval to judge the next preset time interval, thereby obtaining the predicted overload time interval exceeding the self logistics upper limit threshold value and the predicted delay time in each predicted overload time interval, and recording a first delay time caused by the self object flow and a second delay time caused by the accumulation of the previous preset time interval to the current object flow in each expected overload time interval, wherein the expected time interval corresponds to the departure time interval of the logistics network.
As can be seen from the above description, if the accumulated material flow rate in the previous preset time interval exceeds the upper threshold of the material flow rate, the exceeded part is naturally processed in the next preset time interval, so that the material flow rate in the next preset time interval is increased, and therefore the part of the previous preset time interval exceeding the upper threshold of the material flow rate is considered to perform the judgment of the next preset time interval, so as to ensure that the finally obtained predicted delay time is more accurate.
Further, the step S2 specifically includes:
counting all the commodity flow on each commodity flow line of each commodity flow point in each preset time interval in real time to obtain a predicted overload time interval exceeding a self commodity flow upper limit threshold and a corresponding commodity flow line, wherein the self commodity flow upper limit threshold comprises a transportation upper limit threshold of each commodity flow line, and the predicted time interval corresponds to a departure time interval of the commodity flow point;
and calculating the predicted delay time of each logistics line corresponding to each predicted overload time interval.
From the above description, it can be known that, for one logistics line, not only the upper limit exists corresponding to the staff, but also the transportation of all articles can not be completed in time by a certain logistics line caused by different object flow rates of different logistics lines in different preset time intervals, thereby causing the delay of the articles, and therefore, the logistics line corresponding to the logistics network point is used for determining the unit, thereby ensuring that the finally obtained estimated delay time is more accurate.
Further, the first preset time increases with an increase in the total flow rate.
Furthermore, the first preset times are set at even intervals, and the current first preset time is modified to the next preset time when the total object flow increases to the flow threshold of the next preset time.
From the above description, when the total flow rate is large, it indicates that the total flow rate is in the peak period of logistics, delayed delivery of logistics is likely to occur, and the whole calculation pressure increases with the increase of the articles to be optimized, so that the first preset time is increased, and the article data included in each logistics set is also increased, so that the number of logistics sets is reduced, and thus the calculation pressure for subsequent scheduling optimization is reduced, stable operation of the whole logistics scheduling is ensured, and the situation that the articles are delayed to arrive is reduced.
Further, the step S3 is followed by:
step S4, if there is a predicted delay time in the final transportation path of the first article set, identifying the first article in the first article set when the articles are scanned and classified at each logistics network point;
step S5, when the first article is identified, whether actual delay exists in the previous transportation process of the first article is obtained, if yes, actual delay time of the first article in the previous transportation process is obtained, whether the actual delay time is larger than the average delay time of the current logistics is judged, if yes, the first article is marked as preferential transportation and is automatically transmitted to a preferential shipment position, otherwise, the first article is automatically transmitted to a waiting shipment position, and the first article located at the preferential shipment position is preferentially transported by a current transportation vehicle.
From the above description, if there is a delay time expected in one logistics site, not all the articles will be delayed, and there is no delay time in the articles transported by the transportation vehicles of each logistics site on time, but the articles that cannot be transported on time are accumulated to the next departure time, which causes delay. Meanwhile, as for the aging, usually seen in days, sometimes, delay of only one logistics network point does not cause a difference of one day, so that delay in aging caused by accumulated delay time is eliminated as much as possible, the condition that articles are delayed to be delivered is reduced, and the problem that the user is poor in physical examination caused by too long delay can be solved.
Further, the step S1 of classifying all the articles within the first preset time according to the current logistics node and the end logistics node specifically includes:
and classifying all the articles except the end logistics network point as the current logistics network point within the first preset time according to the current logistics network point and the end logistics network point.
From the above description, if the end logistics point of the article is the current logistics point, the transportation of the next logistics point is not needed, and the classification process is naturally not needed, so as to reduce some unnecessary calculations.
Further, the logistics tracking of each article in the transportation process is carried out based on the combination of the GPS technology and the GIS technology.
From the above description, it can be known that the method is based on the combination of the GPS technology and the GIS technology to realize better logistics tracking.
Referring to fig. 1, a first embodiment of the present invention is:
a path optimization method based on logistics tracking comprises the following steps:
step S1, when each article is sent out, classifying all articles in a first preset time according to the current logistics network point and the end logistics network point to obtain a plurality of article sets, generating an initial transportation path of each article set based on historical data of logistics tracking to obtain all logistics network points passed by each article set from the initial logistics network point to the end logistics network point and the predicted time of reaching each logistics network point;
in this embodiment, on a logistics system, there are a plurality of logistics points that need to perform article forwarding, where the historical data of the logistics tracking includes the transportation time between each logistics point and the residence time at each logistics point, so that the existing path planning algorithm can be used to automatically generate the initial transportation path for each type of article set based on the historical data and the requirement of the shortest transportation time. Due to the fixed logistics points, the goods are distributed in the logistics points in a centralized way no matter how the starting point and the end point of the goods are scattered.
Therefore, the logistics process of virtually all goods can be clustered to the starting logistics network and the ending logistics network, for example, a logistics network is provided in city a, a region a1, a region a2 and the like are provided under city a, and then the goods sent out in a room of a cell in region a1 and the goods sent out in a room of a cell in region a2 are finally sent to the logistics network in city a. Therefore, the path planning is finally carried out based on the path planning among the logistics nodes, and the path planning can be carried out by referring to historical paths of the same starting logistics node and ending logistics node in history.
In this embodiment, the first preset time increases as the total flow rate increases. Specifically, the first preset times are set at even intervals, and the current first preset time is modified to the next preset time when the total object flow increases to the flow threshold of the next preset time.
The multiple first preset times are at intervals of 10 minutes, the corresponding flow thresholds are respectively B1 and B2 … …, for example, the flow threshold of the first preset time of 30 minutes is B1, the flow threshold of the first preset time of 40 minutes is B2, and so on, when the total flow rate is between B1 and B2, 30 minutes are used as the first preset time, and when the total flow rate reaches B2, 40 minutes are used as the first preset time, so that the first preset time is increased along with the increase of the total flow rate, the number of the flow sets is decreased instead when the total flow rate is increased, the calculation pressure of subsequent scheduling optimization is reduced, the stable operation of the whole flow schedule is ensured, and the condition that goods arrive delay is reduced.
In step S1, classifying all articles in the first preset time according to the current logistics node and the end logistics node specifically includes:
and classifying all the articles except the end logistics network point as the current logistics network point within the first preset time according to the current logistics network point and the end logistics network point.
If the end logistics point of the article is the current logistics point, the transportation of the next logistics point is not needed, and the classification treatment is naturally not needed.
In this embodiment, the logistics tracking of each article in the transportation process is performed based on the combination of the GPS technology and the GIS technology, and the GPS technology can realize real-time and rapid positioning so as to conveniently realize real-time monitoring of the scheduling end on the transportation condition of the vehicle. Meanwhile, by combining the GIS technology and utilizing the functions of network analysis, path analysis and the like, the optimal path is scientifically and quickly preset.
Step S2, counting all commodity flow of each logistics network point in each preset time interval in real time to obtain a predicted overload time interval exceeding the logistics upper limit threshold of the logistics network point and predicted delay time in each predicted overload time interval, wherein the predicted time interval corresponds to departure time intervals of the logistics network points, and the predicted delay time is set for all commodities arriving at the logistics network points in the predicted overload time interval;
in this embodiment, step S2 specifically includes:
counting all the material flow rates of each logistics point in each preset time interval in real time, gradually calculating backward from the current preset time interval whether the estimated material flow rate of each preset time interval exceeds the self logistics upper limit threshold value, accumulating the exceeded part and the estimated material flow rate in the next preset time interval to judge the next preset time interval when the accumulated material flow rate of the previous preset time interval exceeds the self logistics upper limit threshold value, so as to obtain the estimated overload time interval exceeding the self logistics upper limit threshold value and the corresponding logistics line, calculating the estimated delay time of each logistics line corresponding to each estimated overload time interval, and recording the first delay time caused by the material flow rate and the second delay time caused by the accumulated material flow rate to the current material flow rate in the previous preset time interval in each estimated overload time interval, the self logistics upper limit threshold comprises a transportation upper limit threshold of each logistics line, and the departure time interval of a corresponding logistics network point in a predicted time interval;
for example, if the departure time interval of the logistics site in city a is 6 hours, the logistics site in city a can count all the logistics volume in each 6 hours in three days, including the items which are actually expected to arrive in the preset time interval on the road and have not yet started planning.
For one logistics line, it is calculated that two logistics points have transportation paths, but not necessarily have logistics vehicles to perform calculation, so that the logistics line between the logistics points needs to be considered during calculation, and therefore, the threshold value is not only a worker for sorting articles at the logistics points, but also an upper transportation limit threshold value of each logistics line.
Thus, if the articles are too many in a certain time interval, the articles cannot be consumed in the time interval, and the time is delayed to the next time interval, so that the whole logistics is delayed. Therefore, if the accumulated material flow rate in the previous preset time interval exceeds the material flow upper limit threshold of the previous preset time interval, the exceeded part is naturally processed in the next preset time interval, so that the material flow rate in the next preset time interval is increased, and therefore the part of the previous preset time interval exceeding the material flow upper limit threshold of the previous preset time interval is considered to be judged in the next preset time interval.
And step S3, if the accumulated estimated delay time of one type of the article sets in the process of the initial transportation path exceeds the average delay time of the current logistics, optimizing the initial transportation path of the article sets to obtain a final transportation path with the transportation time shorter than the transportation time of the initial transportation path.
In this embodiment, step S3 specifically includes:
step S31, if the estimated delay time accumulated in the process of the initial transportation path of one type of the article sets exceeds the average delay time of the current logistics, adding the article sets into the article sets to be optimized as the subsets to be optimized, and sequencing the article sets to be optimized from large to small according to the estimated delay time;
therefore, if the predicted delay time does not exceed the average delay time of the current logistics, the current logistics is in an overall delayed state, and therefore, the optimization of the commodity set is not needed, because other logistics sets exceeding the average delay time of the current logistics are necessarily needed to be optimized.
In addition, the items to be optimized are sequentially sorted from large to small according to the expected delay time, then the optimization is performed first, and the object flow rates of all logistics nodes are updated, so that too long delay time is avoided as far as possible, and the delay time is averaged.
Such as half a day and one day for the predicted delay time of the two item sets, then the item set with the predicted delay time of one day is optimized first.
Step S32, generating all selectable transportation paths of all to-be-optimized item sets based on historical data of logistics tracking, and sequentially enabling each to-be-optimized subset to execute step S33 according to the sequence, wherein the paths of two adjacent logistics nodes in the selectable transportation paths are logistics lines opened in advance by the two logistics nodes;
and S33, sorting each selectable transportation path according to transportation time length according to all commodity flow of each current logistics node in each preset time interval, taking the selectable transportation path with the shortest transportation time length as a final transportation path, and updating all commodity flow of each logistics node in each preset time interval in real time according to the final transportation path.
At this time, the increase and decrease of the object flow rate of each optimized path of each logistics set for different logistics nodes may cause the predicted delay time of the initial transportation path of other article sets, so the predicted delay time of the transportation paths of all article sets needs to be updated after the object flow rate is updated.
Therefore, an initial transport path of each type of article set is generated based on historical data of logistics tracking, all the commodity flow of each logistics network point in each preset time interval is obtained, the expected overload time interval exceeding the logistics upper limit threshold value of each logistics network point and the expected delay time in each expected overload time interval are obtained according to the commodity flow, the initial transport path of the article set is optimized according to the expected delay time, a final transport path is obtained, the commodity flow of each logistics network point is balanced, the delay time is shortened, partial articles which possibly delay can be timely delivered, and the condition that the articles delay to the arrival of the articles is reduced.
Referring to fig. 1, the second embodiment of the present invention is:
a method for optimizing a path based on logistics tracking, in the first embodiment, after the step S3, the method further includes:
step S4, if there is a predicted delay time in the final transportation path of the first article set, identifying the first article in the first article set when the articles are scanned and classified at each logistics network point;
if one logistics point has predicted delay time, not all articles can be delayed, and the articles which are transported by the transportation vehicles of each logistics point on time do not have delay time, but the articles which cannot be transported on time are accumulated to the next departure time, so that delay is caused.
Therefore, when each article set arrives at each logistics site, the logistics site performs scanning classification.
Step S5, when the first article is identified, whether the first article has actual delay in the previous transportation process is obtained, if yes, the actual delay time of the first article in the previous transportation process is obtained, whether the actual delay time is larger than the average delay time of the current logistics is judged, if yes, the first article is marked as preferential transportation and is automatically transmitted to a preferential shipment position, otherwise, the first article is automatically transmitted to a waiting shipment position, and the first article located at the preferential shipment position is preferentially transported by the current transportation vehicle.
Assuming that 80% of the articles may be circulated in this time interval, and only 20% of the articles need to be delivered with a delay of 6 hours, at this time, scanning and sorting all the articles in this time interval finds that 10% of the articles have been delivered with a delay of 6 hours in the previous circulation process, and the average delay time of the current logistics is only 3 hours, at this time, the articles delivered with a delay of 6 hours need to be delivered immediately at this time, and the remaining 90% can be selected 70% to get on the bus first according to the time.
Therefore, when the aging is usually seen in days, the delay of only one logistics point sometimes does not cause one day difference, so the delay in the aging caused by the accumulated delay time is eliminated as much as possible, the condition that the articles are delayed to be delivered is reduced, and the problem that the user is poor in physical examination caused by too long delay can be reduced.
Referring to fig. 1, a third embodiment of the present invention is:
based on the first embodiment, the step S1 of generating the initial transportation path for each type of item set based on the historical data of the logistics tracking specifically includes the following steps:
step S11, obtaining historical data of logistics tracking, wherein the historical data comprises historical transportation time of each article among each logistics branch, historical residence time of each article in each logistics branch, historical branch actual logistics flow of each article in the corresponding logistics branch in unit time when corresponding to the logistics branch, and historical logistics line actual logistics flow of each article in the corresponding logistics branch in unit time when corresponding to the logistics branch;
the historical logistics flow threshold value of a logistics network is not changed in real time and is fixed and unchanged in most of time unless expansion is carried out subsequently or transport vehicles on the logistics line are added, so that the unit time can be day-to-day, week-to-week and the like. Meanwhile, the caused delay time not only cannot be timely processed due to the overstocked articles on the logistics network, but also cannot be timely transported under the condition that the logistics line corresponding to the articles is full, and at the moment, the articles can only wait for the departure of the next lying on the logistics network.
Step S12, marking the actual commodity flow of the historical website which does not exceed the historical commodity flow threshold value in each unit time as the normal commodity flow of the historical website, marking the actual commodity flow of the historical website which exceeds the historical website commodity flow threshold value in each unit time as the abnormal commodity flow of the historical website with different grades according to the proportion exceeding the historical commodity flow threshold value, processing the actual commodity flow of the historical commodity flow line in the same way, and carrying out pairwise crossing combination according to the normal commodity flow of the historical website, the abnormal commodity flow of the historical website with different grades, the normal commodity flow of the historical commodity flow line and the abnormal commodity flow of the historical commodity flow line with different grades to obtain a historical commodity flow group, wherein the historical website commodity flow threshold value is the maximum commodity flow which can be processed by the logistic website in time in the corresponding unit time;
the historical site commodity flow threshold in a certain historical unit time is 10000, the grade of the historical site commodity flow threshold can be 0-10%, 10-20% and 20-30%, and at the time of increasing progressively, the actual commodity flow of the historical site in the historical unit time is 8000, the actual commodity flow of the historical site is the normal commodity flow of the historical site, and if the actual commodity flow of the historical site is 12500, the actual commodity flow exceeds 25%, and the actual commodity flow of the historical site belongs to 20-30% of the abnormal commodity flow of the historical site. Meanwhile, the historical logistics line commodity flow threshold on the logistics line leading to city B on the logistics network of city A is 2000, wherein the level is counted according to the carrying capacity of the single-vehicle cargos, for example, the carrying capacity of the single-vehicle cargos is 1000, wherein the historical logistics line commodity flow threshold is a multiple relation of the carrying capacity of the single-vehicle cargos, so that the actual commodity flow of the historical logistics line going to city B is 2800% and the historical logistics line abnormal commodity flow of the level is 0-50% when the commodity is 0-1000 actually 0-50% and the level behind the same is 50-100%.
At this time, the above-mentioned pairwise crossing combination is: the system comprises a historical branch point normal commodity flow and a historical logistics line normal commodity flow, a historical branch point normal commodity flow and a historical logistics line abnormal commodity flow of a first grade, a historical branch point abnormal commodity flow of a last grade and a historical logistics line abnormal commodity flow of a last grade, wherein the historical branch point abnormal commodity flow of the last grade is … ….
Step S13, unifying the historical stay time of all articles in each logistics network point in the first departure time of the logistics network point in each unit time into a historical departure time interval, accumulating the historical departure time interval once every other departure time of the rest historical stay time, and finally obtaining the final historical stay time of all articles in each unit time of each logistics network point;
for example, the historical departure time interval of the logistics point in city a is 6 hours, the article arrives at the logistics point at 19:00, and the first departure time is 24: 00, the historical residence time of the articles is unified to 6 hours. And if the item is at the second departure time of 6:00, accumulating the historical departure time interval, namely 12 hours.
Step S14, converting all final historical stay time of all articles in the historical object flow group of each logistics branch into a proportional coefficient corresponding to the historical departure time interval for averaging to obtain the historical average proportional coefficient of each logistics branch in each historical object flow group;
for example, if the above-mentioned 6 hours is the historical departure time interval, the scale factor is 1 if the historical staying time is 6 hours, and the scale factor is 2 if the historical staying time is 12 hours.
Step S15, distinguishing the historical transportation time of each article among each logistics point according to the traffic jam condition in the historical moment to generate the historical average transportation time under different traffic jam conditions;
that is, when the user arrives at a holiday or other temporary control road sections, the congestion condition of the logistics line is caused to cause the increase of the transportation time, so that the difference is needed, and the problem that the historical transportation time of the congestion condition in a few time periods is easily averaged by the historical transportation time of most normal time periods, so that the accurate prediction time cannot be output when congestion is encountered subsequently, is avoided.
For example, if the transportation time is 8 hours when the congestion situation is serious, and the transportation time is 4 hours when the road is normal, but the time when the congestion situation is serious is only 10 days, and the time when the road is normal is 355 days, if the differentiation is not performed, the final historical average transportation time is 4.11 days, and then when the congestion situation is serious later, the predicted transportation time of 4.11 days is also given, and the actual access is larger. If the traffic congestion situation is classified into a serious congestion situation and a normal road situation as described above, the above problem does not occur. Accordingly, the traffic congestion situations can be divided into three or four, for example, three situations are a normal road situation, a primary congestion situation and a secondary congestion situation. In other words, besides the normal condition of the road, different grades can be divided according to the actual congestion condition so as to calculate the historical average transport time of the traffic congestion under the normal condition of the road and different grades.
In addition, when the numerical values are required to be averaged in the steps S11 to S15, all the numerical values required to be averaged are filtered, and the numerical values exceeding the set threshold are filtered or the numerical values of the highest part and the numerical values of the lowest part are proportionally removed, so as to avoid the numerical value abnormality caused by an accident. For example, if the traffic accident in the transportation process causes that the transportation time is ten times longer than that under the normal condition of the road, the traffic accident can be directly filtered.
And S16, generating all transportable paths for each type of article set according to logistics lines among the logistics nodes, matching the historical average proportion coefficient of the current article flow group corresponding to the moment when the corresponding logistics node is reached and the historical average transport time of the current traffic jam condition when the corresponding logistics line is passed, converting the matched historical average proportion coefficient of the current article flow group and the current departure time interval to obtain a predicted stop time, accumulating the predicted stop time and the historical average transport time of the current traffic jam condition to finally obtain the transport time of each transportable path, and generating the initial transport path of each type of article set according to the principle that the transport time is shortest.
In order to ensure accuracy and while the above mentioned unexpected situation has been avoided, it may be considered that the rounding critical point is between 0.05 and 0.2, i.e. 1 is entered beyond the rounding critical point, and vice versa, i.e. for example, if the rounding critical point is 0.1, the decimal place 0.15 of 1.15 is entered 1 beyond 0.1, and thus 1.15 is changed into 2.
Therefore, compared with the problem of poor accuracy caused by the fact that the average value of historical data is only used for calculating the transportation time of the transportation path in the prior art, the embodiment classifies and combines the residence time of the logistics nodes into the node processing abnormity and the logistics line transportation abnormity, classifies the residence time based on the departure time interval and classifies the transportation time based on the congestion condition, so that the predicted residence time and transportation time are more real and accurate, the transportation time of the transportable path is more real and accurate, and the optimal initial transportation path is obtained.
In summary, the present invention provides a route optimization method based on logistics tracking, which generates an initial transportation route of each type of item set based on historical data of logistics tracking, obtains all the object flow rates of each logistics node in each preset time interval, and obtains a predicted overload time interval exceeding the upper threshold of the logistics node and a predicted delay time in each predicted overload time interval according to the object flow rates, so as to sequentially optimize the initial transportation route of the item set according to the size of the predicted delay time, obtain a final transportation route, average the actual delay time in the actual operation process, balance the object flow rates of each logistics node, reduce the delay time, eliminate the time delay caused by the accumulated delay time, and allow some possibly delayed items to be delivered in time, therefore, the situation that goods are delayed to arrive is reduced, and physical examination of the user is improved.
The above description is only an embodiment of the present invention, and not intended to limit the scope of the present invention, and all equivalent changes made by using the contents of the present specification and the drawings, or applied directly or indirectly to the related technical fields, are included in the scope of the present invention.
Claims (7)
1. A path optimization method based on logistics tracking is characterized by comprising the following steps:
step S1, when each article is sent out, classifying all articles in a first preset time according to the current logistics network point and the end logistics network point to obtain a plurality of article sets, generating an initial transportation path of each article set based on historical data of logistics tracking to obtain all logistics network points passed by each article set from the initial logistics network point to the end logistics network point and the predicted time of reaching each logistics network point;
step S2, counting all the commodity flow of each logistics network point in each preset time interval in real time to obtain a predicted overload time interval exceeding the logistics upper limit threshold of the logistics network point and a predicted delay time in each predicted overload time interval, wherein the predicted time interval corresponds to the departure time interval of the logistics network point, and the predicted delay time is set to exist for all the commodities arriving at the logistics network point in the predicted overload time interval;
step S3, if the estimated delay time accumulated in the process of the initial transportation path of one type of the item sets exceeds the average delay time of the current logistics, optimizing the initial transportation path of the item sets to obtain a final transportation path with the transportation time less than the transportation time of the initial transportation path;
the step S3 is followed by:
step S4, if there is a predicted delay time in the final transportation path of the first article set, identifying the first article in the first article set when the articles are scanned and classified at each logistics network point;
step S5, when the first article is identified, acquiring whether the first article has actual delay in the previous transportation process, if so, acquiring actual delay time of the first article in the previous transportation process, and judging whether the actual delay time is greater than the average delay time of the current logistics, if so, marking the first article as priority transportation and automatically transmitting the first article to a priority shipment position, otherwise, automatically transmitting the first article to a waiting shipment position so that the current transportation vehicle preferentially transports the first article at the priority shipment position;
the step S2 specifically includes:
counting all the commodity flow of each logistics network point in each preset time interval in real time, gradually calculating backwards from the current preset time interval whether the predicted commodity flow of each preset time interval exceeds the upper logistics limit threshold value of the logistics network point, when the accumulated material flow rate in the last preset time interval exceeds the material flow upper limit threshold value of the accumulated material flow rate, accumulating the exceeded part and the predicted material flow rate in the next preset time interval to judge the next preset time interval, thereby obtaining the predicted overload time interval exceeding the self logistics upper limit threshold value and the predicted delay time in each predicted overload time interval, and recording a first delay time caused by the self object flow and a second delay time caused by the accumulation of the previous preset time interval to the current object flow in each expected overload time interval, wherein the expected time interval corresponds to the departure time interval of the logistics network.
2. The method for optimizing a path based on physical distribution tracking according to claim 1, wherein the step S3 specifically includes:
step S31, if the estimated delay time accumulated in the process of the initial transportation path of one type of the item sets exceeds the average delay time of the current logistics, adding the type of the item sets into the item set to be optimized as a subset to be optimized, and sequencing the item sets to be optimized from large to small according to the estimated delay time;
step S32, generating all selectable transportation paths of all the item sets to be optimized based on historical data of logistics tracking, and sequentially enabling each subset to be optimized to execute step S33 according to the sequence, wherein the paths of two adjacent logistics nodes in the selectable transportation paths are logistics lines opened in advance by the two logistics nodes;
and step S33, sorting each selectable transportation path according to transportation time length according to all commodity flow of each current logistics network point in each preset time interval, taking the selectable transportation path with the shortest transportation time length as a final transportation path, and updating all commodity flow of each logistics network point in each preset time interval in real time according to the final transportation path.
3. The method for optimizing a path based on physical distribution tracking according to claim 1, wherein the step S2 specifically includes:
counting all the commodity flow on each commodity flow line of each commodity flow point in each preset time interval in real time to obtain a predicted overload time interval exceeding a self commodity flow upper limit threshold and a corresponding commodity flow line, wherein the self commodity flow upper limit threshold comprises a transportation upper limit threshold of each commodity flow line, and the predicted time interval corresponds to a departure time interval of the commodity flow point;
and calculating the predicted delay time of each logistics line corresponding to each predicted overload time interval.
4. The method for optimizing a path based on material flow tracking according to claim 1, wherein the first preset time is increased with the increase of the total material flow.
5. The method as claimed in claim 4, wherein the first preset times are set at regular intervals, and the current first preset time is modified to the next preset time as the total flow rate of the objects increases to the flow threshold of the next preset time.
6. The method for optimizing a path based on physical distribution tracking according to any one of claims 1 to 5, wherein the step S1 of classifying all the articles within the first preset time according to the current physical distribution point and the ending physical distribution point specifically comprises:
and classifying all the articles except the end logistics network point as the current logistics network point within the first preset time according to the current logistics network point and the end logistics network point.
7. The method for optimizing a path based on physical distribution tracking according to any one of claims 1 to 5, wherein the physical distribution tracking of each article during transportation is based on a combination of GPS technology and GIS technology.
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