Railway freight terminal customer reservation allocation and integrated navigation system

Abstract

The present application provides a railway freight station customer appointment allocation and integrated navigation system, which includes: a customer appointment allocation module, which is used to obtain appointment request information of customers waiting to enter the station from the railway freight platform and historical operation data of the railway freight station through a customer appointment allocation model, and process them to generate a QR code corresponding to the customer allocation strategy; a freight navigation module, which is used to start the configured navigation service component to run the navigation service according to the scanning of the QR code storing the entry time period and the entrance gate allocation information, and generate navigation prompts according to the real-time perception information of the work site and the operation sequence. The navigation prompts are used to guide customers to enter, work and exit the station, and enter the work site and perform the operation tasks according to the real-time perception information of the entrance gate and the work site and the operation sequence, and guide them to exit the station after completing the operation; a freight operation analysis module, which is used to monitor and record the execution sequence information of the entry and exit of customers waiting to enter the station and the operation tasks, and analyze them to generate support data for generating an appointment allocation strategy for customers in the next cycle.

Description

Technical Field

[0001] The present invention relates to the technical field of railway freight transportation, and in particular to a railway freight station customer reservation allocation and integrated navigation system. Background Art

[0002] At present, the customer reservation allocation and integrated navigation system of railway freight stations has achieved real-time data exchange with the 95306 platform and driving system, so that key information about the type, quantity, weight of cargo transportation, arrival and departure status of vehicles, loading and unloading methods, etc. can be shared within the station. Although the existing system has improved the convenience of information sharing to a certain extent, there is still room for optimization in the face of growing freight demand and complex station operation processes.

[0003] Especially with the widespread implementation of the customer appointment system, how to effectively integrate appointment information, automatic scheduling information within the station, container number and location information, and real-time perception information of the terminal has become an urgent problem to be solved. Given that the operations within the station involve weighing and inspection, safety inspection, loading and unloading operations, operation confirmation and other complex links, the traditional entry arrangement method can no longer meet the needs of efficient operation.

[0004] Therefore, in order to further optimize the customer appointment entry process, realize integrated navigation services inside and outside the station, reduce customer waiting time and simplify the operation process, and at the same time help the station to arrange operations reasonably and improve overall operational efficiency, it is particularly important to develop a railway freight station customer appointment allocation and integrated navigation system that can be intelligently optimized according to customer appointment information and station operation needs. Summary of the invention

[0005] In order to solve the above-mentioned technical problems, the present application provides a railway freight terminal customer reservation allocation and integrated navigation system to at least solve or alleviate the above-mentioned problems existing in the prior art.

[0006] A railway freight station customer reservation allocation and integrated navigation system, comprising:

[0007] The customer reservation allocation module is used to obtain the reservation request information of the customers waiting to enter the station from the railway freight platform and the historical operation data of the railway freight station through the customer reservation allocation model, and generate a QR code corresponding to the customer allocation strategy. The QR code stores the information of the entry time period and entrance of the users waiting to enter the station;

[0008] A freight navigation module, which is used to start the configured navigation service component to run the navigation service according to the scanning of the QR code, and generate navigation prompts according to the real-time perception information of the work site and the work sequence. The navigation is used to guide the customers waiting to enter the station to enter and exit the station based on the progress time period, and enter the work site and perform the work tasks according to the real-time perception information of the station entrance and the work site and the work sequence, and guide them to exit the station after completing the work;

[0009] The freight operation analysis module is used to monitor and record the entry and exit information of the customers waiting to enter the station and the execution sequence information of the operation tasks, and analyze them to generate support data for generating an appointment allocation strategy for the customers in the next cycle.

[0010] In this application, the reservation request information of the customer to enter the station is obtained from the railway freight platform through the customer reservation allocation module, and is processed in combination with the historical operation data of the station. Through the customer reservation allocation model, the system can comprehensively consider multiple factors (such as freight type, quantity, weight, vehicle status, loading and unloading method, etc.) to generate an optimized customer allocation strategy. This not only realizes the effective integration of customer appointment information, but also ensures the rationality of the allocation strategy. Further, the generated QR code contains the entry time period and entrance information of the user to enter the station, so that the customer can enter the station according to the scheduled time and route, avoiding congestion and waiting, and improving the efficiency of entering the station. Furthermore, the freight navigation module starts the navigation service component according to the scanned QR code and generates navigation prompts based on the entry time period, entrance, and real-time perception information and operation sequence of the operation site. This integrated navigation service not only guides customers to enter the operation site smoothly, but also adjusts the navigation path according to the real-time situation to ensure that customers can reach the designated location efficiently and accurately. Through the navigation prompts, customers can clearly understand the operation sequence and site conditions, so as to perform operations according to instructions, reduce unnecessary communication and waiting time, and simplify the operation process. Finally, the freight operation analysis module monitors, records, and analyzes the execution sequence information of customers entering and leaving the station and the operation tasks. These data provide valuable feedback to the system, which is used to generate appointment allocation strategies for customers in the next cycle. Through continuous learning and optimization, the system can gradually adapt to changes in freight demand and adjustments to the operation process within the station, improving overall operational efficiency. BRIEF DESCRIPTION OF THE DRAWINGS

[0011] The drawings constituting a part of this application are used to provide a further understanding of this application, so that other features, purposes and advantages of this application become more obvious. The schematic embodiment drawings and their descriptions of this application are used to explain this application and do not constitute an improper limitation on this application. In the drawings:

[0012] Figure 1 It is a structural diagram of a railway freight station customer reservation allocation and integrated navigation system according to an embodiment of the present application;

[0013] Figure 2 It is a framework diagram for generating navigation prompts based on real-time perception information of a work site provided by the present invention;

[0014] Figure 3 It is a schematic diagram of the principle of the customer appointment allocation module provided by the present invention;

[0015] Figure 4 It is a schematic diagram of the principle of planning intra-station navigation of the present invention. DETAILED DESCRIPTION

[0016] In order to enable those skilled in the art to better understand the solution of the present application, the following will be combined with the drawings in the embodiments of the present application to clearly and completely describe the technical solutions in the embodiments of the present application. Obviously, the described embodiments are only part of the embodiments of the present application, not all of the embodiments. Based on the embodiments in the present application, all other embodiments obtained by ordinary technicians in this field without creative work should fall within the scope of protection of this application.

[0017] Figure 1 1 is a structural diagram of a railway freight station customer reservation allocation and integrated navigation system according to an embodiment of the present application. Figure 1 As shown in the figure, the railway freight terminal customer reservation allocation and integrated navigation system includes:

[0018] The customer reservation allocation module is used to obtain the reservation request information of the customers waiting to enter the station from the railway freight platform and the historical operation data of the railway freight station through the customer reservation allocation model, and generate a QR code corresponding to the customer allocation strategy. The QR code stores the information of the entry time period and entrance of the users waiting to enter the station;

[0019] A freight navigation module, which is used to start the configured navigation service component to run the navigation service according to the scanning of the QR code, and generate navigation prompts according to the real-time perception information of the work site and the work sequence. The navigation is used to guide the customers waiting to enter the station to enter and exit the station based on the progress time period, and enter the work site and perform the work tasks according to the real-time perception information of the station entrance and the work site and the work sequence, and guide them to exit the station after completing the work;

[0020] The freight operation analysis module is used to monitor and record the entry and exit information of the customers waiting to enter the station and the execution sequence information of the operation tasks, and analyze them to generate support data for generating an appointment allocation strategy for the customers in the next cycle.

[0021] Optionally, the reservation request information includes the arrival date, cargo name, cargo weight, cargo type, volume, assignee, telephone number, etc. The expected arrival date defaults to within two days of the arrival date.

[0022] Optionally, the customer appointment allocation model is constructed based on a first target model and a second target model, wherein the first target model is used to minimize the time that the customers waiting to enter the station stay in the station, and the second target model is used to maximize the responsiveness of the customers waiting to enter the station to the railway freight allocation strategy.

[0023] Therefore, in the railway freight station customer appointment allocation and integrated navigation system, a customer appointment allocation model based on the first objective model and the second objective model is adopted, which has the following technical benefits:

[0024] 1. Optimizing customer retention time

[0025] The first target model minimizes the time that customers waiting to enter the terminal stay in the terminal. By optimizing the customer residence time, the system can ensure that the turnover of goods in the terminal is accelerated, reducing unnecessary waiting and delays, thereby improving the operational efficiency of the entire terminal. Shorter residence time also means faster cargo processing and higher throughput, which helps to cope with the growing demand for freight.

[0026] In addition, customers usually want to work efficiently after entering the station to reduce time costs. By shortening the residence time, the system can improve customer satisfaction and enhance customer stickiness.

[0027] 2. Enhance customer responsiveness

[0028] The second objective model maximizes the responsiveness of customers waiting to enter the yard to the railway freight allocation strategy. By optimizing the allocation strategy, customers are more inclined to accept and execute appointments on time, thereby improving the appointment execution rate. This helps to reduce idle resources and operation delays caused by customers' missed appointments or late arrivals, and further improves the overall operational efficiency of the yard. In addition, higher customer responsiveness enables the yard to more accurately predict and arrange operation needs, and reasonably allocate resources such as manpower, material resources and time. This helps to reduce resource waste, reduce operating costs, and provide customers with more stable and reliable services.

[0029] Optionally, the customer appointment allocation model is constructed based on a normalized model of the first objective model and the sum of the second objective model.

[0030] To this end, the above-mentioned additive model can balance the two goals of minimizing customer residence time and maximizing customer responsiveness, so that the generated allocation strategy can not only meet the needs of site operation efficiency, but also take into account the actual experience and needs of customers. This helps to improve the overall performance of the system and customer satisfaction.

[0031] Optionally, the customer appointment allocation model also includes a first constraint model, a second constraint model, a third constraint model, and a fourth constraint model. The first constraint model is used to allocate all customers waiting to enter the station to different time periods and entrances; the second constraint model is used to ensure that the number of allocated customers waiting to enter the station does not exceed the upper limit of the carrying capacity that any entrance can pass smoothly; the third constraint model is used to ensure that the waiting time of any number of customers waiting to enter the station at any work site does not exceed the upper limit of the waiting time; the fourth constraint model is used to ensure that all customers waiting to enter the station need to participate in the railway freight allocation strategy.

[0032] To this end, in the railway freight station customer appointment allocation and integrated navigation system, the first constraint model, the second constraint model, the third constraint model, and the fourth constraint model are introduced as a supplement to the customer appointment allocation model, which has the following technical benefits:

[0033] (1) The first constraint model ensures that all customers waiting to enter the terminal are assigned to different time periods and entrances. By ensuring that each customer waiting to enter the terminal is assigned to a different time period and entrance, the constraint model effectively avoids time and space conflicts between customers and ensures the continuity and smoothness of terminal operations. This helps reduce operational delays and resource waste caused by conflicts and improves the overall operational efficiency of the terminal.

[0034] (2) The second constraint model is used to limit the number of customers waiting to enter the station to no more than the upper limit of the load of any entrance. Each entrance has its upper limit. Exceeding this limit may lead to congestion and disorder at the entrance, or even cause safety accidents. This constraint model ensures that the workload of any entrance is within a safe and controllable range by limiting the number of customers waiting to enter the station, providing a safer and more orderly working environment for operators and customers. In addition, the reasonable allocation of entrance resources enables each entrance to handle operating tasks efficiently and evenly, avoiding the situation where some entrances are overloaded while others are idle, thereby improving resource utilization and overall operating efficiency.

[0035] (3) The third constraint model ensures that the waiting time of any customer waiting to enter the site at any work site does not exceed the upper limit of the waiting time: too long a waiting time will reduce customer satisfaction and even lead to customer loss. By setting an upper limit on the waiting time, this constraint model ensures that the waiting time of each customer at the work site is within an acceptable range, thereby improving the overall customer experience and satisfaction. Furthermore, by limiting the waiting time, the system can prompt the site to optimize the working environment, ensure the orderly organization of the work, and improve the work efficiency and response speed.

[0036] (4) The fourth constraint model ensures that all customers waiting to enter the yard participate in the railway freight allocation strategy. This constraint model ensures that all customers waiting to enter the yard are included in the management scope of the railway freight allocation strategy, realizing the comprehensive collection and unified management of customer information. This helps the system to more accurately evaluate and optimize operational needs and improve the rationality and accuracy of allocation. Furthermore, by ensuring that all customers participate in the allocation strategy, the system can better respond to various emergencies and changes in demand, enhancing the robustness and adaptability of the system.

[0037] Optionally, the first target model defines the first target function according to the following formula (1): Minimize the time that the customers waiting to enter the station stay in the station: ,in, represents the allocation decision variable, ,when =1 means The pending customers are assigned to time period, An entrance, Indicates Time period Customers waiting to enter the market from The time spent in the station after entering the station through the entrance, 1≤ ≤L,1≤ ≤M,1≤ ≤N, L, M, N are integers greater than 1.

[0038] To this end, the model in the above formula not only considers the entry efficiency of a single customer, but also balances the entry time and location of all customers through a global optimization strategy, ensuring the continuity and smoothness of the terminal operation. This global perspective optimization maximizes the utilization of terminal resources and reduces resource waste and operation delays caused by conflicts and waiting. The detailed analysis is as follows:

[0039] (1) Reduce operational delays: By avoiding overlaps in time and space, customers can enter the site smoothly as planned, reducing operational delays caused by conflicts and improving the punctuality and reliability of site operations.

[0040] (2) Improve resource utilization: Rationally allocate customers to different time periods and entrances so that resources in each time period and entrance can be fully utilized, avoiding idleness and waste of resources and improving the overall operational efficiency of the station.

[0041] (3) Improve customer satisfaction: Customers spend less time on-site, which means their waiting time is shortened, thereby improving customer satisfaction.

[0042] Optionally, the normalized model of the first target model is: , , , Indicates Estimated time for each client to be on-site. is the ratio of the maximum distance to the average speed of traffic in the station + .

[0043] The formula first defines and , respectively represent the maximum possible value and the minimum estimated value of the operation time. , that is, the normalized value, the model can evaluate and optimize the job time allocation strategy. This strategy has the following technical benefits:

[0044] (1) By ensuring that each customer is assigned to a different time period and entrance, time and space conflicts are effectively avoided, ensuring the continuity and smoothness of terminal operations.

[0045] (2) It reduces operational delays and resource waste caused by conflicts, enabling terminals to handle customer operations more efficiently and improving overall operational efficiency.

[0046] To this end, when constructing the customer appointment allocation model based on the normalized model of the first target model and the sum of the second target model, according to the formula To build, Represents a customer appointment allocation model.

[0047] Optionally, the second objective model is defined by the second objective function of the following formula (2): The responsiveness of the customers waiting to enter the site to the railway freight distribution strategy is maximized: , represents the allocation decision variable, ,when =1 means it is The pending customers are assigned to time period, The entrance of Indicates Customers entering the market Time period for entrance The preference coefficient of , 1≤ ≤L,1≤ ≤M,1≤ ≤N, L, M, N are integers greater than 1.

[0048] To this end, the second objective function is defined to maximize the responsiveness of customers waiting to enter the station to the railway freight allocation strategy. The core is to reasonably allocate customers waiting to enter the station to different time periods and entrances by optimizing the allocation strategy, while considering the customer's preference coefficient to ensure that the allocation plan is both efficient and meets customer needs. This formula can achieve the maximum total responsiveness by adjusting the allocation decision variables under the premise of meeting certain constraints. By optimizing the allocation strategy, customers are more willing to accept and execute appointments on time, thereby significantly improving the appointment execution rate. This helps to reduce idle resources and operation delays caused by customer failures or lateness, and improves the overall operational efficiency of the station. In addition, a higher customer responsiveness enables the station to more accurately predict and arrange operation needs, and then reasonably allocate resources such as manpower, material resources and time. This precise resource allocation helps to reduce resource waste, reduce operating costs, and improve resource utilization. The optimized allocation strategy can ensure that each entrance can handle operation tasks efficiently and evenly, avoiding operation delays and customer dissatisfaction caused by uneven resource allocation. This stable operating environment provides customers with a more reliable service experience.

[0049] Optionally, the first constraint model is used to allocate all customers waiting to enter the station to different time periods and entrances based on formula (3): , Indicates The time period is The number of customers assigned to enter the station through the entrance, represents the allocation decision variable, ,when =1 means it is The pending customers are assigned to time period, The entrance of Indicates The pending customers are assigned to The first entrance time period, , 1≤ ≤L,1≤ ≤M,1≤ ≤N, L, M, N are integers greater than 1.

[0050] To this end, the above formula implements linear constraints to ensure that all customers waiting to enter the station can be efficiently and orderly assigned to different time periods and entrances. Through this mechanism, the model effectively avoids time and space conflicts between customers. The detailed principle is as follows:

[0051] (1) The complex customer allocation problem is transformed into a quantifiable decision-making process. The allocation decision of each customer is controlled by a decision variable. When customer m is assigned to time period n and entrance l, the variable is 1, otherwise it is 0. This binary processing method simplifies the complexity of the problem and makes the allocation process clearer.

[0052] (2) The model ensures that all customers waiting to enter the station can be reasonably allocated through equality constraints. That is, the total number of customers allocated in all time periods and entrances is equal to the total number of customers waiting to enter the station. This constraint not only ensures the comprehensiveness of the allocation, but also avoids the waste and idleness of resources.

[0053] (3) By avoiding time and space conflicts between customers, the continuity and smoothness of terminal operations are significantly improved. In a busy terminal environment, conflicts between customers often lead to operation delays and resource waste, which in turn affects the overall operational efficiency. The model effectively prevents the occurrence of such conflicts through a sophisticated allocation strategy, providing a strong guarantee for the efficient operation of the terminal.

[0054] Optionally, the second constraint model is used to ensure that the number of customers to be assigned to enter the station does not exceed the upper limit of the carrying capacity that can be successfully passed by any entrance based on the following formula (4): , Indicates The time period is The number of customers assigned to each entrance, Indicates The entrance gates ensure that the load limit can be successfully passed through the entrance gate inspection, 1≤ ≤L,1≤ ≤M,1≤ ≤N, L, M, N are integers greater than 1. During the gate inspection, such as verifying the car license plate number, container number, container damage detection, weighing, etc. Customers who have not made an appointment are also required to get off the car and scan the code to fill in the information to enter the station.

[0055] To this end, by controlling the number of customers assigned to each entrance in each time period to ensure that the upper limit of the load is not exceeded, the flow of entrances can be effectively managed. The detailed principles are as follows:

[0056] (1) Ensure operational safety: By setting a load limit and strictly enforcing it, we can avoid congestion and disorder at the entrance caused by excessive customer flow, effectively prevent the occurrence of safety accidents, and create a safe environment for the work site.

[0057] (2) Improve operational efficiency: Rationally allocate customers to various entrances to ensure balanced workload at each entrance. This not only avoids processing bottlenecks caused by overload at certain entrances, but also reduces idle resources at other entrances, thereby improving overall operational efficiency.

[0058] (3) Optimizing resource utilization: Customers are allocated based on the actual carrying capacity of the entrance, maximizing the use of existing resources, reducing resource waste, and improving resource utilization efficiency.

[0059] Optionally, the third constraint model is used to ensure that the waiting time of any number of customers waiting to enter the site at any work site does not exceed the upper limit of the waiting time based on the following formula (5): , Indicated in The upper limit of the waiting time for the jth job sequence to start in a time period, Indicates Customers waiting to enter the market are Time period Estimated start time of work at each work site, 1≤ ≤L,1≤ ≤M,1≤ ≤N, L, M, N are integers greater than 1.

[0060] Optionally, According to the scheduled cargo and delivery information, the operation sequence of incoming customers in the terminal within a certain period of time can be optimized and estimated.

[0061] To this end, the above formula specifies the upper limit of the waiting time for each job sequence (j) in the i-th time period, which sets a clear boundary for the customer's waiting time and prevents indefinite waiting. In addition, the customer's operation time estimate is also specified, indicating the customer's estimated start time for the operation in the time period and operation site. This estimate provides basic data support for system scheduling.

[0062] Optionally, the fourth constraint model is used to ensure that all customers waiting to enter the terminal need to participate in the railway freight distribution strategy based on the following formula (6): , 1≤ ≤L,1≤ ≤M,1≤ ≤N, L, M, N are integers greater than 1, represents the allocation decision variable, ,when =1 means yes The pending customers are assigned to time period, The entrance of the station.

[0063] Optionally, the navigation prompts include off-site navigation prompts and on-site navigation prompts, the off-site navigation prompts are used to guide the customers waiting to enter the site outside the work site to reach the site entrance with reference to the entry time period, and the on-site navigation prompts are used to guide the customers waiting to enter the site from the site entrance to the entry entrance and enter the work site with reference to the entry time period.

[0064] To this end, the navigation prompt system brings the following technical benefits to the railway freight distribution strategy through the organic combination of off-station navigation prompts and on-station navigation prompts:

[0065] (1) First, the off-site navigation prompt system effectively guides customers waiting to enter the station outside the operation site to arrive at the station entrance within the specified time period through accurate information on the time period of entry. This measure not only reduces the waiting time of customers due to information asymmetry, but also avoids congestion and delays in entering the station, significantly improving the efficiency of entry. At the same time, it optimizes resource allocation, ensuring sufficient entry capacity and processing capacity during peak hours, thereby improving resource utilization.

[0066] (2) Secondly, the in-station navigation prompt system further refines the guidance process, guiding customers from the station entrance to the station entrance safely and quickly, and smoothly entering the work site. This process reduces the time customers spend searching in the site and improves overall work efficiency. In addition, clear navigation prompts reduce customers' uncertainty and anxiety, improve customer experience, and enhance customer satisfaction.

[0067] (3) Furthermore, the navigation prompt system ensures the smooth progress of cargo loading and unloading operations by monitoring the customer's location and status in real time. This function reduces delays and errors caused by human factors and improves the safety and accuracy of operations. At the same time, the system can adjust the entry time period and terminal entrance location according to actual conditions, and inform customers in real time through navigation prompts, which enhances the flexibility and adaptability of the system.

[0068] (4) Finally, the navigation prompt system reduces the cost of manual management and coordination by automating the guidance process. The system can automatically update and adjust navigation prompts, reducing management complexity and labor costs and improving management efficiency.

[0069] Optionally, the railway freight terminal customer appointment allocation and integrated navigation system also includes a camera installed at the station entrance and exit to capture images of traffic at the station entrance, so that the freight navigation module can generate real-time perception information of the work site based on the captured images and generate the off-site navigation prompts.

[0070] To this end, the cameras included in the railway freight terminal customer appointment allocation and integrated navigation system are installed at the entrance and exit of the terminal, which has the following technical benefits:

[0071] (1) First, the camera can capture the traffic conditions at the station entrance in real time, providing accurate on-site data for the freight navigation module. This data reflects the real-time traffic conditions at the work site, including traffic volume, vehicle type, driving speed, etc., enabling the navigation module to generate accurate real-time perception information of the work site.

[0072] (2) Secondly, based on the images captured by the camera, the freight navigation module can quickly generate off-site navigation prompts. These prompts provide customers with the best approach route and time suggestions based on real-time traffic conditions, avoiding congestion and delays and improving approach efficiency. At the same time, customers can plan their trips in advance based on the navigation prompts, reducing waiting time and improving customer experience.

[0073] (3) In addition, the installation of cameras can also help improve the safety of the work site. Through real-time monitoring, the system can promptly detect potential safety hazards, such as illegal parking and speeding, and take appropriate measures to intervene to ensure traffic order and safety at the work site.

[0074] (4) Finally, the combination of the camera and the freight navigation module enables intelligent management of traffic conditions at the work site. The system can automatically analyze and process the captured image data, generate accurate navigation prompts and perception information, reduce the cost of manual management and coordination, and improve management efficiency.

[0075] Optionally, the freight navigation module performs the following steps to generate the in-station navigation prompt:

[0076] The edge detection camera installed at the work site collects and identifies the vehicle numbers in the work site and calculates the vehicle queue length and the operation waiting time at the work site to compare with the set operation waiting time threshold;

[0077] The parking space information is collected by the camera installed inside the station. When the estimated waiting time of the queued vehicles and the operation is greater than the set waiting time threshold, the operation sequence is adjusted according to the real-time perception information of the operation site and the operation plan, and voice prompts are given to guide the vehicle to enter the operation site or park in place;

[0078] The position and driving speed of the freight vehicle on the current section of the work site are obtained through the vehicle navigation software and the camera installed in the site. With the station intersection as the node and the current section as the path information, the path planning of the freight vehicle is carried out so that the vehicle arrives at the work site or exits the station. The path planning assigns different weights according to the path information and the situation speed.

[0079] See also Figure 2, a schematic diagram of freight vehicle navigation based on real-time perception information of the work site, which is applied to a specific scenario in this embodiment. The real-time perception information of the work site includes, for example, accessible boundaries of the work site and passenger flow congestion of the path. In addition, in this scenario, historical spatiotemporal data is also used. The historical spatiotemporal data includes historical station entrances, historical traffic flow estimates, and historical station entrance recommendations. The historical spatiotemporal data (i.e., historical work data) is obtained, for example, from the implementation of railway freight station customer appointment allocation and integrated navigation for customers in the previous cycle.

[0080] To this end, the path planning of the freight vehicle is performed Figure 2 It is called in-station path guidance. In the in-station path guidance part, the real-time perception information of the above-mentioned work site, the status of inbound and outbound operations, and the results of the on-site guidance customer allocation are used to guide the inbound and outbound operation paths, and further conduct outbound inspection operations.

[0081] In addition, a customer entry guidance allocation logic is configured, in which all customers who have made entry and exit appointments are collected, and then the customers who are to enter are selected according to the time period. The above-mentioned customer appointment allocation module is started to implement the entry guidance customer allocation and obtain the corresponding results, including at least the above-mentioned QR code.

[0082] Furthermore, by configuring the processing logic of the in-station operation analysis, the freight operation analysis module is triggered to analyze the entry and exit information of the customers waiting to enter the station and the execution sequence information of the operation tasks, so as to generate support data for generating appointment allocation strategies for the customers in the next cycle, so as to optimize the processing process of the customer appointment allocation module for the customers in the next cycle.

[0083] To this end, the freight navigation module performs a series of steps to generate in-station navigation prompts, which has the following technical benefits:

[0084] (1) First, the edge detection camera installed at the work site collects the vehicle number and calculates the length of the vehicle queue, and estimates the waiting time for the operation based on the waybill information. The waiting time for the operation is compared with the set waiting time threshold. This step enables the system to grasp the vehicle situation in the work site in real time, including the number of vehicles, the length of the queue, and the estimated waiting time. This helps the system to detect potential congestion and delays in a timely manner, improve the comfort of the working environment and the orderly operation of the operation, and provide accurate data support for subsequent navigation prompts.

[0085] (2) Secondly, the parking space information is collected by the camera installed inside the station. When the estimated waiting time of the queued vehicles and the operation is greater than the set waiting time threshold, the system will adjust the operation sequence according to the real-time perception information of the operation site and the operation plan, and provide voice prompts to guide the vehicle to enter the operation site or park. This function ensures the efficient use of the operation site, avoids long waiting time for vehicles and wastes resources. At the same time, the addition of voice prompts makes navigation more user-friendly and improves the customer experience.

[0086] (3) Finally, the system obtains the location and driving speed of the freight vehicle on the current section of the work site through the vehicle navigation software and the cameras installed in the site, and plans the route of the freight vehicle with the station intersection as the node and the current section as the path information, so that the vehicle can smoothly reach the work site or exit the station. This step realizes the precise guidance of the vehicle's driving path, improving the traffic efficiency of the work site and the transportation efficiency of the vehicle.

[0087] Optionally, the freight operation analysis module is specifically used to analyze the entry and exit information of the customers waiting to enter the station and the execution sequence information of the operation tasks, so as to determine the customer operation sequence, operation duration, entry and exit time, customer entry and exit ports and customer preference information of the customers waiting to enter the station as the supporting data.

[0088] Specifically, the vectorization unit in the freight operation analysis module vectorizes the execution sequence of the entry and exit of the customer to be entered and the operation task respectively to obtain the entry and exit feature vector and the task execution feature vector, and then performs attention analysis on the entry and exit feature vector and the task execution feature vector based on the attention analysis unit therein to obtain the attention weight, and fuses the entry and exit feature vector and the task execution feature vector based on the attention weight to obtain a fusion vector, and then encodes the fusion vector into a context vector according to the time step based on the encoding unit, and then decodes the context vector based on the adjacent time step based on the decoding unit to predict the customer operation sequence of the customer to be entered; uses the time series regression unit therein to perform time regression analysis on the fusion vector to predict the operation duration and the entry and exit duration, and uses the logistic regression unit therein to perform logistic regression analysis on the fusion vector to predict the customer entry and exit port, and uses the association pattern recognition unit therein to associate and pattern recognize the features in the fusion vector to predict the customer's preference information.

[0089] To this end, the entry and exit of the customer to be entered and the execution sequence of the operation task are converted into feature vectors through the vectorization unit. Through vectorization processing, the unstructured sequence data can be converted into structured feature vectors, which is convenient for subsequent analysis and prediction. Then, based on the attention analysis unit, the entry and exit feature vectors and the task execution feature vectors are analyzed, and the importance of each feature vector (i.e., attention weight) is calculated. Through the attention mechanism, different parts of the input sequence can be dynamically focused on, so as to better capture long-distance dependencies and important features. Then, based on the attention weight, the fusion unit fuses the entry and exit feature vectors and the task execution feature vectors to obtain a fusion vector. The fusion vector integrates the information of entry and exit and task execution, providing more comprehensive feature support for subsequent prediction. Further, the encoding unit encodes the fusion vector into a context vector according to the time step. The context vector captures the time series information and semantic information in the fusion vector. The decoding unit decodes the context vector based on the adjacent time steps to predict the customer operation sequence of the customer to be entered. Through the decoding process, the context vector can be converted into a specific operation sequence prediction result. Based on the time series regression unit, the fusion vector is subjected to time regression analysis to predict the operation duration and the entry and exit duration. Time regression analysis can capture trends and periodic changes in time series data, thereby providing accurate duration predictions. Based on the logistic regression unit, the fusion vector is subjected to logistic regression analysis to predict the customer's entry and exit points. Logistic regression is a classification algorithm that can predict the customer's most likely entry and exit points based on the features in the fusion vector. Based on the association pattern recognition unit, the features in the fusion vector are associated and pattern recognized to predict the customer's preference information. By mining the association relationships and patterns between features, we can gain a deep understanding of the customer's preferences and needs, providing support for providing more personalized logistics services.

[0090] The specific implementation methods of the above-mentioned units are as follows:

[0091] Vectorization unit: Use methods such as feature hashing to convert the execution sequence of inbound and outbound and job tasks into feature vectors. These vectors can capture key information and patterns in the sequence.

[0092] Attention analysis unit: Self-Attention or Multi-Head Attention can be used to assign weights to the inbound and outbound feature vectors and the task execution feature vectors. In this way, the model can pay more attention to the features that have an important impact on the prediction results.

[0093] Encoding unit: This unit can encode the fusion vector into a context vector by time step using a recurrent neural network (RNN), a long short-term memory network (LSTM), or a gated recurrent unit (GRU). These networks can capture temporal dependencies and long-term memory in a sequence.

[0094] Decoding unit: The decoding unit can also use networks such as RNN, LSTM or GRU, but it decodes the context vector based on adjacent time steps to generate a predicted customer job sequence. In addition, strategies such as beam search can be used to improve the accuracy and diversity of the generated sequence.

[0095] Time series regression unit: Linear regression, support vector regression (SVR), or neural network regression can be used to perform time regression analysis on the fusion vector to predict the operation duration and the time of entering and leaving the station. These methods can be selected according to the characteristics of the data and the prediction requirements.

[0096] Logistic regression unit: Logistic regression or softmax regression can be used to perform logistic regression analysis on the fused vector to predict the customer's entry and exit points. These methods can handle classification problems and output the probability of each category.

[0097] Association pattern recognition unit: This unit can use association rule mining or pattern recognition algorithms (such as Apriori algorithm, FP-Growth algorithm, etc.) to associate and recognize patterns of features in the fusion vector. In this way, the association between customer preferences and specific features can be discovered, thereby more accurately predicting customer preference information.

[0098] To this end, the specific function of the freight operation analysis module is to conduct in-depth analysis of the entry and exit information of the incoming customers and the execution sequence information of the operation tasks to determine a series of key parameters, including customer operation sequence, operation duration, entry and exit duration, customer entry and exit ports, and customer preference information. These parameters serve as important supporting data for subsequent operations and have the following technical benefits:

[0099] (1) Improve operational efficiency: By accurately analyzing the customer's operational sequence and operation duration, the freight operation analysis module can help the system arrange the operation plan more reasonably and ensure the efficient use of resources. This helps to reduce waiting time and idle time during the operation process, thereby improving overall operational efficiency.

[0100] (2) By analyzing the time it takes to enter and exit the station and the customer entry and exit points, the module can identify potential bottlenecks and congestion points and make suggestions for improvement. This helps optimize the entry and exit process, reduce congestion and delays, and improve the customer experience.

[0101] (3) It can take into account customer preference information, such as specific entry and exit points, operating hours, etc., to provide customers with more personalized services. This helps to enhance customer satisfaction and loyalty and improve the competitiveness of enterprises.

[0102] See also Figure 3 , is a schematic diagram of applying the above solution of the present application in a specific scenario. In this scenario, the following operation processes are included in sequence:

[0103] Customer appointments;

[0104] Appointment acceptance;

[0105] Determine whether to directly unload or load the goods;

[0106] If it is not direct unloading / direct loading, the processing logic of determining the progress time according to the rules is executed;

[0107] If yes, directly execute the processing logic of determining the progress time according to the rules and the station entrance according to the historical station entrance data to generate the above-mentioned station entrance QR code;

[0108] Execute the entry guidance allocation model logic, in which the customer appointment allocation module is triggered to generate the customer allocation strategy and generate the above-mentioned entry QR code;

[0109] When executing the approach guidance allocation model logic, reference variables that enable the first target model and the second target model to work and constraint conditions that enable each constraint model to work are configured.

[0110] See also Figure 4 , is a schematic diagram of applying the above-mentioned in-site navigation principle provided by the present application to a specific scenario, which specifically includes the following process:

[0111] Conduct entry verification for freight vehicles;

[0112] Determine whether the queue length of the next operation sequence site is less than a specified queue length threshold;

[0113] If yes, the above path planning is performed and the airport wait operation is completed;

[0114] Determine whether the job is completed;

[0115] If completed, the freight vehicle is verified for exit;

[0116] Otherwise, continue to execute the above determination of whether the queue length of the next job sequence site is less than the specified queue length threshold until the job is completed;

[0117] If the queue length of the next operation sequence site is greater than or equal to the specified queue length threshold, determine whether to adjust the operation sequence;

[0118] If not, the freight vehicle is reminded to wait, and the nearest available parking position is calculated according to the vehicle position and the parking space occupancy in the terminal to further perform path planning and wait for the airport to complete the waiting operation;

[0119] If the operation sequence needs to be adjusted, the operation sequence is adjusted and then the process jumps to: determining whether the queue length of the next operation sequence site is less than a specified queue length threshold.

[0120] The above description is only the preferred embodiment of the present application and is not intended to limit the present application. For those skilled in the art, the present application may have various modifications and variations. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present application shall be included in the protection scope of the present application.

Claims

1. A railway freight station customer reservation allocation and integrated navigation system, characterized in that: include: The customer reservation allocation module is used to obtain the reservation request information of the customers waiting to enter the station from the railway freight platform and the historical operation data of the railway freight station through the customer reservation allocation model, and generate a QR code corresponding to the customer allocation strategy. The QR code stores the information of the entry time period and entrance of the users waiting to enter the station; A freight navigation module, for starting the configured navigation service component to run the navigation service according to the scanning of the QR code, generating navigation prompts according to the real-time perception information of the work site and the work sequence, wherein the navigation prompts are used to guide the customers to enter and exit the station based on the entry time period, and enter the work site and perform the work tasks according to the entry gate, the real-time perception information of the work site and the work sequence, and guide the customers to exit the station after the work is completed; The freight operation analysis module is used to monitor and record the entry and exit information of the customers waiting to enter the station and the execution sequence information of the operation tasks, and analyze them to generate support data for generating an appointment allocation strategy for the customers in the next cycle; Among them, the customer appointment allocation model includes an additive model, a first constraint model, a second constraint model, a third constraint model and a fourth constraint model constructed based on the first objective model and the second objective model; wherein the additive model is used to balance the goals of shortest customer residence time and maximum customer responsiveness; the first constraint model is used to ensure that all customers waiting to enter are allocated to different time periods and entrances; the second constraint model is used to ensure that the number of allocated customers waiting to enter does not exceed the upper limit of the load that can be smoothly passed through any entrance; the third constraint model is used to ensure that the waiting time of any number of customers waiting to enter at any work site does not exceed the upper limit of the waiting time; the fourth constraint model is used to ensure that all customers waiting to enter need to participate in the railway freight allocation strategy.

2. The railway freight terminal customer reservation allocation and integrated navigation system according to claim 1 is characterized in that: The first target model is used to minimize the time that the customers waiting to enter the station stay in the station, and the second target model is used to maximize the responsiveness of the customers waiting to enter the station to the railway freight distribution strategy.

3. A railway freight station customer reservation allocation and integrated navigation system according to claim 2, characterized in that: The additive model is constructed based on a normalized model of the first target model and the sum of the second target model.

4. A railway freight terminal customer reservation allocation and integrated navigation system according to claim 2, characterized in that: The first objective model is defined according to the following formula (1): Minimize the time that the customers waiting to enter the station stay in the station: ,in, represents the allocation decision variable, ,when =1 means The pending customers are assigned to time period, An entrance, Indicates Time period Customers waiting to enter the market from The time spent in the station after entering the station through the entrance, 1≤ ≤L,1≤ ≤M,1≤ ≤N, L, M, N are integers greater than 1.

5. A railway freight terminal customer reservation allocation and integrated navigation system according to claim 4, characterized in that: The normalized model of the first target model is: , , , Indicates Estimated time for each client to be on-site.

6. A railway freight terminal customer reservation allocation and integrated navigation system according to claim 2, characterized in that: The second objective model is defined by the second objective function of the following formula (2): The responsiveness of the customers waiting to enter the site to the railway freight distribution strategy is maximized: , represents the allocation decision variable, ,when =1 means it is The pending customers are assigned to time period, The entrance of Indicates Customers entering the market Time period for entrance The preference coefficient of , 1≤ ≤L,1≤ ≤M,1≤ ≤N, L, M, N are integers greater than 1.

7. A railway freight terminal customer reservation allocation and integrated navigation system according to claim 1, characterized in that: The first constraint model is used to allocate all customers waiting to enter the station to different time periods and entrances based on formula (3): , Indicates The time period is The number of customers assigned to enter the station through the entrance, represents the allocation decision variable, ,when =1 means it is The pending customers are assigned to time period, The entrance of Indicates The pending customers are assigned to The first entrance time period, , 1≤ ≤L,1≤ ≤M,1≤ ≤N, L, M, N are integers greater than 1.

8. The railway freight terminal customer reservation allocation and integrated navigation system according to claim 1 is characterized in that: The second constraint model is used to ensure that the number of customers to be assigned to enter the station does not exceed the upper limit of the carrying capacity that can be successfully passed by any entrance based on the following formula (4): , Indicates The time period is The number of customers assigned to each entrance, Indicates The entrance gates ensure that the load limit can be successfully passed through the entrance gate inspection, 1≤ ≤L,1≤ ≤M,1≤ ≤N, L, M, N are integers greater than 1.

9. A railway freight terminal customer reservation allocation and integrated navigation system according to claim 1, characterized in that: The third constraint model is used to ensure that the waiting time of any number of customers waiting to enter the site at any work site cannot exceed the upper limit of the waiting time based on the following formula (5): , Indicated in The upper limit of the waiting time for the jth job sequence to start in a time period, Indicates Customers waiting to enter the market are Time period Estimated start time of work at each work site, 1≤ ≤L,1≤ ≤M,1≤ ≤N, L, M, N are integers greater than 1.

10. A railway freight terminal customer reservation allocation and integrated navigation system according to claim 1, characterized in that: The fourth constraint model is used to ensure that all customers waiting to enter the terminal need to participate in the railway freight distribution strategy based on the following formula (6): , 1≤ ≤L,1≤ ≤M,1≤ ≤N, L, M, N are integers greater than 1, represents the allocation decision variable, ,when =1 means yes The pending customers are assigned to time period, The entrance of a station.

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