CN116341907B - Food supply chain risk assessment system based on artificial intelligence - Google Patents
Food supply chain risk assessment system based on artificial intelligence Download PDFInfo
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- CN116341907B CN116341907B CN202310307005.8A CN202310307005A CN116341907B CN 116341907 B CN116341907 B CN 116341907B CN 202310307005 A CN202310307005 A CN 202310307005A CN 116341907 B CN116341907 B CN 116341907B
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- 235000013305 food Nutrition 0.000 title claims abstract description 74
- 238000013473 artificial intelligence Methods 0.000 title claims abstract description 13
- 238000012502 risk assessment Methods 0.000 title claims abstract description 13
- 238000000034 method Methods 0.000 claims abstract description 23
- 238000007726 management method Methods 0.000 description 6
- 230000006866 deterioration Effects 0.000 description 3
- 238000010586 diagram Methods 0.000 description 2
- 238000011065 in-situ storage Methods 0.000 description 2
- 241000282414 Homo sapiens Species 0.000 description 1
- 230000009286 beneficial effect Effects 0.000 description 1
- 230000007547 defect Effects 0.000 description 1
- 238000013439 planning Methods 0.000 description 1
- 230000004083 survival effect Effects 0.000 description 1
- 238000012384 transportation and delivery Methods 0.000 description 1
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
- G06Q10/00—Administration; Management
- G06Q10/06—Resources, workflows, human or project management; Enterprise or organisation planning; Enterprise or organisation modelling
- G06Q10/063—Operations research, analysis or management
- G06Q10/0635—Risk analysis of enterprise or organisation activities
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
- G06Q10/00—Administration; Management
- G06Q10/06—Resources, workflows, human or project management; Enterprise or organisation planning; Enterprise or organisation modelling
- G06Q10/063—Operations research, analysis or management
- G06Q10/0639—Performance analysis of employees; Performance analysis of enterprise or organisation operations
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- G06Q50/40—
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- Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
- Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
- Y02P—CLIMATE CHANGE MITIGATION TECHNOLOGIES IN THE PRODUCTION OR PROCESSING OF GOODS
- Y02P90/00—Enabling technologies with a potential contribution to greenhouse gas [GHG] emissions mitigation
- Y02P90/30—Computing systems specially adapted for manufacturing
Abstract
The invention provides a food supply chain risk assessment system based on artificial intelligence. The system comprises: the system comprises an environment acquisition module, a task management module and a central processing unit; the environment acquisition module is used for acquiring a plurality of food transportation data and sending the food transportation data to the processor; the task management module is used for acquiring a plurality of task data and sending the task data to the processor; the plurality of items of task data comprise food names, planned routes and real-time positions of transport vehicles; the central processing unit obtains a plurality of standard transportation data corresponding to the food according to the name of the food; the central processing unit obtains a plurality of route nodes according to the planned route; and when the transport vehicle arrives at a route node, according to the arrival time of the transport vehicle, combining a plurality of standard transport data, and evaluating the risk coefficient when the transport vehicle arrives at each route node. The invention solves the technical problem that the transportation risk is inconvenient to evaluate in the food supply process in the prior art.
Description
Technical Field
The invention relates to the technical field of food supply chains, in particular to a food supply chain risk assessment system based on artificial intelligence.
Background
The food is the basis for survival, the safety of the food and the healthy development of the food industry are the guarantee of continuous reproduction of human beings. For many years, food safety accidents frequently emerge, and become a focus of general attention of the society, and a spot is visible on the importance of food. Different from common commodities, the food has the natural attribute of perishable deterioration, is influenced by natural environments such as regions, seasons, climates and the like, and is directly related to the life safety and health of people.
During the process of food supply, the food needs to be transported between various levels of distribution sites by a transport vehicle until the food is delivered to the hands of consumers; food is easy to damage and deteriorate due to various reasons in the transportation process, but is not easy to find in time; the risk during transportation cannot be assessed.
Disclosure of Invention
Aiming at the defects existing in the prior art, the invention provides a food supply chain risk assessment system based on artificial intelligence, which solves the technical problem that the transportation risk is inconvenient to assess in the food supply process in the prior art.
According to the invention, an artificial intelligence-based food supply chain risk assessment system comprises:
the system comprises an environment acquisition module, a task management module and a central processing unit;
the environment acquisition module is used for acquiring a plurality of food transportation data and sending the food transportation data to the processor, wherein the plurality of food transportation data comprise in-vehicle temperature, in-vehicle humidity and in-vehicle vibration data;
the task management module is used for acquiring a plurality of task data and sending the task data to the processor; the plurality of items of task data comprise food names, planned routes and real-time positions of transport vehicles;
the central processing unit acquires a plurality of standard transportation data corresponding to the food according to the name of the food; the plurality of items of standard transportation data comprise standard transportation time, standard temperature, standard humidity and standard vibration data;
the central processing unit obtains a plurality of route nodes according to the planned route; and when the transport vehicle arrives at a route node, evaluating risk coefficients when the transport vehicle arrives at each route node according to a plurality of food transport data of the transport vehicle between the current route node and the upper route node and arrival time of the transport vehicle and combining a plurality of standard transport data.
Further, the method further comprises the following steps:
judging whether the arrival time exceeds a first set time or not when the transport vehicle arrives at the route node;
if so, evaluating risk coefficients when the transport vehicle reaches the current route node according to the timeout period and a plurality of out-of-limit data in a plurality of food transport data between the current route node and the last route node; the plurality of out-of-limit data comprise a temperature-exceeding duration, a humidity-exceeding duration and a vibration-exceeding duration;
if not, evaluating risk coefficients when the transport vehicle reaches the current route node according to a plurality of out-of-limit data in a plurality of food transport data between the current route node and the upper route node.
Further, evaluating a risk factor of the transport vehicle when reaching the route node comprises:
when the arrival time exceeds the first set time and does not exceed the standard transportation time, carrying out dimensionless treatment on the overtime time, the overtemperature time, the overtime time and the overtime time, and acquiring a risk coefficient according to the following formula:
FXXH＝β1*CHSH+β2*YWSH+β3*YSSH+β4*YZSH；
wherein FXXH is a risk factor; CHSH is the timeout period; YWSH is the warm-up time period; YSSH is the wet time period; YZSH is the vibration time length; β1, β2, β3, and β4 are all scaling factors;
when the arrival time does not exceed the first set time, after dimensionless processing is carried out on the overtemperature time, the overtemperature time and the overtemperature time, the risk coefficient is obtained according to the following formula:
FXXH＝γ1*YWSH+γ2*YSSH+γ3*YZSH；
wherein FXXH is a risk factor; YWSH is the warm-up time period; YSSH is the wet time period; YZSH is the vibration time length; γ1, γ2 and γ3 are all scaling factors.
Further, the method further comprises the following steps:
acquiring insurance probability corresponding to the arrival of the transport vehicle at each route node according to the risk coefficient corresponding to the arrival of the transport vehicle at each route node;
constructing a transportation event tree according to the front-back position relation of a plurality of route nodes;
and obtaining the supply risk rate of the food when the transport vehicle arrives at the destination according to the transport event tree.
Further, the central processing unit obtains a plurality of route nodes according to the planned route, including:
acquiring a plurality of running routes with the same departure place and destination as the planned route;
acquiring a running route with the best road condition and a running route with the shortest driving time from a plurality of running routes as alternative routes;
acquiring position information of an intersection communicated with an alternative route on the planned route;
and acquiring the position of the set distance before each intersection as a route node.
Further, acquiring the set distance includes:
obtaining speed limit data of roads corresponding to each road opening;
obtaining the highest running speed corresponding to the transport vehicle according to the speed limit data;
obtaining the longest operation time when a transport vehicle driver runs to an intersection;
and acquiring the set distance before each road junction according to the longest operation time and the highest running speed.
Further, the longest operation time includes: the transport vehicle passes through the lane at the furthest side of the intersection several times to change the operation time at the intersection.
Further, the method further comprises the following steps: when the arrival time of the transport vehicle to a route node exceeds the standard transport time, a reminding signal is generated and sent to a goods supply client, a goods receiving client and a transport client.
Further, when the risk coefficient of the transport vehicle at one of the route nodes is greater than the set risk coefficient, a reminding signal is generated and sent to the goods supply client, the goods receiving client and the transport client.
Compared with the prior art, the invention has the following beneficial effects:
the invention obtains a plurality of task data by setting a task management module, and the central processing unit obtains a plurality of route nodes according to a planned route; setting an environment acquisition module to acquire a plurality of food transportation data in the food transportation process, and uploading the food transportation data to a central processing unit when a transport vehicle arrives at a route node; and evaluating the transportation risk of the transportation vehicle when the current route node is the same as the current route node by comparing a plurality of items of transportation data with a plurality of items of standard transportation data in a one-to-one correspondence manner. The technical problem that transportation risks are inconvenient to evaluate in the food supply process in the prior art is solved.
Drawings
Fig. 1 is a schematic diagram of an embodiment of the present invention.
Fig. 2 is a diagram of steps in a method according to another embodiment of the present invention.
Detailed Description
The technical scheme of the invention is further described below with reference to the accompanying drawings and examples.
As shown in fig. 1-2, an artificial intelligence based food supply chain risk assessment system, the system comprising:
the system comprises an environment acquisition module, a task management module and a central processing unit;
the environment acquisition module is used for acquiring a plurality of food transportation data and sending the food transportation data to the processor, wherein the plurality of food transportation data comprise in-vehicle temperature, in-vehicle humidity and in-vehicle vibration data;
the task management module is used for acquiring a plurality of task data and sending the task data to the processor; the plurality of items of task data comprise food names, planned routes and real-time positions of transport vehicles;
the central processing unit acquires a plurality of standard transportation data corresponding to the food according to the name of the food; the plurality of items of standard transportation data comprise standard transportation time, standard temperature, standard humidity and standard vibration data;
the central processing unit obtains a plurality of route nodes according to the planned route; and when the transport vehicle arrives at a route node, evaluating risk coefficients when the transport vehicle arrives at each route node according to a plurality of food transport data of the transport vehicle between the current route node and the upper route node and arrival time of the transport vehicle and combining a plurality of standard transport data.
The implementation process of the embodiment comprises the following steps:
the environment acquisition module comprises a temperature sensor, a humidity sensor and a vibration sensor, wherein the temperature sensor is used for acquiring the temperature in the vehicle; the humidity sensor is used for collecting humidity in the vehicle, and the vibration sensor is used for collecting vibration data in the vehicle; the temperature sensors and the humidity sensors are distributed at all positions of a carriage of the transport vehicle; the temperature sensors collect temperatures every set period, and an average value of the temperatures collected by the temperature sensors is used as the temperature in the vehicle corresponding to the period; also, a plurality of humidity sensors are provided at each of the carriage of the transport vehicle; the humidity sensors collect humidity every set period, and an average value of the humidity collected by the humidity sensors is used as the humidity in the vehicle corresponding to the period.
In this embodiment, each time the transport vehicle arrives at the route node, it is determined whether the arrival time exceeds a first set time;
if so, evaluating risk coefficients when the transport vehicle reaches the current route node according to the timeout period and a plurality of out-of-limit data in a plurality of food transport data between the current route node and the last route node; the plurality of out-of-limit data comprise a temperature-exceeding duration, a humidity-exceeding duration and a vibration-exceeding duration;
if not, evaluating risk coefficients when the transport vehicle reaches the current route node according to a plurality of out-of-limit data in a plurality of food transport data between the current route node and the upper route node.
Wherein evaluating the risk factor of the transport vehicle when arriving at the route node comprises:
when the arrival time exceeds the first set time and does not exceed the standard transportation time, carrying out dimensionless treatment on the overtime time, the overtemperature time, the overtime time and the overtime time, and acquiring a risk coefficient according to the following formula:
FXXH＝β1*CHSH+β2*YWSH+β3*YSSH+β4*YZSH；
wherein FXXH is a risk factor; CHSH is the timeout period; YWSH is the warm-up time period; YSSH is the wet time period; YZSH is the vibration time length; β1, β2, β3, and β4 are all scaling factors;
when the arrival time does not exceed the first set time, after dimensionless processing is carried out on the overtemperature time, the overtemperature time and the overtemperature time, the risk coefficient is obtained according to the following formula:
FXXH＝γ1*YWSH+γ2*YSSH+γ3*YZSH；
wherein FXXH is a risk factor; YWSH is the warm-up time period; YSSH is the wet time period; YZSH is the vibration time length; γ1, γ2 and γ3 are all scaling factors.
In the embodiment, a task management module is arranged to acquire a plurality of task data, and a central processing unit acquires a plurality of route nodes according to a planned route; setting an environment acquisition module to acquire a plurality of food transportation data in the food transportation process, and uploading the food transportation data to a central processing unit when a transport vehicle arrives at a route node; and evaluating the transportation risk of the transportation vehicle when the current route node is the same as the current route node by comparing a plurality of items of transportation data with a plurality of items of standard transportation data in a one-to-one correspondence manner. The technical problem that transportation risks are inconvenient to evaluate in the food supply process in the prior art is solved.
Another embodiment of the present invention further comprises:
acquiring insurance probability corresponding to the arrival of the transport vehicle at each route node according to the risk coefficient corresponding to the arrival of the transport vehicle at each route node;
constructing a transportation event tree according to the front-back position relation of a plurality of route nodes;
and obtaining the supply risk rate of the food when the transport vehicle arrives at the destination according to the transport event tree.
In this embodiment, the larger the risk coefficient, the smaller the insurance probability; when the risk coefficient is 0, namely, in the process from the previous route node to the current route node, no out-of-limit data is generated, and the insurance probability when the transport vehicle reaches the route node is 1; in the embodiment of the present market, a transportation event tree is constructed according to the number of route nodes; the video supply risk rate when the transport vehicle arrives at the destination can be obtained according to the transport event tree, and the probability of corresponding occurrence risk of the transport vehicle in each transport link; when the supply risk rate of the food when being supplied to the destination is higher than the set value, the receiver can select a corresponding solution according to the supply risk rate before receiving the goods, and the central processor can acquire an auxiliary solution according to the supply risk rate to assist the user in making decisions. .
In another embodiment of the present invention, the central processing unit obtains a plurality of route nodes according to a planned route, including:
acquiring a plurality of running routes with the same departure place and destination as the planned route;
acquiring a running route with the best road condition and a running route with the shortest driving time from a plurality of running routes as alternative routes;
acquiring position information of an intersection communicated with an alternative route on the planned route;
and acquiring the position of the set distance before each intersection as a route node.
The implementation process of the embodiment comprises the following steps:
in this embodiment, acquiring the set distance includes:
obtaining speed limit data of roads corresponding to each road opening;
obtaining the highest running speed corresponding to the transport vehicle according to the speed limit data;
obtaining the longest operation time when a transport vehicle driver runs to an intersection; the longest operation time includes: the operation time of the transport vehicle from the lane at the farthest side of the intersection to the intersection through a plurality of lane changes;
and acquiring the set distance before each road junction according to the longest operation time and the highest running speed.
In the transportation process, a plurality of lines exist between a departure place and a destination, one line is selected as a planning line, and a driving line with the best road condition and a driving line with the shortest driving time are obtained from the rest of other lines as alternative lines; a plurality of intersections for replacing the route exist between the planned route and the alternative route, and the position of the preset distance in front of the intersection is used as a route node, so that a driver of the transport vehicle can replace the route at the intersection in time.
Another embodiment of the present invention further comprises: when the arrival time of the transport vehicle to a route node exceeds the standard transport time, a reminding signal is generated and sent to a goods supply client, a goods receiving client and a transport client.
The implementation process of the embodiment comprises the following steps:
when the arrival time of the transport vehicle to a route node exceeds the standard transport time, the possibility of damage and deterioration of the food caused by untimely transport is greatly increased, and the damage result exceeds the acceptance degree of the receiving client. At this point, the current route can be selected to be driven out at the intersection, and the food on the transport vehicle can be processed in situ to reduce losses.
In another embodiment of the present invention, when the risk coefficient of the transport vehicle at one of the route nodes is greater than the set risk coefficient, a reminder is generated and sent to the delivery client, the receiving client and the transport client.
The implementation process of the embodiment comprises the following steps:
when the risk coefficient of the transport vehicle at one of the route nodes is larger than the set risk coefficient in the same transport process, the possibility of damage and deterioration of the food caused by the factors of the transport environment is greatly increased, and the damage result exceeds the acceptance degree of the receiving client. At this point, it may be selected to drive out the current route at the intersection, change the route of travel, or treat the food product in situ to reduce transportation losses.
The above embodiments are only for illustrating the technical method of the present invention and not for limiting the same, and it should be understood by those skilled in the art that the technical method of the present invention may be modified or substituted without departing from the spirit and scope of the technical method of the present invention.
Claims (6)
1. An artificial intelligence-based food supply chain risk assessment system, which is characterized in that: the system comprises:
the system comprises an environment acquisition module, a task management module and a central processing unit;
the environment acquisition module is used for acquiring a plurality of food transportation data and sending the food transportation data to the processor, wherein the plurality of food transportation data comprise in-vehicle temperature, in-vehicle humidity and in-vehicle vibration data;
the task management module is used for acquiring a plurality of task data and sending the task data to the processor; the plurality of items of task data comprise food names, planned routes and real-time positions of transport vehicles;
the central processing unit acquires a plurality of standard transportation data corresponding to the food according to the name of the food; the plurality of items of standard transportation data comprise standard transportation time, standard temperature, standard humidity and standard vibration data;
the central processing unit obtains a plurality of route nodes according to the planned route; when the transport vehicle arrives at a route node, according to a plurality of food transport data of the transport vehicle between the current route node and the upper route node and the arrival time of the transport vehicle, evaluating risk coefficients when the transport vehicle arrives at each route node by combining a plurality of standard transport data;
the central processing unit obtains a plurality of route nodes according to the planned route, and the method comprises the following steps: acquiring a plurality of running routes with the same departure place and destination as the planned route; acquiring a running route with the best road condition and a running route with the shortest driving time from a plurality of running routes as alternative routes; acquiring position information of an intersection communicated with an alternative route on the planned route; acquiring the position of a preset distance before each intersection as a route node;
acquiring a set distance, including: obtaining speed limit data of roads corresponding to each road opening; obtaining the highest running speed corresponding to the transport vehicle according to the speed limit data; obtaining the longest operation time when a transport vehicle driver runs to an intersection; acquiring a set distance before each road junction according to the longest operation time and the highest running speed; the maximum operating time includes: the transport vehicle passes through the lane at the furthest side of the intersection several times to change the operation time at the intersection.
2. An artificial intelligence based food supply chain risk assessment system according to claim 1, wherein: further comprises:
judging whether the arrival time exceeds a first set time or not when the transport vehicle arrives at the route node;
if so, evaluating risk coefficients when the transport vehicle reaches the current route node according to the timeout period and a plurality of out-of-limit data in a plurality of food transport data between the current route node and the last route node; the plurality of out-of-limit data comprise a temperature-exceeding duration, a humidity-exceeding duration and a vibration-exceeding duration;
if not, evaluating risk coefficients when the transport vehicle reaches the current route node according to a plurality of out-of-limit data in a plurality of food transport data between the current route node and the upper route node.
3. An artificial intelligence based food supply chain risk assessment system according to claim 2, wherein: assessing a risk factor of the transport vehicle when arriving at the route node, comprising:
when the arrival time exceeds the first set time and does not exceed the standard transportation time, carrying out dimensionless treatment on the overtime time, the overtemperature time, the overtime time and the overtime time, and acquiring a risk coefficient according to the following formula:
FXXH＝β1*CHSH+β2*YWSH+β3*YSSH+β4*YZSH；
wherein FXXH is a risk factor; CHSH is the timeout period; YWSH is the warm-up time period; YSSH is the wet time period; YZSH is the vibration time length; β1, β2, β3, and β4 are all scaling factors;
when the arrival time does not exceed the first set time, after dimensionless processing is carried out on the overtemperature time, the overtemperature time and the overtemperature time, the risk coefficient is obtained according to the following formula:
FXXH＝γ1*YWSH+γ2*YSSH+γ3*YZSH；
wherein FXXH is a risk factor; YWSH is the warm-up time period; YSSH is the wet time period; YZSH is the vibration time length; γ1, γ2 and γ3 are all scaling factors.
4. An artificial intelligence based food supply chain risk assessment system according to claim 1, wherein: further comprises:
acquiring insurance probability corresponding to the arrival of the transport vehicle at each route node according to the risk coefficient corresponding to the arrival of the transport vehicle at each route node;
constructing a transportation event tree according to the front-back position relation of a plurality of route nodes;
and obtaining the supply risk rate of the food when the transport vehicle arrives at the destination according to the transport event tree.
5. An artificial intelligence based food supply chain risk assessment system according to claim 1, wherein: further comprises: when the arrival time of the transport vehicle to a route node exceeds the standard transport time, a reminding signal is generated and sent to a goods supply client, a goods receiving client and a transport client.
6. An artificial intelligence based food supply chain risk assessment system according to claim 1, wherein: when the risk coefficient of the transport vehicle at one of the route nodes is larger than the set risk coefficient, a reminding signal is generated and sent to the goods supply client, the goods receiving client and the transport client.
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| CN117079220B (en) * | 2023-10-13 | 2023-12-26 | 凌雄技术(深圳)有限公司 | Supply chain intelligent supervision system and method based on Internet of things |
| CN117350624A (en) * | 2023-11-06 | 2024-01-05 | 太仓泽远供应链管理有限公司 | Logistics supply chain intelligent early warning system based on Internet of things |
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