Food transportation risk early warning method and device, equipment, storage medium and product

Abstract

The invention relates to the technical field of logistics transportation, and provides a food transportation risk early warning method, device and equipment, a storage medium and a product. The method comprises the steps of determining a first risk score based on compartment odor characteristics of a transport vehicle; determining a second risk score based on the historical parking area of the transport vehicle; based on the first risk score and the second risk score, performing weighted aggregation to obtain a food transport risk score of the transport vehicle; and carrying out risk early warning based on the food transportation risk score. Through the above mode, the influence of two factors of the carriage smell and the vehicle historical parking area is comprehensively considered in the risk early warning process, the category of goods transported by the transport vehicle can be accurately judged, accurate food transport risk early warning can be further carried out, the accuracy of risk early warning can be improved, dependence on manpower is not needed, and the risk early warning efficiency is improved. The influence of human subjective factors can be avoided, and potential food safety hazards can be avoided.

Description

Technical Field

[0001] This application relates to the field of logistics and transportation technology, and in particular to a method, device, equipment, storage medium, and product for early warning of food transportation risks. Background Technology

[0002] With the development of the logistics industry, the number of transport vehicles is increasing, but food transportation remains difficult to effectively regulate. Currently, there are still instances of non-food transport vehicles carrying food goods without being cleaned and disinfected. If vehicles that have transported industrial or chemical products (non-food goods) are used to transport food goods without being cleaned and disinfected, unpredictable food safety risks will arise.

[0003] In existing technologies, for vehicles that have transported non-food goods, drivers often need to avoid orders for food goods themselves, or cargo owners need to hire third-party testing agencies to inspect the vehicle before loading food goods to determine whether there are harmful substances in the cargo compartment that could affect food safety, in order to identify potential food transportation risks.

[0004] However, existing methods rely heavily on manual labor, making it difficult to avoid the influence of subjective human factors. This results in low accuracy of risk warnings and potential food safety hazards. Summary of the Invention

[0005] This application provides a food transportation risk warning method, device, equipment, storage medium, and product to solve the technical problems of existing methods being highly dependent on manual labor, difficult to avoid the influence of human subjective factors, having low accuracy in risk warning, and potentially posing food safety hazards.

[0006] In a first aspect, embodiments of this application provide an electronic device detection method, comprising: determining a first risk score based on the odor characteristics of the cargo compartment of a transport vehicle; determining a second risk score based on the historical stopping areas of the transport vehicle; performing weighted aggregation based on the first risk score and the second risk score to obtain a food transport risk score for the transport vehicle; and conducting a risk warning based on the food transport risk score.

[0007] In one embodiment, determining a first risk score based on the odor characteristics of the transport vehicle's compartment includes: collecting odor characteristics of the compartment using an electronic nose; classifying the gases in the compartment based on the odor characteristics to determine multiple gases and their corresponding gas concentration values; determining a first risk level based on each gas and its corresponding gas concentration value, as well as the risk coefficient of the cargo category corresponding to each gas; and determining a first risk score based on the first risk level and multiple historical first risk levels.

[0008] In one embodiment, determining a second risk score based on the historical stopping areas of transport vehicles includes: clustering the historical trajectory data of transport vehicles to determine multiple historical stopping areas of transport vehicles; determining the stopping company information and stopping industry information of transport vehicles based on the POI information of each historical stopping area; determining each historical cargo transportation event and the cargo category corresponding to each historical cargo transportation event based on the stopping company information and stopping industry information; determining a second risk level based on the risk coefficient of the cargo category corresponding to each historical cargo transportation event and the time weighting coefficient of each historical cargo transportation event; and determining a second risk score based on the second risk level.

[0009] In one embodiment, before determining the second risk score based on the historical stopping areas of the transport vehicle, the method further includes: obtaining the location information of the transport vehicle; determining the average travel distance of the transport vehicle based on the location information; determining whether the transport vehicle has stopped based on the average travel distance and a preset stopping point threshold; if it is determined that the transport vehicle has stopped, determining whether the goods of the transport vehicle are food products based on the cargo information of the transport vehicle; if the goods of the transport vehicle are food products, determining that a second risk score needs to be calculated.

[0010] In one embodiment, determining whether a transport vehicle has stopped based on the average travel distance and a preset stop point threshold includes: determining whether the average travel distance is less than the preset stop point threshold; if the average travel distance is less than the preset stop point threshold, then determining that the transport vehicle has stopped.

[0011] In one embodiment, risk warning is generated based on a food transportation risk score, including: determining whether the food transportation risk score is greater than a preset risk threshold; if the food transportation risk score is greater than the preset risk threshold, a risk warning is generated and the risk warning information is sent to the transport vehicle.

[0012] Secondly, embodiments of this application provide a food transportation risk warning device, comprising: a first risk score determination module, used to determine a first risk score based on the odor characteristics of the transport vehicle's cargo compartment; a second risk score determination module, used to determine a second risk score based on the historical stopping areas of the transport vehicle; a comprehensive risk score determination module, used to perform weighted aggregation based on the first risk score and the second risk score to obtain a food transportation risk score for the transport vehicle; and a risk warning module, used to issue a risk warning based on the food transportation risk score.

[0013] Thirdly, embodiments of this application provide an electronic device, including a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the computer program, it implements any of the above-described food transportation risk warning methods.

[0014] Fourthly, embodiments of this application provide a non-transitory computer-readable storage medium storing a computer program thereon, which, when executed by a processor, implements any of the above-described food transportation risk warning methods.

[0015] Fifthly, embodiments of this application provide a computer program product, including a computer program that, when executed by a processor, implements any of the above-described food transportation risk warning methods.

[0016] The food transportation risk warning method, apparatus, equipment, storage medium, and product provided in this application first determine a first risk score based on the odor characteristics of the transport vehicle's cargo compartment, then determine a second risk score based on the historical stopping areas of the transport vehicle, and finally determine the food transportation risk score of the transport vehicle based on the first and second risk scores, thereby conducting risk warning. Through this method, since the cargo compartment odor and the vehicle's historical stopping areas are related to the type of goods transported by the transport vehicle, and the type of goods transported by the transport vehicle is related to the food transportation risk, comprehensively considering the influence of both factors during the risk warning process is beneficial for accurately determining the type of goods transported by the transport vehicle, thus facilitating accurate food transportation risk warning. This improves the accuracy of risk warning, eliminates reliance on manual intervention, avoids the influence of subjective human factors, and prevents potential food safety hazards. Attached Figure Description

[0017] To more clearly illustrate the technical solutions in this application or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are some embodiments of this application. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0018] Figure 1 This is one of the flowcharts illustrating the food transportation risk warning method provided in the embodiments of this application.

[0019] Figure 2 This is a schematic diagram of the structure of the vehicle transport food risk warning system provided in the embodiments of this application.

[0020] Figure 3 This is the second flowchart illustrating the food transportation risk warning method provided in the embodiments of this application.

[0021] Figure 4 This is a schematic diagram of the structure of the food transportation risk warning device provided in the embodiments of this application.

[0022] Figure 5 This is a schematic diagram of the structure of the electronic device provided in the embodiments of this application. Detailed Implementation

[0023] To make the objectives, technical solutions, and advantages of this application clearer, the technical solutions of this application will be clearly and completely described below with reference to the accompanying drawings of the embodiments. Obviously, the described embodiments are only some embodiments of this application, not all embodiments. Based on the embodiments of this application, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this application.

[0024] Please see Figures 1 to 3 , Figure 1 This is one of the flowcharts illustrating the food transportation risk warning method provided in this application embodiment. Figure 2 This is a schematic diagram of the structure of the vehicle-transported food risk warning system provided in this application embodiment. Figure 3 This is the second flowchart illustrating the food transportation risk warning method provided in the embodiments of this application.

[0025] like Figure 1 and Figure 2 As shown, to address the issue of mixed use of food transport vehicles and non-food transport vehicles, this application proposes a food transport risk warning method and a vehicle transport food risk warning system. The food transport risk warning method is applied to the vehicle transport food risk warning system, which includes intelligent vehicle-mounted equipment (which can be installed as a terminal in the transport vehicle), a vehicle management module, an equipment management module, an alarm system, a stop point calculation module, a map POI data module, an electronic nose system, a spatial clustering module (for clustering historical trajectories), a fusion risk assessment module, an odor influencing factor calculation module, and a historical loading location factor calculation module. The map POI data module's functions include, but are not limited to, acquiring data from map providers and acquiring data from big data analysis; the intelligent vehicle-mounted equipment includes a high-precision positioning module, an alarm module, an RFID (Radio Frequency Identification) module, a communication module, and a gas sensor. Map POI (Point of Interest) data typically includes four aspects of information: name, category, longitude, and latitude. In electronic maps and geographic information systems (GIS), POI data or POI information generally refers to point-type data on a map, which basically includes four attributes: name, address, coordinates, and category.

[0026] Specifically, such as Figure 1 As shown, the food transportation risk warning method includes steps S110 to S140, and the specific steps are as follows: S110: Determine the first risk score based on the odor characteristics of the transport vehicle's compartment.

[0027] Understandably, such as Figure 3 As shown, before issuing risk warnings, data preparation and processing are required: POI information is obtained using the map POI data module. POI information includes, but is not limited to, data obtained from map providers, data obtained through big data analysis, etc., or data can be directly accessed using the map provider's API. POI information includes company name, company service type, and other company and industry information. The vehicle management module and equipment management module are used to input the license plate number, vehicle information, equipment information, etc. of the transport vehicles. Model training is performed to enable the electronic nose system to distinguish odor categories and perform concentration detection. A risk coefficient dictionary is prepared, with different risk coefficients preset for different types of goods.

[0028] After data preparation is completed, the electronic nose system is used to monitor the cargo compartment of the transport vehicle at fixed times and frequencies, and to calculate the risk factor impact score of the transported food (i.e., the first risk score) in real time.

[0029] Specifically, the electronic nose system can collect the odor characteristics of the passenger compartment of a transport vehicle through gas sensor arrays, data acquisition circuits, and other components on intelligent in-vehicle equipment.

[0030] Furthermore, the odor characteristics of the vehicle compartment are input into a support vector machine model to classify the gases inside the compartment and identify various gases, such as food-related gases, chemical gases, and industrial gases. Based on prior knowledge of the categories, decision tree regression is performed on the collected odor characteristics of the vehicle compartment to determine the gas concentration value C for each gas.

[0031] Furthermore, obtain the risk coefficient for the cargo category corresponding to each gas. (For example, the cargo category corresponding to food-grade gases is food, and the risk factor for food-grade gases is...) Similarly, the cargo category corresponding to chemical gases is chemical, and the risk factor for chemical cargo is... Based on each gas and its corresponding gas concentration, as well as the risk coefficient of the cargo category corresponding to each gas, the risk level of this data collection is calculated to determine the first risk level of the transport vehicle for this data collection. .

[0032] Among them, the first risk level The calculation formula is as follows: ; in, The transport vehicle was classified as having the highest risk level in this incident. This represents the total number of gas categories collected in this study. Indicates the first Risk coefficient of the cargo category corresponding to the gas; Indicates the first The gas concentration value corresponding to each gas.

[0033] Furthermore, based on the first risk level and multiple historical first risk levels, a first risk score is determined. .

[0034] In this embodiment, the final first risk score needs to be calculated using the first risk level. First risk score The calculation formula is as follows: ; in, This represents the total number of data collections at the highest historical risk level. For the first This collection represents the highest historical risk level. It is a parameter used to control the decay rate; Indicates the current time; Indicates the first The time of each collection; It is a natural constant.

[0035] Among them, the transport vehicles were classified as the first-risk category. This can be understood as the first This is the highest risk level in history for this collection.

[0036] S120: Determine the second risk score based on the historical stopping areas of transport vehicles.

[0037] By analyzing the historical stopping points of transport vehicles (i.e., historical stopping areas), a risk score can be calculated based on the historical loading location factors affecting the risk of transporting food by truck (i.e., the second risk score).

[0038] Specifically, historical trajectory data of transport vehicles is obtained; after cleaning the historical trajectory data, the DBSCAN algorithm is used to cluster the historical trajectory data and identify multiple cluster results. Each cluster consists of a series of points, which represent different frequent stopping areas of transport vehicles, i.e., multiple historical stopping areas of transport vehicles.

[0039] Furthermore, by querying map services based on these historical stop areas, POI information for each historical stop area is obtained. The POI information includes enterprise information, industry information, etc. Based on the POI information for each historical stop area, the enterprise information and industry information of the transport vehicle where it stopped are determined. Based on the enterprise information and industry information of the vehicle where it stopped, each historical cargo transport event and the cargo category corresponding to each historical cargo transport event are determined.

[0040] Generally, each stop of a transport vehicle can be considered as loading and unloading cargo in the corresponding stop area. Therefore, each historical stop area can be considered to correspond to a historical cargo transport event. Based on this, according to the POI information of each historical stop area, the enterprise information and industry information of each stop of the transport vehicle can be determined. At this time, the transport vehicle is likely loading and unloading cargo for the enterprise it stops at. The cargo categories of enterprises or industries are usually relatively fixed. Therefore, based on the enterprise information and industry information of the stop of the transport vehicle, each historical cargo transport event and the corresponding cargo category can be determined, such as food, chemicals, industrial products, etc.

[0041] Furthermore, obtain the risk coefficient of the cargo category corresponding to each historical cargo transportation event. (For example, the risk factor for food products is...) The risk factor for chemical products is Based on the risk coefficient of the cargo category corresponding to each historical cargo transportation event and the time-weighted coefficient of each historical cargo transportation event, the second risk level is determined. .

[0042] The formula for calculating the time weighting coefficient is as follows: ; in, Indicates the first The time-weighted coefficients for historical cargo transportation events are such that the more recent the historical cargo transportation event is, the greater its weighting importance. It is a natural constant; It is a parameter used to control the decay rate; Indicates the current time; Indicates the first The transportation time of a historical cargo transportation event.

[0043] By weighting each historical cargo transportation event, a second risk level can be obtained. Level 2 risk The calculation formula is as follows: ; in, This represents the total number of historical cargo transportation events, i.e., the total number of times cargo was transported by transport vehicles. For the first Risk coefficient of cargo category corresponding to each historical cargo transportation event; Indicates the first Time-weighted coefficients for each historical cargo transportation event.

[0044] Furthermore, based on the second risk level, a second risk score is determined. .

[0045] Among them, the second risk score The calculation formula is as follows: ; in, This represents the total number of historical cargo transportation events, i.e., the total number of times cargo was transported by transport vehicles. It is classified as the second risk level.

[0046] S130: Based on the first risk score and the second risk score, a weighted aggregation is performed to obtain the food transportation risk score of the transport vehicle.

[0047] Food transportation risk score for transport vehicles The calculation formula is as follows: ; in, Indicates the second risk score The corresponding weighting coefficients; Indicates the first risk score The corresponding weighting coefficients.

[0048] S140: Conduct risk warnings based on food transportation risk scores.

[0049] Specifically, assessing the risk score of food transportation. Is it greater than the preset risk threshold? .

[0050] If food transportation risk score Greater than the preset risk threshold If a risk warning is generated, risk warning information will be sent to the transport vehicle, such as by sending a text message to the cargo owner and the driver of the transport vehicle, and the information will be reported to the superior regulatory platform.

[0051] The food transportation risk warning method provided in this application first determines a first risk score based on the odor characteristics of the transport vehicle's cargo compartment, then determines a second risk score based on the historical stopping areas of the transport vehicle, and finally determines the food transportation risk score of the transport vehicle based on the first and second risk scores, thereby providing a risk warning. Through this method, since the cargo compartment odor and the vehicle's historical stopping areas are related to the type of goods transported by the transport vehicle, and the type of goods transported by the transport vehicle is related to the food transportation risk, comprehensively considering the influence of both factors during the risk warning process is beneficial for accurately determining the type of goods transported by the transport vehicle, thus facilitating accurate food transportation risk warnings. This improves the accuracy of risk warnings, eliminates reliance on manual intervention, avoids the influence of subjective human factors, and prevents potential food safety hazards.

[0052] In some embodiments, determining a first risk score based on the odor characteristics of the transport vehicle compartment includes: collecting odor characteristics of the compartment based on an electronic nose; classifying the gases in the compartment based on the odor characteristics to determine multiple gases and their corresponding gas concentration values; determining a first risk level based on each gas and its corresponding gas concentration value, as well as the risk coefficient of the cargo category corresponding to each gas; and determining a first risk score based on the first risk level and multiple historical first risk levels.

[0053] Specifically, the electronic nose system can collect the odor characteristics of the passenger compartment of a transport vehicle through gas sensor arrays, data acquisition circuits, and other components on intelligent in-vehicle equipment.

[0054] Furthermore, the odor characteristics of the vehicle compartment are input into a support vector machine model to classify the gases inside the compartment and identify various gases, such as food-related gases, chemical gases, and industrial gases. Based on prior knowledge of the categories, decision tree regression is performed on the collected odor characteristics of the vehicle compartment to determine the gas concentration value C for each gas.

[0055] Furthermore, obtain the risk coefficient for the cargo category corresponding to each gas. (For example, the cargo category corresponding to food-grade gases is food, and the risk factor for food-grade gases is...) Similarly, the cargo category corresponding to chemical gases is chemical, and the risk factor for chemical cargo is... Based on each gas and its corresponding gas concentration, as well as the risk coefficient of the cargo category corresponding to each gas, the risk level of this data collection is calculated to determine the first risk level of the transport vehicle for this data collection. .

[0056] Among them, the first risk level The calculation formula is as follows: ; in, The transport vehicle was classified as having the highest risk level in this incident. This represents the total number of gas categories collected in this study. Indicates the first Risk coefficient of the cargo category corresponding to the gas; Indicates the first The gas concentration value corresponding to each gas.

[0057] Furthermore, based on the first risk level and multiple historical first risk levels, a first risk score is determined. .

[0058] In this embodiment, the final first risk score needs to be calculated using the first risk level. First risk score The calculation formula is as follows: ; in, This represents the total number of data collections at the highest historical risk level. For the first This collection represents the highest historical risk level. It is a parameter used to control the decay rate; Indicates the current time; Indicates the first The time of each collection; It is a natural constant.

[0059] Among them, the transport vehicles were classified as the first-risk category. This can be understood as the first This is the highest risk level in history for this collection.

[0060] In some embodiments, determining a second risk score based on the historical stopping areas of transport vehicles includes: clustering the historical trajectory data of transport vehicles to determine multiple historical stopping areas of transport vehicles; determining the stopping company information and stopping industry information of transport vehicles based on the POI information of each historical stopping area; determining each historical cargo transportation event and the cargo category corresponding to each historical cargo transportation event based on the stopping company information and stopping industry information; determining a second risk level based on the risk coefficient of the cargo category corresponding to each historical cargo transportation event and the time weighting coefficient of each historical cargo transportation event; and determining a second risk score based on the second risk level.

[0061] By analyzing the historical stopping points of transport vehicles (i.e., historical stopping areas), a risk score can be calculated based on the historical loading location factors affecting the risk of transporting food by truck (i.e., the second risk score).

[0062] Specifically, historical trajectory data of transport vehicles is obtained; after cleaning the historical trajectory data, the DBSCAN algorithm is used to cluster the historical trajectory data and identify multiple cluster results. Each cluster consists of a series of points, which represent different frequent stopping areas of transport vehicles, i.e., multiple historical stopping areas of transport vehicles.

[0063] Furthermore, by querying map services based on these historical stop areas, POI information for each historical stop area is obtained. The POI information includes enterprise information, industry information, etc. Based on the POI information for each historical stop area, the enterprise information and industry information of the transport vehicle where it stopped are determined. Based on the enterprise information and industry information of the vehicle where it stopped, each historical cargo transport event and the cargo category corresponding to each historical cargo transport event are determined.

[0064] Generally, each stop of a transport vehicle can be considered as loading and unloading cargo in the corresponding stop area. Therefore, each historical stop area can be considered to correspond to a historical cargo transport event. Based on this, according to the POI information of each historical stop area, the enterprise information and industry information of each stop of the transport vehicle can be determined. At this time, the transport vehicle is likely loading and unloading cargo for the enterprise it stops at. The cargo categories of enterprises or industries are usually relatively fixed. Therefore, based on the enterprise information and industry information of the stop of the transport vehicle, each historical cargo transport event and the corresponding cargo category can be determined, such as food, chemicals, industrial products, etc.

[0065] Furthermore, obtain the risk coefficient of the cargo category corresponding to each historical cargo transportation event. (For example, the risk factor for food products is...) The risk factor for chemical products is Based on the risk coefficient of the cargo category corresponding to each historical cargo transportation event and the time-weighted coefficient of each historical cargo transportation event, the second risk level is determined. .

[0066] The formula for calculating the time weighting coefficient is as follows: ; in, Indicates the first The time-weighted coefficients for historical cargo transportation events are such that the more recent the historical cargo transportation event is, the greater its weighting importance. It is a natural constant; It is a parameter used to control the decay rate; Indicates the current time; Indicates the first The transportation time of a historical cargo transportation event.

[0067] By weighting each historical cargo transportation event, a second risk level can be obtained. Level 2 risk The calculation formula is as follows: ; in, This represents the total number of historical cargo transportation events, i.e., the total number of times cargo was transported by transport vehicles. For the first Risk coefficient of cargo category corresponding to each historical cargo transportation event; Indicates the first Time-weighted coefficients for each historical cargo transportation event.

[0068] Furthermore, based on the second risk level, a second risk score is determined. .

[0069] Among them, the second risk score The calculation formula is as follows: ; in, This represents the total number of historical cargo transportation events, i.e., the total number of times cargo was transported by transport vehicles. It is classified as the second risk level.

[0070] In some embodiments, before determining the second risk score based on the historical stopping areas of the transport vehicle, the method further includes: obtaining the location information of the transport vehicle; determining the average travel distance of the transport vehicle based on the location information; determining whether the transport vehicle has stopped based on the average travel distance and a preset stopping point threshold; if it is determined that the transport vehicle has stopped, determining whether the goods of the transport vehicle are food products based on the cargo information of the transport vehicle; if the goods of the transport vehicle are food products, determining that a second risk score needs to be calculated.

[0071] Intelligent vehicle-mounted devices can report the location information of transport vehicles to the platform at fixed times and frequencies, every [time period]. The vehicle reports its location to the platform every second (if the vehicle is off, the platform uses the same location point for calculation). The location message includes the vehicle's current location information, such as the latitude and longitude of the current location. Simultaneously, the platform stores the vehicle's previous location information, including the latitude and longitude of the previous location, and uses the Pythagorean theorem to calculate the distance between the vehicle's current location and the previous location. , The calculation formula is as follows: ; in, The radius of the Earth; It is the longitude of the previous location. It is the latitude of the previous location; It is the longitude of the current location. It is the latitude of the current location.

[0072] Specifically, after obtaining the location information of the transport vehicles, the average distance traveled by the transport vehicles can be determined based on the location information. .

[0073] Assuming the platform has already received For each location message, the average distance traveled by the transport vehicle is... The calculation formula is as follows: ; Among them, intelligent vehicle equipment every The location of the transport vehicle is reported to the platform once per second. Indicates the number of transport vehicles The distance between each location point and the previous location point.

[0074] Furthermore, based on the average travel distance and the preset stop point threshold, it is determined whether the transport vehicle stops.

[0075] Specifically, determining the average distance traveled. Is it less than the preset dwell point threshold? .

[0076] If the average distance traveled Less than the preset dwell point threshold If so, it can be determined that the transport vehicle has stopped.

[0077] If it is determined that the transport vehicle is stopped, it is determined that the transport vehicle is loading or unloading. At this time, the cargo information of the transport vehicle can be obtained through the RFID identification module, and it can be determined whether the cargo of the transport vehicle is food based on the cargo information.

[0078] If the goods transported by the vehicle are food products, then a second risk score needs to be calculated.

[0079] The food transportation risk warning method provided in this application only calculates the subsequent risk score when the goods in the transport vehicle are food products, and does not calculate the subsequent risk score when the goods in the transport vehicle are non-food products, thus avoiding invalid calculations.

[0080] In some embodiments, determining whether a transport vehicle has stopped based on the average travel distance and a preset stop point threshold includes: determining whether the average travel distance is less than the preset stop point threshold; if the average travel distance is less than the preset stop point threshold, then determining that the transport vehicle has stopped.

[0081] In some embodiments, risk warning is generated based on food transportation risk scores, including: determining whether the food transportation risk score is greater than a preset risk threshold; if the food transportation risk score is greater than the preset risk threshold, a risk warning is generated and the risk warning information is sent to the transport vehicle.

[0082] Specifically, assessing the risk score of food transportation. Is it greater than the preset risk threshold? .

[0083] If food transportation risk score Greater than the preset risk threshold If a risk warning is generated, risk warning information will be sent to the transport vehicle, such as by sending a text message to the cargo owner and the driver of the transport vehicle, and the information will be reported to the superior regulatory platform.

[0084] The food transportation risk warning method provided in this application reduces labor costs and improves the accuracy of risk warning compared with the prior art, which can effectively reduce food safety risks and help protect people's health.

[0085] This application also provides a food transportation risk warning device. Please refer to [link / reference]. Figure 4 , Figure 4 This is a schematic diagram of the structure of the food transportation risk warning device provided in this application embodiment. In this application embodiment, the food transportation risk warning device includes a first risk score determination module 410, a second risk score determination module 420, a comprehensive risk score determination module 430, and a risk warning module 440.

[0086] The first risk score determination module 410 is used to determine the first risk score based on the odor characteristics of the transport vehicle's compartment.

[0087] The second risk score determination module 420 is used to determine the second risk score based on the historical stopping areas of the transport vehicle.

[0088] The comprehensive risk score determination module 430 is used to perform weighted aggregation based on the first risk score and the second risk score to obtain the food transportation risk score of the transport vehicle.

[0089] The risk warning module 440 is used to issue risk warnings based on food transportation risk scores.

[0090] In some embodiments, the first risk score determination module 410 is used to collect the odor characteristics of the carriage based on an electronic nose; classify the gases in the carriage based on the odor characteristics, determine multiple gases and the gas concentration value corresponding to each gas; determine a first risk level based on each gas and the gas concentration value corresponding to each gas, as well as the risk coefficient of the cargo category corresponding to each gas; and determine a first risk score based on the first risk level and multiple historical first risk levels.

[0091] In some embodiments, the second risk score determination module 420 is used to cluster the historical trajectory data of the transport vehicles to determine multiple historical stopping areas of the transport vehicles; based on the POI information of each historical stopping area, determine the stopping enterprise information and stopping industry information of the transport vehicles; based on the stopping enterprise information and stopping industry information, determine each historical cargo transport event and the cargo category corresponding to each historical cargo transport event; based on the risk coefficient of the cargo category corresponding to each historical cargo transport event and the time weighting coefficient of each historical cargo transport event, determine a second risk level; and based on the second risk level, determine a second risk score.

[0092] In some embodiments, the second risk score determination module 420 is used to obtain the location information of the transport vehicle; determine the average travel distance of the transport vehicle based on the location information; determine whether the transport vehicle stops based on the average travel distance and a preset stop point threshold; if it is determined that the transport vehicle stops, determine whether the goods of the transport vehicle are food products based on the cargo information of the transport vehicle; if the goods of the transport vehicle are food products, determine that a second risk score needs to be calculated.

[0093] In some embodiments, the second risk scoring determination module 420 is used to determine whether the average travel distance is less than a preset stop point threshold; if the average travel distance is less than the preset stop point threshold, it is determined that the transport vehicle has stopped.

[0094] In some embodiments, the risk warning module 440 is used to determine whether the food transportation risk score is greater than a preset risk threshold; if the food transportation risk score is greater than the preset risk threshold, a risk warning is generated and the risk warning information is sent to the transport vehicle.

[0095] This application also provides an electronic device. Figure 5 This is a schematic diagram of the structure of the electronic device provided in the embodiments of this application, such as... Figure 5As shown, the electronic device may include a processor 510, a communications interface 520, a memory 530, and a communication bus 540. The processor 510, communications interface 520, and memory 530 communicate with each other via the communication bus 540. The processor 510 can call logical instructions from the memory 530 to execute a food transportation risk warning method.

[0096] Furthermore, the logical instructions in the aforementioned memory 530 can be implemented as software functional units and, when sold or used as independent products, can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention, or the part that contributes to the prior art, or a part of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods described in the various embodiments of the present invention. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.

[0097] This application also provides a non-transitory computer-readable storage medium storing a computer program thereon, which, when executed by a processor, implements the food transportation risk warning method provided by the above methods.

[0098] This application also provides a computer program product, which includes a computer program that can be stored on a non-transitory computer-readable storage medium. When the computer program is executed by a processor, the computer can execute the food transportation risk warning method provided by the above methods.

[0099] The device embodiments described above are merely illustrative. The units described as separate components may or may not be physically separate. The components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the modules can be selected to achieve the purpose of this embodiment according to actual needs. Those skilled in the art can understand and implement this without any creative effort.

[0100] Through the above description of the embodiments, those skilled in the art can clearly understand that each embodiment can be implemented by means of software plus necessary general-purpose hardware platforms, and of course, it can also be implemented by hardware. Based on this understanding, the above technical solutions, in essence or the part that contributes to the prior art, can be embodied in the form of a software product. This computer software product can be stored in a computer-readable storage medium, such as ROM / RAM, magnetic disk, optical disk, etc., and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute the methods described in the various embodiments or some parts of the embodiments.

[0101] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, and not to limit them; although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features; and these modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of the present invention.

Claims

1. A method for early warning of food transportation risks, characterized in that, include: The first risk score is determined based on the odor characteristics of the transport vehicle's compartment; A second risk score is determined based on the historical stopping areas of the transport vehicles; Based on the first risk score and the second risk score, a weighted aggregation is performed to obtain the food transportation risk score of the transport vehicle. Risk warnings are issued based on the aforementioned food transportation risk score.

2. The food transportation risk early warning method according to claim 1, characterized in that, The determination of the first risk score based on the odor characteristics of the transport vehicle's compartment includes: Based on an electronic nose, the odor characteristics of the carriage were collected; Based on the odor characteristics of the carriage, the gases inside the carriage are classified, and the various gases and their corresponding gas concentration values ​​are determined. Based on each gas and its corresponding gas concentration value, as well as the risk coefficient of the cargo category corresponding to each gas, a first risk level is determined; The first risk score is determined based on the first risk level and multiple historical first risk levels.

3. The food transportation risk early warning method according to claim 1, characterized in that, The determination of the second risk score based on the historical stopping areas of the transport vehicle includes: Cluster the historical trajectory data of the transport vehicles to determine multiple historical stopping areas of the transport vehicles; Based on the POI information of each historical stop area, determine the company information and industry information of the transport vehicle where it stops; Based on the information of the enterprise where the goods are staying and the information of the industry where the goods are staying, each historical cargo transportation event and the cargo category corresponding to each historical cargo transportation event are determined. The second risk level is determined based on the risk coefficient of the cargo category corresponding to each of the historical cargo transportation events and the time weighting coefficient of each of the historical cargo transportation events. Based on the second risk level, the second risk score is determined.

4. The food transportation risk early warning method according to claim 3, characterized in that, Before determining the second risk score based on the historical stopping areas of the transport vehicle, the method further includes: Obtain the location information of the transport vehicle; Based on the location information, the average travel distance of the transport vehicle is determined; Based on the average travel distance and the preset stop point threshold, it is determined whether the transport vehicle stops. If it is determined that the transport vehicle is stopped, then based on the cargo information of the transport vehicle, it is determined whether the cargo of the transport vehicle is food. If the goods transported by the vehicle are food products, then it is determined that the second risk score needs to be calculated.

5. The food transportation risk early warning method according to claim 4, characterized in that, The step of determining whether the transport vehicle stops based on the average travel distance and a preset stop point threshold includes: Determine whether the average moving distance is less than the preset stopping point threshold; If the average travel distance is less than the preset stop point threshold, then it is determined that the transport vehicle has stopped.

6. The food transportation risk early warning method according to claim 1, characterized in that, The risk warning based on the food transportation risk score includes: Determine whether the food transportation risk score is greater than a preset risk threshold; If the food transportation risk score is greater than the preset risk threshold, a risk warning is generated and the risk warning information is sent to the transport vehicle.

7. A food transportation risk early warning device, characterized in that, include: The first risk score determination module is used to determine the first risk score based on the odor characteristics of the transport vehicle's cargo compartment. The second risk score determination module is used to determine a second risk score based on the historical stopping areas of the transport vehicle. The comprehensive risk score determination module is used to perform weighted aggregation based on the first risk score and the second risk score to obtain the food transportation risk score of the transport vehicle. The risk warning module is used to issue risk warnings based on the food transportation risk score.

8. An electronic device comprising a memory, a processor, and a computer program stored in the memory and running on the processor, characterized in that, When the processor executes the computer program, it implements the food transportation risk warning method as described in any one of claims 1 to 6.

9. A non-transitory computer-readable storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by the processor, it implements the food transportation risk warning method as described in any one of claims 1 to 6.

10. A computer program product, comprising a computer program, characterized in that, When the computer program is executed by the processor, it implements the food transportation risk warning method as described in any one of claims 1 to 6.

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