Logistics management optimization method and system based on Internet of Things

Abstract

The invention provides a logistics management optimization method and system based on the Internet of Things, and relates to the technical field of logistics management. The method comprises the following steps: acquiring cargo order information of a plurality of cargo orders in a preset optimization time period; according to the delivery addresses, obtaining transportation routes and transportation distances of a plurality of cargo orders; in a plurality of transportation dates, obtaining weather forecast information of a plurality of cargo orders on the transportation route; according to the weather forecast information, the transportation distance, the cargo weight and the cargo quantity, determining a predicted transportation day number; obtaining the number of remaining days from the current date to the delivery date required by the client; determining a cargo emergency result according to the predicted number of transportation days and the number of remaining days; and according to the cargo emergency result and the order placing time, determining the cargo which is transported preferentially in a preset optimization time period. According to the method and the device, the cargo emergency result can be accurately analyzed, so that the cargo transportation sequence is optimized, and the logistics efficiency is improved.

Description

Technical Field

[0001] The present invention relates to the technical field of logistics management, and in particular to a logistics management optimization method and system based on the Internet of Things. Background Art

[0002] In the related technologies, the logistics operation and maintenance optimization management system of CN104809607A includes a cloud service platform based on the Internet of Things, an intelligent vehicle management system, and an intelligent warehousing center. The three rely on the Internet of Things to achieve security and efficient scheduling of the entire logistics transportation process, and build an automated, visualized, controllable, intelligent, and systematic modern logistics service system.

[0003] CN118569446A discloses a logistics planning and management system based on the Internet of Things, which relates to the field of logistics management technology, including: obtaining the logistics transportation network of the first enterprise; establishing a first transportation sub-network between the first logistics node and the second logistics node according to the logistics transportation network and the transportation attribute mark; collecting transportation resources of the first logistics node through the Internet of Things monitoring platform to generate first reserve transportation resource information; establishing an anti-disturbance prediction auxiliary factor, combining the first reserve transportation resource information to predict the anti-disturbance capability, and generating a first anti-disturbance performance index; if the first anti-disturbance performance index is less than the preset anti-disturbance index, optimizing the transportation network, generating a first transportation network optimization plan, and performing logistics planning and management on the first transportation sub-network. This solution solves the technical problems of low efficiency and low reliability of logistics planning and management in the prior art, and achieves the technical effect of improving the efficiency and reliability of logistics planning and management.

[0004] Therefore, although the related technology can improve the efficiency of logistics transportation, the related technology does not consider the impact of the emergency results of the goods on the order of goods transportation, that is, it is impossible to optimize the order of goods transportation according to the emergency results of the goods.

[0005] The information disclosed in the background technology section of this application is only intended to deepen the understanding of the general background technology of this application, and should not be regarded as an admission or any form of suggestion that the information constitutes the prior art already known to those skilled in the art. Summary of the invention

[0006] The present invention provides a logistics management optimization method and system based on the Internet of Things, which can solve the technical problem that the related technology cannot optimize the cargo transportation sequence according to the cargo emergency results.

[0007] According to a first aspect of the present invention, there is provided a method for optimizing logistics management based on the Internet of Things, comprising: obtaining cargo order information of multiple cargo orders within a preset optimization time period, wherein the cargo order information comprises a delivery address, cargo weight, cargo quantity, a customer-required delivery date, and an order time; obtaining, according to the delivery address, transportation routes and transportation distances of the multiple cargo orders; obtaining, on multiple transportation dates, weather forecast information of the multiple cargo orders on the transportation routes, wherein the weather forecast information comprises average temperature data, average humidity data, and average wind speed data; determining an estimated transportation day according to the weather forecast information, the transportation distance, the cargo weight, and the cargo quantity; obtaining the remaining days from the current date to the customer-required delivery date; determining an emergency result of the cargo according to the estimated transportation day and the remaining days; and determining, according to the emergency result of the cargo and the order time, cargo for priority transportation within the preset optimization time period.

[0008] According to a second aspect of the present invention, there is provided a logistics management optimization system based on the Internet of Things, comprising: a cargo order information module, used to obtain cargo order information of multiple cargo orders within a preset optimization time period, wherein the cargo order information includes a delivery address, cargo weight, cargo quantity, customer-required delivery date and order time; a transportation route and transportation distance module, used to obtain the transportation routes and transportation distances of multiple cargo orders according to the delivery address; a weather forecast information module, used to obtain weather forecast information of multiple cargo orders on the transportation routes on multiple transportation dates, wherein the weather forecast information includes average temperature data, average humidity data and average wind data; an estimated transportation days module, used to determine the estimated transportation days according to the weather forecast information, the transportation distance, the cargo weight and the cargo quantity; a remaining days module, used to obtain the remaining days from the current date to the customer-required delivery date; a cargo emergency result module, used to determine the cargo emergency result according to the estimated transportation days and the remaining days; a priority transportation cargo module, used to determine the priority transportation cargo within the preset optimization time period according to the cargo emergency result and the order time.

[0009] Technical effect: According to the present invention, by comprehensively considering the cargo order information and weather forecast information, the transportation days can be predicted more accurately, so as to reasonably arrange the transportation plan and reduce the delay caused by weather factors. According to the expected transportation days and the delivery date required by the customer, the cargo emergency results can be accurately analyzed, so as to optimize the cargo transportation sequence and improve the logistics efficiency. When determining the driving distance relationship function, the influence of historical weather conditions, historical cargo weight and historical cargo quantity on the historical driving distance can be expressed by the undetermined coefficient equation of the driving distance relationship function, and then the undetermined coefficient is solved to determine the driving distance relationship function, accurately analyze the relationship between the historical driving distance and the historical weather conditions, historical cargo weight and historical cargo quantity, and improve the accuracy of the driving distance relationship function. When determining the driving distance, the driving distance can be determined by the driving distance relationship function, weather forecast information, cargo weight and cargo quantity, and various factors affecting the driving distance can be comprehensively considered to improve the comprehensiveness, scientificity and accuracy of the driving distance. When determining the estimated transportation days, the estimated transportation days can be determined by accumulating the daily driving distance and comparing it with the transportation distance, which helps to predict the time required for transportation and improve the scientificity and reliability of the estimated transportation days. When determining the emergency results of goods, the emergency results of goods can be determined by the estimated transportation days and the remaining days. The urgency of the cargo order can be quantitatively evaluated to better allocate and manage resources, avoid waste of resources, and improve overall operational efficiency. When determining the priority score, the priority score can be determined by the emergency results of the cargo, the order time, and the preset optimization time period, so that urgent and important goods can be processed first, which helps to optimize logistics management, improve decision-making efficiency, and reduce resource waste and delays.

[0010] It should be understood that the above general description and the following detailed description are exemplary and explanatory only and do not limit the present invention. Other features and aspects of the present invention will become more apparent from the following detailed description of exemplary embodiments with reference to the accompanying drawings. BRIEF DESCRIPTION OF THE DRAWINGS

[0011] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings in the following description are only some embodiments of the present invention. For those of ordinary skill in the art, other embodiments can be obtained based on these drawings without creative work.

[0012] Figure 1 A schematic diagram of a flow chart of a method for optimizing logistics management based on the Internet of Things according to an embodiment of the present invention is exemplarily shown;

[0013] Figure 2A block diagram of a logistics management optimization system based on the Internet of Things according to an embodiment of the present invention is exemplarily shown. DETAILED DESCRIPTION

[0014] In order to make the purpose, technical solution and advantages of the embodiments of the present invention clearer, the technical solution in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0015] The technical solution of the present invention is described in detail with specific embodiments below. The following specific embodiments can be combined with each other, and the same or similar concepts or processes may not be described in detail in some embodiments.

[0016] Figure 1 A flow chart of a logistics management optimization method based on the Internet of Things according to an embodiment of the present invention is exemplarily shown, and the method comprises: step S101, obtaining cargo order information of multiple cargo orders within a preset optimization time period, wherein the cargo order information comprises a delivery address, cargo weight, cargo quantity, customer required delivery date and order time; step S102, obtaining transportation routes and transportation distances of multiple cargo orders according to the delivery address; step S103, obtaining weather forecast information of multiple cargo orders on the transportation routes on multiple transportation dates, wherein the weather forecast information comprises average temperature data, average humidity data and average wind data; step S104, determining an estimated transportation day according to the weather forecast information, the transportation distance, the cargo weight and the cargo quantity; step S105, obtaining the remaining days from the current date to the customer required delivery date; step S106, determining the cargo emergency result according to the estimated transportation day and the remaining days; step S107, determining the cargo to be transported with priority within the preset optimization time period according to the cargo emergency result and the order time.

[0017] According to the IoT-based logistics management optimization method of an embodiment of the present invention, by comprehensively considering cargo order information and weather forecast information, the transportation days can be predicted more accurately, thereby reasonably arranging transportation plans and reducing delays caused by weather factors. According to the estimated transportation days and the delivery date required by the customer, the emergency results of the cargo can be accurately analyzed, thereby optimizing the cargo transportation sequence and improving logistics efficiency.

[0018] According to one embodiment of the present invention, in step S101, within a preset optimization time period, the cargo order information of multiple cargo orders is obtained, wherein the preset optimization time period can be set to 24 hours, 48 ​​hours, etc., and the present invention does not limit this. The cargo order includes the cargo orders newly added within the preset optimization time period and the cargo orders to be transported. The customer requires the delivery date to be the delivery date specified by the customer. If the customer does not specify the delivery date, the customer requires the delivery date to be set to 30 days, 60 days, etc., and the present invention does not limit this.

[0019] According to one embodiment of the present invention, in step S102, the delivery address is input through a map service or a navigation system to perform a query to obtain the transportation route and transportation distance of each cargo order.

[0020] According to an embodiment of the present invention, in step S103, daily weather forecast information for multiple cargo orders on the transportation routes is obtained on multiple transportation dates, wherein the multiple transportation dates include transportation dates for the next 7 days or 15 days, which is not limited by the present invention. The average temperature data is the average value of the temperature data for one day, the average humidity data is the average value of the humidity data for one day, and the average wind speed data is the average value of the wind speed data for one day.

[0021] According to one embodiment of the present invention, in step S104, the estimated transportation days are determined based on the weather forecast information, the transportation distance, the cargo weight and the cargo quantity.

[0022] According to one embodiment of the present invention, step S104 includes: obtaining historical cargo weights, historical cargo quantities and historical transportation routes of multiple historical cargo orders; obtaining historical weather condition information of multiple historical cargo orders on the historical transportation routes on multiple historical transportation dates, wherein the historical weather condition information includes historical average temperature data, historical average humidity data and historical average wind data; obtaining historical driving distances of multiple historical cargo orders on multiple historical transportation dates; determining a driving distance relationship function based on the historical weather condition information, the historical cargo weight, the historical cargo quantity and the historical driving distance; determining the driving distance based on the driving distance relationship function, the weather forecast information, the cargo weight and the cargo quantity; determining the expected number of transportation days based on the driving distance and the transportation distance.

[0023] According to one embodiment of the present invention, the historical cargo order is a completed cargo order, from which the historical cargo weight, historical cargo quantity and historical transportation route of the order can be obtained. The multiple historical transportation dates include the transportation dates of the past 7 days or 15 days, which is not limited by the present invention. The historical driving distance is the driving distance of the historical cargo order on a historical transportation date. The historical average temperature data, the historical average humidity data, the historical average wind data, the historical cargo weight and the historical cargo quantity will affect the historical driving distance to a certain extent. For example, in stormy weather, the historical average humidity data and the historical average wind data will be higher. Due to the poor traffic conditions in stormy weather, the driving distance will be shorter. At the same time, the greater the historical cargo weight and the more historical cargo quantity, the slower the driving speed and the shorter the driving distance. Based on the correlation of the above data, the driving distance relationship function between the historical average temperature data, the historical average humidity data, the historical average wind data, the historical cargo weight, the historical cargo quantity and the historical driving distance can be determined. The driving distance relationship function can reflect the changing law of the driving distance with the cargo orders and weather conditions under different cargo orders and weather conditions. By inputting weather forecast information, cargo weight and cargo quantity into the established route length relationship function, the daily route length for different cargo orders and weather forecast information can be predicted. By comparing the predicted route length with the transportation distance, the estimated transportation days can be obtained.

[0024] According to one embodiment of the present invention, determining the travel distance relationship function according to the historical weather condition information, the historical cargo weight, the historical cargo quantity and the historical travel distance includes: obtaining an undetermined coefficient equation of the travel distance relationship function according to formula (1);

[0025]

[0026] Among them, L s,i is the historical travel distance of the sth historical cargo order on the i-th historical transportation date, W s is the historical cargo weight of the sth historical cargo order, N s is the historical quantity of goods for the sth historical goods order, is the historical average temperature data of the i-th historical transportation date, T p To preset the average temperature data, is the historical average humidity data of the i-th historical transportation date, is the historical average wind data of the ith historical transportation date, α1, α2, α3, α4, α5, α6, α7 and α8 are the undetermined coefficients of the undetermined coefficient equation; according to the historical weather condition information, the historical cargo weight, the historical cargo quantity and the historical driving distance in multiple historical transportation dates of multiple historical cargo orders, the undetermined coefficients are solved to obtain the solved values ​​of the undetermined coefficients; according to the solved values ​​of the undetermined coefficients and the undetermined coefficient equation, the driving distance relationship function is determined.

[0027] According to one embodiment of the present invention, in formula (1), is the relative difference between the historical average temperature data and the preset average temperature data on the i-th historical transportation date. The smaller the relative difference is, the longer the historical driving distance is. The result of 1 minus the relative difference can be obtained. Indicates that the historical driving distance is positively correlated with this result. The larger the result, the longer the historical driving distance. For example, in high temperatures, the driver is prone to fatigue and sleepiness or the engine overheats and causes failures, which shortens the driving distance. In low temperature environments, fog or frost is easily condensed on the windows and windshield, and the road may be iced or snowy, affecting the driving speed, thereby shortening the driving distance. It indicates that the historical cargo weight, historical cargo quantity, historical average humidity data, historical average wind speed data and historical driving distance are negatively correlated. For example, in stormy weather, the greater the historical average humidity data and historical average wind speed data, the worse the traffic conditions and the shorter the driving distance. At the same time, the greater the historical cargo weight and the greater the historical cargo quantity, the slower the driving speed and the shorter the driving distance. Based on the above relationship, the undetermined coefficient can be set to form the undetermined coefficient equation of the driving distance relationship function represented by formula (1).

[0028] According to one embodiment of the present invention, fitting can be performed based on multiple parameters involved in the above undetermined coefficient equation, that is, fitting can be performed based on historical average temperature data, historical average humidity data, historical average wind speed data, historical cargo weight, historical cargo quantity and historical driving distance to solve the above multiple undetermined coefficients. There are 8 undetermined coefficients, namely, α1, α2, α3, α4, α5, α6, α7 and α8. According to the historical weather condition information, the historical cargo weight, the historical cargo quantity and the historical driving distance in multiple historical transportation dates of multiple historical cargo orders, the above 8 undetermined coefficients are solved to obtain the solution values ​​of the above 8 undetermined coefficients, and the solution values ​​of the above 8 undetermined coefficients are substituted into the undetermined coefficient equation to determine the driving distance relationship function.

[0029] In this way, the influence of historical weather conditions, historical cargo weight and historical cargo quantity on historical driving distance can be expressed by the undetermined coefficient equation of the driving distance relationship function, and then the undetermined coefficients can be solved to determine the driving distance relationship function, and the relationship between historical driving distance and historical weather conditions, historical cargo weight and historical cargo quantity can be accurately analyzed, thereby improving the accuracy of the driving distance relationship function.

[0030] According to one embodiment of the present invention, the driving distance is determined according to the driving distance relationship function, the weather forecast information, the cargo weight and the cargo quantity, including: determining the driving distance L of the e-th cargo order on the j-th transportation date according to formula (2): e,j ,

[0031]

[0032] Among them, W e is the weight of the e-th order, N e is the quantity of goods in the e-th order, is the average temperature data of the jth transportation date, T p To preset the average temperature data, is the average humidity data on the jth transportation date, is the average wind speed data on the jth transportation date, α 1,F is the solution value of α1, α 2,F is the solution value of α2, α 3,F is the solution value of α3, α 4,F is the solution value of α4, α 5,F is the solution value of α5, α 6,F is the solution value of α6, α 7,F is the solution value of α7, α 8,F is the solution value of α8.

[0033] According to one embodiment of the present invention, α 1,F , α 2,F , α 3,F , α 4,F , α 5,F , α 6,F , α 7,F and α 8,F Substitute into formula (1) to obtain the travel distance relationship function, substitute the cargo weight of the e-th cargo order, the cargo quantity of the e-th cargo order, the average temperature data of the j-th transportation date, the average humidity data of the j-th transportation date and the average wind speed data of the j-th transportation date into the travel distance relationship function, and the travel distance of the e-th cargo order on the j-th transportation date can be obtained.

[0034] In this way, the driving distance can be determined through the driving distance relationship function, weather forecast information, cargo weight and cargo quantity. Various factors affecting the driving distance can be comprehensively considered to improve the comprehensiveness, scientificity and accuracy of the driving distance.

[0035] According to an embodiment of the present invention, the estimated transportation days are determined according to the driving distance and the transportation distance, including: determining the estimated transportation days t of the e-th cargo order according to formula (3): e,p , t e,p =a+1,L e,1 +L e,2 +…+L e,a <L e ≤L e,1 +L e,2 +…+L e,a +L e,a+1 (3)

[0036] Among them, L e is the transportation distance of the e-th order, L e,1 , L e,2 ,…,L e,a , L e,a+1 are the travel distances of the e-th cargo order on the 1st, 2nd, …, a, and a+1 transportation dates respectively, and a is a positive integer.

[0037] According to one embodiment of the present invention, in formula (3), when L e,1 +L e,2 +…+L e,a <L e ≤L e,1 +L e,2 +…+L e,a +L e,a+1 That is, when the transportation distance of the e-th cargo order is greater than the sum of the travel distances of the e-th cargo order on the 1st, 2nd, …, a transportation dates, and less than or equal to the sum of the travel distances of the e-th cargo order on the 1st, 2nd, …, a, a+1 transportation dates, the estimated transportation days for the e-th cargo order is a+1 days.

[0038] In this way, the estimated transportation days can be determined by accumulating the daily driving distance and comparing it with the transportation distance, which helps to predict the time required for transportation and improve the scientificity and reliability of the estimated transportation days.

[0039] According to an embodiment of the present invention, in step S105, the current date can be obtained through a date function or calendar tool in a programming language, and the difference between the customer's required delivery date and the current date is calculated in days.

[0040] According to an embodiment of the present invention, in step S106, the urgency result of the cargo is determined according to the estimated transportation days and the remaining days.

[0041] According to an embodiment of the present invention, determining the cargo emergency result according to the estimated transportation days and the remaining days includes: determining the cargo emergency result R of the e-th cargo order according to formula (4): e ,

[0042]

[0043] Among them, t e,p is the estimated shipping days for the e-th order, t e,q The remaining days for the e-th order.

[0044] According to one embodiment of the present invention, in formula (4), when t e,p ≤t e,q , that is, when the estimated transportation days of the e-th cargo order is less than or equal to the remaining days of the e-th cargo order, the cargo emergency result is is the relative difference between the remaining days of the e-th cargo order and the estimated transportation days of the e-th cargo order. The larger the relative difference, the more remaining days of the cargo order. The result of 1 minus the relative difference can be obtained. The larger the result, the fewer remaining days of the cargo order, the higher the cargo urgency, and the greater the cargo urgency result. For example, the estimated transportation days of a cargo order are 2 days, and the remaining days are 4 days. The cargo urgency result of the cargo order is The estimated delivery days for the cargo order are 2 days, and the remaining days are 3 days. The cargo urgency result of this cargo order is When t e,p >t e,q , that is, when the estimated transportation days of the e-th cargo order is greater than the remaining days of the e-th cargo order, the cargo urgency result is is the relative difference between the estimated shipping days of the e-th cargo order and the remaining days of the e-th cargo order. The larger the relative difference, the more the estimated shipping days of the cargo order. The result of 1 plus the relative difference can be obtained. The larger the result, the more the estimated shipping days of the cargo order exceed the remaining days, the higher the cargo urgency, and the greater the cargo urgency result. For example, the estimated shipping days of a cargo order are 4 days, and the remaining days are 3 days. The cargo urgency result of the cargo order is The estimated delivery days for the cargo order are 5 days, and the remaining days are 3 days. The cargo urgency result of this cargo order is

[0045] In this way, the urgency of cargo orders can be determined by estimating the number of shipping days and the remaining days, and the urgency of cargo orders can be quantitatively assessed to better allocate and manage resources, avoid wasting resources, and improve overall operational efficiency.

[0046] According to an embodiment of the present invention, in step S107, the goods to be transported with priority within a preset optimization time period are determined according to the urgency result of the goods and the order time.

[0047] According to one embodiment of the present invention, step S107 includes: determining a priority score based on the urgency result of the goods, the order time and the preset optimization time period; arranging the priority scores of multiple goods orders in descending order to obtain a priority transportation sequence of goods orders; and determining the goods for priority transportation within the preset optimization time period based on the goods order corresponding to the first element of the priority transportation sequence of goods orders.

[0048] According to one embodiment of the present invention, the priority score reflects the urgency of the cargo order, and the higher the priority score, the more urgent the cargo order. The priority scores of multiple cargo orders are arranged in descending order to obtain a cargo order priority transportation sequence, and the first element of the cargo order priority transportation sequence is the element with the largest priority score, and the cargo order corresponding to the element is determined as the cargo for priority transportation within the preset optimization time period.

[0049] According to an embodiment of the present invention, determining a priority score according to the cargo urgency result, the order time and the preset optimization time period includes: determining the priority score U of the e-th cargo order according to formula (5): e ,

[0050]

[0051] Among them, R e is the cargo urgency result of the e-th cargo order, D o is the current time, D t,e is the order time of the e-th order, D p is the preset optimization time period, and β1 and β2 are the preset weights.

[0052] According to one embodiment of the present invention, in formula (5), The ratio of the difference between the current time and the order time of the e-th cargo order to the preset optimization time period. The larger the ratio, the earlier the order time of the cargo order, indicating that the e-th cargo order has a higher urgency. The weighted sum of the above ratio and the cargo urgency result can be obtained to obtain the priority score of the e-th cargo order. The higher the priority score, the higher the urgency of the cargo order, indicating that the cargo needs to be transported first.

[0053] In this way, priority scores can be determined based on the urgency of the goods, the time of ordering, and the preset optimization time period, so that urgent and important goods can be handled first, which helps to optimize logistics management, improve decision-making efficiency, and reduce resource waste and delays.

[0054] According to the logistics management optimization method based on the Internet of Things in the embodiment of the present invention, by comprehensively considering the cargo order information and weather forecast information, the transportation days can be predicted more accurately, so as to reasonably arrange the transportation plan and reduce the delay caused by weather factors. According to the expected transportation days and the delivery date required by the customer, the cargo emergency results can be accurately analyzed, so as to optimize the cargo transportation sequence and improve the logistics efficiency. When determining the driving distance relationship function, the influence of historical weather condition information, historical cargo weight and historical cargo quantity on the historical driving distance can be expressed by the undetermined coefficient equation of the driving distance relationship function, and then the undetermined coefficient is solved to determine the driving distance relationship function, accurately analyze the relationship between the historical driving distance and the historical weather condition information, the historical cargo weight and the historical cargo quantity, and improve the accuracy of the driving distance relationship function. When determining the driving distance, the driving distance can be determined by the driving distance relationship function, weather forecast information, cargo weight and cargo quantity, and various factors affecting the driving distance can be comprehensively considered to improve the comprehensiveness, scientificity and accuracy of the driving distance. When determining the estimated transportation days, the estimated transportation days can be determined by accumulating the daily driving distance and comparing it with the transportation distance, which helps to predict the time required for transportation and improve the scientificity and reliability of the estimated transportation days. When determining the emergency results of goods, the emergency results of goods can be determined by the estimated transportation days and the remaining days. The urgency of the cargo order can be quantitatively evaluated to better allocate and manage resources, avoid waste of resources, and improve overall operational efficiency. When determining the priority score, the priority score can be determined by the emergency results of the cargo, the order time, and the preset optimization time period, so that urgent and important goods can be processed first, which helps to optimize logistics management, improve decision-making efficiency, and reduce resource waste and delays.

[0055] Figure 2The block diagram of the logistics management optimization system based on the Internet of Things according to an embodiment of the present invention is exemplarily shown, and the system includes: a cargo order information module, which is used to obtain cargo order information of multiple cargo orders within a preset optimization time period, wherein the cargo order information includes a delivery address, cargo weight, cargo quantity, customer required delivery date and order time; a transportation route and transportation distance module, which is used to obtain the transportation routes and transportation distances of multiple cargo orders according to the delivery address; a weather forecast information module, which is used to obtain weather forecast information of multiple cargo orders on the transportation routes on multiple transportation dates, wherein the weather forecast information includes average temperature data, average humidity data and average wind data; an estimated transportation days module, which is used to determine the estimated transportation days according to the weather forecast information, the transportation distance, the cargo weight and the cargo quantity; a remaining days module, which is used to obtain the remaining days from the current date to the customer required delivery date; a cargo emergency result module, which is used to determine the cargo emergency result according to the estimated transportation days and the remaining days; a priority transportation cargo module, which is used to determine the priority transportation cargo within the preset optimization time period according to the cargo emergency result and the order time.

[0056] It should be understood by those skilled in the art that the embodiments of the present invention described above and shown in the accompanying drawings are only examples and do not limit the present invention. The purpose of the present invention has been fully and effectively achieved. The functional and structural principles of the present invention have been demonstrated and explained in the embodiments, and the embodiments of the present invention may be deformed or modified in any way without departing from the principles.

Claims

1. A logistics management optimization method based on the Internet of Things, characterized in that: include: Obtain cargo order information of multiple cargo orders within a preset optimization time period, wherein the cargo order information includes the delivery address, cargo weight, cargo quantity, customer-required delivery date and order time; obtain the transportation routes and transportation distances of the multiple cargo orders based on the delivery address; obtain weather forecast information of the multiple cargo orders on the transportation routes on multiple transportation dates, wherein the weather forecast information includes average temperature data, average humidity data and average wind speed data; determine the estimated transportation days based on the weather forecast information, the transportation distance, the cargo weight and the cargo quantity; obtain the remaining days from the current date to the customer-required delivery date; determine the cargo emergency result based on the estimated transportation days and the remaining days; determine the cargo priority transportation within the preset optimization time period based on the cargo emergency result and the order time.

2. The method for optimizing logistics management based on the Internet of Things according to claim 1, characterized in that: Determine the expected number of transportation days according to the weather forecast information, the transportation distance, the cargo weight and the cargo quantity, including: obtaining the historical cargo weight, the historical cargo quantity and the historical transportation routes of multiple historical cargo orders; obtaining the historical weather condition information of multiple historical cargo orders on the historical transportation routes on multiple historical transportation dates, wherein the historical weather condition information includes historical average temperature data, historical average humidity data and historical average wind data; obtaining the historical driving distance of multiple historical cargo orders on multiple historical transportation dates; determine the driving distance relationship function according to the historical weather condition information, the historical cargo weight, the historical cargo quantity and the historical driving distance; determine the driving distance according to the driving distance relationship function, the weather forecast information, the cargo weight and the cargo quantity; determine the expected number of transportation days according to the driving distance and the transportation distance.

3. The method for optimizing logistics management based on the Internet of Things according to claim 2 is characterized in that: Determine the travel distance relationship function according to the historical weather condition information, the historical cargo weight, the historical cargo quantity and the historical travel distance, including: according to the formula Obtain the unknown coefficient equation of the driving road length relationship function, where L s,i is the historical travel distance of the sth historical cargo order on the i-th historical transportation date, W s is the historical cargo weight of the sth historical cargo order, N s is the historical quantity of goods for the sth historical goods order, is the historical average temperature data of the i-th historical transportation date, T p To preset the average temperature data, is the historical average humidity data of the i-th historical transportation date, is the historical average wind data of the ith historical transportation date, α1, α2, α3, α4, α5, α6, α7 and α8 are the undetermined coefficients of the undetermined coefficient equation; according to the historical weather condition information, the historical cargo weight, the historical cargo quantity and the historical driving distance in multiple historical transportation dates of multiple historical cargo orders, the undetermined coefficients are solved to obtain the solved values ​​of the undetermined coefficients; according to the solved values ​​of the undetermined coefficients and the undetermined coefficient equation, the driving distance relationship function is determined.

4. The method for optimizing logistics management based on the Internet of Things according to claim 3 is characterized in that: Determine the driving distance according to the driving distance relationship function, the weather forecast information, the cargo weight and the cargo quantity, including according to the formula Determine the travel distance L for the e-th cargo order on the j-th transportation date e，j , where W e is the weight of the e-th order, N e is the quantity of goods in the e-th order, is the average temperature data of the jth transportation date, T p To preset the average temperature data, is the average humidity data on the jth transportation date, is the average wind speed data on the jth transportation date, α 1,F is the solution value of α1, α 2,F is the solution value of α2, α 3,F is the solution value of α3, α 4,F is the solution value of α4, α 5,F is the solution value of α5, α 6,F is the solution value of α6, α 7,F is the solution value of α7, α 8,F is the solution value of α8.

5. The method for optimizing logistics management based on the Internet of Things according to claim 4 is characterized in that: According to the driving distance and the transportation distance, the estimated transportation days are determined, including: according to the formula t e，p =a+1,L e，1 +L e，2 +…+L e，a <L e ≤L e，1 +L e，2 +…+L e，a +L e，a+1 Determine the estimated shipping days t for the e-th order e，p , where L e is the transportation distance of the e-th order, L e，1 , L e，2 ,…,L e，a , L e，a+1 are the travel distances of the e-th cargo order on the 1st, 2nd, …, a, and a+1 transportation dates respectively, and a is a positive integer.

6. The method for optimizing logistics management based on the Internet of Things according to claim 1, characterized in that: Determine the cargo emergency result based on the estimated transportation days and the remaining days, including: according to the formula Determine the cargo urgency result R of the e-th cargo order e , where t e,p is the estimated shipping days for the e-th order, t e,q The remaining days for the e-th order.

7. The method for optimizing logistics management based on the Internet of Things according to claim 1, characterized in that: The method comprises: determining a priority score according to the urgency result of the goods, the order time and the preset optimization time period; arranging the priority scores of multiple goods orders in descending order to obtain a goods order priority transportation sequence; and determining the goods for priority transportation within the preset optimization time period according to the goods order corresponding to the first element of the goods order priority transportation sequence.

8. The method for optimizing logistics management based on the Internet of Things according to claim 7 is characterized in that: Determine the priority score according to the urgency result of the goods, the order time and the preset optimization time period, including: according to the formula Determine the priority score U of the e-th order e , where R e is the cargo urgency result of the e-th cargo order, D o is the current time, D t,e is the order time of the e-th order, D p is the preset optimization time period, and β1 and β2 are the preset weights.

9. A logistics management optimization system based on the Internet of Things, characterized in that: include: A cargo order information module is used to obtain cargo order information of multiple cargo orders within a preset optimization time period, wherein the cargo order information includes the delivery address, cargo weight, cargo quantity, customer-required delivery date and order time; a transportation route and transportation distance module is used to obtain the transportation routes and transportation distances of multiple cargo orders according to the delivery address; a weather forecast information module is used to obtain weather forecast information of multiple cargo orders on the transportation routes on multiple transportation dates, wherein the weather forecast information includes average temperature data, average humidity data and average wind data; an estimated transportation days module is used to determine the estimated transportation days according to the weather forecast information, the transportation distance, the cargo weight and the cargo quantity; a remaining days module is used to obtain the remaining days from the current date to the customer-required delivery date; a cargo emergency result module is used to determine the cargo emergency result according to the estimated transportation days and the remaining days; a priority transportation cargo module is used to determine the priority transportation cargo within the preset optimization time period according to the cargo emergency result and the order time.

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