A smart logistics logistics vehicle transport vehicle-road collaborative control system and method

Abstract

The present invention relates to the field of traffic control, and in particular to a smart logistics logistics vehicle-road collaborative management and control system and method. It includes: obtaining real-time road network status information data and real-time operation status information data of logistics vehicles, and pre-processing and fusing them to obtain integrated information data after fusion; based on the integrated information data after fusion, introducing a preliminary path optimization algorithm to obtain a preliminary optimal transportation path; collecting environmental monitoring information and quantifying it to obtain quantified environmental factors; further optimizing the preliminary optimal transportation path; generating a scheduling strategy based on the optimized optimal transportation path, quantified environmental factors and integrated comprehensive information data, generating a scheduling instruction based on the scheduling strategy, and sending the scheduling instruction to the logistics vehicle. It solves the technical problems of insufficient understanding and management of the complex interactions between logistics vehicles and road network status, as well as low transportation efficiency and adaptability in a constantly changing environment.

Description

Technical Field

[0001] The present invention relates to the field of traffic control, and in particular to a smart logistics logistics vehicle-road collaborative management and control system and method. Background Art

[0002] In traditional logistics and transportation management systems, the path planning and scheduling decisions of logistics vehicles often face many challenges. First, traditional methods often cannot accurately capture the road network status and the real-time operation status of vehicles in real time, resulting in a lack of flexibility and adaptability in path planning, and an inability to effectively respond to sudden changes in the road network, such as traffic congestion and accidents. In addition, environmental factors, such as bad weather and road construction, have a significant impact on the safety and efficiency of logistics and transportation, but are often ignored or not fully considered in many traditional logistics systems. At the same time, traditional logistics and transportation path planning mostly relies on simple models, such as the shortest path algorithm, which often only considers the distance factor and ignores dynamic information such as traffic flow, accident risk, and road condition changes, resulting in the planned path may not be the optimal choice in actual execution. Moreover, these methods rarely consider the impact of environmental changes on the transportation path, lack a flexible adjustment mechanism, and make it difficult for the logistics and transportation system to respond quickly when encountering unpredictable environmental changes.

[0003] However, the above technologies have at least the following technical problems: incomplete understanding and management of the complex interactions between logistics vehicles and road network status, and low transportation efficiency and adaptability in a changing environment. Summary of the invention

[0004] The present invention provides a smart logistics logistics vehicle-road collaborative management and control system and method to solve the technical problems of insufficient understanding and management of the complex interactions between logistics vehicles and road network status, as well as low transportation efficiency and adaptability in a constantly changing environment.

[0005] The present invention provides a smart logistics logistics vehicle transport vehicle-road collaborative control system and method, which specifically includes the following technical solutions:

[0006] A smart logistics logistics vehicle transport vehicle-road collaborative control method includes the following steps:

[0007] S1. Acquire real-time road network status information data and real-time operation status information data of logistics vehicles, and pre-process and fuse the real-time road network status information data and the real-time operation status information data of logistics vehicles to obtain fused comprehensive information data;

[0008] S2. Based on the integrated information data after fusion, a preliminary path optimization algorithm is introduced to model the integrated information data after fusion into a graph. ,in is a set of vertices, representing intersections or important nodes in the road network; is an edge set, representing a road segment; each edge ; and define the weight, indicating the passing road segment The cost of transporting goods; define a comprehensive cost function based on weights; use the comprehensive cost function as the criterion for path selection, and select the path with the lowest cost from all potential paths as the initial optimal transportation path for each logistics vehicle;

[0009] S3. Collect environmental monitoring information and quantify it to obtain quantified environmental factors; further optimize the preliminary optimal transportation path; generate a scheduling strategy based on the optimized optimal transportation path, quantified environmental factors and integrated comprehensive information data, generate scheduling instructions based on the scheduling strategy, and send the scheduling instructions to the logistics vehicles.

[0010] Preferably, the S1 specifically includes:

[0011] The real-time road network status information data and the real-time operation status information data of logistics vehicles are cleaned separately; then the cleaned real-time road network status information data and the real-time operation status information data of logistics vehicles are standardized; through the matching of time and space, the standardized real-time road network status information data and the real-time operation status information data of logistics vehicles are integrated together to create a comprehensive view with temporal and spatial consistency.

[0012] Preferably, the S1 further includes:

[0013] For time matching, the temporal correspondence between the real-time operation status information data of the logistics vehicles after standardization and the real-time road network status information data is determined to obtain the real-time operation status information data of the logistics vehicles after time matching; for space matching, the real-time operation status information data of the logistics vehicles after standardization and the real-time road network status information data are matched in geographical location to obtain the real-time road network status information data after space matching.

[0014] Preferably, the S1 further includes:

[0015] Integrate the real-time operation status information data of logistics vehicles after time matching and the real-time road network status information data after space matching, create a comprehensive view, and obtain fused comprehensive information data.

[0016] Preferably, the S3 specifically includes:

[0017] Collect environmental monitoring information in the park and external factors affecting logistics and transportation in real time, convert the environmental monitoring information into quantitative indicators, and obtain quantified environmental factors; evaluate the impact of quantified environmental factors and external factors affecting logistics and transportation on the preliminary optimal transportation path based on the impact assessment model; optimize the preliminary optimal transportation path based on the impact of quantified environmental factors and external factors affecting logistics and transportation on the preliminary optimal transportation path, and obtain the optimized optimal transportation path.

[0018] Preferably, the S3 further includes:

[0019] The specific implementation process of the optimized optimal transportation path is:

[0020] Firstly, based on the impact assessment of quantified environmental factors and external factors affecting logistics and transportation, the influencing indicators of the weights in the preliminary optimal transportation path are obtained; further, the weights in the preliminary optimal transportation path are optimized; and the weights in the preliminary path selection algorithm are replaced with the optimized weights to finally obtain the optimized optimal transportation path.

[0021] A smart logistics logistics vehicle transport vehicle-road collaborative control system, applied to the above-mentioned smart logistics logistics vehicle transport vehicle-road collaborative control method, includes the following parts:

[0022] Road network monitoring module, vehicle monitoring module, data processing and fusion module, path planning module, environment perception module, intelligent dispatching control module;

[0023] The road network monitoring module monitors the road network status in the park, obtains real-time road network status information data, and sends the real-time road network status information data to the data processing and fusion module;

[0024] The vehicle monitoring module monitors the real-time operating status of all logistics vehicles in the park, obtains the real-time operating status information data of the logistics vehicles, and sends the real-time operating status information data of the logistics vehicles to the data processing and fusion module;

[0025] The data processing and fusion module pre-processes and fuses the real-time road network status information data and the real-time operation status information data of the logistics vehicle to obtain the integrated information data after fusion, which provides data basis for the path planning module and the intelligent scheduling control module;

[0026] The environmental perception module collects environmental monitoring information in the park and external factors affecting logistics and transportation in real time, quantifies the environmental monitoring information to obtain quantified environmental factors, and sends the quantified environmental factors and external factors affecting logistics and transportation to the path planning module and the intelligent scheduling control module;

[0027] The path planning module plans a preliminary optimal transportation path for each logistics vehicle based on the integrated information data; and optimizes the preliminary optimal transportation path based on the quantified environmental factors and external factors affecting logistics transportation to obtain the optimized optimal transportation path, and sends the optimized optimal transportation path to the intelligent scheduling control module;

[0028] The intelligent scheduling control module generates a scheduling strategy based on the optimized optimal transportation path, quantified environmental factors and integrated comprehensive information data, generates scheduling instructions based on the scheduling strategy, and sends the scheduling instructions to each logistics vehicle.

[0029] The beneficial effects of the technical solution of the present invention are:

[0030] 1. The present invention effectively integrates the real-time road network status information data and the real-time operation status information data of the logistics vehicle through the matching technology of time and space, and creates a comprehensive view with time and space consistency; the comprehensive view can provide a more comprehensive and in-depth analysis perspective, helping decision makers to better understand the interaction and dependency between logistics vehicles and road network status; the integrated comprehensive information data provides rich and multi-dimensional data support for the path planning and scheduling decisions of the logistics vehicle transport vehicle-road collaborative management and control system; the data support can enhance the decision support capability of the logistics vehicle transport vehicle-road collaborative management and control system, making the path planning more reasonable and the scheduling decision more scientific, thereby improving the efficiency and effectiveness of the entire logistics vehicle transport vehicle-road collaborative management and control system.

[0031] 2. The present invention adopts integrated information data to enable route planning based on the latest road network conditions and vehicle status information, so that the logistics vehicle-road collaborative management and control system can quickly respond to changes in road network conditions (such as sudden congestion, accidents, etc.), and adjust the transportation route in time to avoid possible delays and losses; through comprehensive consideration of multiple influencing factors, it can provide a more scientific and reasonable basis for route selection, greatly improving the decision-making quality of logistics scheduling.

[0032] 3. By considering the impact of environmental factors on the transportation route, the present invention makes the path planning process more adaptable to the actual transportation environment, improves the flexibility of logistics transportation, and ensures that logistics transportation can quickly adjust and respond when facing unpredictable environmental changes, thereby reducing delays and potential losses; by quantifying environmental factors and incorporating the quantified environmental factors into the path optimization process, it can effectively avoid road sections with higher risks due to environmental factors (such as bad weather, road construction, etc.), reduce the risk of accidents caused by environmental factors, and improve the overall safety of cargo transportation and vehicle driving; the scheduling strategy generated based on the optimized optimal transportation route and quantified environmental influencing factors can ensure the optimization of logistics transportation in terms of efficiency and cost control. The application of multi-factor decision analysis methods allows the logistics vehicle-road collaborative management and control system to comprehensively consider various factors and formulate the best transportation plan and scheduling instructions for logistics vehicles, further improving the overall operational efficiency of the logistics vehicle-road collaborative management and control system. BRIEF DESCRIPTION OF THE DRAWINGS

[0033] Figure 1 This is a structural diagram of the logistics vehicle transport vehicle-road collaborative management and control system of the smart logistics described in the present invention;

[0034] Figure 2 This is a flow chart of the method for coordinated control of logistics vehicles and transport vehicles and roads in the smart logistics described in the present invention. DETAILED DESCRIPTION

[0035] In order to further explain the technical means and effects adopted by the present invention to achieve the predetermined invention purpose, the technical scheme in the embodiment of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiment of the present invention. Obviously, the described embodiment is only a part of the embodiment of the present invention, not all the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0036] Unless defined otherwise, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this invention belongs.

[0037] The following is a detailed description of a specific scheme of a smart logistics logistics vehicle transport vehicle-road collaborative management and control system and method provided by the present invention in conjunction with the accompanying drawings.

[0038] See attached Figure 1 , which shows a structure diagram of a smart logistics logistics vehicle transport vehicle-road collaborative management and control system provided by an embodiment of the present invention, the system includes the following parts:

[0039] Road network monitoring module, vehicle monitoring module, data processing and fusion module, path planning module, environment perception module, intelligent dispatching control module;

[0040] The road network monitoring module monitors the road network status in the entire park, obtains real-time road network status information data, and sends the real-time road network status information data to the data processing and fusion module;

[0041] The road network refers to the overall structure of all roads in the park and their interconnections, such as the layout of roads, connection points between roads (such as intersections), types of roads (such as main roads, secondary roads, service roads), and road usage (such as one-way driving, speed limit areas);

[0042] Road network status such as traffic flow, accident occurrences, and changes in road conditions;

[0043] Real-time road network status information such as congestion, accident locations, and road conditions;

[0044] The vehicle monitoring module monitors the real-time operating status of all logistics vehicles in the park, obtains the real-time operating status information data of the logistics vehicles, and sends the real-time operating status information data of the logistics vehicles to the data processing and fusion module;

[0045] The real-time operation status information such as position, speed and operation status;

[0046] The data processing and fusion module performs intelligent preprocessing and fusion processing on the real-time road network status information data and the real-time operation status information data of the logistics vehicle to obtain the fused comprehensive information data, which provides data basis for the path planning module and the intelligent scheduling control module;

[0047] The environmental perception module collects environmental monitoring information in the park and external factors affecting logistics transportation in real time, quantifies the environmental monitoring information, obtains quantified environmental factors, and sends the quantified environmental factors to the path planning module and the intelligent scheduling control module;

[0048] The path planning module plans a preliminary optimal transportation path for each logistics vehicle based on the integrated information data, and optimizes the preliminary optimal transportation path based on the quantified environmental factors and external factors affecting logistics transportation to obtain the optimized optimal transportation path, and sends the optimized optimal transportation path to the intelligent scheduling control module;

[0049] The intelligent scheduling control module generates a scheduling strategy based on the optimized optimal transportation path, quantified environmental factors and integrated comprehensive information data, generates scheduling instructions based on the scheduling strategy, and sends the scheduling instructions to each logistics vehicle to achieve coordinated management and control of logistics vehicles and roads in smart logistics.

[0050] See attached Figure 2, which shows a flow chart of a method for coordinated management and control of logistics vehicles and roads in smart logistics provided by an embodiment of the present invention, the method comprising the following steps:

[0051] S1. Acquire real-time road network status information data and real-time operation status information data of logistics vehicles, and pre-process and fuse the real-time road network status information data and the real-time operation status information data of logistics vehicles to obtain fused comprehensive information data;

[0052] Monitoring equipment such as high-definition cameras and traffic flow monitors are installed at key nodes in the park, and vehicle detectors are installed at major intersections and sections. The road network status information data of the entire park is obtained in real time through monitoring equipment and vehicle detectors, and real-time road network status information data is obtained; key nodes, major intersections and sections are determined according to expert experience; high-definition cameras are used for real-time video monitoring to capture accidents and congestion conditions; traffic flow monitors are used to collect traffic flow data of each section; real-time road network status information, such as congestion conditions, accident locations and road conditions; monitoring equipment such as GPS positioning equipment and speed sensors are installed for all logistics vehicles in the park to capture key operating data such as the location and speed of logistics vehicles in real time, and obtain real-time operating status information data of logistics vehicles; real-time operating status information of logistics vehicles such as location, speed and operating status;

[0053] The real-time road network status information data and the real-time operation status information data of logistics vehicles are intelligently pre-processed and fused to obtain the fused comprehensive information data; the specific implementation process is as follows:

[0054] Firstly, the real-time road network status information data and the real-time operation status information data of logistics vehicles are cleaned respectively using existing data cleaning technologies to remove noise data, such as outliers or damaged data points, and to correct erroneous values ​​or missing values, so as to ensure the quality of the data and provide a data basis for subsequent analysis; then the cleaned real-time road network status information data and the real-time operation status information data of logistics vehicles are standardized to obtain a unified data format and measurement unit, so that data from different data sources can be compared and analyzed on the same platform;

[0055] Furthermore, by matching time and space, the standardized real-time road network status information data and the real-time operation status information data of logistics vehicles are integrated to create a comprehensive view with temporal and spatial consistency; specifically:

[0056] For time matching, determine the time correspondence between the standardized real-time operation status information data of logistics vehicles and the real-time road network status information data; preset any time window based on expert experience , ,in For the The start time of the time window, For the The end time of the time window; , is the number of time windows; the real-time operation status information data of the logistics vehicles and the real-time road network status information data are segmented according to the time windows, and the real-time operation status information data of the logistics vehicles after time matching is obtained. , describes the logistics vehicles in time , the geographic coordinates of the logistics vehicle Ensure the time consistency during comparison and analysis;

[0057] For spatial matching, the real-time operation status information data of the logistics vehicles after standardization is matched with the real-time road network status information data in terms of geographical location; the location data of the logistics vehicles and the real-time road network status information data are mapped to the same map using geographic information system technology. For each logistics vehicle, the nearest road section to the location of the logistics vehicle is found, and spatial matching is performed by calculating the shortest distance between the vehicle location point and each road section in the road network to obtain the real-time road network status information data after spatial matching. , describes the Road section in time , at the position coordinates Real-time road network status information data;

[0058] Real-time operation status information data of logistics vehicles after time matching Real-time road network status information data after matching with space In time The values ​​fall in the same time window In the case of , it means that the real-time road network status information data after standardization and the real-time operation status information data of the logistics vehicle meet the time matching conditions, and the time matching is achieved; when the real-time operation status information data of the logistics vehicle after time matching Real-time road network status information data after matching with space In If the real-time road network status information data after time matching and the real-time road network status information data after space matching are located at the same position, it means that the real-time road network status information data after standardization and the real-time operation status information data of the logistics vehicles meet the spatial matching conditions and realize spatial matching; finally, data fusion is performed to integrate the real-time operation status information data of the logistics vehicles after time matching and the real-time road network status information data after space matching to create a comprehensive view; within each time window, the real-time operation status information data of the logistics vehicles (such as position and speed) are fused with the real-time road network status information data (such as traffic flow and accident information) of the vehicle space matching to obtain the fused comprehensive information data; the fused comprehensive information data is represented in the form of multidimensional data, including time mark, location, vehicle status and status of the matching road section; the fused comprehensive information data is represented as ,in, It is a timestamp, used to identify time; It is the unique identification of logistics vehicles; is the geographical location coordinates of the logistics vehicle; is the speed of the logistics vehicle; It is the unique identifier of the road section where the logistics vehicle is located; is the flow rate of the road section; and Respectively represent other status information of logistics vehicles and road sections;

[0059] After the above processing, the integrated information data is obtained, providing complete data for subsequent processing.

[0060] S2. Based on the integrated information data after fusion, a preliminary path optimization algorithm is introduced to model the integrated information data after fusion into a graph. ,in is a set of vertices, representing intersections or important nodes in the road network; is an edge set, representing a road segment; each edge ; and define the weight, indicating the passing road segment The cost of transporting goods; define a comprehensive cost function based on weights; use the comprehensive cost function as the criterion for path selection, and select the path with the lowest cost from all potential paths as the initial optimal transportation path for each logistics vehicle;

[0061] Based on the integrated information data, the preliminary optimal transportation path is planned for each logistics vehicle through the preliminary path optimization algorithm; the implementation process of the preliminary path optimization algorithm is as follows:

[0062] First, a comprehensive cost function is defined based on the integrated information data and the total path cost. The factors affecting the total path cost include distance, estimated time, congestion cost, and accident risk. The comprehensive cost function quantifies the above factors and assigns a cost value to each possible path. The path with the lowest cost is the preliminary optimal transportation path.

[0063] The fused comprehensive information data is modeled as a graph ,in is a set of vertices, representing intersections or important nodes in the road network; is a set of edges, representing road segments; each edge, i.e., a road segment , weights are defined based on the length of the road segment, traffic flow, accident frequency, and road conditions. , indicating that the road segment cost; weight It is expressed as:

[0064]

[0065] in, Representative road segment The length directly affects the driving distance and time; Representative road segment Current traffic flow; is the critical value of traffic flow, when When , the road segment is considered to be congested; The value of is determined based on historical traffic flow data from the database and traffic engineering experience; and It reflects the real-time usage and congestion of the road segment, which has a direct impact on travel time; Representative road segment The frequency of accidents; Represents road segment The road condition score ranges from 0 to 1, with 1 indicating good road conditions and 0 indicating very bad road conditions; It represents the influence of other influencing factors, which is determined according to the expert experience method; , , , , is the weight coefficient, which indicates the relative importance of different influencing factors in the total weight calculation. The value of the weight coefficient needs to be adjusted according to the specific application and actual situation;

[0066] The impact of traffic flow is modeled using a logistic function, ,here is an exponential function used to convert traffic flow into a congestion probability so that the weight is close to the critical value when the traffic flow is close to the critical value. This shows that congestion has a significant impact on path selection. is a positive proportional constant that determines the steepness of the function curve;

[0067] Defining the comprehensive cost function For path The total cost, the comprehensive cost function calculation formula is as follows:

[0068]

[0069] Further adopting the existing graph search algorithm, using the comprehensive cost function defined As the criterion for path selection, the path with the lowest cost is selected from all possible paths as the preliminary optimal transportation path.

[0070] S3. Collect environmental monitoring information and quantify it to obtain quantified environmental factors; further optimize the preliminary optimal transportation path; generate a scheduling strategy based on the optimized optimal transportation path, quantified environmental factors and integrated comprehensive information data, generate a scheduling instruction based on the scheduling strategy, and send the scheduling instruction to the logistics vehicle;

[0071] Environmental monitoring sensors are installed at major intersections and sections of roads to collect real-time environmental monitoring information in the park and external factors that affect logistics and transportation; environmental monitoring information includes rainfall, temperature, wind speed, etc.; external factors that affect logistics and transportation include road construction, large-scale events, traffic accidents, etc.; the collected environmental monitoring information is converted into quantitative indicators using expert experience, such as converting rainfall into a road slippery index and converting temperature into a risk level that affects the performance of logistics vehicles, to achieve quantitative processing of environmental monitoring information and obtain quantitative environmental factors; further, the impact assessment model pre-defined according to the expert experience method is used to assess the specific impact of the quantified environmental factors and external factors that affect logistics and transportation on the preliminary optimal transportation path, the specific impacts include the resulting delays and route closures, to achieve an impact assessment of the quantified environmental factors and external factors that affect logistics and transportation, and obtain the specific impact on the preliminary optimal transportation path;

[0072] Furthermore, based on the specific impact of the quantified environmental factors and external factors affecting logistics and transportation on the preliminary optimal transportation path, the preliminary optimal transportation path is optimized to obtain the optimized optimal transportation path; specifically:

[0073] Based on the impact assessment of quantified environmental factors and external factors affecting logistics and transportation, the weights in the preliminary optimal transportation path are obtained. Impact indicators ::

[0074]

[0075] in, Indicates the degree of influence of rainfall; Indicates the degree of influence of snowfall; Indicates the degree of impact of fog conditions; Indicates the degree of impact of the construction situation; Indicates the degree of impact of a special event; , , , , They are the weight coefficients of various environmental factors, which are used to adjust the contribution of each environmental factor to the total impact; Represents the natural logarithm, used in the calculation of the impact of rainfall; Exponential decay is used to represent the negative impact of fog on visibility. The heavier the fog, the more significant the impact. The logistic function form is used to quantify the impact of construction conditions and simulate the saturation effect of construction impact;

[0076] Further weights in the preliminary optimal transport path Optimize and get the optimized weight :

[0077] Then, the optimized weights are used to replace the weights in the preliminary path optimization algorithm, and subsequent processing is performed to finally obtain the optimized optimal transportation path;

[0078] Based on the optimized optimal transportation path, quantified environmental factors and integrated comprehensive information data, a multi-factor decision analysis method is used to generate the best scheduling strategy; the multi-factor decision analysis method is to combine the existing fuzzy logic, priority arrangement and weighted factor analysis technology according to the expert experience method; based on the above-mentioned optimal scheduling strategy, the existing rule engine or expert system is used to generate specific scheduling instructions, and the scheduling instructions specifically indicate the key parameters of the transportation vehicle, such as the driving route, start and end time and speed limit; finally, the existing Internet of Vehicles technology is used to send the scheduling instructions to the target logistics vehicle, so as to realize the coordinated management and control of logistics vehicles and transportation vehicles in smart logistics.

[0079] To sum up, a smart logistics logistics vehicle transport vehicle-road collaborative management and control system and method has been completed.

[0080] The order of the embodiments of the invention is for description only and does not represent the advantages and disadvantages of the embodiments. The processes depicted in the drawings do not necessarily require the specific order or continuous order shown to achieve the desired results. In some embodiments, multitasking and parallel processing are also possible or may be advantageous.

[0081] The various embodiments in this specification are described in a progressive manner, and the same or similar parts between the various embodiments can be referenced to each other, and each embodiment focuses on the differences from other embodiments.

[0082] The above embodiments are only used to illustrate the technical solutions of the present invention, rather than to limit the same. Although the present invention has been described in detail with reference to the above embodiments, those skilled in the art should understand that the technical solutions described in the above embodiments may still be modified, or some of the technical features may be replaced by equivalents. Such modifications or replacements do not deviate the essence of the corresponding technical solutions from the spirit and scope of the technical solutions of the embodiments of the present invention, and should be included in the protection scope of the present invention.

Claims

1. A smart logistics logistics vehicle transport vehicle-road collaborative control method, characterized in that: The following steps are involved: S1. Acquire real-time road network status information data and real-time operation status information data of logistics vehicles, pre-process the real-time road network status information data and real-time operation status information data of logistics vehicles, and perform time and space matching on the real-time road network status information data and real-time operation status information data of logistics vehicles after the pre-processing and standardization processing: For time matching, determine the time correspondence between the real-time operation status information data of the logistics vehicle after standardization and the real-time road network status information data; segment the real-time operation status information data of the logistics vehicle and the real-time road network status information data according to the preset time window, and obtain the real-time operation status information data of the logistics vehicle after time matching; For spatial matching, the standardized real-time operation status information data of logistics vehicles and the real-time road network status information data are matched in terms of geographical location; Use geographic information system technology to map the location data of logistics vehicles and real-time road network status information data onto the same map, calculate the shortest distance between the vehicle location point and each road section in the road network for spatial matching, and obtain real-time road network status information data after spatial matching; Based on the real-time operation status information data of the logistics vehicles after time matching and the real-time road network status information data after space matching, the real-time operation status information data of the logistics vehicles and the real-time road network status information data of the vehicles after space matching are fused within each time window to obtain the fused comprehensive information data; S2. Based on the integrated information data after fusion, a preliminary path optimization algorithm is introduced to model the integrated information data after fusion into a graph. ,in is a set of vertices, representing intersections or important nodes in the road network; is an edge set, representing a road segment; each edge ; and define the weight, indicating the passing road segment The cost, weight It is expressed as: in, Representative road segment Length; Representative road segment Current traffic flow; is the critical value of traffic flow; Representative road segment The frequency of accidents; Represents road segment 's road condition rating; Indicates the influence of other influencing factors; , , , , is the weight coefficient; is a positive proportional constant; define a comprehensive cost function based on weights; use the comprehensive cost function as the criterion for path selection, select the path with the lowest cost from all potential paths, and use it as the initial optimal transportation path for each logistics vehicle; S3, collect environmental monitoring information in the park and external factors affecting logistics transportation, and quantify the environmental monitoring information to obtain quantified environmental factors; evaluate the weight impact indicators in the initial optimal transportation path based on the quantified environmental factors and external factors affecting logistics transportation : in, Indicates the degree of influence of rainfall; Indicates the degree of influence of snowfall; Indicates the degree of impact of fog conditions; Indicates the degree of impact of the construction situation; Indicates the degree of impact of a special event; , , , , are the weight coefficients of various environmental factors respectively; based on the influencing index of the weight in the preliminary optimal transportation path, the weight in the preliminary optimal transportation path is optimized to obtain the optimized weight : Based on the optimized weights, the preliminary optimal transportation path is further optimized to obtain the optimized optimal transportation path; a scheduling strategy is generated according to the optimized optimal transportation path, quantified environmental factors and integrated comprehensive information data, a scheduling instruction is generated based on the scheduling strategy, and the scheduling instruction is sent to the logistics vehicle.

2. The method for coordinated management and control of logistics vehicles and transportation vehicles of smart logistics according to claim 1 is characterized in that: The S1 specifically includes: The specific implementation process of the preprocessing is as follows: the first step is to clean the real-time road network status information data and the real-time operation status information data of the logistics vehicles respectively; the second step is to standardize the cleaned real-time road network status information data and the real-time operation status information data of the logistics vehicles to obtain the standardized real-time road network status information data and the real-time operation status information data of the logistics vehicles.

3. The method for coordinated management and control of logistics vehicles and transportation vehicles of smart logistics according to claim 1 is characterized in that: The S1 also includes: integrating the real-time operation status information data of the logistics vehicles after time matching and the real-time road network status information data after space matching, creating a comprehensive view with time and space consistency, and obtaining fused comprehensive information data.

4. The method for coordinated management and control of logistics vehicles and transportation vehicles of smart logistics according to claim 1 is characterized in that: The S3 specifically includes: evaluating the impact of quantified environmental factors and external factors affecting logistics and transportation on the preliminary optimal transportation path based on the impact assessment model; optimizing the preliminary optimal transportation path based on the impact of quantified environmental factors and external factors affecting logistics and transportation on the preliminary optimal transportation path to obtain the optimized optimal transportation path.

5. A smart logistics logistics vehicle transport vehicle-road collaborative control system, applied to the smart logistics logistics vehicle transport vehicle-road collaborative control method as claimed in claim 1, characterized in that: Includes the following parts: Road network monitoring module, vehicle monitoring module, data processing and fusion module, path planning module, environment perception module, intelligent dispatching control module; The road network monitoring module monitors the road network status in the park, obtains real-time road network status information data, and sends the real-time road network status information data to the data processing and fusion module; The vehicle monitoring module monitors the real-time operating status of all logistics vehicles in the park, obtains the real-time operating status information data of the logistics vehicles, and sends the real-time operating status information data of the logistics vehicles to the data processing and fusion module; The data processing and fusion module pre-processes and fuses the real-time road network status information data and the real-time operation status information data of the logistics vehicle to obtain the integrated information data after fusion, which provides data basis for the path planning module and the intelligent scheduling control module; The environmental perception module collects environmental monitoring information in the park and external factors affecting logistics and transportation in real time, quantifies the environmental monitoring information to obtain quantified environmental factors, and sends the quantified environmental factors and external factors affecting logistics and transportation to the path planning module and the intelligent scheduling control module; The path planning module plans a preliminary optimal transportation path for each logistics vehicle based on the integrated information data; and optimizes the preliminary optimal transportation path based on the quantified environmental factors and external factors affecting logistics transportation to obtain the optimized optimal transportation path, and sends the optimized optimal transportation path to the intelligent scheduling control module; The intelligent scheduling control module generates a scheduling strategy based on the optimized optimal transportation path, quantified environmental factors and integrated comprehensive information data, generates scheduling instructions based on the scheduling strategy, and sends the scheduling instructions to each logistics vehicle.

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