Task allocation method and system for IGV logistics vehicles

Abstract

The invention discloses a task allocation method and system for IGV logistics vehicles, and relates to the technical field of logistics transportation. The method aims to solve the problems that a traditional IGV task allocation mode is opaque in information, low in efficiency and incapable of quickly responding to complex and changeable logistics requirements, and the coexistence of task overstock and vehicle idling often occurs. Through a block chain technology, an intelligent matching algorithm and a dynamic weight adjustment mechanism, factors such as a task emergency degree, cargo characteristics, a vehicle state and a driving path are comprehensively considered, tasks and vehicles are rapidly and accurately matched, the task allocation efficiency is improved, resource allocation is optimized, and through real-time collection of vehicle and cargo state data, the task allocation efficiency is improved. According to the technical scheme, fault early warning is carried out in combination with historical data, various abnormal conditions can be found and processed in time, it is ensured that tasks are completed on time, real-time feedback of task progress and driving tracks is achieved through a constructed communication link, clients and logistics parties can conveniently obtain information, the customer satisfaction degree is improved, and efficient development of the logistics industry is promoted.

Description

Technical Field

[0001] The present invention relates to the technical field of logistics transportation, and particularly relates to a task allocation method and system for an IGV logistics vehicle. Background Art

[0002] With the rapid development of the logistics industry, intelligent guided vehicles (IGVs) are increasingly widely used in the logistics warehousing and transportation links. For example, the patent application with the publication number CN118863423A discloses an Internet of Vehicles logistics scheduling system and method based on artificial intelligence, which includes: a logistics task receiving module for obtaining logistics task information; an Internet of Vehicles module for obtaining vehicle information of all transport vehicles accessing the Internet of Vehicles; an initial scheduling and allocation module for sending the logistics task information to the nearest idle vehicle to complete the initial scheduling and allocation; a path planning module for generating a matching transport path; a road right calculation module for generating a road right value corresponding to the path; a vehicle right calculation module for generating a vehicle right value of the transport vehicle; a real-time scheduling and allocation module for adding a transfer logistics station as a scheduling station when the road right value is higher than a preset value; and it is further used for obtaining the vehicle right value and performing real-time scheduling and allocation of loading or unloading tasks for the transport vehicle according to the vehicle right value.

[0003] Although the above patent improves the scheduling flexibility during the transportation process, the traditional IGV task allocation method has problems such as opaque information, low efficiency, and difficulty in coping with complex and changeable logistics environments, and cannot quickly respond to complex and changeable logistics demands. When facing a large number of tasks and vehicle scheduling, there is often a coexistence of task backlogs and vehicle idleness. Summary of the Invention

[0004] The purpose of the present invention is to provide a task allocation method and system for an IGV logistics vehicle, which realizes intelligent matching and dynamic adjustment through blockchain technology, improves task allocation efficiency and resource utilization rate, constructs a communication link to achieve real-time information sharing, and enhances customer satisfaction and the overall operation efficiency of logistics, so as to solve the problems raised in the above background art.

[0005] To achieve the above purpose, the present invention provides the following technical solutions:

[0006] A task allocation method for an IGV logistics vehicle, comprising:

[0007] Obtaining transportation task information based on a task management system, and parsing the obtained transportation task information to extract key data;

[0008] Real-time obtaining the status information of each IGV logistics vehicle, and performing two-way matching through blockchain technology according to the parsing result of the transportation task information and the status information of the IGV logistics vehicle;

[0009] Based on the matching results, after receiving the transportation task, the IGV logistics vehicle executes the corresponding transportation task according to the planned route, and during the execution process, it constructs communication links for both the customer side and the logistics side to provide real-time feedback on the task progress and driving trajectory.

[0010] Furthermore, the obtained transportation task information is parsed, specifically including:

[0011] Read the obtained transportation task information to determine the transportation plan of the transportation task information, including the starting position of the goods, the target position, and the weight of the goods;

[0012] Analyze the transportation plan to determine the priority of the transportation task information, and arrange each transportation task information based on the priority;

[0013] Extract the geographical location coordinates of the starting position and the target position of the goods through the coordinate parsing algorithm, classify the transportation task according to the logistics area division rules, and at the same time, extract the weight value of the goods;

[0014] Extract the key data of the area classification result and the weight value of the goods and perform anomaly detection to determine whether the starting position and the target position of the goods are within the reasonable logistics operation area range, and whether the weight value of the goods meets the load capacity range of the IGV logistics vehicle.

[0015] Furthermore, determine the priority of the transportation task information, specifically including:

[0016] Delivery time urgency assessment: Extract the difference between the expected delivery time and the current time of the task from the transportation plan, determine the time urgency value of the transportation task information, establish the mapping relationship between the time urgency and the priority, and determine the time urgency weight;

[0017] Goods value assessment: Extract the goods information in the transportation plan, evaluate the goods value based on the goods information, and establish the mapping relationship between the goods value assessment result and the priority, and determine the goods value weight;

[0018] Analysis of the impact on associated business: Analyze the upstream and downstream business links associated with the transportation task information, judge the impact degree of the delay or early completion of the transportation task information on other business links, and determine the impact degree weight based on the judgment result;

[0019] Priority assessment: Based on the comprehensive calculation of the time urgency weight, the goods value weight, and the impact degree weight, obtain the priority score of each transportation task information, and arrange the priority of each transportation task information according to the priority score.

[0020] Further, a two-way matching is performed on the parsing result of the transportation task information and the status information of the IGV logistics vehicle through blockchain technology, specifically including:

[0021] Based on the parsing result of the transportation task information and the status information of the IGV logistics vehicle, a task allocation blockchain is built, and index keywords are constructed for the transportation task information and the IGV logistics vehicle status information respectively on the task allocation blockchain;

[0022] Combined with the index keywords, a two-way retrieval and matching of the transportation task information and the IGV logistics vehicle status information is performed based on the matching rules, and the two-way retrieval and matching results are adjusted according to the weights of each matching rule;

[0023] Among them, the matching rules include: preferentially matching the IGV logistics vehicle that is close to the starting position of the goods and in an idle state; for high-priority tasks, preferentially allocate them to vehicles with sufficient remaining power, suitable load capacity, and relatively short driving paths; comprehensively consider the driving speed of the vehicle, the estimated arrival time, and the delivery time requirement of the task for matching;

[0024] After the two-way matching is completed, the matching result is recorded on the task matching blockchain. At the same time, the matching result is fed back to the task management system and the corresponding IGV logistics vehicle.

[0025] Further, a two-way retrieval and matching of the transportation task information and the IGV logistics vehicle status information is performed based on the matching rules, specifically including:

[0026] Based on the index keywords of the transportation task information, the IGV logistics vehicle status information is retrieved on the task allocation blockchain, and the IGV logistics vehicle status information records are screened based on the coordinate characteristics of the starting position of the goods;

[0027] At the same time, based on the index keywords of the IGV logistics vehicle status information, a reverse retrieval of the transportation task information is performed. For the IGV logistics vehicle in an idle state and with the remaining power greater than the preset power range, the transportation task information is screened;

[0028] Based on the screening results, an initial matching set of the transportation task information and the IGV logistics vehicle is obtained;

[0029] From the initial matching set, for each transportation task information, among the candidate IGV logistics vehicles, they are sorted according to the distance between the vehicle position and the starting position of the goods;

[0030] The transportation tasks with a high priority level are screened out, and among the corresponding candidate IGV logistics vehicles, the operating state performance of the vehicles is evaluated;

[0031] Construct a matching subset based on the screening results in the initial matching set, and predict the driving speed of the vehicle to perform the task based on the matching subset according to the historical driving speed data of the IGV logistics vehicle. Combine the starting position and the target position of the goods of the task to determine the estimated driving time;

[0032] Determine the estimated arrival time of the transportation task information based on the estimated driving time, compare the estimated arrival time with the delivery time requirement, and obtain the matching pairs whose estimated arrival time does not exceed the delivery time requirement;

[0033] Calculate the matching degree for each obtained matching pair, obtain the comprehensive score of the matching pair, and re - sort and screen the matching results according to the comprehensive score to obtain the final two - way matching result.

[0034] Further, the comprehensive score of the matching pair includes the distance score between the vehicle and the starting position of the goods, the vehicle idle state score, the vehicle operation state performance score under high - priority tasks, and the matching degree score of the estimated delivery time requirement.

[0035] Further, after the IGV logistics vehicle receives the transportation task and executes the corresponding transportation task according to the planned path, it further includes: real - time collecting various operation data of the vehicle, monitoring the vehicle state of the IGV logistics vehicle. At the same time, the sensor monitors the state of the transported goods, adjusts the driving path according to the real - time road condition information, and timely feedbacks the path change information to the task management system.

[0036] Further, monitoring the vehicle state of the IGV logistics vehicle further includes: performing fault warning on the IGV logistics vehicle based on the historical vehicle state data. When it is predicted that the IGV logistics vehicle may have an abnormality during the task execution, the abnormality warning information is quickly synchronized and notified to the customer side and the logistics side based on the communication link.

[0037] Further, constructing the communication link between the customer side and the logistics side further includes:

[0038] Setting data synchronization nodes in the communication link, establishing a real - time data synchronization mechanism, and obtaining the task progress and driving trajectory data real - time fed back by the IGV logistics vehicle based on the real - time synchronization mechanism;

[0039] The customer side receives the task progress data in real - time and synchronously obtains the remaining distance from the IGV logistics vehicle to the goods destination and the estimated arrival time; at the same time, the logistics side synchronously obtains the real - time position and vehicle state information of the vehicle.

[0040] The present invention provides another technical solution, a task assignment system for an IGV logistics vehicle, including:

[0041] A task acquisition module, configured to obtain transportation task information from a task management system, read and parse it, determine a transportation plan, and arrange it based on the priorities of each transportation task information;

[0042] A vehicle status monitoring module, configured to obtain the status information of each IGV logistics vehicle in real time, monitor the real-time status of the vehicle, and perform fault warning for the IGV logistics vehicle based on historical vehicle status data;

[0043] A blockchain platform, configured to build a task allocation blockchain based on the parsing results of transportation task information and the status information of IGV logistics vehicles, and perform two-way retrieval and matching of transportation task information and the status information of IGV logistics vehicles according to matching rules;

[0044] A path planning module, configured to plan a driving path for the IGV logistics vehicle that receives a transportation task according to the task allocation result, and timely feedback path change information to the task management system and the vehicle status monitoring module.

[0045] Compared with the prior art, the beneficial effects of the present invention are:

[0046] Through blockchain technology, intelligent matching algorithms, and dynamic weight adjustment mechanisms, comprehensively considering factors such as task urgency, cargo characteristics, vehicle status, and driving path, quickly and accurately match tasks with vehicles, improve task allocation efficiency, optimize resource allocation, reduce operating costs, ensure fair and just task allocation, through real-time collection of vehicle and cargo status data, combined with historical data for fault warning, can timely detect and handle various abnormal situations, effectively avoid task interruption and cargo loss, ensure task completion on time, the constructed communication link realizes real-time feedback of task progress and driving trajectory, facilitates customers and logistics parties to obtain information, enhances information transparency and collaboration, improves customer satisfaction, and promotes the efficient development of the logistics industry. BRIEF DESCRIPTION OF THE DRAWINGS

[0047] Figure 1 It is a flowchart of the task allocation method for the IGV logistics vehicle of the present invention;

[0048] Figure 2 It is a logic block diagram for determining the priority of transportation tasks of the present invention;

[0049] Figure 3 It is a module diagram of the task allocation system for the IGV logistics vehicle of the present invention. DETAILED DESCRIPTION OF THE INVENTION

[0050] Next, the technical solutions in the embodiments of the present invention will be clearly and completely described in conjunction with the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are only a part of the embodiments of the present invention, rather than all the embodiments. All other embodiments obtained by those of ordinary skill in the art based on the embodiments of the present invention without creative efforts shall fall within the protection scope of the present invention.

[0051] To solve the problems existing in the traditional IGV task allocation method, such as opaque information, low efficiency, and difficulty in coping with complex and changeable logistics environments, and being unable to quickly respond to complex and changeable logistics demands. When facing a large number of tasks and vehicle scheduling, there are often technical problems of task backlog and vehicle idleness coexisting. Please refer to Figure 1 - Figure 2 , the following technical solutions are provided in this embodiment:

[0052] A task allocation method for an IGV logistics vehicle, including:

[0053] Obtain transportation task information based on the task management system, including data such as the starting position, target position, weight, and priority of the goods, and parse the obtained transportation task information to extract key data for subsequent processing. Specifically, it includes:

[0054] Read the obtained transportation task information to determine the transportation plan of the transportation task information, including the starting position, target position, and weight of the goods;

[0055] Analyze the transportation plan to determine the priority of the transportation task information, and arrange each transportation task information based on the priority;

[0056] Extract the geographical location coordinates of the starting position and target position of the goods through a coordinate parsing algorithm, and classify the transportation tasks according to the logistics area division rules. At the same time, extract the weight value of the goods;

[0057] Extract the key data of the area classification result and the weight value of the goods and perform anomaly detection to determine whether the starting position and target position of the goods are within the reasonable logistics operation area range, and whether the weight value of the goods meets the load capacity range of the IGV logistics vehicle;

[0058] Obtain the status information of each IGV logistics vehicle in real time, including vehicle position, remaining power, current load condition, and whether it is in an idle state, etc. Perform two-way matching through blockchain technology according to the parsing result of the transportation task information and the status information of the IGV logistics vehicle;

[0059] Based on the matching result, after receiving the transportation task, the IGV logistics vehicle executes the corresponding transportation task according to the planned path, and during the execution process, constructs a communication link between the customer side and the logistics side, and real-time feedbacks the task progress and driving trajectory. It also includes:

[0060] Set data synchronization nodes in the communication link, establish a real-time data synchronization mechanism, and obtain the task progress and driving trajectory data of the IGV logistics vehicle in real-time based on the real-time synchronization mechanism;

[0061] The logistics tracking APP of the customer side can receive the task progress data in real-time, and synchronously obtain the remaining distance and estimated arrival time of the IGV logistics vehicle from the cargo destination; at the same time, the dispatching center of the logistics side can synchronously obtain the real-time position and vehicle status information of the vehicle for global task scheduling and resource allocation.

[0062] In this embodiment, the intelligent matching of tasks and vehicles is realized through blockchain technology, and dynamic adjustment is carried out according to the IGV status information and task priorities, effectively improving the task allocation efficiency and resource utilization rate, providing real-time feedback on task progress and driving trajectory, providing transparent and convenient logistics services for customers, enhancing system reliability through a fault warning mechanism, realizing data sharing and collaboration, supporting personalized services, providing strong support for the development of the logistics industry, and improving the overall operation efficiency and customer satisfaction.

[0063] In this embodiment, determining the priority of the transportation task information specifically includes:

[0064] Assessment of delivery time urgency: Extract the difference between the expected delivery time and the current time of the task from the transportation plan, determine the time urgency value of the transportation task information, establish a mapping relationship between the time urgency and the priority, and determine the time urgency weight;

[0065] In this embodiment, if the time urgency value is less than the preset emergency time threshold, it is determined that the task has a high time urgency. For example, the emergency time threshold is preset to 2 hours. When the delivery time of a certain task is less than 2 hours from the current time, its time urgency value can be set to a high level. The higher the time urgency, the greater the weight of the corresponding task in the priority ranking. For tasks with a high time urgency level, a higher priority promotion coefficient is given, such as 1.5; for medium-level ones, 1.2; for low-level ones, 1.0;

[0066] Assessment of cargo value: Extract the cargo information from the transportation plan, evaluate the cargo value based on the cargo information, and establish a mapping relationship between the cargo value evaluation result and the priority, and determine the cargo value weight;

[0067] In this embodiment, for goods with high market prices, strong scarcity, and high damage risks, a higher value assessment score is assigned. For example, by analyzing market data, industry reports, and historical damage rate data during the logistics transportation process, the value of different types of goods is evaluated, and different value ranges are set corresponding to different priority adjustment coefficients. For tasks with goods value in the high-value range, the priority adjustment coefficient is 1.3; for the medium-value range, it is 1.1; for the low-value range, it is 1.0;

[0068] Analysis of associated business impacts: Analyze the upstream and downstream business links associated with the transportation task information to determine the degree of impact of the delay or early completion of the transportation task information on other business links, and determine the impact degree weight based on the judgment results;

[0069] In this embodiment, tasks with a high degree of impact are directly promoted by several levels in the priority ranking; tasks with a low degree of impact maintain the original priority or appropriately reduce the priority. For example, tasks with a high degree of impact are promoted by 2 levels based on the original priority; for tasks with a low degree of impact, if the current priority is medium, it is reduced by 1 level;

[0070] Priority assessment: Based on the time urgency weight, goods value weight, and impact degree weight, a comprehensive calculation is performed to obtain the priority score of each transportation task information, and the transportation task information is prioritized according to the priority score. For example: Using the weighted summation method, different weights are assigned to each evaluation factor. For example, the weight of delivery time urgency is 0.4, the weight of goods value is 0.3, and the weight of the impact on associated business links is 0.3. The calculation formula for the priority score is as follows:

[0071] S = C t × w t + C v × w v +(L0 + ΔL)× w i

[0072] C t is the priority improvement coefficient corresponding to the time urgency, C v is the priority adjustment coefficient corresponding to the goods value, ΔL is the level adjustment value corresponding to the associated business impact, L0 is the initial priority level, S is the priority score, and the higher the score, the higher the priority, as shown in Table 1:

[0073] Table 1 Example table of transportation task information

[0074] Task Number Starting Position of Goods Target Position Goods Weight Delivery Time Initial Priority 001 (10，20) (50，80) 50kg After 2h 3 002 (30，40) (70，90) 30kg After 1h 2 003 (25，35) (60，75) 40kg After 3h 1

[0075] In this embodiment, according to the parsing result of the transportation task information and the status information of the IGV logistics vehicle, a two-way matching is performed through blockchain technology, specifically including:

[0076] Build a task allocation blockchain based on the parsing results of transportation task information and the status information of IGV logistics vehicles, and construct index keywords for the transportation task information and IGV logistics vehicle status information respectively on the task allocation blockchain;

[0077] Combined with the index keywords, perform two-way retrieval and matching on the transportation task information and the status information of IGV logistics vehicles based on matching rules, and adjust the two-way retrieval and matching results according to the weights of each matching rule. For example, during the peak logistics period, the weights of vehicle idle status and distance factors can be appropriately increased to improve task allocation efficiency; in the scenario of transporting special goods, the weights of load capacity adaptation and vehicle safety factors can be increased;

[0078] Among them, the matching rules include: preferentially match IGV logistics vehicles that are close to the starting position of the goods and in an idle state; for high-priority tasks, preferentially allocate them to vehicles with sufficient remaining power, suitable load capacity, and relatively short driving routes; comprehensively consider the driving speed of the vehicle, the estimated arrival time, and the delivery time requirements of the task for matching;

[0079] After completing the two-way matching, record the matching results on the task matching blockchain, including information such as the correspondence between tasks and vehicles, the estimated execution time, and the driving route plan (preliminary plan, which can be adjusted according to real-time road conditions in the future). At the same time, feedback the matching results to the task management system and the corresponding IGV logistics vehicles;

[0080] After receiving the results, the task management system can perform subsequent task scheduling and monitoring arrangements; after receiving the assigned tasks, the IGV logistics vehicles are ready to execute the transportation tasks according to the planned routes and update their own status information to the blockchain in real time so that the task management system and other vehicles can obtain the latest situation.

[0081] In this embodiment, the two-way retrieval and matching of the transportation task information and the status information of IGV logistics vehicles based on matching rules specifically includes:

[0082] Retrieve the status information of IGV logistics vehicles on the task allocation blockchain based on the index keywords of transportation task information (such as the unique task identifier, key coordinate features of the starting and target positions of the goods, priority level, etc.), and screen the status information records of IGV logistics vehicles based on the coordinate features of the starting position of the goods;

[0083] In this embodiment, if the coordinate of the starting position of the goods of the transportation task is (x0, y0) and the search radius is r, then retrieve all vehicle position coordinates (x, y) that satisfy the IGV logistics vehicle information;

[0084] Meanwhile, based on the IGV logistics vehicle status information index keywords (such as vehicle ID, current position coordinates, remaining power range, load condition category, etc.), the transportation task information is retrieved in reverse. For IGV logistics vehicles in the idle state and with the remaining power greater than the preset power range (such as greater than 60% of the total power), the transportation task information is screened;

[0085] Based on the screening results, an initial matching set of transportation task information and IGV logistics vehicles is obtained;

[0086] From the initial matching set, for each transportation task information, among the candidate IGV logistics vehicles, they are sorted according to the distance between the vehicle position and the cargo starting position, and the IGV logistics vehicle that is the closest and in the idle state is preferentially selected;

[0087] The transportation tasks with high-priority levels are screened out, and among the corresponding candidate IGV logistics vehicles, the operating state performance of the vehicles is evaluated, including whether the remaining power is sufficient (such as the remaining power being greater than 1.5 times the estimated power consumption for executing this task), whether the load capacity is suitable (the cargo weight is within the vehicle load capacity range), and estimating the driving path length according to the map information and path planning algorithm, and the vehicle with a relatively shorter driving path is preferentially selected;

[0088] Based on the screening results in the initial matching set, a matching subset is constructed, and based on the matching subset, the driving speed of the vehicle for executing the task is predicted according to the historical driving speed data of the IGV logistics vehicle, and combined with the cargo starting position and the target position of the task, the estimated driving time is determined;

[0089] Based on the estimated driving time, the estimated arrival time of the transportation task information is determined, the estimated arrival time is compared with the delivery time requirement, and the matching pairs whose estimated arrival time does not exceed the delivery time requirement are obtained;

[0090] For each obtained matching pair, the matching degree is calculated, and the comprehensive score of the matching pair is obtained, including the distance score between the vehicle and the cargo starting position, the vehicle idle state score, the vehicle operating state performance score under high-priority tasks, and the matching degree score of the estimated delivery time requirement. According to the comprehensive score, the matching results are sorted and screened again to obtain the final two-way matching results;

[0091] In this embodiment, according to the weights of each matching rule, the scores under each matching rule are calculated, including:

[0092] The distance score S1 between the vehicle and the cargo starting position, and the calculation formula is as follows:

[0093]

[0094] Where d is the actual distance between the vehicle position and the cargo starting position, d maxd is the maximum distance between the vehicle position and the starting position of the goods min d is the minimum distance between the vehicle position and the starting position of the goods. When d ≤ d min , S1 = 1; when d ≥ d max , S1 = 0;

[0095] Vehicle idle state score S2: If the vehicle is in an idle state, S2 = 1; otherwise, S2 = 0;

[0096] Vehicle operation state performance score S3 under high-priority tasks, the calculation formula is as follows:

[0097]

[0098] Among them, S 31 is the remaining power score. If the vehicle power is greater than the preset power threshold, it means that the remaining power is sufficient to meet the task requirements, then S 31 = 1; if the remaining power cannot support the completion of the task, then S 31 = 0; if the power is insufficient to meet the task order, then calculate the power part score in the vehicle operation state performance score according to the proportional relationship between the remaining power of the vehicle and the expected power consumption for executing the task;

[0099] S 32 is the load capacity score. If the goods weight is within the vehicle load range, then S 32 = 1; if the goods are overweight and the vehicle cannot carry them, then S 32 = 0;

[0100] S 33 is the driving path length score. If the vehicle driving path reaches or is better than the ideal shortest path, then S 33 = 1; if the driving path is too long and exceeds the acceptable range, then S 33 = 0;

[0101] Degree of match score S4 for the expected delivery time requirement, the calculation formula is as follows:

[0102]

[0103] Among them, T est is the expected arrival time, T req is the delivery time requirement, T max is the maximum acceptable delay time. When (T est - T req ) ≤ 0, S4 = 1; when (T est - T req ) ≥ ΔT max , S4 = 0;

[0104] Then, the comprehensive score S is:

[0105] S = m1S1 + m2S2 + m3S3 + m4S4

[0106] Wherein, m1 is the distance weight of the starting position of the vehicle and the goods. For example, during the peak logistics period, this weight can be appropriately increased and set to 0.3. m2 is the weight of the vehicle idle state. During the peak logistics period, this weight can be increased and set to 0.2. m3 is the comprehensive weight of the remaining battery power, load capacity, and driving path length of the vehicle under high-priority tasks, and is set to 0.3. m4 is the weight of the matching degree between the estimated arrival time and the delivery time requirement, and is set to 0.2.

[0107] In this embodiment, through the intelligent matching algorithm and the dynamic weight adjustment mechanism, tasks and vehicles can be quickly and accurately matched, and the rationality and effectiveness of the matching results can be ensured. Factors such as task urgency, cargo characteristics, vehicle status, and driving path are fully considered, and flexible adjustments can be made according to the actual situation, thereby improving task allocation efficiency, optimizing resource allocation, and reducing operating costs. It can effectively ensure the fairness and justice of the task allocation process, facilitate information sharing and collaboration among all parties, and provide strong support for building an efficient and reliable logistics system.

[0108] In this embodiment, after the IGV logistics vehicle receives the transportation task, it executes the corresponding transportation task according to the planned path, and also includes: real-time collecting various operation data of the vehicle, such as driving speed, acceleration, steering angle, battery power consumption rate, etc., monitoring the vehicle status of the IGV logistics vehicle, and performing fault warning on the IGV logistics vehicle based on historical vehicle status data. When it is predicted that the IGV logistics vehicle may have abnormalities during the task execution, such as vehicle failure, cargo damage, road congestion, etc., based on the communication link, the abnormal warning information is quickly synchronized and notified to the customer side and the logistics side. At the same time, the sensor monitors the status of the transported goods, such as whether the goods are stable, whether there are signs of displacement or damage, etc. For example, through the pressure sensor and displacement sensor installed in the cargo hold, the position change and force condition of the goods during transportation are sensed in real time. Once an abnormality is found, an alarm is immediately issued; it can timely detect and handle abnormal situations such as vehicle failure, cargo damage, and road congestion, avoid task interruption or cargo loss, improve the reliability and safety of the logistics system, adjust the driving path according to real-time road conditions information to ensure the task is completed on time, and timely feedback the path change information to the task management system to improve information transparency, enhance the control of all parties over the logistics process, and improve customer satisfaction and logistics efficiency.

[0109] To better implement the task allocation method of the IGV logistics vehicle, please refer to Figure 3 The present invention provides a task allocation system for an IGV logistics vehicle, including:

[0110] A task acquisition module, configured to obtain transportation task information from a task management system, read and parse it, determine a transportation plan, and arrange it based on the priorities of the transportation task information;

[0111] A vehicle status monitoring module, configured to obtain the status information of each IGV logistics vehicle in real time, monitor the real-time status of the vehicle, and perform fault warnings for the IGV logistics vehicle based on historical vehicle status data;

[0112] A blockchain platform, configured to build a task allocation blockchain based on the parsing results of transportation task information and the status information of IGV logistics vehicles, and perform two-way retrieval and matching on the transportation task information and the status information of IGV logistics vehicles according to matching rules;

[0113] A path planning module, configured to plan a driving path for the IGV logistics vehicle that receives the transportation task according to the task allocation result, and timely feedback the path change information to the task management system and the vehicle status monitoring module.

[0114] In this embodiment, by integrating functional modules such as task management, vehicle status monitoring, blockchain platform, and path planning, automatic acquisition, parsing, priority sorting, and intelligent matching of tasks are realized, the running status of IGV logistics vehicles is monitored in real time and fault warnings are performed, the optimal driving path is dynamically planned, and data sharing and collaboration are achieved, effectively improving task allocation efficiency, resource utilization rate, and system reliability, providing an intelligent, efficient, and reliable solution for the development of the logistics industry.

[0115] The above is only a preferred specific embodiment of the present invention, but the protection scope of the present invention is not limited thereto. Any person skilled in the art within the technical scope disclosed by the present invention, according to the technical solution and inventive concept of the present invention, makes equivalent substitutions or changes, and should be covered by the protection scope of the present invention.

Claims

1. A task allocation method for an IGV logistics vehicle, characterized in that, Including: Obtain transportation task information based on the task management system, parse the obtained transportation task information, and extract key data; Obtain the status information of each IGV logistics vehicle in real time, and perform two-way matching through blockchain technology according to the parsing result of the transportation task information and the status information of the IGV logistics vehicle; Based on the matching result, after the IGV logistics vehicle receives the transportation task, execute the corresponding transportation task according to the planned path, and during the execution process, build a communication link between the customer side and the logistics side, and feedback the task progress and driving track in real time.

2. The task allocation method of an IGV logistics vehicle according to claim 1, characterized in that, Parse the obtained transportation task information, specifically including: Read the obtained transportation task information, determine the transportation plan of the transportation task information, including the starting position, target position and weight of the goods; Analyze the transportation plan, determine the priority of the transportation task information, and arrange each transportation task information based on the priority; Extract the geographical location coordinates of the starting position and target position of the goods through the coordinate parsing algorithm, divide the transportation task into regional categories according to the logistics area division rules, and at the same time, extract the weight value of the goods; Extract the key data of the regional category division result and the weight value of the goods and perform anomaly detection to judge whether the starting position and target position of the goods are within the reasonable logistics operation area range, and whether the weight value of the goods meets the load capacity range of the IGV logistics vehicle.

3. The task allocation method of an IGV logistics vehicle according to claim 2, characterized in that Determine the priority of the transportation task information, specifically including: Delivery time urgency assessment: Extract the difference between the expected delivery time of the task and the current time from the transportation plan, determine the time urgency value of the transportation task information, establish the mapping relationship between the time urgency and the priority, and determine the time urgency weight; Goods value assessment: Extract the goods information in the transportation plan, evaluate the goods value based on the goods information, establish the mapping relationship between the goods value assessment result and the priority, and determine the goods value weight; Associated business impact analysis: Analyze the upstream and downstream business links associated with the transportation task information, judge the impact degree of the delay or early completion of the transportation task information on other business links, and determine the impact degree weight based on the judgment result; Priority assessment: Based on the time urgency weight, goods value weight and impact degree weight, perform comprehensive calculation to obtain the priority score of each transportation task information, and arrange the priority of each transportation task information according to the priority score.

4. The task allocation method of an IGV logistics vehicle according to claim 1, wherein, Perform two-way matching through blockchain technology according to the parsing result of the transportation task information and the status information of the IGV logistics vehicle, specifically including: Build a task allocation blockchain based on the parsing result of the transportation task information and the status information of the IGV logistics vehicle, and build index keywords for the transportation task information and the IGV logistics vehicle status information on the task allocation blockchain respectively; Combined with the index keywords, perform two-way retrieval and matching on the transportation task information and the status information of the IGV logistics vehicle based on the matching rules, and adjust the two-way retrieval and matching results according to the weight of each matching rule; Among them, the matching rules include: preferentially matching the IGV logistics vehicle that is close to the starting position of the goods and is in an idle state; for high-priority tasks, preferentially allocating to vehicles with sufficient remaining power, suitable load capacity, and relatively short driving paths; comprehensively considering the driving speed of the vehicle, the expected arrival time, and the delivery time requirement of the task for matching; After completing the two-way matching, record the matching result on the task matching blockchain. At the same time, feedback the matching result to the task management system and the corresponding IGV logistics vehicle.

5. The task allocation method of an IGV logistics vehicle according to claim 4, characterized in that, Conduct two-way retrieval and matching on the transportation task information and the status information of the IGV logistics vehicle based on the matching rules, specifically including: Retrieve the status information of the IGV logistics vehicle on the task allocation blockchain based on the index keywords of the transportation task information, and screen the status information records of the IGV logistics vehicle based on the coordinate characteristics of the starting position of the goods; At the same time, conduct a reverse retrieval of the transportation task information based on the index keywords of the IGV logistics vehicle status information. For the IGV logistics vehicle in an idle state and with remaining power greater than the preset power range, screen the transportation task information; Based on the screening results, obtain the initial matching set of the transportation task information and the IGV logistics vehicle; From the initial matching set, for each transportation task information, sort the candidate IGV logistics vehicles according to the distance between the vehicle position and the starting position of the goods; Screen out the transportation tasks with high-priority levels, and evaluate the operating state performance of the vehicles among the corresponding candidate IGV logistics vehicles; Based on the screening results in the initial matching set, construct a matching subset, and based on the matching subset, predict the driving speed of the vehicle to execute the task according to the historical driving speed data of the IGV logistics vehicle. Combine the starting position and the target position of the goods in the task to determine the expected driving time; Determine the expected arrival time of the transportation task information based on the expected driving time, compare the expected arrival time with the delivery time requirement, and obtain the matching pairs whose expected arrival time does not exceed the delivery time requirement; Calculate the matching degree of each obtained matching pair, obtain the comprehensive score of the matching pair, and re-sort and screen the matching results according to the comprehensive score to obtain the final two-way matching result.

6. The task allocation method of an IGV logistics vehicle according to claim 5, characterized in that, The comprehensive score of the matching pair includes the distance score between the vehicle and the starting position of the goods, the vehicle idle state score, the vehicle operating state performance score under high-priority tasks, and the matching degree score of the expected delivery time requirement.

7. The task allocation method of an IGV logistics vehicle according to claim 1, characterized in that After receiving the transportation task, the IGV logistics vehicle executes the corresponding transportation task according to the planned path, and also includes: real-time collecting various operation data of the vehicle, monitoring the vehicle state of the IGV logistics vehicle. At the same time, the sensor monitors the state of the transported goods, adjusts the driving path according to the real-time road condition information, and promptly feedbacks the path change information to the task management system.

8. The task allocation method of an IGV logistics vehicle according to claim 1, characterized in that, Monitoring the vehicle state of the IGV logistics vehicle also includes: conducting fault warning for the IGV logistics vehicle based on historical vehicle state data. When it is predicted that the IGV logistics vehicle may have an abnormality during the task execution, quickly synchronize the abnormality warning information to the customer side and the logistics side based on the communication link.

9. The task allocation method of an IGV logistics vehicle according to claim 1, characterized in that, Constructing the communication link between the customer side and the logistics side also includes: Set up data synchronization nodes in the communication link, establish a real-time data synchronization mechanism, and obtain the task progress and driving trajectory data of the IGV logistics vehicle in real time based on the real-time synchronization mechanism; The customer side receives the task progress data in real time and synchronously obtains the remaining distance and estimated arrival time of the IGV logistics vehicle from the goods destination; at the same time, the logistics side synchronously obtains the real-time position and vehicle status information of the vehicle.

10. A task allocation system for an IGV logistics vehicle, which is applied in a task allocation method for an IGV logistics vehicle as described in claim 1, characterized in that, It includes: A task acquisition module configured to obtain transportation task information from the task management system, read and parse it, determine the transportation plan, and arrange it based on the priority of each transportation task information; A vehicle status monitoring module configured to obtain the status information of each IGV logistics vehicle in real time, monitor the real-time status of the vehicle, and issue a fault warning for the IGV logistics vehicle based on the historical vehicle status data; A blockchain platform configured to build a task assignment blockchain based on the parsing results of the transportation task information and the status information of the IGV logistics vehicle, and perform two-way retrieval and matching on the transportation task information and the status information of the IGV logistics vehicle according to the matching rules; A path planning module configured to plan a driving path for the IGV logistics vehicle that receives the transportation task according to the task assignment result, and timely feedback the path change information to the task management system and the vehicle status monitoring module.

Images