CN116050971A - Multi-temperature co-distribution platform based on Internet of things technology - Google Patents

Multi-temperature co-distribution platform based on Internet of things technology Download PDF

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CN116050971A
CN116050971A CN202310050678.XA CN202310050678A CN116050971A CN 116050971 A CN116050971 A CN 116050971A CN 202310050678 A CN202310050678 A CN 202310050678A CN 116050971 A CN116050971 A CN 116050971A
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高羽佳
徐礼前
余俊岚
张伟
管敏
洪扬
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Anhui Agricultural University AHAU
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Abstract

The invention relates to a cold chain logistics, in particular to a multi-temperature co-distribution platform based on the internet of things technology, which comprises an equipment layer, a connecting layer and an internet of things cloud layer; the equipment layer is used for extracting static data from the transportation management system as an important data source for fulfilling orders and collecting real-time data of transportation vehicles; the connection layer receives the acquired data sent by the equipment layer and sends the received acquired data to the cloud layer of the Internet of things; the cloud layer of the Internet of things receives acquisition data sent by the equipment layer through the connection layer, develops an internet of things (IoT) -MTDPS system and realizes event management in order fulfilling process; the technical scheme provided by the invention can effectively overcome the defects that the transportation path of the combined delivery vehicle cannot be effectively optimized and the whole delivery process cannot be effectively monitored in the prior art.

Description

Multi-temperature co-distribution platform based on Internet of things technology
Technical Field
The invention relates to a cold chain logistics, in particular to a multi-temperature co-distribution platform based on the technology of the Internet of things.
Background
The cold chain logistics is a logistics network which keeps a low-temperature state in the whole process from the production of the commodity to the delivery of the commodity to a client according to the characteristics of the commodity so as to ensure the quality of the commodity. At present, the cold chain logistics market coverage of China is wide, and the demand is also continuously increasing. Compared with normal-temperature logistics, the cold-chain logistics planning construction requires more funds and has higher requirements on the whole system. For goods with low requirements on storage conditions, the goods can be transported through common logistics, while for foods with high requirements on storage conditions, such as fresh foods, frozen foods and perishable foods, the goods are transported through cold chain logistics, so that the goods loss in the distribution process is reduced to the greatest extent.
In general, most cold chain streams are required to follow the "3T" principle, namely Time of storage and circulation in the cold chain (Time), temperature (Temperature) and product storage resistance (Tolerance), which determine the quality of the cold chain commodity. For different types of cold chain commodities, the cold chain commodities have self specificity and are required to be kept in a low-temperature state before being distributed into customers, so that unnecessary commodity loss caused by improper storage temperature is prevented.
The Multi-temperature co-distribution (Multi-TemperatureJointDistribution, MTJD) refers to a refrigeration technology for loading low-temperature foods, wherein a refrigerator, a refrigeration plate and a cold accumulation heat preservation box are placed in a refrigeration area to provide and maintain different temperature conditions required by conveying the low-temperature foods, and the Multi-temperature co-distribution is matched with normal-temperature cargoes, so that common vehicles are adopted to realize the co-distribution of multiple temperature layers. The principle of multi-temperature co-distribution is similar to that of a refrigerator, and the refrigerator is provided with a refrigerating layer and a freezing layer, so that cold chain commodities can be stored.
In the distribution process, the multi-temperature co-distribution can provide different storage temperatures for different types of cold chain commodities, and unnecessary commodity losses caused by the storage temperatures in the distribution process are prevented as much as possible. In short distance delivery, achieving high quality, low cost delivery has been the goal sought by many logistics enterprises. The temperature of the ordinary cold chain distribution is fixed, and for the cold chain commodity, although the cold chain commodity has a certain fresh-keeping effect, the high quality assurance is not achieved. The multi-temperature co-distribution has the significance of not only being capable of collocating and transporting different commodities, reducing the no-load rate and saving the cost; meanwhile, each commodity can be guaranteed to be at the respective optimal storage temperature, and the requirements of consumers on commodity quality are met.
However, existing multi-temperature co-dispensing platforms do not allow for efficient optimization of the joint dispensing vehicle transportation path and for efficient supervision of the entire dispensing process.
Disclosure of Invention
(one) solving the technical problems
Aiming at the defects existing in the prior art, the invention provides the multi-temperature co-distribution platform based on the internet of things technology, which can effectively overcome the defects that the transportation path of the combined distribution vehicle cannot be effectively optimized and the whole distribution process cannot be effectively monitored in the prior art.
(II) technical scheme
In order to achieve the above purpose, the invention is realized by the following technical scheme:
the multi-temperature co-distribution platform based on the Internet of things technology comprises an equipment layer, a connecting layer and an Internet of things cloud layer;
the equipment layer is used for extracting static data from the transportation management system as an important data source for fulfilling orders and collecting real-time data of transportation vehicles;
the connection layer receives the acquired data sent by the equipment layer and sends the received acquired data to the cloud layer of the Internet of things;
and the cloud layer of the Internet of things receives acquisition data sent by the equipment layer through the connection layer, develops an IoT-MTDPS system and realizes event management in order fulfilling process.
Preferably, the static data extracted from the transportation management system by the equipment layer includes customer data, order data, transportation data and driver information, and the real-time data of the transportation vehicle collected by the equipment layer includes real-time environment data and real-time traffic data.
Preferably, the real-time environmental data is collected by a sensing device installed in the container area of the transportation vehicle, the real-time environmental data including an environmental temperature and a relative humidity, the sensing device having an organization ID, a device type, a device ID, and an authentication token configured therein.
Preferably, the real-time traffic data is collected by establishing external connection of a map API, and the real-time traffic data comprises real-time positioning data, a position distance matrix and real-time traffic conditions;
the map API embeds the map into a webpage API through JavaScript, the webpage API provides a large number of practical tools for processing the map, and adds contents to the map through various services, so that a user creates a map application program with comprehensive functions on a website;
the location distance matrix is used to evaluate the location between two customers, and to evaluate order delivery time in combination with real-time traffic conditions.
Preferably, the sensing equipment in the equipment layer is paired with the edge router in advance by using a wireless communication technology under a 2.4GHz ISM frequency band wireless communication protocol, and the acquired real-time environment data is transmitted to the cloud layer of the Internet of things through the edge router;
the real-time traffic data is directly transmitted to the cloud layer of the Internet of things through the 4G/LTE network.
Preferably, the cloud layer of the internet of things comprises a cloud computing database and an internet of things development platform;
the calculation database uses NoSQL database service and stores acquired data sent by the equipment layer through the connection layer in json format;
the Internet of things development platform applies web development, algorithm deployment and backend development to build the IoT-MTDPS system.
Preferably, the IoT-MTDPS system includes a registration login, a company-oriented function module, a driver-oriented function module, and a user-oriented function module;
the company-oriented functional module comprises order classification collection, logistics information signboards, vehicle driver information, locker distribution and locker information control;
the driver-oriented functional module comprises personal order summarization, a driver information signboard and in-vehicle real-time monitoring;
the user-oriented function module comprises short message service and opinion feedback.
Preferably, the system further comprises a transportation path optimizing unit, wherein the transportation path optimizing unit determines an optimizing target of the transportation path of the combined delivery vehicle, and builds constraint conditions based on customer satisfaction degree considering time demand sensitivity and combined delivery cost, and builds a combined delivery vehicle transportation path optimizing model based on maximization of the customer satisfaction degree and minimization of the combined delivery cost.
Preferably, the optimization objective of the delivery path of the joint delivery vehicle includes maximizing customer satisfaction and minimizing joint delivery cost, and the objective function of maximizing customer satisfaction is expressed by the following formula:
Figure BDA0004057782560000041
wherein mu i (t i ) Representing a customer satisfaction function that considers time demand sensitivity;
the objective function of minimizing the joint distribution cost is expressed by the following formula:
Figure BDA0004057782560000042
wherein TC is ij To combine the total delivery costs of delivery vehicles from customer i to customer j, TC ij ＝TC 1 +TC 2ij +TC 3ij +TC 4ij ，TC 1 Indicating fixed cost of departure of vehicle, TC 2ij Representing fuel consumption costs of vehicles from customer i to customer j, TC 3ij Representing penalty cost of vehicle from customer i to customer j, TC 4ij Representing the cooling cost of the vehicle from customer i to customer j;
Figure BDA0004057782560000043
preferably, the customer satisfaction function μ taking into account the time demand sensitivity i (t i ) Expressed by the following formula:
Figure BDA0004057782560000044
in the above, t i For vehicle arrival time [ ET ] i d ，ET i ]Represents the range of early arrival times acceptable to the client, [ LT ] i ，LT i d ]Represents the range of deferred arrival times that a client can accept lambda i For the time demand sensitivity of client i, 0 < lambda i ＜1；
Cost of fuel consumption TC of the vehicle from customer i to customer j 2ij Expressed by the following formula:
Figure BDA0004057782560000051
wherein c is the unit fuel consumption cost, FR is the fuel consumption rate function,
Figure BDA0004057782560000053
Figure BDA0004057782560000054
is the mass ratio of fuel to air, gamma is the engine friction factor, N is the engine speed, V s For engine displacement, P ij For the total traction of the vehicle, eta is the efficiency parameter of the engine, d ij Mu is the heat value of the fuel, v, for the distance between client i and client j ij Zeta is a unit conversion coefficient for vehicle speed;
penalty cost TC of the vehicle from customer i to customer j 3ij Expressed by the following formula:
TC 3ij ＝TC' 3ij +TC” 3ij
wherein, TC' 3ij For time penalty cost, the following formula is used:
TC' 3ij ＝p i (t i )＝c 1 max((ET i -t i ),0)+c 2 max((t i -LT i ),0)
in the above, p i (t i ) Representing a time penalty cost function, expressed by the following formula:
Figure BDA0004057782560000052
in the above, t i For vehicle arrival time [ ET ] i d ，ET i ]Represents the range of early arrival times acceptable to the client, [ LT ] i ，LT i d ]Representing a deferred arrival time range acceptable to the client c 1 Penalty cost per unit time for vehicle arrival ahead, c 2 Punishment cost per unit time generated by deferring arrival of the vehicle, wherein W is the maximum punishment cost per unit time;
TC” 3ij for the cost of goods loss, the following formula is adopted:
TC” 3ij ＝p·θ·q j
in the above formula, p is a punishment coefficient, θ is a cargo loss ratio per unit weight, q j The weight of the cargo required for customer j;
refrigeration cost TC of the vehicle from customer i to customer j 4ij Expressed by the following formula:
TC 4ij ＝TC' 4ij +TC” 4ij
wherein, TC' 4ij For the refrigeration cost in the driving process, the following formula is adopted for expression:
Figure BDA0004057782560000061
in the above, c 3 Refrigeration cost per unit heat load, G t G is the thermal load of the carriage t = (1+phi) ·r·s·Δt, phi is the cabin degradation degree coefficient, R is the thermal conductivity, S is the vehicle body surface area, Δt is the temperature difference between the cabin exterior temperature and the cabin interior temperature, T ij Is the delivery time from client i to client j;
TC” 4ij for the refrigeration cost when opening the door, the following formula is adopted:
TC” 4ij ＝c 3 ·Q s ·ΔT·t i s
in the above, c 3 Refrigeration cost per unit heat load, Q s The delta T is the temperature difference between the outside temperature of the carriage and the inside temperature of the carriage, and T is the heat load generated by the unit temperature difference of unit time i s Is the door opening time.
(III) beneficial effects
Compared with the prior art, the multi-temperature co-distribution platform based on the internet of things technology has the following beneficial effects:
1) The equipment layer extracts static data from the transportation management system as an important data source for fulfilling orders, and simultaneously collects real-time data of transportation vehicles; the connection layer receives the acquired data sent by the equipment layer and sends the received acquired data to the cloud layer of the Internet of things; the cloud layer of the Internet of things receives the acquired data sent by the equipment layer through the connection layer, develops an IoT-MTDPS system, realizes event management in the process of fulfilling orders, can effectively monitor and control the whole distribution process including distribution orders, commodity storage environments and distribution traffic conditions, and realizes timely control over the whole distribution process;
2) The transportation path optimization unit determines an optimization target of the transportation path of the combined delivery vehicle, builds a combined delivery vehicle transportation path optimization model based on the maximization of the customer satisfaction degree and the minimization of the combined delivery cost based on the customer satisfaction degree and the establishment of constraint conditions of the combined delivery cost considering the time demand sensitivity, and utilizes the two-stage multi-target genetic algorithm optimizer to perform multi-target optimization on the combined delivery vehicle transportation path optimization model, so that the effective optimization of the transportation path of the combined delivery vehicle can be realized, the delivery cost is reduced, the delivery time is reduced, and the requirements of consumers on commodity quality are met.
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In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings used in the description of the embodiments or the prior art will be briefly described below. It is evident that the drawings in the following description are only some embodiments of the present invention and that other drawings may be obtained from these drawings without inventive effort for a person of ordinary skill in the art.
FIG. 1 is a schematic view of a platform frame according to the present invention;
FIG. 2 is a system diagram of an IoT-MTDPS system in accordance with the invention.
Detailed Description
In order to make the objects, technical solutions and advantages of the embodiments of the present invention more clear, the technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings in the embodiments of the present invention. It will be apparent that the described embodiments are some, but not all, embodiments of the invention. All other embodiments, which can be made by those skilled in the art based on the embodiments of the invention without making any inventive effort, are intended to be within the scope of the invention.
The multi-temperature co-distribution platform based on the Internet of things technology comprises a device layer, a connection layer and an Internet of things cloud layer as shown in fig. 1;
the equipment layer is used for extracting static data from the transportation management system as an important data source for fulfilling orders and collecting real-time data of transportation vehicles;
the connection layer receives the acquired data sent by the equipment layer and sends the received acquired data to the cloud layer of the Internet of things;
and the cloud layer of the Internet of things receives acquisition data sent by the equipment layer through the connection layer, develops an IoT-MTDPS system and realizes event management in order fulfilling process.
(1) The static data extracted from the transportation management system by the equipment layer includes customer data (such as customer name, contact phone, delivery address, etc.), order data (such as required delivery time and delivery list, etc.), transportation data (such as capacity of MCV vehicle, transportation limit, etc.), and driver information (such as total amount of delivered orders by driver, etc.), and the real-time data of the equipment layer collected transportation vehicle (MCV vehicle) includes real-time environment data and real-time traffic data.
1) Real-time environmental data is collected by a sensing device installed in the container area of the transport vehicle, the real-time environmental data including an ambient temperature and a relative humidity, and the sensing device is configured with an organization ID, a device type, a device ID, and an authentication token.
2) The real-time traffic data is collected by establishing external connection of a map API, and the real-time traffic data comprises real-time positioning data, a position distance matrix and real-time traffic conditions;
the map API embeds the map into a webpage API through JavaScript, the webpage API provides a large number of practical tools for processing the map, and adds contents to the map through various services, so that a user creates a map application program with comprehensive functions on a website;
the location distance matrix is used to evaluate the location between two customers, in combination with real-time traffic conditions, to evaluate order delivery times.
(2) The sensing equipment in the equipment layer is paired with an edge router (supporting 3G/4G/LTE connection) in advance by using a wireless communication technology (such as low-power consumption Bluetooth, wi-Fi and the like) under a 2.4GHz ISM frequency band wireless communication protocol, and acquired real-time environment data is transmitted to an Internet of things cloud layer through the edge router;
the real-time traffic data is directly transmitted to the cloud layer of the Internet of things through the 4G/LTE network.
(3) The cloud layer of the Internet of things comprises a cloud computing database and an Internet of things development platform;
the calculation database uses NoSQL database service and stores acquired data sent by the equipment layer through the connection layer in json format;
the Internet of things development platform applies web development, algorithm deployment and backend development to build the IoT-MTDPS system.
Wherein the IoT-MTDPS system includes a login-oriented company function module, a driver-oriented function module, and a user-oriented function module;
the company-oriented functional module comprises order classification collection, logistics information signboards, vehicle driver information, locker distribution and locker information control;
the driver-oriented functional module comprises personal order summarization, a driver information signboard and in-vehicle real-time monitoring;
the user-oriented function module comprises short message service and opinion feedback.
In the company oriented functional module:
1) The order classification collection is carried out, and the order classification collection page can be skipped by clicking the order data of the data visualization page, wherein the order data such as order number, order confirmation time, required delivery time, order loading time and the like, and the customer data such as customer name, contact phone and delivery address can be intuitively seen on the page; in addition, after the order data is uploaded to the platform, the platform can automatically classify according to the interval of the storage temperature of the order goods;
2) The logistics information board is clicked on the data visualization of the navigation bar on the upper part of the system platform, so that the logistics information data board can appear, the page displays the information such as the quantity of the current waybills, the data quality rule, the personnel information, the month waybill statistical chart, the total quantity of the waybills, the order data and the like, and intuitively displays the waybill conditions of the current day and the current month; the map API is inserted into the webpage, and the positions of the delivery vehicles and the deposit cabinets are displayed on the map through GIS-based secondary development;
3) The information of the vehicle driver can be jumped to a vehicle driver information page when a truck button in the map is clicked, and the working rest condition, the working time and the driver information of the delivery vehicle can be seen on the page;
4) The distribution of the locker is realized, and the locker in the map in the 'data visualization' page is clicked, so that the locker distribution page can be jumped, and the specific distribution condition of the locker in a certain area is displayed on the page;
5) The control of the information of the locker, click the "locker control" of the upper navigation bar of the system platform, can jump to the locker information control page, this page can control the switch of refrigerating fan, door lock and ultraviolet lamp inside locker, can realize the relevant function through clicking the correspondent button, thus can carry on the remote control to the locker; the page is also provided with an early warning function, when the temperature and humidity in the locker exceeds a set threshold value, an alarm lamp on the page is lightened to give an alarm in time, and a short message is sent to a user for reminding.
In the driver oriented functional module:
1) The personal order summarization, clicking the 'driver personal order' on the upper part of the system platform, namely jumping to a driver personal order summarization page, wherein the page displays the number of 'normal orders', 'temporary orders', 'abnormal orders' finished by the driver personally and can be visually seen in a table;
2) The driver information board clicks the 'driver information board' on the upper part of the system platform, namely, the page of the driver information board can be jumped, and the number of the present waybills and weather information can be checked on the page; besides, the driving route of the user and the position required to pass through the locker can be intuitively seen on the map, and the positions are sequentially distributed according to the serial numbers on the locker;
3) The method comprises the steps of monitoring in a vehicle in real time, clicking the temperature and humidity display of a navigation bar on the upper part of a system platform, namely jumping to a real-time environment monitoring page in the vehicle, wherein the page is used for statistically drawing temperature and humidity values of 24 hours in the vehicle into a line graph, more intuitively reflecting temperature and humidity changes of 24 hours in the vehicle, timely finding out abnormal temperature and humidity conditions according to the data, and triggering a temperature and humidity early warning system to automatically alarm when the temperature and humidity exceed a set threshold range so as to remind a driver to timely take corresponding countermeasures.
In the user-oriented function module: the opinion feedback is performed by clicking an opinion feedback button of a navigation bar on the upper part of the system platform, namely, the opinion feedback page can be jumped, and after inputting names, opinion types, mailbox information and opinion contents, a user can submit own opinion of the platform.
In the technical scheme, the system further comprises a transportation path optimizing unit, wherein the transportation path optimizing unit determines an optimizing target of the transportation path of the combined delivery vehicle, and builds a constraint condition based on customer satisfaction degree considering time demand sensitivity and combined delivery cost, and builds a combined delivery vehicle transportation path optimizing model based on maximization of the customer satisfaction degree and minimization of the combined delivery cost.
Optimization objectives for the joint delivery vehicle transportation path include customer satisfaction maximization, joint delivery cost minimization:
(1) the objective function for maximizing customer satisfaction is expressed by the following formula:
Figure BDA0004057782560000111
wherein mu i (t i ) A customer satisfaction function that accounts for time demand sensitivity, the function being represented by:
Figure BDA0004057782560000112
in the above, t i For vehicle arrival time [ ET ] i d ，ET i ]Represents the range of early arrival times acceptable to the client, [ LT ] i ，LT i d ]Represents the range of deferred arrival times that a client can accept lambda i For the time demand sensitivity of client i, 0 < lambda i ＜1。
(2) The objective function for minimizing joint delivery costs is represented by the following formula:
Figure BDA0004057782560000113
wherein TC is ij To combine the total delivery costs of delivery vehicles from customer i to customer j, TC ij ＝TC 1 +TC 2ij +TC 3ij +TC 4ij ，TC 1 Indicating fixed cost of departure of vehicle, TC 2ij Representing fuel consumption costs of vehicles from customer i to customer j, TC 3ij Representing penalty cost of vehicle from customer i to customer j, TC 4ij Representing the cooling cost of the vehicle from customer i to customer j;
Figure BDA0004057782560000114
1) Fixed cost TC for departure of vehicle 1 Is a fixed value, generally does not change along with the transportation time, the transportation distance or the transportation environment, and mainly comprises depreciation cost, maintenance cost, fixed wages of drivers and the like of the delivery vehicles;
2) Cost of fuel consumption TC for vehicle from customer i to customer j 2ij Expressed by the following formula:
Figure BDA0004057782560000121
wherein c is the unit fuel consumption cost, FR is the fuel consumption rate function,
Figure BDA0004057782560000123
Figure BDA0004057782560000124
is the mass ratio of fuel to air, gamma is the engine friction factor, N is the engine speed, V s For engine displacement, P ij For the total traction of the vehicle, eta is the efficiency parameter of the engine, d ij Mu is the heat value of the fuel, v, for the distance between client i and client j ij Zeta is a unit conversion coefficient for vehicle speed; />
3) Penalty cost TC for vehicle from customer i to customer j 3ij Expressed by the following formula:
TC 3ij ＝TC' 3ij +TC” 3ij
wherein, TC' 3ij For time penalty cost, the following formula is used:
TC' 3ij ＝p i (t i )＝c 1 max((ET i -t i ),0)+c 2 max((t i -LT i ),0)
in the above, p i (t i ) Representing a time penalty cost function, expressed by the following formula:
Figure BDA0004057782560000122
in the above, t i For vehicle arrival time [ ET ] i d ，ET i ]Represents the range of early arrival times acceptable to the client, [ LT ] i ，LT i d ]Representing a deferred arrival time range acceptable to the client c 1 Penalty cost per unit time for vehicle arrival ahead, c 2 Punishment cost per unit time generated by deferring arrival of the vehicle, wherein W is the maximum punishment cost per unit time;
TC” 3ij for the cost of goods loss, the following formula is adopted:
TC” 3ij ＝p·θ·q j
in the above formula, p is a punishment coefficient, θ is a cargo loss ratio per unit weight, q j The weight of the cargo required for customer j;
4) Cost of refrigeration TC for vehicle from customer i to customer j 4ij Expressed by the following formula:
TC 4ij ＝TC' 4ij +TC” 4ij
wherein, TC' 4ij For the refrigeration cost in the driving process, the following table is adoptedThe illustration is:
Figure BDA0004057782560000131
in the above, c 3 Refrigeration cost per unit heat load, G t G is the thermal load of the carriage t = (1+phi) ·r·s·Δt, phi is the cabin degradation degree coefficient, R is the thermal conductivity, S is the vehicle body surface area, Δt is the temperature difference between the cabin exterior temperature and the cabin interior temperature, T ij Is the delivery time from client i to client j;
TC” 4ij for the refrigeration cost when opening the door, the following formula is adopted:
TC” 4ij ＝c 3 ·Q s ·ΔT·t i s
in the above, c 3 Refrigeration cost per unit heat load, Q s The delta T is the temperature difference between the outside temperature of the carriage and the inside temperature of the carriage, and T is the heat load generated by the unit temperature difference of unit time i s Is the door opening time.
According to the technical scheme, the two-stage multi-objective genetic algorithm (2 PMOGA) optimizer is utilized to conduct multi-objective optimization on the combined delivery vehicle transportation path optimization model, and effective optimization on the combined delivery vehicle transportation path can be achieved. When dealing with the multi-objective optimization problem, the multi-objective genetic algorithm (MOGA) can search for pareto optimal solutions instead of exact solutions, and the two-stage multi-objective genetic algorithm plays a role in integrating multi-objective optimization in the transportation path optimization and fuzzy logic methods.
Multi-objective optimization of a joint delivery vehicle transportation path optimization model using a two-stage multi-objective genetic algorithm (2 PMOGA) optimizer, comprising the following two stages:
in the first stage, when an unexpected event is detected, activating a chromosome to optimize a membership function, thereby obtaining better precision in the proposed fuzzy logic method;
in the second phase, a group of clients is assigned to the MCV cars by a binary sequence in order to make the transport path.
Prior to using this method, membership functions in a two-stage multi-objective genetic algorithm optimizer need to be trained using a given set of training data sets (collected from 10 active clients out of 210 clients by conducting surveys of the clients, knowing their beliefs about changes in importance factors and their influence on satisfaction by the occurrence of unexpected events), so that the base length of the membership functions is optimized.
After optimizing the membership functions, the fuzzy logic method can be used to adjust α i (customer satisfaction), beta i These two important factors (customer importance) make it possible to re-optimize the transport path. In the process, the fuzzy logic library skfuzzy in pyrm is used for performing operations such as fuzzification, reasoning engine, reverse fuzzification and the like, so that alpha is realized i (customer satisfaction), beta i Adjustment of two important factors (customer importance) while the transportation path can be re-optimized by taking into account parameter adjustments in the delivery schedule.
The above embodiments are only for illustrating the technical solution of the present invention, and are not limiting; although the invention has been described in detail with reference to the foregoing embodiments, it will be understood by those of ordinary skill in the art that: the technical scheme described in the foregoing embodiments can be modified or some technical features thereof can be replaced by equivalents; such modifications and substitutions do not depart from the spirit and scope of the technical solutions of the embodiments of the present invention.

Claims (10)

1. Multi-temperature co-distribution platform based on Internet of things technology, and is characterized in that: the device comprises a device layer, a connection layer and an Internet of things cloud layer;
the equipment layer is used for extracting static data from the transportation management system as an important data source for fulfilling orders and collecting real-time data of transportation vehicles;
the connection layer receives the acquired data sent by the equipment layer and sends the received acquired data to the cloud layer of the Internet of things;
and the cloud layer of the Internet of things receives acquisition data sent by the equipment layer through the connection layer, develops an IoT-MTDPS system and realizes event management in order fulfilling process.
2. The internet of things multi-temperature co-distribution platform according to claim 1, wherein: the static data extracted from the transportation management system by the equipment layer comprises client data, order data, transportation data and driver information, and the real-time data of the transportation vehicle collected by the equipment layer comprises real-time environment data and real-time traffic data.
3. The internet of things multi-temperature co-distribution platform according to claim 2, wherein: the real-time environmental data is collected by a sensing device installed in a container area of the transport vehicle, the real-time environmental data comprises an environmental temperature and a relative humidity, and an organization ID, a device type, a device ID and an authentication token are configured in the sensing device.
4. The internet of things multi-temperature co-distribution platform according to claim 2, wherein: the real-time traffic data is collected by establishing external connection of a map API, and the real-time traffic data comprises real-time positioning data, a position distance matrix and real-time traffic conditions;
the map API embeds the map into a webpage API through JavaScript, the webpage API provides a large number of practical tools for processing the map, and adds contents to the map through various services, so that a user creates a map application program with comprehensive functions on a website;
the location distance matrix is used to evaluate the location between two customers, and to evaluate order delivery time in combination with real-time traffic conditions.
5. The internet of things multi-temperature co-distribution platform according to claim 2, wherein: the sensing equipment in the equipment layer is paired with an edge router in advance by using a wireless communication technology under a 2.4GHzISM frequency band wireless communication protocol, and acquired real-time environment data are transmitted to an Internet of things cloud layer through the edge router;
the real-time traffic data is directly transmitted to the cloud layer of the Internet of things through the 4G/LTE network.
6. The internet of things multi-temperature co-distribution platform according to claim 1, wherein: the cloud layer of the Internet of things comprises a cloud computing database and an Internet of things development platform;
the calculation database uses NoSQL database service and stores acquired data sent by the equipment layer through the connection layer in json format;
the Internet of things development platform applies web development, algorithm deployment and backend development to build the IoT-MTDPS system.
7. The internet of things multi-temperature co-distribution platform according to claim 6, wherein: the IoT-MTDPS system includes a login, a company-oriented functional module, a driver-oriented functional module, and a user-oriented functional module;
the company-oriented functional module comprises order classification collection, logistics information signboards, vehicle driver information, locker distribution and locker information control;
the driver-oriented functional module comprises personal order summarization, a driver information signboard and in-vehicle real-time monitoring;
the user-oriented function module comprises short message service and opinion feedback.
8. The internet of things multi-temperature co-distribution platform according to claim 1, wherein: the system further comprises a transportation path optimizing unit, wherein the transportation path optimizing unit determines an optimizing target of the transportation path of the combined delivery vehicle, and builds a constraint condition based on customer satisfaction degree considering time demand sensitivity and combined delivery cost, and builds a combined delivery vehicle transportation path optimizing model based on maximization of the customer satisfaction degree and minimization of the combined delivery cost.
9. The internet of things multi-temperature co-distribution platform of claim 8, wherein: the optimization objective of the joint delivery vehicle transportation path comprises maximization of customer satisfaction and minimization of joint delivery cost, and the objective function of the maximization of customer satisfaction is expressed by the following formula:
Figure FDA0004057782550000021
wherein mu i (t i ) Representing a customer satisfaction function that considers time demand sensitivity;
the objective function of minimizing the joint distribution cost is expressed by the following formula:
Figure FDA0004057782550000031
wherein TC is ij To combine the total delivery costs of delivery vehicles from customer i to customer j, TC ij ＝TC 1 +TC 2ij +TC 3ij +TC 4ij ，TC 1 Indicating fixed cost of departure of vehicle, TC 2ij Representing fuel consumption costs of vehicles from customer i to customer j, TC 3ij Representing penalty cost of vehicle from customer i to customer j, TC 4ij Representing the cooling cost of the vehicle from customer i to customer j;
Figure FDA0004057782550000032
10. the internet of things multi-temperature co-distribution platform according to claim 9, wherein: the customer satisfaction function mu taking into account the time demand sensitivity i (t i ) Expressed by the following formula:
Figure FDA0004057782550000033
in the above, t i For vehicle arrival time [ ET ] i d ，ET i ]Represents the range of early arrival times acceptable to the client, [ LT ] i ，LT i d ]Represents the range of deferred arrival times that a client can accept lambda i For the time demand sensitivity of client i, 0 < lambda i ＜1；
Cost of fuel consumption TC of the vehicle from customer i to customer j 2ij Expressed by the following formula:
Figure FDA0004057782550000034
wherein c is the unit fuel consumption cost, FR is the fuel consumption rate function,
Figure FDA0004057782550000035
Figure FDA0004057782550000036
is the mass ratio of fuel to air, gamma is the engine friction factor, N is the engine speed, V s For engine displacement, P ij For the total traction of the vehicle, eta is the efficiency parameter of the engine, d ij Mu is the heat value of the fuel, v, for the distance between client i and client j ij For vehicle speed +.>
Figure FDA0004057782550000041
Is a unit conversion coefficient;
penalty cost TC of the vehicle from customer i to customer j 3ij Expressed by the following formula:
TC 3ij ＝TC' 3ij +TC” 3ij
wherein, TC' 3ij For time penalty cost, the following formula is used:
TC' 3ij ＝p i (t i )＝c 1 max((ET i -t i ),0)+c 2 max((t i -LT i ),0)
in the above, p i (t i ) Representing a time penalty cost function, employingThe formula is:
Figure FDA0004057782550000042
in the above, t i For vehicle arrival time [ ET ] i d ，ET i ]Represents the range of early arrival times acceptable to the client, [ LT ] i ，LT i d ]Representing a deferred arrival time range acceptable to the client c 1 Penalty cost per unit time for vehicle arrival ahead, c 2 Punishment cost per unit time generated by deferring arrival of the vehicle, wherein W is the maximum punishment cost per unit time;
TC” 3ij for the cost of goods loss, the following formula is adopted:
TC” 3ij ＝p·θ·q j
in the above formula, p is a punishment coefficient, θ is a cargo loss ratio per unit weight, q j The weight of the cargo required for customer j;
refrigeration cost TC of the vehicle from customer i to customer j 4ij Expressed by the following formula:
TC 4ij ＝TC' 4ij +TC” 4ij
wherein, TC' 4ij For the refrigeration cost in the driving process, the following formula is adopted for expression:
TC' 4ij ＝c 3 ·G t ·t ij ＝c 3 ·((1+φ)·R·S·ΔT)·t ij
in the above, c 3 Refrigeration cost per unit heat load, G t G is the thermal load of the carriage t = (1+phi) ·r·s·Δt, phi is the cabin degradation degree coefficient, R is the thermal conductivity, S is the vehicle body surface area, Δt is the temperature difference between the cabin exterior temperature and the cabin interior temperature, T ij Is the delivery time from client i to client j;
TC” 4ij for the refrigeration cost when opening the door, the following formula is adopted:
Figure FDA0004057782550000051
in the above, c 3 Refrigeration cost per unit heat load, Q s The delta T is the temperature difference between the outside temperature of the carriage and the inside temperature of the carriage, and T is the heat load generated by the unit temperature difference of unit time i s Is the door opening time.
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Cited By (1)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN117709830A (en) * 2024-02-05 2024-03-15 南京迅集科技有限公司 Intelligent supply chain management system and method realized by artificial intelligence and Internet of things technology

Cited By (2)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN117709830A (en) * 2024-02-05 2024-03-15 南京迅集科技有限公司 Intelligent supply chain management system and method realized by artificial intelligence and Internet of things technology
CN117709830B (en) * 2024-02-05 2024-04-16 南京迅集科技有限公司 Intelligent supply chain management system and method realized by artificial intelligence and Internet of things technology

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