Passenger-ro ship index configuration method and device based on discrete passenger and cargo flow information

Abstract

The invention belongs to the technical field of data processing, and particularly discloses a passenger rolling ship index configuration method and device based on discrete passenger and cargo flow information, and the method comprises the steps: obtaining a static index and a ship portrait of a ship, and obtaining a market fluctuation factor; determining a current season according to a ship index distribution date, and calculating trolley information and truck information respectively in combination with historical statistical data; the historical average trolley loading number, the trolley market fluctuation factor, the trolley vehicle information, the historical average truck loading number, the truck market fluctuation factor and the truck vehicle information are combined with static indexes, calculation is carried out by utilizing an established stowage allocation formula, and a stowage index allocation scheme is obtained and determined; and the index distribution scheme is optimized based on the stowage evaluation parameters until stowage indexes of the stowage index distribution scheme reach target values, and an optimized ship stowage scheme is obtained. According to the invention, the shipping stowage efficiency of the ship can be improved.

Description

Technical Field

[0001] The present application belongs to the field of data processing technology, and more specifically, to a method and device for configuring ro-ro passenger ship indicators based on discrete passenger and cargo flow information. Background Art

[0002] The passenger and vehicle ferry in the Qiongzhou Strait currently adopts a reservation system. Users can directly buy tickets for the corresponding ship schedule on the ticket purchase website, and arrive at the port 1 to 3 hours in advance according to the ship's departure time to wait for the ferry. However, objectively, there are three problems that are difficult to solve: First, although the crossing time of most family cars is relatively stable, their arrival order is disordered, which makes it difficult to organize the ship loading according to the pre-planned plan; second, it is often difficult to estimate the crossing time in advance for most freight trucks (the cargo loading and unloading time is uncertain), so the basic model now adopted is to make an appointment to arrive at the port, that is, not to make an appointment for a specific schedule but to make an appointment for a time period. After the vehicle arrives at the site, the on-site mobilization personnel will arrange it to board the ship; third, with the increasing number of new energy vehicles, there are many restrictions on ship loading due to their safety hazards, resulting in the situation where new energy vehicles cannot be loaded on board and cross the sea. Although some measures have been taken to improve, there are still the following shortcomings:

[0003] 1. Due to the lack of accurate assessment of the market demand for crossing the sea, it is difficult to reasonably allocate the share of tickets for small cars and trucks.

[0004] 2. Currently, trucks are mostly delivered to the port by appointment. Therefore, the loading and stowage is mainly based on the loading situation of the cars. The on-site stowage personnel arrange the trucks for loading operations according to the current loading situation of the ship. The loading efficiency is often limited by the experience and ability of the stowage personnel.

[0005] 3. New energy vehicles are subject to loading requirements such as the loading capacity of a single ship not exceeding 10% of the total loading capacity and a safety distance of 5 meters. Due to the lack of pre-planned loading index design, the efficiency of on-site loading and unloading is often restricted.

[0006] In summary, in the transportation of multiple vehicles and different types of goods, how to effectively schedule and load and improve the loading and loading efficiency of ships is an important challenge facing current technology. Summary of the invention

[0007] In view of the defects of the prior art, the purpose of the present application is to provide a method and device for configuring passenger and cargo ship indicators based on discrete passenger and cargo flow information, aiming to solve the problem of low loading and stowage efficiency of ships.

[0008] In a first aspect, the present application provides a method for configuring ro-ro passenger ship indicators based on discrete passenger and cargo flow information, comprising:

[0009] Obtain static indicators and ship portraits of ships, as well as market volatility factors; the ship portraits include the upper limit of ship loading quotas, historical average loading rates, historical average number of cars loaded, and historical average number of trucks loaded; the market volatility factors include car market volatility factors, truck market volatility factors, and overall market volatility factors; the market volatility factors are automatically calculated based on big data;

[0010] Determine the current season according to the ship index allocation date, and calculate the vehicle information of cars and trucks respectively in combination with historical statistical data; the vehicle information of cars includes average vehicle size and weight information of cars, and the vehicle information of trucks includes average vehicle size and weight information of trucks;

[0011] The historical average number of cars loaded, car market fluctuation factor, car vehicle information, historical average number of trucks loaded, truck market fluctuation factor, truck vehicle information are combined with the static index, and the established loading allocation formula is used for calculation to obtain a determined loading index allocation plan;

[0012] The index allocation scheme is optimized based on the loading evaluation parameters until the loading index of the loading index allocation scheme reaches the target value, thereby obtaining an optimized ship loading scheme.

[0013] Optionally, the optimizing the index allocation scheme based on the loading evaluation parameter until the loading index of the loading index allocation scheme reaches a target value to obtain an optimized ship loading scheme includes:

[0014] The historical average loading number of small cars, the small car market fluctuation factor, the small car vehicle information, the historical average loading number of trucks, the truck market fluctuation factor, the truck vehicle information and the upper limit of the ship loading quota are combined and calculated using the established loading constraint formula to obtain the loading evaluation parameters;

[0015] A comparison is performed based on a preset fixed threshold, a first fluctuation factor threshold and the loading evaluation parameter to obtain a comparison result; the first fluctuation factor threshold is determined based on the overall fluctuation factor and the first parameter, and the truck market fluctuation factor is increased or decreased based on the comparison result until the loading evaluation parameter meets the parameter range determined by the fixed threshold and the loading quota threshold;

[0016] The adjusted truck market volatility factor is cyclically substituted into the loading constraint formula to obtain an optimized loading evaluation parameter, until the loading evaluation parameter reaches the fixed threshold, thereby obtaining an optimized first ship loading solution.

[0017] Optionally, it also includes:

[0018] When it is determined that the preliminary quota allocation has been completed and dynamic adjustment is required, the quota value of the trolley and the ticket purchase information to be allocated are obtained, and the trolley quota value and the ticket purchase information, the trolley vehicle information, the historical average loading number of trucks, the market volatility factor, and the truck vehicle information are combined with the static quota to obtain the loading plan parameters;

[0019] The loading assessment parameter and the loading scheme parameter are adjusted cyclically until the loading scheme parameter is greater than a preset second fluctuation factor threshold, thereby obtaining an optimized ship loading second scheme; the second fluctuation factor threshold is determined based on the overall fluctuation factor and the second parameter.

[0020] Optionally, the truck ticket quotas of the first ship loading scheme and the second ship loading scheme are both determined based on the truck market volatility factor and the historical average truck loading number;

[0021] The trolley ticket quota of the first ship loading scheme is determined based on the trolley market volatility factor and the historical average trolley loading number, and the trolley ticket quota of the second ship loading scheme is determined based on the trolley index value and ticket purchase information.

[0022] Optionally, the current season is determined according to the ship index allocation date, and the vehicle information of cars and trucks is calculated separately in combination with historical statistical data, including:

[0023] Obtaining the license plate number of the vehicle for which the ticket has been purchased, comparing the license plate number of the vehicle with the historical statistical data, and obtaining the vehicle size and weight information of the vehicle;

[0024] The current season is determined according to the ship index allocation date, and the trucks are classified according to the truck model and station proportion in the current season combined with the historical statistical data to determine the vehicle size and weight information of the trucks.

[0025] Optionally, the obtaining of static indicators and ship portraits of the ship includes:

[0026] According to the ship name and the ship index allocation date to be calculated, the ship lane line length is obtained, and the historical data of the ship portrait within the target time range of the ship index allocation date of the previous year is automatically obtained;

[0027] The historical average loading rate, the historical average loading number of small cars and the historical average loading number of trucks within the target time range are calculated based on the historical data.

[0028] Optionally, the market volatility factor is determined based on seasonal fluctuations in the market and the crossing of cars and trucks at different time periods each day; the overall market volatility factor is determined based on the overall off-season and peak-season conditions of the market;

[0029] The determination methods of the car market volatility factor and the truck market volatility factor include:

[0030] Determine the first factor proportion of cars and trucks based on seasonal market fluctuations;

[0031] Determine the second factor proportion of cars and trucks according to the crossing of cars and trucks at different time periods every day;

[0032] The market volatility factor of the car is obtained by multiplying the proportion of the first factor and the proportion of the second factor of the car, and the market volatility factor of the truck is obtained by multiplying the proportion of the first factor and the proportion of the second factor of the truck.

[0033] In a second aspect, the present application further provides a device for configuring ro-ro passenger ship indicators based on discrete passenger and cargo flow information, comprising:

[0034] An input acquisition module is used to obtain static indicators and ship portraits of ships, as well as market volatility factors; the ship portraits include the upper limit of ship loading quotas, historical average loading rates, historical average loading numbers of small cars, and historical average loading numbers of trucks; the market volatility factors include small car market volatility factors, truck market volatility factors, and overall market volatility factors; the market volatility factors are automatically calculated based on big data;

[0035] A vehicle information determination module is used to determine the current season according to the ship index allocation date, and to calculate the vehicle information of cars and trucks respectively in combination with historical statistical data; the vehicle information of cars includes the average vehicle size and weight information of cars, and the vehicle information of trucks includes the average vehicle size and weight information of trucks;

[0036] A scheme calculation module is used to combine the historical average number of small cars loaded, the small car market fluctuation factor, the small car vehicle information, the historical average number of trucks loaded, the truck market fluctuation factor, and the truck vehicle information with the static index, and use the established loading allocation formula to calculate and determine the loading index allocation scheme;

[0037] The scheme optimization module is used to optimize the index allocation scheme based on the loading evaluation parameters until the loading index of the loading index allocation scheme reaches the target value, thereby obtaining an optimized ship loading scheme.

[0038] In a third aspect, the present application provides an electronic device comprising: at least one memory for storing programs; and at least one processor for executing the programs stored in the memory. When the program stored in the memory is executed, the processor is used to execute the method described in the first aspect or any possible implementation of the first aspect.

[0039] In a fourth aspect, the present application provides a computer-readable storage medium, which stores a computer program. When the computer program runs on a processor, the processor executes the method described in the first aspect or any possible implementation of the first aspect.

[0040] In a fifth aspect, the present application provides a computer program product. When the computer program product runs on a processor, the processor executes the method described in the first aspect or any possible implementation of the first aspect.

[0041] It can be understood that the beneficial effects of the second to fifth aspects mentioned above can be found in the relevant description of the first aspect mentioned above, and will not be repeated here.

[0042] In general, the above technical solutions conceived by this application have the following beneficial effects compared with the prior art:

[0043] (1) This application combines the static indicators of ships, ship portraits and market volatility factors to more accurately calculate the loading requirements of different types of ships, reduce resource waste and improve the loading efficiency of ships. And by using the dynamic evaluation and calculation of market volatility factors, the loading plan can dynamically adapt to market changes, so that the loading and operating efficiency of ships in different seasons and market conditions can be improved. Furthermore, by analyzing market volatility factors and historical data, it is possible to timely identify the fluctuation trend of demand to adapt to market changes and improve the loading efficiency of ships.

[0044] (2) The loading scheme of this application can flexibly allocate the ticket quotas of cars and trucks according to the historical average loading numbers of different models and market fluctuations, ensuring that resources are reasonably allocated when demand fluctuates, avoiding economic losses caused by over- or under-allocation. By combining historical data analysis with market fluctuation factors, it is possible to reduce the number of empty ships during transportation, thereby reducing operating costs and improving economic benefits.

[0045] (3) This application ensures that each ship is as close to its rated load as possible by accurately calculating and optimizing the ship's loading plan, so that the ship can transport more cargo, thereby directly increasing the revenue of each operation, that is, improving resource utilization and saving operating expenses. By optimizing the ship's loading plan and increasing the loading limit of each ship as close to the rated load as possible, the overall profitability of the ferry crossing business can be significantly improved. BRIEF DESCRIPTION OF THE DRAWINGS

[0046] Figure 1 This is one of the flow diagrams of the method for configuring the index of a ro-ro passenger ship based on discrete passenger and cargo flow information provided in an embodiment of the present application;

[0047] Figure 2It is a market fluctuation diagram of different seasons provided by the embodiment of the present application;

[0048] Figure 3 It is a market fluctuation diagram of different time periods provided in the embodiment of the present application;

[0049] Figure 4 It is a graph of overall market fluctuations during off-season and peak-season provided in the embodiment of the present application;

[0050] Figure 5 This is the second flow chart of the method for configuring the index of a ro-ro passenger ship based on discrete passenger and cargo flow information provided in the embodiment of the present application;

[0051] Figure 6 It is a structural schematic diagram of a ro-ro passenger ship index configuration device based on discrete passenger and cargo flow information provided in an embodiment of the present application;

[0052] Figure 7 It is a schematic diagram of the structure of an electronic device provided in an embodiment of the present application. DETAILED DESCRIPTION

[0053] In order to make the purpose, technical solution and advantages of the present application more clearly understood, the present application is further described in detail below in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present application and are not used to limit the present application.

[0054] The term "and / or" in this article is a description of the association relationship of associated objects, indicating that there can be three relationships. For example, A and / or B can represent: A exists alone, A and B exist at the same time, and B exists alone. The symbol " / " in this article indicates that the associated objects are in an or relationship, for example, A / B means A or B.

[0055] The terms "first" and "second" in the specification and claims herein are used to distinguish different objects rather than to describe a specific order of the objects. For example, a first response message and a second response message are used to distinguish different response messages rather than to describe a specific order of the response messages.

[0056] In the embodiments of the present application, words such as "exemplary" or "for example" are used to indicate examples, illustrations or descriptions. Any embodiment or design described as "exemplary" or "for example" in the embodiments of the present application should not be interpreted as being more preferred or more advantageous than other embodiments or designs. Specifically, the use of words such as "exemplary" or "for example" is intended to present related concepts in a specific way.

[0057] In the description of the embodiments of the present application, unless otherwise specified, "multiple" means two or more than two. For example, multiple processing units refer to two or more processing units, etc.; multiple elements refer to two or more elements, etc.

[0058] Next, the technical solutions provided in the embodiments of the present application are introduced.

[0059] Reference Figure 1 The present application provides a method for configuring ro-ro passenger ship indicators based on discrete passenger and cargo flow information, including:

[0060] S101. Obtain static indicators and ship portraits of ships, and obtain market volatility factors; the ship portraits include the upper limit of ship loading quota, historical average loading rate, historical average number of cars loaded, and historical average number of trucks loaded; the market volatility factors include car market volatility factors, truck market volatility factors, and overall market volatility factors; the market volatility factors are automatically calculated based on big data;

[0061] S102. Determine the current season according to the ship index allocation date, and calculate the vehicle information of cars and trucks respectively in combination with historical statistical data; the vehicle information of cars includes the average vehicle size and weight information of cars, and the vehicle information of trucks includes the average vehicle size and weight information of trucks;

[0062] S103. The historical average number of cars loaded, car market volatility factor, car vehicle information, historical average number of trucks loaded, truck market volatility factor, truck vehicle information are combined with the static index and calculated using the established loading allocation formula to obtain a determined loading index allocation plan;

[0063] S104. Optimizing the index allocation scheme based on the loading evaluation parameters until the loading index of the loading index allocation scheme reaches the target value, thereby obtaining an optimized ship loading scheme.

[0064] Specifically, in the above step S101, the static index and market volatility factor of the ship are obtained. First, the above characteristics are described:

[0065] Ship loading quota upper limit: refers to the maximum amount of cargo that a ship can carry under specific sailing conditions, recorded as Mh;

[0066] Historical average loading rate: refers to the average proportion of a ship loaded in historical operations, which can be used to evaluate the operational efficiency of the ship, denoted as Mz;

[0067] Historical average number of trolleys: refers to the average number of trolleys carried by the ship on each voyage in historical operations, recorded as Mc;

[0068] Historical average truck load: Similar to the historical average car load, it refers to the average number of trucks carried by the ship on each voyage in historical operations, recorded as Mt.

[0069] Small car market volatility factor: small car market demand change parameter obtained based on big data analysis, denoted as Sc;

[0070] Truck market volatility factor: The truck market demand change parameter obtained based on big data analysis, denoted as St.

[0071] Overall market volatility factor: Taking into account the market fluctuations of cars and trucks, we get the overall market demand change factor, denoted as Sf.

[0072] Furthermore, by analyzing the ship quota allocation date and combining historical data, the current seasonal characteristics are determined in order to allocate appropriate quotas. On the one hand, the average vehicle size and weight information of cars is obtained through market data to obtain the common size and weight of cars in order to accurately calculate the loading capacity of ships. On the other hand, the average vehicle size and weight information of trucks is used to obtain truck information through the same method.

[0073] Furthermore, the historical average loading number of small cars, the volatility factor of the small car market, the vehicle information of small cars, the historical average loading number of trucks, the volatility factor of the truck market and the vehicle information of trucks are combined with the static index and calculated using the established loading allocation formula to obtain the initial loading index allocation plan, as shown below:

[0074]

[0075] Among them, B is the specific flight schedule, C is the car ticket quota, and T is the truck ticket quota.

[0076] This formula integrates various indicators to determine the loading index allocation plan for cars and trucks in a quantitative manner to ensure maximum utilization of the ship's carrying capacity.

[0077] Finally, through the above S104, the preliminary loading index allocation plan is refined and optimized based on the loading evaluation parameters. The optimization process is a cyclic iterative process until the set target value is reached. In this embodiment, the parameter value of the loading index allocation plan is as close to 1 as possible, which means that it is infinitely close to the upper limit of the ship's ship loading quota.

[0078] The embodiment of the present application formulates a dynamically adaptive loading strategy by making full use of the static indicators of the ship and the dynamic market data, which helps to improve the overall operating efficiency and revenue of the ferry crossing business.

[0079] This application combines the static indicators of ships (loading quota upper limit, historical average loading rate, etc.) with market volatility factors (car market volatility factor, truck market volatility factor) to more accurately calculate the loading requirements of different models, reduce resource waste, and improve the loading efficiency of ships. By utilizing the dynamic evaluation and calculation of market volatility factors, the loading plan can dynamically adapt to market changes, so that the operating efficiency of ships in different seasons and market conditions is improved. By analyzing market volatility factors and historical data, it is possible to identify the fluctuation trend of demand in a timely manner to adapt to market changes and improve the overall resource allocation efficiency.

[0080] Optionally, the optimizing the index allocation scheme based on the loading evaluation parameter until the loading index of the loading index allocation scheme reaches a target value to obtain an optimized ship loading scheme includes:

[0081] The historical average loading number of small cars, the small car market fluctuation factor, the small car vehicle information, the historical average loading number of trucks, the truck market fluctuation factor, the truck vehicle information are combined with the upper limit of the ship loading quota, and the established loading constraint formula is used to calculate to obtain the loading evaluation parameters;

[0082] A comparison is performed based on a preset fixed threshold, a first fluctuation factor threshold and the loading assessment parameter to obtain a comparison result; the first fluctuation factor threshold is determined based on the overall fluctuation factor and the first parameter;

[0083] Based on the comparison result, it is determined whether to increase or decrease the truck market volatility factor until the loading evaluation parameter satisfies the parameter range determined by the fixed threshold and the loading quota threshold;

[0084] The adjusted truck market volatility factor is cyclically substituted into the loading constraint formula to obtain an optimized loading evaluation parameter, until the loading evaluation parameter reaches the fixed threshold, thereby obtaining an optimized first ship loading solution.

[0085] Specifically, in this embodiment, the historical average loading number of small cars, the small car market fluctuation factor, the small car vehicle information, the historical average loading number of trucks, the truck market fluctuation factor, the truck vehicle information and the upper limit of the ship loading quota are combined and applied to the configuration constraint formula to obtain the loading evaluation parameters, as shown in the following formula:

[0086]

[0087] Among them, {B, M} are load evaluation parameters. The preset fixed threshold is 1, the first fluctuation factor threshold is 0.9×Sf, and 0.9 is the first parameter, which is a basic parameter manually set in the system based on the experience of dispatchers and market fluctuations.

[0088] In the embodiment of the present application, a fixed reference value is set for comparing and judging the validity of the loading assessment parameter, that is, the fixed threshold is set to 1, that is, the loading assessment parameter is less than 1 (the ratio of the total loading weight of cars and trucks to the upper limit of the ship loading quota is less than 1).

[0089] The obtained loading evaluation parameter is compared with the above-mentioned fixed threshold and the first fluctuation factor threshold. Based on the comparison result, it is decided whether to increase or decrease the truck market fluctuation factor, and the adjustment is made step by step until the configuration evaluation parameter is greater than the loading quota threshold (0.9Sf) and less than the fixed threshold (1), so as to determine the adjusted truck market fluctuation factor.

[0090] Substitute the adjusted truck market volatility factor into the loading constraint formula again to calculate the new loading evaluation parameter. Repeat this process until the loading evaluation parameter reaches the preset fixed threshold, and the final first ship loading plan is obtained.

[0091] In the first ship stowage plan:

[0092] Truck ticket quotas are determined based on the truck market volatility factor and the historical average truck load number, as shown in the following formula:

[0093] T=Mt×St

[0094] The trolley ticket quota of the first ship loading scheme is determined based on the trolley market volatility factor and the historical average trolley loading number, as shown in the following formula:

[0095] C=Mc×Sc

[0096] Among them, Mt is the historical average loading number of trucks, St is the truck market volatility factor; Mc is the historical average loading number of cars, Sc is the car market volatility factor.

[0097] The embodiment of the present application obtains an optimized first ship loading scheme to ensure that the loading evaluation parameters are within a predetermined range, so that the overall operation achieves the expected benefits. It not only includes the rational use of the ship's carrying capacity, but also ensures that it can cope with changes in cargo demand in a fluctuating market, thereby improving the economic benefits and operational efficiency of the ship.

[0098] Optionally, it also includes:

[0099] When it is determined that the preliminary quota allocation has been completed and dynamic adjustment is required, the quota value of the trolley and the ticket purchase information to be allocated are obtained, and the trolley quota value and the ticket purchase information, the trolley vehicle information, the historical average loading number of trucks, the market volatility factor, and the truck vehicle information are combined with the static quota to obtain the loading plan parameters;

[0100] The loading assessment parameter and the loading scheme parameter are adjusted cyclically until the loading scheme parameter is greater than a preset second fluctuation factor threshold, thereby obtaining an optimized ship loading second scheme; the second fluctuation factor threshold is determined based on the overall fluctuation factor and the second parameter.

[0101] Specifically, in this embodiment, the vehicle index value, ticket purchase information, vehicle information, historical average truck loading number, market volatility factor, truck information and established static indicators are comprehensively analyzed to obtain the loading plan parameters. As shown in the following formula:

[0102]

[0103] Among them, {B, C1, T1} are the loading plan parameters, that is, the loading plan data of the ship obtained when it is determined that the preliminary index allocation has been completed and dynamic adjustment is required. A comprehensive evaluation is performed based on the preliminary allocated index and the calculated loading plan parameters.

[0104] Analyze and adjust the current loading evaluation parameters to ensure continuous optimization. Compare the loading plan parameters with the second volatility factor threshold. If the current loading plan parameters are less than the second volatility factor threshold, it is necessary to adjust the car index value, ticket purchase information or market volatility factor to improve the plan parameters. Continuously adjust the parameters, use the actual market volatility data and analysis, and feed it back to the loading plan until the loading plan parameters are greater than the set second volatility factor threshold.

[0105] The embodiment of the present application makes the most of the loading capacity of the ship and optimizes the logistics efficiency through dynamic adjustment, and can flexibly adjust the loading strategy in a complex market environment to ensure the maximization of economic benefits. The dynamic adjustment method based on data analysis and feedback greatly improves the efficiency of resource allocation.

[0106] In the second ship loading plan:

[0107] Truck ticket quotas are determined based on the truck market volatility factor and the historical average truck load number, as shown in the following formula:

[0108] T1=Mt×St

[0109] The car ticket quota is determined based on the car index value and the ferry ticket purchase information, as shown in the following formula:

[0110] C1=Pc+Cc

[0111] Among them, T1 is the truck ticket quota, C1 is the car ticket quota, Cc is the car index value, and Pc is the ship ticket purchase information.

[0112] Optionally, the current season is determined according to the ship index allocation date, and the vehicle information of cars and trucks is calculated separately in combination with historical statistical data, including:

[0113] Obtaining the license plate number of the vehicle for which the ticket has been purchased, comparing the license plate number of the vehicle with the historical statistical data, and obtaining the vehicle size and weight information of the vehicle;

[0114] The current season is determined according to the ship index allocation date, and the trucks are classified according to the truck model and station proportion in the current season combined with the historical statistical data to determine the vehicle size and weight information of the trucks.

[0115] Specifically, in this embodiment, the car vehicle information Dc is automatically matched and generated according to the car license plate number of the system ticket purchase information, and the truck vehicle information Dt is obtained by mining the market volatility factor and truck model data. The specific method is as follows:

[0116] a.Dc is classified based on historical statistical data of major cross-sea vehicle types and is generally relatively fixed. For small vehicles that have purchased tickets, since the system has obtained their license plate numbers, the vehicle size and weight information of the vehicle can be directly compared with the vehicle data retained by the system.

[0117] b.Dt is obtained by classification based on historical statistical data of truck models crossing the sea, and has obvious seasonal characteristics. Generally speaking, during the Spring Festival, vehicles mainly transport fruits and vegetables, while there are more large-scale transport vehicles in summer. Different seasons have different vehicle models and weights, with obvious seasonal fluctuations, which can be obtained through big data analysis.

[0118] Optionally, the obtaining of static indicators and ship portraits of the ship includes:

[0119] According to the ship name and the ship index allocation date to be calculated, the ship lane line length is obtained, and the historical data of the ship portrait within the target time range of the ship index allocation date of the previous year is automatically obtained;

[0120] The historical average loading rate, the historical average loading number of small cars and the historical average loading number of trucks within the target time range are calculated based on the historical data.

[0121] In this embodiment, the L and M values, including the upper limit of the loading quota of a certain type of ship Mh, the historical average loading rate Mz, and the historical average loading number of cars and trucks (Mc and Mt), are all historical average values ​​within a certain period of time obtained based on the statistical data of the ship. The determination method and steps are as follows:

[0122] a. Enter the ship name and the ship quota allocation date to be calculated;

[0123] b. The system automatically gives the static index L and Mh values ​​of the ship, and automatically obtains the ship portrait related data 30 days before and after the allocation date of the index in the previous year;

[0124] c. Calculate the values ​​of Mz, Mc and Mt during this period based on historical data.

[0125] Optionally, the market volatility factor is determined based on seasonal fluctuations in the market and the crossing of cars and trucks at different time periods each day; the overall market volatility factor is determined based on the overall off-season and peak-season conditions of the market;

[0126] The determination methods of the car market volatility factor and the truck market volatility factor include:

[0127] Determine the first factor proportion of cars and trucks based on seasonal market fluctuations;

[0128] Determine the second factor proportion of cars and trucks according to the crossing of cars and trucks at different time periods every day;

[0129] The market volatility factor of the car is obtained by multiplying the proportion of the first factor and the proportion of the second factor of the car, and the market volatility factor of the truck is obtained by multiplying the proportion of the first factor and the proportion of the second factor of the truck.

[0130] It should be noted that S in the embodiment of the present application is a market volatility factor, which is determined by multiple factors and can be determined in the following way:

[0131] a. S1 is the seasonal fluctuation of the market. Generally, Figure 2 Value, Figure 2 It is a market fluctuation chart under different seasons. Among them, car S1 is used to calculate car quota data, and truck S1 is used to calculate truck quota data.

[0132] b. S2 is the proportion of cars and trucks crossing the sea at different time periods every day. Figure 3 Value, Figure 3 It is a chart of market fluctuations in different time periods, where car S2 is used to calculate car quota data and truck S2 is used to calculate truck quota data.

[0133] c. The market volatility factor of car quota Sc = car S1 × car S2.

[0134] d. The market volatility factor of truck quota St = truck S1 × truck S2.

[0135] e. Sf is the overall off-season and peak-season situation of the market. Generally speaking, Figure 4 Value, Figure 4 This is a chart showing the overall market fluctuations during the peak and off-peak seasons.

[0136] As can be seen from the figure, the overall market volatility factor is a comprehensive consideration of the fluctuations of cars and trucks, and is usually determined based on analysis of the market's off-season and peak season conditions.

[0137] The embodiment of the present application adjusts the loading strategy of cars and trucks in real time according to the changes in market volatility factors to ensure the best utilization of resources, uses market volatility factors to predict demand changes, optimizes transportation plans, and reduces operating costs.

[0138] The following is the complete process of the embodiment of this application:

[0139] 1) Preprocessing stage: The system inputs the name of the ship and the date of ship index allocation to be calculated. Based on the ship portrait and other relevant data information, the system automatically calculates the ship lane line length L and the ship loading quota upper limit Mh, the historical average loading rate Mz, the historical average car loading number Mc and the historical average truck loading number Mt.

[0140] 2) Set the ship schedule data, mainly the schedule time. According to the schedule time, the system automatically calculates the market volatility factor Sc of the car quota, the market volatility factor St of the truck quota and the overall market volatility factor Sf.

[0141] 3) The system determines the current season of the shift based on the indicator allocation date, and calculates the average vehicle size and weight information Dc of cars and the average vehicle size and weight information Dt of trucks based on historical statistical data.

[0142] 4) The system calculates the values ​​of {B, C, T} according to the following formula (1), and calculates the values ​​of {B, M} according to formula (2). If the values ​​of {B, M} meet the constraint requirements of formula (2), then T = Mt × St, C = Mc × Sc.

[0143]

[0144]

[0145] 5) If the value of {B, M} is greater than 1, then reduce the market volatility factor St of truck quotas, that is, reduce the total amount of truck quotas. If the value of {B, M} is less than 0.9×Sf, then increase the market volatility factor St of truck quotas to make the value of {B, M} as close to 1 as possible.

[0146] 6) Substitute the adjusted St into formula (1). If the value of {B, C, T} does not exceed 1, then T = Mt × St, C = Mc × Sc. If the value exceeds 1, reduce the value of St until the value of {B, C, T} is less than 1, then T = Mt × adjusted St, C = Mc × Sc.

[0147] 7) For the case where the preliminary index allocation has been completed and needs to be dynamically adjusted, the trolley index value Cc to be allocated can be manually entered and calculated according to the following formula (3) to ensure that {B, C1, T1} is greater than 0.95×Sf, and the load weight is calculated according to formula (2) to ensure that it is less than Mh.

[0148]

[0149] 8) Repeat steps 4 to 7 until the value of {B, C, T} is as close to 1 as possible or {B, C1, T1} is greater than 0.95×Sf, then stop the calculation and obtain the final calculation result.

[0150] 9) The final result is:

[0151] a. For {B, C, T}, the truck ticket quota T = Mt × St, the car ticket quota C = Mc × Sc;

[0152] b. For {B, C1, T1}, the truck ticket quota T1 = Mt×St, and the car ticket quota C1 = Pc+Cc.

[0153] Reference Figure 5 , Figure 5 The complete process diagram of the embodiment of the present application includes the following steps:

[0154] Enter the ship name, ship schedule, and key parameters;

[0155] One sentence of ship portrait to obtain L and M data;

[0156] Calculate market volatility factors;

[0157] Calculate the car quota fluctuation factor Sc;

[0158] Calculate the truck quota fluctuation factor St;

[0159] Calculate the overall market volatility factor Sf;

[0160] Calculate vehicle history statistics;

[0161] Calculate the average vehicle size and weight information Dc of the car;

[0162] Calculate the average vehicle size and weight information Dt of trucks;

[0163] Calculate vehicle quota allocation plan;

[0164] Determine whether the solution exceeds the ship's load capacity limit;

[0165] If yes, then the truck quota is manually adjusted and the process returns to the step of calculating the vehicle quota allocation plan;

[0166] If not, determine whether the loading condition of the solution is lower than the loading threshold;

[0167] If so, the truck quota is adjusted manually;

[0168] If not, determine whether to adjust the quota during the bill collection period;

[0169] If so, return to calculate the vehicle index allocation plan;

[0170] If not, the process ends.

[0171] Reference Figure 6 The present application also provides a device for configuring ro-ro passenger ship indicators based on discrete passenger and cargo flow information, comprising:

[0172] Input acquisition module 610, used to acquire static indicators and ship portraits of ships, and acquire market volatility factors; the ship portraits include the upper limit of ship loading quota, historical average loading rate, historical average number of small cars loaded, and historical average number of trucks loaded; the market volatility factors include small car market volatility factors, truck market volatility factors, and overall market volatility factors; the market volatility factors are automatically calculated based on big data;

[0173] The vehicle information determination module 620 is used to determine the current season according to the ship index allocation date, and calculate the vehicle information of cars and trucks respectively in combination with historical statistical data; the vehicle information of cars includes the average vehicle size and weight information of cars, and the vehicle information of trucks includes the average vehicle size and weight information of trucks;

[0174] A solution calculation module 630 is used to combine the historical average number of small cars loaded, the small car market fluctuation factor, the small car vehicle information, the historical average number of trucks loaded, the truck market fluctuation factor, and the truck vehicle information with the static index, and use the established loading allocation formula to calculate and determine the loading index allocation solution;

[0175] The scheme optimization module 640 is used to optimize the index allocation scheme based on the loading evaluation parameters until the loading index of the loading index allocation scheme reaches the target value, thereby obtaining an optimized ship loading scheme.

[0176] Optionally, the solution optimization module is specifically used to:

[0177] The historical average loading number of small cars, the small car market fluctuation factor, the small car vehicle information, the historical average loading number of trucks, the truck market fluctuation factor, the truck vehicle information and the upper limit of the ship loading quota are combined and calculated using the established loading constraint formula to obtain the loading evaluation parameters;

[0178] A comparison is performed based on a preset fixed threshold, a first fluctuation factor threshold and the loading evaluation parameter to obtain a comparison result; the first fluctuation factor threshold is determined based on the overall fluctuation factor and the first parameter, and the truck market fluctuation factor is increased or decreased based on the comparison result until the loading evaluation parameter meets the parameter range determined by the fixed threshold and the loading quota threshold;

[0179] The adjusted truck market volatility factor is cyclically substituted into the loading constraint formula to obtain an optimized loading evaluation parameter, until the loading evaluation parameter reaches the fixed threshold, thereby obtaining an optimized first ship loading solution.

[0180] Optionally, the solution optimization module is further used to:

[0181] When it is determined that the preliminary quota allocation has been completed and dynamic adjustment is required, the quota value of the trolley and the ticket purchase information to be allocated are obtained, and the trolley quota value and the ticket purchase information, the trolley vehicle information, the historical average loading number of trucks, the market volatility factor, and the truck vehicle information are combined with the static quota to obtain the loading plan parameters;

[0182] The loading assessment parameter and the loading scheme parameter are adjusted cyclically until the loading scheme parameter is greater than a preset second fluctuation factor threshold, thereby obtaining an optimized ship loading second scheme; the second fluctuation factor threshold is determined based on the overall fluctuation factor and the second parameter.

[0183] Optionally, the truck ticket quotas of the first ship loading scheme and the second ship loading scheme are both determined based on the truck market volatility factor and the historical average truck loading number;

[0184] The trolley ticket quota of the first ship loading scheme is determined based on the trolley market volatility factor and the historical average trolley loading number, and the trolley ticket quota of the second ship loading scheme is determined based on the trolley index value and ticket purchase information.

[0185] Optionally, the vehicle information determination module is specifically used to:

[0186] Obtaining the license plate number of the vehicle for which the ticket has been purchased, comparing the license plate number of the vehicle with the historical statistical data, and obtaining the vehicle size and weight information of the vehicle;

[0187] The current season is determined according to the ship index allocation date, and the trucks are classified according to the truck model and station proportion in the current season combined with the historical statistical data to determine the vehicle size and weight information of the trucks.

[0188] Optionally, the input acquisition module is specifically used to:

[0189] According to the ship name and the ship index allocation date to be calculated, the ship lane line length is obtained, and the historical data of the ship portrait within the target time range of the ship index allocation date of the previous year is automatically obtained;

[0190] The historical average loading rate, the historical average loading number of small cars and the historical average loading number of trucks within the target time range are calculated based on the historical data.

[0191] Optionally, the market volatility factor is determined based on seasonal fluctuations in the market and the crossing of cars and trucks at different time periods each day; the overall market volatility factor is determined based on the overall off-season and peak-season conditions of the market;

[0192] The determination methods of the car market volatility factor and the truck market volatility factor include:

[0193] Determine the first factor proportion of cars and trucks based on seasonal market fluctuations;

[0194] Determine the second factor proportion of cars and trucks according to the crossing of cars and trucks at different time periods every day;

[0195] The market volatility factor of the car is obtained by multiplying the proportion of the first factor and the proportion of the second factor of the car, and the market volatility factor of the truck is obtained by multiplying the proportion of the first factor and the proportion of the second factor of the truck.

[0196] It can be understood that the detailed functional implementation of each of the above-mentioned units / modules can be found in the introduction of the aforementioned method embodiment, and will not be repeated here.

[0197] It should be understood that the above-mentioned device is used to execute the method in the above-mentioned embodiment. The implementation principle and technical effect of the corresponding program module in the device are similar to those described in the above-mentioned method. The working process of the device can refer to the corresponding process in the above-mentioned method, which will not be repeated here.

[0198] Reference Figure 7 Based on the method in the above embodiment, the embodiment of the present application provides an electronic device, which may include: a processor (processor) 710, a communication interface (CommunicationsInterface) 720, a memory (memory) 730 and a communication bus 740, wherein the processor 710, the communication interface 720, and the memory 730 communicate with each other through the communication bus 740. The processor 710 can call the logic instructions in the memory 730 to execute the method in the above embodiment.

[0199] In addition, the logic instructions in the above-mentioned memory 730 can be implemented in the form of software functional units and can be stored in a computer-readable storage medium when sold or used as an independent product. Based on this understanding, the technical solution of the present application, or the part that contributes to the prior art, or the part of the technical solution, can be embodied in the form of a software product, which is stored in a storage medium and includes a number of instructions for a computer device (which can be a personal computer, a server, or a network device, etc.) to perform all or part of the steps of the method described in each embodiment of the present application.

[0200] Based on the method in the above embodiment, an embodiment of the present application provides a computer-readable storage medium, which stores a computer program. When the computer program runs on a processor, the processor executes the method in the above embodiment.

[0201] Based on the method in the above embodiment, an embodiment of the present application provides a computer program product. When the computer program product runs on a processor, the processor executes the method in the above embodiment.

[0202] It is understandable that the processor in the embodiment of the present application can be a central processing unit (CPU), or other general-purpose processors, digital signal processors (DSP), application specific integrated circuits (ASIC), field programmable gate arrays (FPGA) or other programmable logic devices, transistor logic devices, hardware components or any combination thereof. The general-purpose processor can be a microprocessor or any conventional processor.

[0203] The method steps in the embodiments of the present application can be implemented by hardware or by a processor executing software instructions. The software instructions can be composed of corresponding software modules, and the software modules can be stored in random access memory (RAM), flash memory, read-only memory (ROM), programmable read-only memory (PROM), erasable programmable read-only memory (EPROM), electrically erasable programmable read-only memory (EEPROM), registers, hard disks, mobile hard disks, CD-ROMs, or any other form of storage medium known in the art. An exemplary storage medium is coupled to a processor so that the processor can read information from the storage medium and write information to the storage medium. Of course, the storage medium can also be a component of the processor. The processor and the storage medium can be located in an ASIC.

[0204] In the above embodiments, it can be implemented in whole or in part by software, hardware, firmware or any combination thereof. When implemented using software, it can be implemented in whole or in part in the form of a computer program product. The computer program product includes one or more computer instructions. When the computer program instructions are loaded and executed on a computer, the process or function described in the embodiment of the present application is generated in whole or in part. The computer may be a general-purpose computer, a special-purpose computer, a computer network, or other programmable device. The computer instructions may be stored in a computer-readable storage medium or transmitted through the computer-readable storage medium. The computer instructions may be transmitted from a website site, a computer, a server or a data center to another website site, a computer, a server or a data center by wired (e.g., coaxial cable, optical fiber, digital subscriber line (DSL)) or wireless (e.g., infrared, wireless, microwave, etc.) means. The computer-readable storage medium may be any available medium that a computer can access or a data storage device such as a server or a data center that includes one or more available media integrated. The available medium may be a magnetic medium (e.g., a floppy disk, a hard disk, a tape), an optical medium (e.g., a DVD), or a semiconductor medium (e.g., a solid state disk (SSD)), etc.

[0205] It should be understood that the various numerical numbers involved in the embodiments of the present application are only used for the convenience of description and are not used to limit the scope of the embodiments of the present application.

[0206] It will be easily understood by those skilled in the art that the above description is only a preferred embodiment of the present application and is not intended to limit the present application. Any modifications, equivalent substitutions and improvements made within the spirit and principles of the present application shall be included in the scope of protection of the present application.

Claims

1. A method for configuring ro-ro passenger ship indicators based on discrete passenger and cargo flow information, characterized in that: include: Obtain static indicators and ship portraits of ships, as well as market volatility factors; the ship portraits include the upper limit of ship loading quotas, historical average loading rates, historical average number of cars loaded, and historical average number of trucks loaded; the market volatility factors include car market volatility factors, truck market volatility factors, and overall market volatility factors; the market volatility factors are automatically calculated based on big data; Determine the current season according to the ship index allocation date, and calculate the vehicle information of cars and trucks respectively in combination with historical statistical data; the vehicle information of cars includes average vehicle size and weight information of cars, and the vehicle information of trucks includes average vehicle size and weight information of trucks; The historical average number of cars loaded, car market fluctuation factor, car vehicle information, historical average number of trucks loaded, truck market fluctuation factor, truck vehicle information are combined with the static index, and the established loading allocation formula is used for calculation to obtain a determined loading index allocation plan; The index allocation scheme is optimized based on the loading evaluation parameters until the loading index of the loading index allocation scheme reaches the target value, thereby obtaining an optimized ship loading scheme.

2. The method for configuring ro-passenger ship indicators according to claim 1, characterized in that: The optimizing the index allocation scheme based on the loading evaluation parameter until the loading index of the loading index allocation scheme reaches the target value to obtain the optimized ship loading scheme includes: The historical average loading number of small cars, the small car market fluctuation factor, the small car vehicle information, the historical average loading number of trucks, the truck market fluctuation factor, the truck vehicle information and the upper limit of the ship loading quota are combined and calculated using the established loading constraint formula to obtain the loading evaluation parameters; A comparison is performed based on a preset fixed threshold, a first fluctuation factor threshold and the loading assessment parameter to obtain a comparison result; the first fluctuation factor threshold is determined based on the overall fluctuation factor and the first parameter; Based on the comparison result, it is determined whether to increase or decrease the truck market volatility factor until the loading evaluation parameter satisfies the parameter range determined by the fixed threshold and the loading quota threshold; The adjusted truck market volatility factor is cyclically substituted into the loading constraint formula to obtain an optimized loading evaluation parameter, until the loading evaluation parameter reaches the fixed threshold, thereby obtaining an optimized first ship loading solution.

3. The method for configuring ro-passenger ship indicators according to claim 2, characterized in that: Also includes: When it is determined that the preliminary quota allocation has been completed and dynamic adjustment is required, the quota value of the trolley and the ticket purchase information to be allocated are obtained, and the trolley quota value and the ticket purchase information, the trolley vehicle information, the historical average loading number of trucks, the market volatility factor, and the truck vehicle information are combined with the static quota to obtain the loading plan parameters; Circularly adjusting the loading evaluation parameter and the loading scheme parameter until the loading scheme parameter is greater than a preset second fluctuation factor threshold, thereby obtaining an optimized ship loading second scheme; The second fluctuation factor threshold is determined based on the overall fluctuation factor and a second parameter.

4. The method for configuring indicators for a passenger and vehicle ship according to claim 3, characterized in that: The truck ticket quotas of the first ship loading scheme and the second ship loading scheme are both determined based on the truck market volatility factor and the historical average truck loading number; The trolley ticket quota of the first ship loading scheme is determined based on the trolley market volatility factor and the historical average trolley loading number, and the trolley ticket quota of the second ship loading scheme is determined based on the trolley index value and ticket purchase information.

5. The method for configuring ro-passenger ship indicators according to claim 1, characterized in that: The current season is determined according to the ship index allocation date, and the vehicle information of cars and trucks is calculated respectively in combination with historical statistical data, including: Obtaining the license plate number of the vehicle for which the ticket has been purchased, comparing the license plate number of the vehicle with the historical statistical data, and obtaining the vehicle size and weight information of the vehicle; The current season is determined according to the ship index allocation date, and the trucks are classified according to the truck model and station proportion in the current season combined with the historical statistical data to determine the vehicle size and weight information of the trucks.

6. The method for configuring indicators of a ro-pax ship according to claim 1, characterized in that: The obtaining of static indicators and ship portraits of the ship includes: According to the ship name and the ship index allocation date to be calculated, the ship lane line length is obtained, and the historical data of the ship portrait within the target time range of the ship index allocation date of the previous year is automatically obtained; The historical average loading rate, the historical average loading number of small cars and the historical average loading number of trucks within the target time range are calculated based on the historical data.

7. The method for configuring ro-passenger ship indicators according to claim 1, characterized in that: The market volatility factor is determined based on the seasonal fluctuations of the market and the crossing of cars and trucks at different time periods each day; the overall market volatility factor is determined based on the overall off-season and peak-season conditions of the market; The determination methods of the car market volatility factor and the truck market volatility factor include: Determine the first factor proportion of cars and trucks based on seasonal market fluctuations; Determine the second factor proportion of cars and trucks according to the crossing of cars and trucks at different time periods every day; The market volatility factor of the car is obtained by multiplying the proportion of the first factor and the proportion of the second factor of the car, and the market volatility factor of the truck is obtained by multiplying the proportion of the first factor and the proportion of the second factor of the truck.

8. A device for configuring ro-ro passenger ship index based on discrete passenger and cargo flow information, characterized in that: include: An input acquisition module is used to obtain static indicators and ship portraits of ships, as well as market volatility factors; the ship portraits include the upper limit of ship loading quotas, historical average loading rates, historical average loading numbers of small cars, and historical average loading numbers of trucks; the market volatility factors include small car market volatility factors, truck market volatility factors, and overall market volatility factors; the market volatility factors are automatically calculated based on big data; A vehicle information determination module is used to determine the current season according to the ship index allocation date, and to calculate the vehicle information of cars and trucks respectively in combination with historical statistical data; the vehicle information of cars includes the average vehicle size and weight information of cars, and the vehicle information of trucks includes the average vehicle size and weight information of trucks; A scheme calculation module is used to combine the historical average number of small cars loaded, the small car market fluctuation factor, the small car vehicle information, the historical average number of trucks loaded, the truck market fluctuation factor, and the truck vehicle information with the static index, and use the established loading allocation formula to calculate and determine the loading index allocation scheme; The scheme optimization module is used to optimize the index allocation scheme based on the loading evaluation parameters until the loading index of the loading index allocation scheme reaches the target value, thereby obtaining an optimized ship loading scheme.

9. An electronic device, characterized in that: include: at least one memory for storing a computer program; At least one processor is used to execute the program stored in the memory. When the program stored in the memory is executed, the processor is used to execute the method according to any one of claims 1 to 7.

10. A computer-readable storage medium storing a computer program, characterized in that: When the computer program runs on a processor, the processor is caused to execute the method according to any one of claims 1 to 7.

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