Cold chain freight vehicle classification method based on travel task complexity

Abstract

The application belongs to the technical field of cold chain highway freight, and discloses a cold chain freight vehicle classification method based on travel task complexity. The method steps are as follows: collecting cold chain truck trajectory point data based on a Beidou satellite positioning system; pre-processing the collected cold chain truck trajectory point data; constructing an OD pair based on the pre-processed cold chain truck trajectory point data; constructing a cold chain freight vehicle travel chain based on the constructed OD pair; extracting travel chain features based on the constructed cold chain freight vehicle travel chain; obtaining a travel characteristic portrait of the cold chain freight vehicle based on the extracted travel chain features; and constructing a vehicle multi-task scheduling optimization model based on the obtained travel characteristic portrait of the cold chain freight vehicle. The application aims to provide support for fine management and operation optimization of cold chain logistics.

Description

Technical Field

[0001] This invention belongs to the field of cold chain road freight technology, specifically relating to a classification method for cold chain freight vehicles based on the complexity of their travel tasks. Background Technology

[0002] In recent years, with the continuous growth of cold chain freight business, cold chain logistics companies have an increasingly urgent need for optimizing transportation routes and rationally arranging vehicle scheduling; government departments have attached increasing importance to strengthening the supervision of cold chain logistics and ensuring the safety of transported goods; the widespread application of my country's Beidou satellite navigation system, with its advantages in precise positioning, short message service, and domestic independent research and development, has also provided reliable data support for the study of the travel characteristics of cold chain vehicles.

[0003] Currently, traditional cold chain freight research mainly focuses on facility site selection or energy consumption, lacking detailed descriptions of the dynamic travel behavior of vehicles. Most studies rely on GPS data, with relatively little analysis and application of BeiDou data. Much research focuses on general freight or specific types (such as container trucks), neglecting the refined and differentiated analysis of the travel chain characteristics, operational models, and influencing factors of cold chain freight, a niche sector with specific requirements for timeliness and temperature control. Cold chain vehicle travel is highly complex, involving not only diverse cargo types and strict temperature control requirements, but also nonlinear route planning, frequent multi-point deliveries, and irregular time rhythms such as morning and evening rush hours and nighttime travel. Furthermore, its operational behavior is influenced by multiple factors such as weather, urban traffic, cargo nature, and customer demand timeframes, resulting in highly dynamic and variable operating trajectories. These characteristics make the cold chain transportation travel chain highly heterogeneous, and traditional methods based on static OD (Origin-Destination) modeling or single parameters such as average speed are no longer sufficient to accurately identify its operational patterns. Summary of the Invention

[0004] To address the shortcomings of existing technologies, this invention processes BeiDou data to identify and construct key origin-destination (OD) pairs and travel chains. This enables the extraction of multi-dimensional feature indicators reflecting operational status and efficiency from the travel chains, thus creating a profile of cold chain truck travel and providing scientific basis and decision support for the refined management, operational optimization, and network resilience enhancement of cold chain logistics companies.

[0005] To achieve the above objectives, this invention provides a method for classifying cold chain freight vehicles based on the complexity of travel tasks, the steps of which are as follows:

[0006] Step S1: Collect trajectory point data of cold chain trucks based on the BeiDou satellite positioning system; preprocess the collected trajectory point data of cold chain trucks;

[0007] Step S2: Construct OD pairs based on the preprocessed cold chain truck trajectory point data;

[0008] Step S3: Construct the cold chain freight vehicle travel chain based on the constructed OD pair;

[0009] Step S4: Extract travel chain features based on the constructed cold chain freight vehicle travel chain;

[0010] Step S5: Based on the extracted travel chain features, obtain a travel characteristic profile of cold chain freight vehicles.

[0011] Step S6: Construct a vehicle multi-task scheduling optimization model based on the obtained travel characteristic profile of cold chain freight vehicles.

[0012] Furthermore, in step S1, the collected cold chain truck trajectory point data is preprocessed; a spatiotemporal deduplication algorithm is used for preprocessing; the specific steps are as follows:

[0013] Step S11: Standardize the time format;

[0014] Step S12: Based on the BeiDou coding, the collected cold chain truck trajectory point data are grouped, and duplicate data are identified using a set dynamic time window of 5 seconds.

[0015] Step S13: Based on the temperature inside the compartment, exclude missing data that has not been available for more than 30 consecutive minutes;

[0016] Step S14: Based on the dual verification mechanism of time window and spatial unique key, duplicate data is removed, and only the first record of each group in the dynamic time window is retained. Then, a triplet of "BeiDou number + time + latitude and longitude" is constructed as a unique index to remove duplicate data.

[0017] Step S15 introduces a spatial fence mechanism, establishing geographical boundary constraints covering the entire land area based on geographical boundary data; intercepting cross-border data, device location drift data, and contaminated data.

[0018] Furthermore, in step S2, OD pairs are constructed based on the preprocessed cold chain truck trajectory point data; the specific steps are as follows:

[0019] Step S21: Group the preprocessed cold chain truck trajectory point data according to Beidou coding, and sort them in ascending order by time field within each group;

[0020] Step S22: Convert latitude and longitude to radian values, calculate the spherical distance between adjacent trajectory points using the semi-sine formula, and calculate the speed in combination with the data collection time interval; when the speed exceeds the legal speed limit threshold, it is judged as abnormal data and removed. At the same time, the vehicle's motion status is divided into five categories according to the speed: stationary, low speed, normal driving, high speed driving, and speeding, and marked accordingly.

[0021] Step S23: Select stationary and low-speed trajectory points with speeds ≤ 5 km / h as candidate stopping points;

[0022] Step S24: Use a density-based spatial clustering algorithm with noise to process the data; determine a suitable distance radius threshold and minimum number of neighbor points through the silhouette coefficient to achieve clustering of candidate stopping points, remove isolated points and noise points, and extract well-structured and continuous clusters.

[0023] Step S25: When there are ≥2 clusters, extract the center point of each cluster as the docking point;

[0024] Step S26: Select stops with a stop time of ≥10 minutes, and extract the trajectory segments between two adjacent stops to construct OD pairs.

[0025] Furthermore, in step S3, a cold chain freight vehicle travel chain is constructed based on the constructed OD; the specific steps are as follows:

[0026] Step S31: Using the first OD pair of each vehicle as the starting point of the travel chain, initialize the travel chain object, and record the travel chain ID, vehicle number and the start and end time of the current trip to form a travel chain trip list.

[0027] Step S32: If the time interval between the current trip and the previous trip meets the continuity requirement and the spatial distance meets the proximity condition, the trip chain is extended. If the continuity requirement is not met, a new trip chain is initialized, and the complete trip chain set is finally output. The specific steps are as follows:

[0028] Step S321: Perform spatiotemporal correlation judgment through time interval constraints, that is, calculate the time interval between the end of the previous trip and the start of the current trip. If the time interval does not exceed 0.5 hours, the trip is considered to be continuous.

[0029] Step S322: When the time interval is ≤0.5 hours and the spatial distance meets the proximity condition of 0.001 kilometers, it is determined that the current trip belongs to the same trip chain as the last trip in the trip chain being constructed.

[0030] Furthermore, in step S4, travel chain features are extracted based on the constructed cold chain freight vehicle travel chain; the specific steps are as follows:

[0031] Based on the constructed cold chain freight vehicle travel chain, travel chain features are extracted from four aspects: basic features, efficiency-related features, time pattern features, and travel complexity features. Among them, basic features include the number of trips, total trip duration, and total trip mileage; efficiency features include average speed and number of trips per hour; time pattern features include departure time, morning and evening peak departure signs, and nighttime travel signs; and travel complexity features include temperature control level and temperature control stability.

[0032] Furthermore, in step S5, a travel characteristic profile of cold chain freight vehicles is obtained based on the extracted travel chain features; the specific steps are as follows:

[0033] Step S51: Robust standardization is used to standardize the travel chain features;

[0034] Step S52: Principal component analysis is used to reduce the dimensionality of the standardized travel chain features. Principal component analysis is set to retain 90% of the feature variance.

[0035] Step S53: Based on the dimensionality-reduced travel chain features, a density-based point ranking clustering algorithm is used for clustering. The noise points identified by the density-based point ranking clustering algorithm are treated as independent categories for analysis. The density-based point ranking clustering algorithm classifies the travel chain into three types: cluster 0, cluster 1, and a noise point category labeled -1. -1 is regarded as category one, cluster 0 as category two, and cluster 1 as category three. Among them, category one represents the fragmented and inefficient operation mode with the lowest transportation efficiency; category two represents the operation mode of high-frequency short-haul and multi-point delivery within the city; and category three represents the regional / intercity dedicated line transportation mode with the highest single-point direct transportation efficiency.

[0036] Furthermore, in step S6, a multi-task scheduling optimization model for vehicles is constructed based on the obtained travel characteristic profiles of cold chain freight vehicles; the specific steps are as follows:

[0037] Step S61: Construct a task complexity feature matrix based on the task complexity characteristics, and construct a vehicle complexity feature matrix based on the vehicle capability characteristics.

[0038] ;

[0039] in, This represents the task complexity feature matrix. Let g represent the complexity feature vector of the r-th task, where r = 1, 2, ..., g; g represents the maximum value of the task number.

[0040] The complexity characteristics of the task include path complexity, cargo temperature control requirements, and timeliness. Path complexity is measured by the task path type in the vehicle travel profile, i.e., multi-point delivery within the city or direct delivery to a single point. Cargo temperature control requirements are measured by the refrigeration temperature requirements of the cargo in the vehicle travel profile. Timeliness is measured by the latest allowed delivery time of the cargo in the vehicle travel profile.

[0041] ;

[0042] in, This represents the vehicle complexity feature matrix. Let f represent the complexity feature vector of the c-th vehicle, where c = 1, 2, ..., f; and f represents the maximum value of the vehicle number.

[0043] Vehicle capability characteristics include temperature control capability, transportation frequency, and task adaptability; among them, temperature control capability is measured by the lowest temperature of the vehicle's temperature control system in the vehicle's trip profile; transportation frequency is measured by the number of trips per hour in the vehicle's trip profile; and task adaptability is measured by the vehicle's trip time pattern in the vehicle's trip profile.

[0044] Step S62: Calculate the complexity matching degree between the task and the vehicle; the formula is as follows:

[0045] ;

[0046] in, Indicates task With vehicles The complexity of matching degree, The feature vector representing the complexity of the task. The feature vector representing the complexity of the vehicle;

[0047] Step S63: Set the constraints for the objective function, including time window constraints, temperature control matching constraints, capacity and load constraints, and complexity capability boundary constraints; the time window constraint formula is as follows:

[0048] ;

[0049] in, Indicates the earliest expected completion time of the task. Indicates the actual time taken to complete the task. Indicates the penalty time for task timeout;

[0050] Temperature control matching constraint, formula as follows:

[0051] ;

[0052] in, This indicates the lowest temperature inside the refrigerated freight truck compartment. This indicates the required refrigeration temperature for the task;

[0053] The capacity and load-bearing constraints are expressed by the following formula:

[0054] ;

[0055] in, The total number of tasks represents the set of tasks. For the task Volume; Indicates task The weight; Let be the decision variable, representing the task. Whether to assign to a vehicle If the task Assigned to vehicles ,but ,otherwise ; For vehicles Maximum volumetric capacity; Indicates vehicle Maximum load capacity;

[0056] The complexity capability boundary constraint is given by the following formula:

[0057] ;

[0058] in, For the task The complexity; This is the difference in complexity that the vehicle can withstand, that is, the maximum complexity that the vehicle can still bear after performing the current task and the tasks that have been assigned.

[0059] Step S64: Construct an objective function with complexity matching degree as weight, and use linear programming and heuristic algorithms to solve the task allocation scheme that minimizes the total cost under the premise of satisfying constraints;

[0060] The objective function is as follows:

[0061] ;

[0062] in, This indicates taking the minimum value. This indicates the total number of vehicles. Indicates task The transportation cost allocated to vehicle j; Indicates task With vehicles The complexity of the matching degree; These represent the weights of the matching degree and cost in the objective function, respectively.

[0063] Step S65: The task allocation scheme is used as the scheduling result after matching vehicles with tasks as input. Combined with real-time traffic data and road condition information, the optimal OD pair sequence route for each vehicle is calculated.

[0064] Step S66: Output the task list, optimal route, and estimated completion time for each vehicle.

[0065] The beneficial effects of this invention are:

[0066] 1. This invention integrates high-precision spatiotemporal data from the BeiDou Navigation Satellite System, identifies key stopping points through speed threshold segmentation, and refines OD pairs using the DBSCAN clustering algorithm. This effectively solves the unique timeliness requirements and dynamic stopping behavior issues in cold chain transportation, providing accurate analysis for understanding the specific characteristics of cold chain vehicles.

[0067] 2. This invention utilizes the OPTICS clustering algorithm to analyze the characteristics of the travel chain, which can clearly distinguish different operating modes of cold chain vehicles, transforming complex feature data into intuitive and differentiated vehicle operation profiles, revealing the characteristic differences of various operating modes, and providing a basis for decision-making for logistics companies in vehicle scheduling, route planning, and resource optimization. Attached Figure Description

[0068] Figure 1 This is a flowchart illustrating the present invention. Detailed Implementation

[0069] This embodiment provides a classification method for cold chain freight vehicles based on the complexity of travel tasks, such as... Figure 1 As shown, it includes the following steps:

[0070] Step S1: Collect trajectory point data of cold chain trucks based on the BeiDou satellite positioning system; preprocess the collected trajectory point data of cold chain trucks;

[0071] Step S2: Construct OD pairs based on the preprocessed cold chain truck trajectory point data;

[0072] Step S3: Construct the cold chain freight vehicle travel chain based on the constructed OD pair;

[0073] Step S4: Extract travel chain features based on the constructed cold chain freight vehicle travel chain;

[0074] Step S5: Based on the extracted travel chain features, obtain a travel characteristic profile of cold chain freight vehicles.

[0075] Step S6: Construct a vehicle multi-task scheduling optimization model based on the obtained travel characteristic profile of cold chain freight vehicles.

[0076] Furthermore, in step S1, the collected cold chain truck trajectory point data is preprocessed; a spatiotemporal deduplication algorithm is used for preprocessing; the specific steps are as follows:

[0077] Step S11: Standardize the time format;

[0078] Step S12: Based on the BeiDou coding, the collected cold chain truck trajectory point data are grouped, and duplicate data are identified using a set dynamic time window of 5 seconds.

[0079] Step S13: Based on the temperature inside the compartment, exclude missing data that has not been available for more than 30 consecutive minutes;

[0080] Step S14: Based on the dual verification mechanism of time window and spatial unique key, duplicate data is removed, and only the first record of each group in the dynamic time window is retained. Then, a triplet of "BeiDou number + time + latitude and longitude" is constructed as a unique index to remove duplicate data.

[0081] Step S15 introduces a spatial fence mechanism, establishing geographical boundary constraints covering the entire land area based on geographical boundary data; intercepting cross-border data, device location drift data, and contaminated data.

[0082] Furthermore, in step S2, OD pairs are constructed based on the preprocessed cold chain truck trajectory point data; the specific steps are as follows:

[0083] Step S21: Group the preprocessed cold chain truck trajectory point data according to Beidou coding, and sort them in ascending order by time field within each group;

[0084] Step S22: Convert latitude and longitude to radian values, calculate the spherical distance between adjacent trajectory points using the semi-sine formula, and calculate the speed in combination with the data collection time interval; when the speed exceeds the legal speed limit threshold, it is judged as abnormal data and removed. At the same time, the vehicle's motion status is divided into five categories according to the speed: stationary, low speed, normal driving, high speed driving, and speeding, and marked accordingly.

[0085] Step S23: Select stationary and low-speed trajectory points with speeds ≤ 5 km / h as candidate stopping points;

[0086] Step S24: Use a density-based spatial clustering algorithm with noise to process the data; determine a suitable distance radius threshold and minimum number of neighbor points through the silhouette coefficient to achieve clustering of candidate stopping points, remove isolated points and noise points, and extract well-structured and continuous clusters.

[0087] Step S25: When there are ≥2 clusters, extract the center point of each cluster as the docking point;

[0088] Step S26: Select stops with a stop time of ≥10 minutes, and extract the trajectory segments between two adjacent stops to construct OD pairs.

[0089] Furthermore, in step S3, a cold chain freight vehicle travel chain is constructed based on the constructed OD; the specific steps are as follows:

[0090] Step S31: Using the first OD pair of each vehicle as the starting point of the travel chain, initialize the travel chain object, and record the travel chain ID, vehicle number and the start and end time of the current trip to form a travel chain trip list.

[0091] Step S32: If the time interval between the current trip and the previous trip meets the continuity requirement and the spatial distance meets the proximity condition, the trip chain is extended. If the continuity requirement is not met, a new trip chain is initialized, and the complete trip chain set is finally output. The specific steps are as follows:

[0092] Step S321: Perform spatiotemporal correlation judgment through time interval constraints, that is, calculate the time interval between the end of the previous trip and the start of the current trip. If the time interval does not exceed 0.5 hours, the trip is considered to be continuous.

[0093] Step S322: When the time interval is ≤0.5 hours and the spatial distance meets the proximity condition of 0.001 kilometers, it is determined that the current trip belongs to the same trip chain as the last trip in the trip chain being constructed.

[0094] Furthermore, in step S4, travel chain features are extracted based on the constructed cold chain freight vehicle travel chain; the specific steps are as follows:

[0095] Based on the constructed cold chain freight vehicle travel chain, travel chain features are extracted from four aspects: basic features, efficiency-related features, time pattern features, and travel complexity features. Among them, basic features include the number of trips, total trip duration, and total trip mileage; efficiency features include average speed and number of trips per hour; time pattern features include departure time, morning and evening peak departure signs, and nighttime travel signs; and travel complexity features include temperature control level and temperature control stability.

[0096] Furthermore, in step S5, a travel characteristic profile of cold chain freight vehicles is obtained based on the extracted travel chain features; the specific steps are as follows:

[0097] Step S51: Robust standardization is used to standardize the travel chain features;

[0098] Step S52: Principal component analysis is used to reduce the dimensionality of the standardized travel chain features. Principal component analysis is set to retain 90% of the feature variance.

[0099] Step S53: Based on the dimensionality-reduced travel chain features, a density-based point ranking clustering algorithm is used for clustering. The noise points identified by the density-based point ranking clustering algorithm are treated as independent categories for analysis. The density-based point ranking clustering algorithm classifies the travel chain into three types: cluster 0, cluster 1, and a noise point category labeled -1. -1 is regarded as category one, cluster 0 as category two, and cluster 1 as category three. Among them, category one represents the fragmented and inefficient operation mode with the lowest transportation efficiency; category two represents the operation mode of high-frequency short-haul and multi-point delivery within the city; and category three represents the regional / intercity dedicated line transportation mode with the highest single-point direct transportation efficiency.

[0100] Furthermore, in step S6, a multi-task scheduling optimization model for vehicles is constructed based on the obtained travel characteristic profiles of cold chain freight vehicles; the specific steps are as follows:

[0101] Step S61: Construct a task complexity feature matrix based on the task complexity characteristics, and construct a vehicle complexity feature matrix based on the vehicle capability characteristics.

[0102] ;

[0103] in, This represents the task complexity feature matrix. Let g represent the complexity feature vector of the r-th task, where r = 1, 2, ..., g; g represents the maximum value of the task number.

[0104] The complexity characteristics of the task include path complexity, cargo temperature control requirements, and timeliness. Path complexity is measured by the task path type in the vehicle travel profile, i.e., multi-point delivery within the city or direct delivery to a single point. Cargo temperature control requirements are measured by the refrigeration temperature requirements of the cargo in the vehicle travel profile. Timeliness is measured by the latest allowed delivery time of the cargo in the vehicle travel profile.

[0105] ;

[0106] in, This represents the vehicle complexity feature matrix. Let f represent the complexity feature vector of the c-th vehicle, where c = 1, 2, ..., f; and f represents the maximum value of the vehicle number.

[0107] Vehicle capability characteristics include temperature control capability, transportation frequency, and task adaptability; among them, temperature control capability is measured by the lowest temperature of the vehicle's temperature control system in the vehicle's trip profile; transportation frequency is measured by the number of trips per hour in the vehicle's trip profile; and task adaptability is measured by the vehicle's trip time pattern in the vehicle's trip profile.

[0108] Step S62: Calculate the complexity matching degree between the task and the vehicle; the formula is as follows:

[0109] ;

[0110] in, Indicates task With vehicles The complexity of matching degree, The feature vector representing the complexity of the task. The feature vector representing the complexity of the vehicle;

[0111] Step S63: Set the constraints for the objective function, including time window constraints, temperature control matching constraints, capacity and load constraints, and complexity capability boundary constraints; the time window constraint formula is as follows:

[0112] ;

[0113] in, Indicates the earliest expected completion time of the task. Indicates the actual time taken to complete the task. Indicates the penalty time for task timeout;

[0114] Temperature control matching constraint, formula as follows:

[0115] ;

[0116] in, This indicates the lowest temperature inside the refrigerated freight truck compartment. This indicates the required refrigeration temperature for the task;

[0117] The capacity and load-bearing constraints are expressed by the following formula:

[0118] ;

[0119] in, The total number of tasks represents the set of tasks. For the task Volume; Indicates task The weight; Let be the decision variable, representing the task. Whether to assign to a vehicle If the task Assigned to vehicles ,but ,otherwise ; For vehicles Maximum volumetric capacity; Indicates vehicle Maximum load capacity;

[0120] The complexity capability boundary constraint is given by the following formula:

[0121] ;

[0122] in, For the task The complexity; This is the difference in complexity that the vehicle can withstand, that is, the maximum complexity that the vehicle can still bear after performing the current task and the tasks that have been assigned.

[0123] Step S64: Construct an objective function with complexity matching degree as weight, and use linear programming and heuristic algorithms to solve the task allocation scheme that minimizes the total cost under the premise of satisfying constraints;

[0124] The objective function is as follows:

[0125] ;

[0126] in, This indicates taking the minimum value. This indicates the total number of vehicles. Indicates task The transportation cost allocated to vehicle j; Indicates task With vehicles The complexity of the matching degree; These represent the weights of the matching degree and cost in the objective function, respectively.

[0127] Step S65: The task allocation scheme is used as the scheduling result after matching vehicles with tasks as input. Combined with real-time traffic data and road condition information, the optimal OD pair sequence route for each vehicle is calculated.

[0128] Step S66: Output the task list, optimal route, and estimated completion time for each vehicle.

[0129] In this process, latitude and longitude are converted into radians, and the spherical distance between adjacent trajectory points is calculated using the semi-versus formula, which is:

[0130] ;

[0131] in, Indicates speed, Represents the spherical distance between adjacent trajectory points. Indicates the time interval for data collection;

[0132] The spherical distance between adjacent trajectory points is calculated using the following formula:

[0133] ;

[0134] in, This indicates the difference in latitude and longitude between two points. Indicates latitude, 'c' represents longitude, and 'c' represents spherical distance. Represents the distance between two points on the Earth's surface. Indicates the Earth's radius;

[0135] Among them, for the travel chain Calculate the profile coefficient The formula is as follows:

[0136] ;

[0137] in, Indicates travel chain The cluster it belongs to, Indicates travel chain Other clusters that are not present Indicates the first The first travel chain and the first The distance between each travel chain; Indicates the first Travel chain to the same cluster The average distance of other travel chains within the chain; Indicates the first The travel chain to the nearest other cluster The average distance;

[0138] The formula for calculating the basic features is as follows:

[0139] ;

[0140] ;

[0141] ;

[0142] in, Let z represent the OD pairs of cold chain freight vehicles; This indicates the end time, i.e., the point in time when the trip is completed; This indicates the start time, i.e., the point in time when the trip begins; Indicates the total number of seconds; This represents the travel distance of a single OD pair; Indicates the total trip duration. Indicates the total travel distance. Indicates the number of trips;

[0143] The efficiency characteristics are calculated using the following formula:

[0144] ;

[0145] ;

[0146] in, Indicates average speed. Indicates the number of trips per hour;

[0147] The formula for calculating time characteristics is as follows:

[0148] ;

[0149] ;

[0150] ;

[0151] ;

[0152] Indicates the departure time in hours. A binary indicator indicating whether the departure was in the morning. A binary indicator indicating whether the departure is in the evening. A binary indicator representing whether or not nighttime travel occurs;

[0153] The formula for calculating travel complexity is as follows:

[0154] ;

[0155] It indicates the complexity of travel, reflecting the complexity of tasks and the diversity of routes during vehicle transportation;

[0156] Among them, robust standardization is used to standardize the travel chain features;

[0157] ;

[0158] in, This represents the characteristics of the travel chain after standardization. Indicates the characteristics of the travel chain. This represents the median of the input travel chain feature X. It represents the interquartile range, which is the difference between the 75th percentile and the 25th percentile of the data.

[0159] As shown in Table 1, based on the constructed cold chain freight vehicle travel chain, the travel chain features are extracted from four aspects: basic features, efficiency-related features, time pattern features, and travel complexity features. Among them, the basic features include the number of trips, the total trip duration, and the total trip mileage; the efficiency features include the average speed and the number of trips per hour; the time pattern features include the departure time (i.e., the number of hours of departure), the departure indicators for morning and evening peak hours, and the night travel indicators (i.e., whether the departure is in the morning, whether it is in the evening, and whether it is at night); and the travel complexity features include the temperature control level and the temperature control stability.

[0160] Table 1 Some features

[0161]

[0162] Although embodiments of the invention have been shown and described, it will be understood by those skilled in the art that various changes, modifications, substitutions and alterations can be made to these embodiments without departing from the principles and spirit of the invention, the scope of which is defined by the appended claims and their equivalents.

Claims

1. A classification method for cold chain freight vehicles based on the complexity of travel tasks, characterized in that, The methods include: Step S1: Collect trajectory point data of cold chain trucks based on the BeiDou satellite positioning system; preprocess the collected trajectory point data of cold chain trucks; Step S2: Construct OD pairs based on the preprocessed cold chain truck trajectory point data; Step S3: Construct the cold chain freight vehicle travel chain based on the constructed OD pair; Step S4: Extract travel chain features based on the constructed cold chain freight vehicle travel chain; Step S5: Based on the extracted travel chain features, obtain a travel characteristic profile of cold chain freight vehicles. Step S6: Construct a multi-task scheduling optimization model for vehicles based on the obtained travel characteristic profiles of cold chain freight vehicles; the specific steps are as follows: Step S61: Construct a task complexity feature matrix based on the task complexity characteristics, and construct a vehicle complexity feature matrix based on the vehicle capability characteristics. ； in, This represents the task complexity feature matrix. Let g represent the complexity feature vector of the r-th task, where r = 1, 2, ..., g; g represents the maximum value of the task number. The complexity characteristics of the task include path complexity, cargo temperature control requirements, and timeliness. Path complexity is measured by the task path type in the vehicle travel profile, i.e., multi-point delivery within the city or direct delivery to a single point. Cargo temperature control requirements are measured by the refrigeration temperature requirements of the cargo in the vehicle travel profile. Timeliness is measured by the latest allowed delivery time of the cargo in the vehicle travel profile. ； in, This represents the vehicle complexity feature matrix. Let f represent the complexity feature vector of the c-th vehicle, where c = 1, 2, ..., f; and f represents the maximum value of the vehicle number. Vehicle capability characteristics include temperature control capability, transportation frequency, and task adaptability; among them, temperature control capability is measured by the lowest temperature of the vehicle's temperature control system in the vehicle's trip profile; transportation frequency is measured by the number of trips per hour in the vehicle's trip profile; and task adaptability is measured by the vehicle's trip time pattern in the vehicle's trip profile. Step S62: Calculate the complexity matching degree between the task and the vehicle; the formula is as follows: ； in, Indicates task With vehicles The complexity of the matching degree; Step S63: Set the constraints for the objective function, including time window constraints, temperature control matching constraints, capacity and load constraints, and complexity capability boundary constraints; the time window constraint formula is as follows: ； in, Indicates the earliest expected completion time of the task. Indicates the actual time taken to complete the task. Indicates the penalty time for task timeout; Temperature control matching constraint, formula as follows: ； in, This indicates the lowest temperature inside the refrigerated freight truck compartment. This indicates the required refrigeration temperature for the task; The capacity and load-bearing constraints are expressed by the following formula: ； in, The total number of tasks represents the set of tasks. For the task Volume; Indicates task The weight; Let be the decision variable, representing the task. Whether to assign to a vehicle If the task Assigned to vehicles ,but ,otherwise ; For vehicles Maximum volumetric capacity; Indicates vehicle Maximum load capacity; The complexity capability boundary constraint is given by the following formula: ； in, For the task The complexity; This is the difference in complexity that the vehicle can withstand, that is, the maximum complexity that the vehicle can still bear after performing the current task and the tasks that have been assigned. Step S64: Construct an objective function with complexity matching degree as weight, and use linear programming and heuristic algorithms to solve the task allocation scheme that minimizes the total cost under the premise of satisfying constraints; The objective function is as follows: ; in, This indicates taking the minimum value. Indicates the total number of vehicles. Indicates task The transportation cost allocated to vehicle j; Indicates task With vehicles The complexity of the matching degree; These represent the weights of the matching degree and cost in the objective function, respectively. Step S65: The task allocation scheme is used as the scheduling result after matching vehicles with tasks as input. Combined with real-time traffic data and road condition information, the optimal OD pair sequence route for each vehicle is calculated. Step S66: Output the task list, optimal route, and estimated completion time for each vehicle.

2. The method for classifying cold chain freight vehicles based on the complexity of travel tasks according to claim 1, characterized in that, Step S1 involves preprocessing the collected cold chain truck trajectory point data; a spatiotemporal deduplication algorithm is used for preprocessing; the specific steps are as follows: Step S11: Standardize the time format; Step S12: Based on the BeiDou coding, the collected cold chain truck trajectory point data are grouped, and duplicate data are identified using a set dynamic time window of 5 seconds. Step S13: Based on the temperature inside the compartment, exclude missing data that has not been available for more than 30 consecutive minutes; Step S14: Based on the dual verification mechanism of time window and spatial unique key, duplicate data is removed, and only the first record of each group in the dynamic time window is retained. Then, a triplet of "BeiDou number + time + latitude and longitude" is constructed as a unique index to remove duplicate data. Step S15 introduces a spatial fence mechanism, establishing geographical boundary constraints covering the entire land area based on geographical boundary data; intercepting cross-border data, device location drift data, and contaminated data.

3. The method for classifying cold chain freight vehicles based on the complexity of travel tasks according to claim 2, characterized in that, In step S2, OD pairs are constructed based on the preprocessed cold chain truck trajectory point data; the specific steps are as follows: Step S21: Group the preprocessed cold chain truck trajectory point data according to Beidou coding, and sort them in ascending order by time field within each group; Step S22: Convert latitude and longitude to radian values, calculate the spherical distance between adjacent trajectory points using the semi-sine formula, and calculate the speed in combination with the data collection time interval; when the speed exceeds the legal speed limit threshold, it is judged as abnormal data and removed. At the same time, the vehicle's motion status is divided into five categories according to the speed: stationary, low speed, normal driving, high speed driving, and speeding, and marked accordingly. Step S23: Select stationary and low-speed trajectory points with speeds ≤ 5 km / h as candidate stopping points; Step S24: Use a density-based spatial clustering algorithm with noise to process the data; determine a suitable distance radius threshold and minimum number of neighbor points through the silhouette coefficient to achieve clustering of candidate stopping points, remove isolated points and noise points, and extract well-structured and continuous clusters. Step S25: When there are ≥2 clusters, extract the center point of each cluster as the docking point; Step S26: Select stops with a stop time of ≥10 minutes, and extract the trajectory segments between two adjacent stops to construct OD pairs.

4. The method for classifying cold chain freight vehicles based on the complexity of travel tasks according to claim 3, characterized in that, Step S3 involves constructing a cold chain freight vehicle travel chain based on the constructed OD pair; the specific steps are as follows: Step S31: Using the first OD pair of each vehicle as the starting point of the travel chain, initialize the travel chain object, and record the travel chain ID, vehicle number and the start and end time of the current trip to form a travel chain trip list. Step S32: If the time interval between the current trip and the previous trip meets the continuity requirement and the spatial distance meets the proximity condition, the trip chain is extended. If the continuity requirement is not met, a new trip chain is initialized, and the complete trip chain set is finally output. The specific steps are as follows: Step S321: Perform spatiotemporal correlation judgment through time interval constraints, that is, calculate the time interval between the end of the previous trip and the start of the current trip. If the time interval does not exceed 0.5 hours, the trip is considered to be continuous. Step S322: When the time interval is ≤0.5 hours and the spatial distance meets the proximity condition of 0.001 kilometers, it is determined that the current trip belongs to the same trip chain as the last trip in the trip chain being constructed.

5. The method for classifying cold chain freight vehicles based on the complexity of travel tasks according to claim 4, characterized in that, In step S4, travel chain features are extracted based on the constructed cold chain freight vehicle travel chain. The specific steps are as follows: Based on the constructed cold chain freight vehicle travel chain, travel chain features are extracted from four aspects: basic features, efficiency-related features, time pattern features, and travel complexity features. Among them, basic features include the number of trips, total trip duration, and total trip mileage; efficiency features include average speed and number of trips per hour; time pattern features include departure time, morning and evening peak departure signs, and nighttime travel signs; and travel complexity features include temperature control level and temperature control stability.

6. The method for classifying cold chain freight vehicles based on the complexity of travel tasks according to claim 5, characterized in that, Step S5 involves obtaining a travel characteristic profile of cold chain freight vehicles based on the extracted travel chain features; the specific steps are as follows: Step S51: Robust standardization is used to standardize the travel chain features; Step S52: Principal component analysis is used to reduce the dimensionality of the standardized travel chain features. Principal component analysis is set to retain 90% of the feature variance. Step S53: Based on the dimensionality-reduced travel chain features, a density-based point ranking clustering algorithm is used for clustering. The noise points identified by the density-based point ranking clustering algorithm are treated as independent categories for analysis. The density-based point ranking clustering algorithm classifies the travel chain into three types: cluster 0, cluster 1, and a noise point category labeled -1. -1 is regarded as category one, cluster 0 as category two, and cluster 1 as category three. Among them, category one represents the fragmented and inefficient operation mode with the lowest transportation efficiency; category two represents the operation mode of high-frequency short-haul and multi-point delivery within the city; and category three represents the regional / intercity dedicated line transportation mode with the highest single-point direct transportation efficiency.

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