Data processing method and device based on aircraft cargo hold loading and electronic equipment

Abstract

The invention discloses a data processing method and device based on aircraft cargo hold loading and electronic equipment. Relates to the field of air transportation. The method comprises the steps that cargo information of a target flight and N cargo hold layout templates associated with the target flight are acquired, the cargo information comprises information of an aircraft to-be-loaded object associated with the target flight, and N is a positive integer; based on the cargo information and the N cargo hold layout templates, S initial loading strategies of the to-be-loaded object are determined, and S is a positive integer; the S initial loading strategies are scored, a scoring result is obtained, a target loading strategy is determined based on the scoring result, and the target loading strategy comprises the initial loading strategy with the highest score in the S initial loading strategies. According to the invention, the technical problem of poor loading effect due to the fact that the to-be-loaded cargo is loaded in the cargo hold of the aircraft by a stowage man according to the experience of the stowage man in the prior art is solved.

Description

Technical Field

[0001] This invention relates to the field of air transport, and more specifically, to a data processing method, apparatus, and electronic equipment based on aircraft cargo hold loading. Background Technology

[0002] Cargo loading is a necessary procedure before aircraft takeoff. The selection of a cargo loading scheme requires a load manager to allocate appropriate storage locations for each item based on flight loading requirements, including information on cargo, mail, and baggage, as well as the physical layout of the aircraft's cargo hold, and drawing on their experience. However, this method has the following drawbacks: First, the load manager needs to allocate storage locations for each item sequentially and make continuous adjustments according to loading rules, making the process complex and inefficient. Second, human error is possible during operation, such as failing to fully consider hazardous materials segregation rules and placing hazardous materials that should not be placed in the same location together, posing risks and hazards to production safety. Finally, the loading scheme provided by the load manager is often only a feasible option and cannot provide an optimal loading scheme based on the actual situation.

[0003] There is currently no effective solution to the above problems. Summary of the Invention

[0004] This invention provides a data processing method, apparatus, and electronic device based on aircraft cargo hold loading, to at least solve the technical problem in the related art where loading of goods in the aircraft cargo hold by loading personnel based on their own experience results in poor loading effect.

[0005] According to one aspect of the present invention, a data processing method based on aircraft cargo hold loading is provided, comprising: acquiring cargo information of a target flight and N cargo hold layout templates associated with the target flight, wherein the cargo information includes information on aircraft objects to be loaded associated with the target flight, and N is a positive integer; determining S initial loading strategies for the objects to be loaded based on the cargo information and the N cargo hold layout templates, wherein S is a positive integer; scoring the S initial loading strategies to obtain a scoring result, and determining a target loading strategy based on the scoring result, wherein the target loading strategy includes the initial loading strategy with the highest score among the S initial loading strategies.

[0006] Further, based on the cargo information and the N cargo hold layout templates, determining S initial loading strategies for the object to be loaded includes: acquiring static data information of the target flight, wherein the static data information includes: restriction data for limiting cargo loading; filtering the N cargo hold layout templates based on the cargo information to obtain M cargo hold layout templates, wherein M is a positive integer less than N; and determining the S initial loading strategies based on the cargo information, the M cargo hold layout templates, and the static data information.

[0007] Further, the objects to be loaded include: containerized cargo that needs to be placed in containers, palletized cargo that needs to be placed on pallets, and loose cargo that needs to be placed in individual containers. The cargo information includes: containerized cargo information, palletized cargo information, and loose cargo information. The cargo hold layout template includes: container cargo location information, palletized cargo location information, and loose cargo location information. Based on the cargo information, M types of cargo hold layout templates, and the static data information, S types of initial loading strategies are determined, including: generating a loading strategy for loose cargo based on the loose cargo information and the loose cargo location information in the M types of cargo hold layout templates; for each of the M types of cargo hold layout templates, generating a palletized cargo loading strategy based on the palletized cargo information and the palletized cargo location information, and filtering out cargo that cannot be loaded from the M types of cargo hold layout templates. P cargo hold layout templates are obtained for loading strategies of palletized cargo, where P is a positive integer less than M. For each of the P cargo hold layout templates, a loading strategy for the containerized cargo is generated based on the containerized cargo information and the containerized cargo location information. Cargo hold layout templates that cannot generate a loading strategy for containerized cargo are filtered out from the P cargo hold layout templates, resulting in Q cargo hold layout templates, where Q is a positive integer less than P. For each of the Q cargo hold layout templates, the loading strategies for bulk cargo, palletized cargo, and containerized cargo are combined to obtain all loading strategies associated with each cargo hold layout template. Based on the static data information, all loading strategies associated with the Q cargo hold layout templates are filtered to obtain S initial loading strategies.

[0008] Further, based on the cargo information, the N types of cargo hold layout templates are filtered to obtain M types of cargo hold layout templates, including: based on the cargo information, calculating the quantity of container cargo and the quantity of pallet cargo in the object to be loaded; among the N types of cargo hold layout templates, selecting cargo hold layout templates where the number of container cargo spaces is greater than or equal to the quantity of container cargo, and the number of pallet cargo spaces is greater than or equal to the quantity of pallet cargo, to obtain M types of cargo hold layout templates.

[0009] Further, based on the loose cargo information and the loose cargo location information in the M types of cargo hold layout templates, a loading strategy for loose cargo is generated, including: sorting the loose cargo based on its weight to obtain a first sorting result, and based on the first sorting result; traversing all loose cargo based on the first sorting result, and generating a loading strategy for the loose cargo based on the weight, volume, object type of the loose cargo and the loose cargo location information in the M types of cargo hold layout templates, wherein the object type includes at least one of the following: cargo, mail, and baggage.

[0010] Further, the S initial loading strategies are scored to obtain a scoring result, including: calculating the score value of multiple evaluation items associated with each initial loading strategy, and determining the weight of each evaluation item, wherein the multiple evaluation items include: indicators used to evaluate the initial loading strategy; determining the comprehensive score value of each initial loading strategy using the superior-inferior solution distance method based on the score value of the multiple evaluation items associated with each initial loading strategy and the weight of each evaluation item; and obtaining the scoring result based on the comprehensive score value of the S initial loading strategies.

[0011] Further, the score values ​​of multiple evaluation items associated with each of the initial loading strategies are calculated, including: for each of the initial loading strategies, obtaining the relative position of the cargo and the aircraft door of each destination station associated with the target flight to obtain relative position information; obtaining the center of gravity value of the target flight at takeoff and at landing to obtain center of gravity information; obtaining the aircraft side balance information of the target flight; and determining the score values ​​of multiple evaluation items associated with each of the initial loading strategies based on the relative position information, the center of gravity information, and the aircraft side balance information.

[0012] Further, determining the weight of each evaluation item includes: determining a first weight of each evaluation item based on the score values ​​of multiple evaluation items associated with each initial loading strategy using the entropy weight method; obtaining the importance level of each evaluation item set by the target object, and determining a second weight of each evaluation item based on the importance level of multiple evaluation items using the hierarchical analysis strategy; and determining the weight of each evaluation item based on the first weight and the second weight of each evaluation item.

[0013] According to another aspect of the present invention, a data processing apparatus based on aircraft cargo hold loading is also provided, comprising: an acquisition unit, configured to acquire cargo information of a target flight and N cargo hold layout templates associated with the target flight, wherein the cargo information includes information on aircraft objects to be loaded associated with the target flight, and N is a positive integer; a determination unit, configured to determine S initial loading strategies for the objects to be loaded based on the cargo information and the N cargo hold layout templates, wherein S is a positive integer; and a processing unit, configured to score the S initial loading strategies, obtain a scoring result, and determine a target loading strategy based on the scoring result, wherein the target loading strategy includes the initial loading strategy with the highest score among the S initial loading strategies.

[0014] Further, the determining unit includes: an acquisition subunit, used to acquire static data information of the target flight, wherein the static data information includes: restriction data for limiting cargo loading; a filtering subunit, used to filter N types of cargo hold layout templates based on the cargo information to obtain M types of cargo hold layout templates, wherein M is a positive integer less than N; and a determining subunit, used to determine S types of initial loading strategies based on the cargo information, the M types of cargo hold layout templates, and the static data information.

[0015] Further, the objects to be loaded include: containerized cargo that needs to be placed in containers, palletized cargo that needs to be placed in pallets, and loose cargo that needs to be placed in bulk. The cargo information includes: containerized cargo information, palletized cargo information, and loose cargo information. The cargo hold layout template includes: container cargo location information, palletized cargo location information, and loose cargo location information. The determining subunit includes: a generation module, used to generate a loading strategy for loose cargo based on the loose cargo information and the loose cargo location information in the M types of cargo hold layout templates; and a first processing module, used to generate a palletized cargo loading strategy for each of the M types of cargo hold layout templates based on the palletized cargo information and the palletized cargo location information, and to filter out cargo hold layout templates that cannot generate a palletized cargo loading strategy from the M types of cargo hold layout templates, to obtain P types of cargo hold layout templates. The system comprises: a cargo hold layout template, wherein P is a positive integer less than M; a second processing module, configured to generate a loading strategy for container cargo based on the container cargo information and the container cargo location information for each of the P cargo hold layout templates, and filter out cargo hold layout templates that cannot generate a loading strategy for container cargo from the P cargo hold layout templates, to obtain Q cargo hold layout templates, wherein Q is a positive integer less than P; a combination module, configured to combine the loading strategies for bulk cargo, pallet cargo, and container cargo for each of the Q cargo hold layout templates, to obtain all loading strategies associated with each cargo hold layout template; and a first filtering module, configured to filter all loading strategies associated with the Q cargo hold layout templates based on the static data information, to obtain S initial loading strategies.

[0016] Further, the screening subunit includes: a calculation module, used to calculate the quantity of container cargo and the quantity of pallet cargo in the object to be loaded based on the cargo information; and a second screening module, used to screen from N cargo hold layout templates where the number of container cargo spaces is greater than or equal to the number of container cargo and the number of pallet cargo spaces is greater than or equal to the number of pallet cargo, to obtain M cargo hold layout templates.

[0017] Further, the generation module includes: a sorting submodule, used to sort the bulk cargo based on its weight to obtain a first sorting result, and based on the first sorting result; and a processing submodule, used to traverse all bulk cargo based on the first sorting result, and based on the weight, volume, object type, and bulk cargo location information in the M types of cargo hold layout templates, to generate a loading strategy for the bulk cargo, wherein the object type includes at least one of the following: cargo, mail, and baggage.

[0018] Further, the processing unit includes: a first processing subunit, configured to calculate the score values ​​of multiple evaluation items associated with each initial loading strategy, and determine the weight of each evaluation item, wherein the multiple evaluation items include: indicators for evaluating the initial loading strategy; a second processing subunit, configured to determine the comprehensive score value of each initial loading strategy based on the score values ​​of the multiple evaluation items associated with each initial loading strategy and the weight of each evaluation item, using the superior-inferior solution distance method; and a third processing subunit, configured to obtain the scoring result based on the comprehensive score values ​​of the S initial loading strategies.

[0019] Further, the second processing subunit includes: a first acquisition module, used to acquire, for each of the initial loading strategies, the relative position of the cargo and the aircraft door of each arrival station associated with the target flight, to obtain relative position information; a second acquisition module, used to acquire the center of gravity value of the target flight at takeoff and at landing, to obtain center of gravity information; a third acquisition module, used to acquire the aircraft side balance information of the target flight; and a first determination module, used to determine the score value of multiple evaluation items associated with each of the initial loading strategies based on the relative position information, the center of gravity information, and the aircraft side balance information.

[0020] Further, the first processing subunit includes: a second determining module, configured to determine a first weight for each of the evaluation items based on the score values ​​of multiple evaluation items associated with each of the initial loading strategies, using the entropy weight method; a third determining module, configured to obtain the importance level of each of the evaluation items set by the target object, and determine a second weight for each of the evaluation items based on the importance level of the multiple evaluation items, using the hierarchical analysis strategy; and a fourth determining module, configured to determine the weight of each of the evaluation items based on the first weight and the second weight of each of the evaluation items.

[0021] According to another aspect of the present invention, an electronic device is also provided, including: a processor; and a memory for storing executable instructions of the processor; wherein the processor is configured to perform the data processing method based on aircraft cargo hold loading of any of the above-mentioned methods by executing the executable instructions.

[0022] According to another aspect of the present invention, a computer-readable storage medium is also provided, which stores a computer program, wherein the computer program controls the device where the computer-readable storage medium is located to execute the data processing method based on aircraft cargo hold loading as described above when the computer program is running.

[0023] In this invention, the following methods are used: First, cargo information of the target flight and N cargo hold layout templates associated with the target flight are obtained. The cargo information includes information about the objects to be loaded onto the aircraft associated with the target flight, where N is a positive integer. Second, based on the cargo information and the N cargo hold layout templates, S initial loading strategies for the objects to be loaded are determined, where S is a positive integer. Third, the S initial loading strategies are scored to obtain a score result, and a target loading strategy is determined based on the score result. The target loading strategy includes the initial loading strategy with the highest score among the S initial loading strategies. This solves the technical problem in related technologies where loading personnel load cargo into the aircraft cargo hold based on their own experience, resulting in poor loading efficiency. In this invention, based on cargo information or cargo hold layout information, a full set of initial loading strategies is generated, and each initial loading strategy is comprehensively scored to select the target loading strategy with the highest score. This avoids the situation in related technologies where cargo hold loading schemes are obtained based on the loading personnel's own experience, leading to low efficiency and low safety. Therefore, this invention achieves the technical effect of improving the efficiency and safety of aircraft cargo hold loading. Attached Figure Description

[0024] The accompanying drawings, which are included to provide a further understanding of the invention and form part of this application, illustrate exemplary embodiments of the invention and, together with their description, serve to explain the invention and do not constitute an undue limitation thereof. In the drawings:

[0025] Figure 1 This is a schematic diagram of data processing based on aircraft cargo hold loading, according to existing technology;

[0026] Figure 2 This is an example diagram of an optional cargo hold layout according to an embodiment of the present invention;

[0027] Figure 3 This is a schematic diagram of an optional data processing device based on aircraft cargo hold loading according to an embodiment of the present invention. Detailed Implementation

[0028] To enable those skilled in the art to better understand the present invention, the technical solutions of the present invention will be clearly and completely described below with reference to the accompanying drawings of the embodiments of the present invention. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort should fall within the scope of protection of the present invention.

[0029] It should be noted that the terms "first," "second," etc., in the specification, claims, and accompanying drawings of this invention are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence. It should be understood that such data can be interchanged where appropriate so that the embodiments of the invention described herein can be implemented in orders other than those illustrated or described herein. Furthermore, the terms "comprising" and "having," and any variations thereof, are intended to cover a non-exclusive inclusion; for example, a process, method, system, product, or apparatus that comprises a series of steps or units is not necessarily limited to those steps or units explicitly listed, but may include other steps or units not explicitly listed or inherent to such processes, methods, products, or apparatus.

[0030] Example 1

[0031] According to an embodiment of the present invention, an optional method embodiment of a data processing method based on aircraft cargo hold loading is provided. It should be noted that the steps shown in the flowchart in the accompanying drawings can be executed in a computer system such as a set of computer-executable instructions. Furthermore, although a logical order is shown in the flowchart, in some cases, the steps shown or described may be executed in a different order than that shown here.

[0032] Figure 1 This is a flowchart of an optional data processing method based on aircraft cargo hold loading according to an embodiment of the present invention, such as... Figure 1 As shown, the method includes the following steps:

[0033] Step S101: Obtain cargo information of the target flight and N cargo hold layout templates associated with the target flight. The cargo information includes information on the aircraft objects to be loaded associated with the target flight, where N is a positive integer.

[0034] The objects to be loaded can be divided into cargo, mail, and baggage. The cargo information may include, but is not limited to, the basic information of cargo, mail, and baggage, including the destination station of the target flight, cargo type, cargo weight, cargo volume, special cargo code, container type, etc. Cargo type includes three categories: C (Cargo), M (Mail), and B (Bag). Container type is the container type code used for cargo. These codes can divide cargo into three categories: Container (container cargo, cargo is placed in a container), Pallet (palletized cargo, cargo is placed on pallets), and BULK (bulk cargo, cargo is scattered).

[0035] Each of the N cargo hold layout templates (i.e., the cargo hold layout information of the aircraft corresponding to the target flight) can include: front and rear hold positions, compartment positions, cargo position positions, and door positions (the aircraft cargo hold includes the front and rear cargo holds, each with one or more compartments, and each compartment contains one or more cargo positions). Cargo positions can be further divided into container cargo positions, pallet cargo positions, and bulk cargo positions. Each cargo position can define the type of cargo that can be stored at that position, the type of container, the impact of a unit weight of cargo on the center of gravity, and the impact of a unit weight of cargo on the lateral balance. Bulk cargo positions can store 0-N (N>=1) bulk cargo, container cargo positions can store 0-1 container cargo, and pallet cargo positions can store 0-1 pallet cargo.

[0036] It should be noted that each aircraft can have one or more layout templates, and the number and location of container and pallet cargo bays can vary from template to template. An example cargo hold layout diagram is shown below. Figure 2 As shown, the bold black vertical line in the middle divides the forward and aft cargo holds. The forward cargo hold contains two compartments, CPT1 and CPT2, while the aft cargo hold contains three compartments, CPT3, CPT4, and CPT5. CPT1 contains six container bays: 11L, 11R, 12L, 12R, 13L, and 13R. CPT2 contains six container bays and two pallet bays. The container bays include 21L... 21R, 22L, 22R, 23L, 23R; pallet positions include 23P, 24P; CPT3 includes 2 container positions and 2 pallet positions, with container positions including 34L, 34R, and pallet positions including 31P, 32P; CPT4 includes 6 container positions, including 41L, 41R, 42L, 42R, 43L, 43R; CPT5 includes 1 bulk cargo position; Example Figure 2 The two bold horizontal lines at the bottom indicate the location of the hatch.

[0037] Step S102: Based on cargo information and N cargo hold layout templates, determine S initial loading strategies for the objects to be loaded, where S is a positive integer.

[0038] In this embodiment, a suitable cargo hold layout template can be selected from N cargo hold layout templates based on the cargo information of the current flight (i.e., the target flight). Based on the selected cargo hold layout template, cargo information, flight center of gravity information, static data information, and loading rules, all feasible loading schemes (i.e., S initial loading strategies) can be generated. The static data information may include, but is not limited to: weight limit static data, center of gravity limit static data, volume limit static data, special cargo isolation rule static data, optimal center of gravity static data, and optimal lateral balance static data.

[0039] Step S103: S initial loading strategies are scored to obtain the scoring results, and the target loading strategy is determined based on the scoring results. The target loading strategy includes the initial loading strategy with the highest score among the S initial loading strategies.

[0040] In this embodiment, TOPSIS (Technique for Order of Preference by Similarity to Ideal Solution, a method in multi-objective decision analysis, also known as the superior-inferior solution distance method) can be used to score S initial loading strategies, obtain scoring results, and determine the target loading strategy based on the scoring results. For example, for each loading scheme (i.e., initial loading strategy), each optimization evaluation item (referred to as evaluation item) can be scored, and the weight of each optimization evaluation item can be calculated. Finally, the comprehensive score of the loading scheme (i.e., initial loading strategy) is calculated according to the TOPSIS algorithm, and the loading scheme with the highest score is the optimal loading scheme (i.e., target loading strategy).

[0041] Through the above steps, in this embodiment, based on cargo information or cargo hold layout information, a full set of initial loading strategies is generated. Each initial loading strategy is then comprehensively scored, and the target loading strategy with the highest score is selected. This avoids the situation in related technologies where cargo hold loading schemes are determined based on the loader's own experience, resulting in low efficiency and low safety. Therefore, it achieves the technical effect of improving the efficiency and safety of aircraft cargo hold loading. Furthermore, it solves the technical problem in related technologies where loading is done by the loader based on their own experience, leading to poor loading results.

[0042] In one alternative example, data processing based on aircraft cargo hold loading may also include the following steps:

[0043] Step (1): Obtain the basic information of the current flight (corresponding to the target flight); Step (2): Obtain the cargo, mail and baggage data of the current flight and classify them into container cargo, pallet cargo and loose cargo; Step (3): Obtain the aircraft cargo hold layout information of the current flight, where each aircraft can have one or more layout templates to choose from; Step (4): Obtain the static data information of the current flight, including static data of weight limit, static data of center of gravity limit, static data of volume limit, static data of special cargo isolation rules, static data of optimal center of gravity, and static data of optimal side balance; Step (5): Select a suitable cargo hold layout template according to the cargo information of the current flight; Step (6): Generate all feasible loose cargo loading schemes according to the loose cargo location information and loose cargo information of the current flight. If there is no feasible loading scheme (or loading strategy), an error will be reported directly; Step (7): For each cargo hold layout template obtained in step (5), generate all feasible container cargo according to the pallet location information and pallet cargo information. Cargo loading scheme: If a certain layout template does not have a feasible loading scheme, it is filtered out; Step (8): For each cargo hold layout template after step (7) screening, generate all feasible container pallet cargo loading schemes based on container cargo information and container cargo information. If a certain layout template does not have a feasible loading scheme, it is filtered out; Step (9): For each cargo hold layout template after step (8) screening, combine the loading schemes generated in steps (6), (7), and (8) to obtain all complete loading schemes for each template; Step (10): Screen the loading schemes in step (9) based on the current flight center of gravity information, static data information, and loading rules, and filter out loading schemes that do not conform to the loading rules; Step (11): Score each optimization evaluation item of the loading schemes obtained after step (10) screening (corresponding to S initial loading strategies), calculate the weight of each optimization evaluation item, and finally calculate the comprehensive score of the loading schemes based on the TOPSIS algorithm. The loading scheme with the highest score is the optimal loading scheme.

[0044] Optionally, based on cargo information and N cargo hold layout templates, S initial loading strategies for the object to be loaded are determined, including: obtaining static data information of the target flight, wherein the static data information includes: restriction data for limiting cargo loading; filtering the N cargo hold layout templates based on cargo information to obtain M cargo hold layout templates, wherein M is a positive integer less than N; and determining S initial loading strategies based on cargo information, M cargo hold layout templates, and static data information.

[0045] The aforementioned static data information may include: restrictive data that limits cargo loading. For example, static data information may include, but is not limited to: weight restriction static data, center of gravity restriction static data, volume restriction static data, special cargo segregation rule static data, optimal center of gravity static data, and optimal lateral balance static data. Specifically, weight restriction static data includes front and rear hold weight limits, compartment weight limits, cargo location weight limits, combined cargo location weight limits, and combined compartment weight limits. Front and rear hold weight limits define the maximum weight of cargo that can be loaded in the front and rear holds; compartment weight limits define the maximum weight of cargo that can be loaded in each compartment; cargo location weight limits define the maximum weight of cargo that can be loaded in each cargo location; combined cargo location weight limits define the sum of the maximum weights of cargo that can be loaded in multiple cargo locations; and combined compartment weight limits define the sum of the maximum weights of cargo that can be loaded in multiple compartments.

[0046] Center of gravity constraint static data may include, but is not limited to: takeoff center of gravity envelope static data, zero-fuel center of gravity envelope static data, and landing center of gravity envelope static data. Zero-fuel center of gravity envelope static data defines the index range for different aircraft weights without refueling; the aircraft's index value must be within this range. Takeoff center of gravity envelope static data defines the index range for different aircraft takeoff weights; the aircraft's index value (center of gravity value) must be within this range. Landing center of gravity envelope static data defines the index range for different aircraft landing weights; the aircraft's index value must be within this range. Volume constraint static data may include, but is not limited to: the maximum volume of cargo that can be loaded in each bulk cargo bay. Special cargo segregation rule static data may include, but is not limited to: the minimum distance required between any two types of special cargo. Optimal center of gravity static data may include, but is not limited to: the optimal index range for different aircraft weights; if the aircraft's index value is within this range during flight, it helps the aircraft save fuel. Optimal lateral balance static data includes takeoff optimal lateral balance static data and landing optimal lateral balance static data. Takeoff optimal lateral balance static data defines the optimal Lateral Index value for different takeoff weights of the aircraft. The closer the Lateral Index of the aircraft is to this optimal value during takeoff, the better. Landing optimal lateral balance static data defines the optimal Lateral Index value (lateral balance center of gravity value) for different landing weights of the aircraft. The closer the Lateral Index of the aircraft is to this optimal value during landing, the better.

[0047] In this embodiment, N cargo hold layout templates can be filtered based on cargo information to obtain M cargo hold layout templates. Then, based on cargo information, M cargo hold layout templates, and static data information, S initial loading strategies can be determined, achieving the technical effect of accurately generating all feasible loading schemes.

[0048] Optionally, the objects to be loaded include: containerized cargo that needs to be placed in containers, palletized cargo that needs to be placed on pallets, and loose cargo that needs to be placed in bulk. The cargo information includes: containerized cargo information, palletized cargo information, and loose cargo information. The cargo hold layout template includes: container cargo location information, palletized cargo location information, and loose cargo location information. Based on the cargo information, M types of cargo hold layout templates, and static data information, S initial loading strategies are determined, including: generating a loading strategy for loose cargo based on the loose cargo information and the loose cargo location information in the M types of cargo hold layout templates; for each of the M types of cargo hold layout templates, generating a loading strategy for palletized cargo based on the palletized cargo information and palletized cargo location information, and filtering out palletized cargo that cannot be generated from the M types of cargo hold layout templates. The cargo hold layout templates for cargo loading strategies are used to obtain P types of cargo hold layout templates, where P is a positive integer less than M. For each of the P types of cargo hold layout templates, a loading strategy for container cargo is generated based on container cargo information and container cargo location information. Cargo hold layout templates that cannot generate a loading strategy for container cargo are filtered out from the P types of cargo hold layout templates, resulting in Q types of cargo hold layout templates, where Q is a positive integer less than P. For each of the Q types of cargo hold layout templates, the loading strategies for bulk cargo, palletized cargo, and container cargo are combined to obtain all loading strategies associated with each cargo hold layout template. Based on static data information, all loading strategies associated with the Q types of cargo hold layout templates are filtered to obtain S initial loading strategies.

[0049] For example, in this embodiment, all feasible bulk cargo loading plans can be generated based on the current flight's bulk cargo location information and bulk cargo information. If no feasible loading plan is found, an error will be reported directly.

[0050] In this embodiment, for each of the M cargo hold layout templates, all feasible container cargo loading schemes can be generated based on the pallet location information and pallet cargo information. If a certain layout template does not have a feasible loading scheme, it will be filtered out, as follows.

[0051] For each of the M cargo hold layout templates, all feasible pallet loading schemes can be generated based on the pallet location information and pallet cargo information. To find all feasible pallet loading schemes (corresponding to pallet cargo loading strategies) for a specific layout template, the steps are as follows: (a) Sort the pallet cargo in reverse chronological order based on its weight; (b) Select the first pallet cargo and iterate through all pallet locations of the template in a fixed order. Based on the pallet cargo weight, cargo type, and container type information, the pallet locations can be... (c) Select a suitable storage location based on the type of goods, container type, and static weight limit data of the storage location. If no suitable storage location is found, it means that there is no feasible loading plan for this template. (d) Select the next containerized goods for storage location allocation. If no suitable storage location can be found, it means that there is no feasible loading plan for this template. (e) Repeat step (c). If all containerized goods can be allocated to storage locations, a feasible loading plan can be obtained. (f) Change the storage location allocated to the goods until all feasible loading plans are found, that is, the loading strategy of containerized goods is obtained.

[0052] For each cargo hold layout template (i.e., each cargo hold layout template in P types) selected during the process of determining the loading strategy for palletized cargo, all feasible palletized cargo loading schemes can be generated based on container location information and container cargo information. If a certain layout template does not have a feasible loading scheme, it is filtered out, resulting in the loading strategy for containerized cargo and the selection of Q types of cargo hold layout templates, as detailed below.

[0053] For each of the P types of cargo hold layout templates, all feasible container cargo loading schemes are generated based on container location information and container cargo information. For this description, to find all feasible container cargo loading schemes for a specific layout template, the steps are as follows: (a) Sort the container cargo in reverse order based on its weight; (b) Select the first container cargo and iterate through all container locations in the template in a fixed order. Based on the container cargo weight, cargo type, container type information, the types of cargo and containers that can be stored in the container location, and the static weight limit data of the location, select a suitable location for storage. If no suitable location is found, it means that this template has no feasible loading scheme; (c) Select the next container cargo and assign it a location. If no suitable location can be found, it means that this template has no feasible loading scheme; (d) Repeat step (c). If all container cargo can be assigned a location, a feasible loading scheme is obtained; (e) Change the location assigned to the cargo until all feasible loading schemes are found.

[0054] In this embodiment, for each of the Q types of cargo hold layout templates, the loading schemes generated in steps (6), (7), and (8) can be combined to obtain all complete loading schemes for each template. Specifically, for each of the Q types of cargo hold layout templates, the loading strategies for bulk cargo, pallet cargo, and container cargo of that cargo hold layout template can be combined to obtain all complete loading methods for each template (corresponding to all loading strategies associated with each cargo hold layout template); if a certain layout template i has NP pallet cargo loading schemes... i There are NC container cargo loading solutions. i If there are NB loading schemes for bulk cargo, then the total number of complete loading schemes for this layout template is T. i =NP i ×NC i ×NB, if a total of n layout templates are selected, then the number of feasible loading schemes is...

[0055] In this embodiment, all loading schemes (i.e. loading strategies) associated with each cargo hold layout template can be filtered based on the current flight center of gravity information, static data information, and loading rules. Loading schemes that do not conform to the loading rules are filtered out to obtain S initial loading strategies, thus achieving the technical effect of accurately determining all available aircraft cargo hold loading schemes.

[0056] It should be noted that loading rules may include, but are not limited to: (a) Weight limit rules: the total weight of cargo in the forward cargo hold cannot exceed the forward cargo hold weight limit, and the total weight of cargo in the aft cargo hold cannot exceed the aft cargo hold weight limit; the total weight of cargo in each compartment cannot exceed the corresponding compartment weight limit; for each set of combined weight limit static data for cargo positions, the total weight of cargo loaded in these cargo positions cannot exceed the maximum value defined by the static data; for each set of combined weight limit static data for compartments, the total weight of cargo loaded in these compartments cannot exceed the maximum value defined by the static data; (b) Center of gravity limit rules: calculate the zero-fuel weight, zero-fuel center of gravity index, takeoff weight, and takeoff center of gravity in the aircraft after loading cargo. dex, landing weight, landing center of gravity index; based on the zero-fuel weight, find the zero-fuel center of gravity envelope static data to obtain the zero-fuel center of gravity index range, at which time the zero-fuel center of gravity index cannot exceed this range; based on the takeoff weight, find the takeoff center of gravity envelope static data to obtain the takeoff center of gravity index range, at which time the takeoff center of gravity index cannot exceed this range; based on the landing weight, find the landing center of gravity envelope static data to obtain the landing center of gravity index range, at which time the landing center of gravity index cannot exceed this range; (c) Special cargo isolation rules: all currently loaded special cargo (the cargo special cargo code is not empty) must meet the restriction rules in the special cargo isolation rules static data.

[0057] Optionally, based on cargo information, N types of cargo hold layout templates are filtered to obtain M types of cargo hold layout templates, including: based on cargo information, calculating the quantity of container cargo and the quantity of pallet cargo in the object to be loaded; among the N types of cargo hold layout templates, selecting cargo hold layout templates where the number of container berths is greater than or equal to the quantity of container cargo and the number of pallet berths is greater than or equal to the quantity of pallet cargo, to obtain M types of cargo hold layout templates.

[0058] For example, based on the cargo information of the current flight, a suitable cargo hold layout template is selected. The specific steps are as follows: (The location and number of bulk cargo positions are the same for each cargo hold layout template): (a) Calculate the number of container cargo and pallet cargo for the current flight; (b) From all cargo hold layout templates for the current flight, search for cargo hold layout templates that satisfy the condition that the number of pallet cargo positions is greater than or equal to the number of pallet cargo; (c) From the cargo hold layout templates obtained in step (b), search for cargo hold layout templates that satisfy the condition that the number of container cargo positions is greater than or equal to the number of container cargo, thus achieving the purpose of preliminary screening of cargo hold layout templates.

[0059] Optionally, based on the information of loose cargo and the information of loose cargo positions in the M types of cargo hold layout templates, a loading strategy for loose cargo is generated, including: sorting the loose cargo based on its weight to obtain a first sorting result, and based on the first sorting result; traversing all loose cargo based on the first sorting result, and generating a loading strategy for loose cargo based on the weight, volume, object type of the loose cargo and the information of loose cargo positions in the M types of cargo hold layout templates, wherein the object type includes at least one of the following: cargo, mail, and baggage.

[0060] For example, based on the information of bulk cargo locations and bulk cargo, all feasible bulk cargo loading plans are generated. The specific steps include: (a) sorting the bulk cargo in reverse order according to its weight; (b) selecting the first bulk cargo, traversing all bulk cargo locations in a fixed order, and selecting a suitable location for storage based on the bulk cargo's weight, volume, cargo type, the types of cargo that can be stored in the bulk cargo location, and the static data of the location's weight and volume limits. If no suitable location is found, an error is reported; (c) selecting the next bulk cargo for location allocation, which may involve the previous bulk cargo. (d) Allocate cargo to the same cargo hold location. At this time, the maximum loadable weight of the cargo hold location must be greater than or equal to the sum of the weights of all cargo in that location, and the maximum loadable volume must be greater than or equal to the sum of the volumes of all cargo in that location. If a suitable cargo hold location cannot be found, an error will be reported directly. (e) Repeat step (c). If all cargo can be allocated to a cargo hold location, a feasible loading plan can be obtained. (f) Change the cargo hold location to which the cargo is allocated until all feasible loading plans are found. The above-mentioned cargo loading strategy for cargo holds is obtained, which achieves the technical effect of accurately generating all feasible cargo loading strategies for each feasible cargo hold layout template.

[0061] Optionally, the S initial loading strategies are scored to obtain a scoring result, including: calculating the score value of multiple evaluation items associated with each initial loading strategy and determining the weight of each evaluation item, wherein the multiple evaluation items include: indicators used to evaluate the initial loading strategy; determining the comprehensive score value of each initial loading strategy using the superior-inferior solution distance method based on the score value of the multiple evaluation items associated with each initial loading strategy and the weight of each evaluation item; and obtaining the scoring result based on the comprehensive score value of the S initial loading strategies.

[0062] In this embodiment, the optimization evaluation items (hereinafter referred to as evaluation items) may include, but are not limited to: (a) Optimization evaluation item DH: used to evaluate whether the cargo, mail, and baggage arriving at the current station are closer to the cabin door than the cargo, mail, and baggage arriving at subsequent stations; (b) Optimization evaluation item CH: used to evaluate whether the baggage arriving at the current station is closer to the cabin door than the cargo and mail; (c) Optimization evaluation items TOI and LAI: used to evaluate whether the center of gravity of the aircraft during flight is within the optimal center of gravity static data range. Since it is impossible to obtain the center of gravity value at a certain moment during flight, this item is simplified and divided into whether the center of gravity of the aircraft is within the optimal center of gravity static data range (TOI) and whether the center of gravity is within the optimal center of gravity static data range (LAI) during landing; (d) Optimization evaluation items TOLI and LALI: used to evaluate whether the lateral balance (horizontal center of gravity) of the aircraft is closer to the optimal lateral balance static data. This item is further divided into whether the lateral balance of the aircraft during takeoff is closer to the optimal lateral balance static data during takeoff (TOLI) and whether the lateral balance of the aircraft during landing is closer to the optimal lateral balance static data during landing (TOLI).

[0063] In this embodiment, each optimization evaluation item of all feasible loading schemes (i.e., S initial loading strategies) can be scored. The weight of each optimization evaluation item is calculated according to the entropy weight method and the analytic hierarchy process. Based on the weight of each optimization evaluation item, the comprehensive score of the loading scheme is calculated according to the TOPSIS algorithm to obtain the optimal loading scheme.

[0064] Optionally, the scores of multiple evaluation items associated with each initial loading strategy are calculated, including: for each initial loading strategy, obtaining the relative position of the cargo and the aircraft door of each destination station associated with the target flight to obtain relative position information; obtaining the center of gravity value of the target flight at takeoff and at landing to obtain center of gravity information; obtaining the aircraft side balance information of the target flight; and determining the scores of multiple evaluation items associated with each initial loading strategy based on the relative position information, center of gravity information, and aircraft side balance information.

[0065] In this embodiment, for each initial loading strategy, scores can be assigned to each optimization evaluation item (hereinafter referred to as evaluation item) to obtain a score value for each evaluation item. The specific scoring method is as follows:

[0066] (a) For loading scheme j (i.e., initial loading strategy j), score the optimization evaluation term CH to obtain the score value SDH. j Specifically, it counts how many cargo, mail, and baggage arriving at subsequent stations are closer to the cabin door than the cargo, mail, and baggage arriving at the current station (corresponding to the target flight). If a certain loading scheme j has DH jIf cargo, mail, and baggage arriving at a subsequent station are closer to the hatch than cargo, mail, and baggage arriving at the current station, then the corresponding score will be SDH. j The calculation formula is:

[0067]

[0068] For example, if the current arrival station of the current flight is PEK and the second arrival station is CAN, mail in cargo bay 23L (arriving at CAN) is closer to the door than cargo in cargo bay 24L (arriving at PEK) and baggage in cargo bay 25L (arriving at PEK); mail in cargo bay 21R (arriving at CAN) and cargo in cargo bay 22R (arriving at CAN) are closer to the door than cargo in cargo bay 24R (arriving at SHA). Therefore, there are a total of 3 cargo, mail, and baggage arriving at subsequent stations (mail in cargo bay 23L, mail in cargo bay 21R, and cargo in cargo bay 22R) that are closer to the door than the cargo, mail, and baggage arriving at the current station. Calculations show DH = 3 and SDH = 1 / 4.

[0069] (b) For loading scheme j (i.e., initial loading strategy j), score the optimization evaluation term CH to obtain the score value SCH. j Specifically, it counts how many cargo and mail arriving at the current station are closer to the cabin door than baggage arriving at the current station on the current flight. If a certain loading scheme j has CH j If cargo or mail arriving at the current station is closer to the cabin door than baggage arriving at the current station, then the corresponding score is SCH. j The calculation formula is as follows:

[0070]

[0071] For example, if the current arrival station of the current flight is PEK, the mail in cargo slots 13L and 21L is closer to the door than the baggage in cargo slot 25L. Therefore, there are a total of 2 cargo and mail items (mail in cargo slots 13L and 21L) that are closer to the door than the baggage in the current arrival station. The calculation results in CH=2 and SCH=1 / 3.

[0072] (c) For loading scheme j (i.e., initial loading strategy j), score the optimization evaluation term TOI to obtain the score value STOI. j Specifically, if for a certain loading scheme j, the center of gravity of the aircraft at takeoff is TOI j The corresponding optimal centroid range is [IDI] min IDI max The range of the center of gravity corresponding to the takeoff center of gravity envelope is [TOI]. min TOI max The corresponding score is STOI.j The calculation formula is:

[0073]

[0074] (d) For loading scheme j (i.e., initial loading strategy j), score the optimization evaluation term LAI to obtain the score value SLAI. j Specifically, if for a certain loading scheme j, the center of gravity of the aircraft upon landing is LAI j The corresponding optimal deployment range is [IDI] min IDI max The corresponding range of the center of gravity envelope is [LAI]. min LAI max The corresponding score is SLAI. j The calculation formula is:

[0075]

[0076] (e) For loading scheme j (i.e., initial loading strategy j), score the optimization evaluation term TOLI to obtain the score value STOLI. j Specifically, if for a certain loading scheme j, the lateral balance value of the aircraft at takeoff is TOLI j The corresponding optimal takeoff side balance is TOLI best The corresponding score is STOLI j The calculation formula is:

[0077]

[0078] Where T sum This represents the total number of loading schemes.

[0079] If, for a certain loading scheme j, the lateral balance value of the aircraft upon landing is LALI j The corresponding optimal landing-side balance is LALI best The corresponding score is SLALI j The calculation formula is:

[0080]

[0081] Among them, T sum This represents the total number of loading schemes.

[0082] By calculating the optimization criteria DH, CH, TOI and LAI, TOLI and LALI, and the score, the technical effect of accurately scoring the initial loading strategy is achieved.

[0083] Optionally, determining the weight of each evaluation item includes: determining the first weight of each evaluation item using the entropy weight method based on the score values ​​of multiple evaluation items associated with each initial loading strategy; obtaining the importance of each evaluation item set by the target object, and determining the second weight of each evaluation item using the hierarchical analysis strategy based on the importance of multiple evaluation items; and determining the weight of each evaluation item based on the first weight and the second weight of each evaluation item.

[0084] In this embodiment, the weights of each optimization evaluation item (hereinafter referred to as the evaluation item) are obtained by using the analytic hierarchy process (AHP) and the entropy weight method, and the weights are determined according to the entropy weight method weight r set by the target object (e.g., the user). EWM (0≤r EWM≤ 1) And the importance of each optimization evaluation item set for the target object, calculate the weight of each optimization evaluation item. The specific calculation method is as follows:

[0085] (a) Based on the scores of each optimization evaluation item for all loading schemes (i.e., S initial loading strategies), the weights of each optimization evaluation item are calculated using the entropy weight method. For optimization evaluation item k, the calculated weight is ωk. EWM ;

[0086] (b) Based on the importance of each optimization evaluation item set for the target object, calculate the relative importance of each optimization evaluation item. Simultaneously, based on the scores of each optimization evaluation item for all loading schemes, input them into the hierarchical analysis algorithm to calculate the weight of each optimization evaluation item. For optimization evaluation item k, the calculated weight is: Based on the importance of each optimization criterion set for the target object, the relative importance of each optimization criterion is calculated. For example, assuming there are three optimization criterions, the first optimization criterion has an importance of 2, the second optimization criterion has an importance of 1, and the third optimization criterion has an importance of 3, the relative importance of each optimization criterion can be obtained as shown in Table 1.

[0087] Table 1

[0088] Optimize evaluation item 1 Optimize evaluation item 2 Optimize evaluation item 3 Optimize evaluation item 1 1 2 2 / 3 Optimize evaluation item 2 1 / 2 1 1 / 3 Optimize evaluation item 3 3 / 2 3 1

[0089] (c) For the optimization evaluation term k, its comprehensive weight ω k The formula for calculating the weight of evaluation item k is as follows:

[0090]

[0091] In this embodiment, the weights of each optimization evaluation item are calculated using the entropy weight method and the analytic hierarchy process, thereby achieving the technical effect of accurately determining the weights of each optimization evaluation item.

[0092] In this embodiment, the overall score of the loading scheme can also be calculated based on the TOPSIS algorithm. The specific calculation method is as follows:

[0093] (a) Based on the scores of each optimization evaluation item for all loading schemes, the original matrix X is constructed as follows:

[0094]

[0095] (b) Obtain the positive and negative ideal loading schemes:

[0096]

[0097]

[0098] (c) Calculate the correlation coefficient between each loading scheme and the positive and negative ideal loading schemes, and use this as the evaluation value of the loading scheme. For loading scheme j, its correlation coefficient C j The calculation formula is as follows:

[0099]

[0100] Where ω k To optimize the overall weight of evaluation item k.

[0101] This embodiment allows for the automatic selection of feasible loading schemes (corresponding to S initial loading strategies) based on the cargo, mail, and baggage to be loaded, as well as the aircraft's cargo hold layout information, taking into full account loading rules, including weight limit rules, center of gravity limit rules, volume limit rules, and special cargo isolation rules. Then, each optimization evaluation item of these feasible schemes is scored, and the comprehensive score of the loading scheme is calculated according to the TOPSIS algorithm. The loading scheme with the highest score is the optimal loading scheme (corresponding to the target loading strategy). Applying this embodiment can effectively improve the shortcomings of existing loading schemes: First, this embodiment can automatically select a loading scheme, eliminating the need for loading personnel to adjust the scheme and improving their work efficiency; second, the cargo hold loading scheme provided by this embodiment fully considers loading rules and eliminates human interference, reducing production safety hazards; finally, this embodiment fully considers various optimization items, such as whether the cargo, mail, and baggage arriving at the current station are closer to the hatch than the cargo, mail, and baggage arriving at subsequent stations, whether the baggage arriving at the current station is closer to the hatch than the cargo and mail, whether the aircraft's center of gravity is within the optimal range during flight, and whether the aircraft's lateral balance (horizontal center of gravity) is closer to the optimal value, etc., and comprehensively evaluates the scores of these optimization items to provide the optimal loading scheme.

[0102] Example 2

[0103] Embodiment 2 of the present invention provides an optional data processing method based on aircraft cargo hold loading, comprising the following steps:

[0104] Step (1): Obtain basic information about the current flight;

[0105] Step (2): Obtain the cargo, mail and baggage data of the current flight and classify them into container cargo, palletized cargo and loose cargo;

[0106] Step (3): Obtain the cargo hold layout information of the current flight. Each aircraft can have one or more layout templates to choose from.

[0107] Step (4): Obtain the static data information of the current flight, including static data of weight limit, static data of center of gravity limit, static data of volume limit, static data of special cargo isolation rules, static data of optimal center of gravity, and static data of optimal lateral balance;

[0108] Step (5): Select a suitable cargo hold layout template based on the cargo information of the current flight;

[0109] Step (6): Based on the current flight's loose cargo space information and loose cargo information, generate all feasible loose cargo loading plans. If there is no feasible loading plan, report an error directly.

[0110] Step (7): For each cargo hold layout template obtained in step (5), generate all feasible container cargo loading schemes based on the pallet location information and pallet cargo information. If a layout template does not have a feasible loading scheme, filter it out.

[0111] Step (8): For each cargo hold layout template after step (7) filtering, generate all feasible pallet cargo loading schemes based on container cargo location information and container cargo information. If a layout template does not have a feasible loading scheme, filter it out.

[0112] Step (9): For each cargo hold layout template after filtering in step (8), combine the loading schemes generated in steps (6), (7), and (8) to obtain all complete loading schemes for each template.

[0113] Step (10) Filter the loading schemes in step (9) based on the current flight center of gravity information, static data information, and loading rules, and filter out loading schemes that do not conform to the loading rules;

[0114] Step (11): Score each optimization evaluation item of the loading scheme obtained after screening in step (10), calculate the weight of each optimization evaluation item, and finally calculate the comprehensive score of the loading scheme according to the TOPSIS algorithm. The loading scheme with the highest score is the optimal loading scheme.

[0115] Step (1) Basic flight information includes airline, flight number, flight date, departure station, arrival station, aircraft registration number, aircraft cabin layout, remaining payload, current center of gravity information, and current side balance information. The current center of gravity information includes the center of gravity values ​​(index values) corresponding to ZFW (Zero Fuel Weight), TOW (Take-Off Weight), and LAW (Landing Weight). The current side balance information includes the side balance center of gravity values ​​(Lateral Index values) corresponding to TOW (Take-Off Weight) and LAW (Landing Weight).

[0116] Step (2) Cargo, mail, and baggage data are the basic information of the cargo, mail, and baggage to be loaded, including the destination station, cargo type, cargo weight, cargo volume, special cargo code, container type, etc.; cargo type includes three categories, namely C (Cargo), M (Mail), and B (Bag); container type is the container type code used for cargo. These codes can divide cargo into three categories, namely Container (container cargo, cargo is placed in a container), Pallet (palletized cargo, cargo is placed on a pallet), and BULK (bulk cargo, cargo is scattered).

[0117] Step (3) The aircraft cargo hold layout information includes the front and rear cargo hold positions, partition compartment positions, cargo position positions, and door positions (the aircraft cargo hold includes the front cargo hold and the rear cargo hold, and the front and rear cargo holds each have one or more partition compartments, and each partition compartment contains one or more cargo positions). Among them, the cargo positions can be divided into container cargo positions, pallet cargo positions, and bulk cargo positions. Each cargo position defines the type of goods that can be stored at that position, the type of container, the influence value of a unit weight of goods on the center of gravity, and the influence value of a unit weight of goods on the lateral balance. Among them, the bulk cargo position can store 0-N (N>=1) bulk cargo, the container cargo position can store 0-1 container cargo, and the pallet cargo position can store 0-1 pallet cargo. Each aircraft can have one or more layout templates, and the number and position of the container cargo positions and pallet cargo positions are different for each layout template.

[0118] Example diagram of cargo hold layout as follows Figure 2As shown, the bold black vertical line in the middle divides the forward and aft cargo holds. The forward cargo hold contains two compartments, CPT1 and CPT2, while the aft cargo hold contains three compartments, CPT3, CPT4, and CPT5. CPT1 contains six container bays: 11L, 11R, 12L, 12R, 13L, and 13R. CPT2 contains six container bays and two pallet bays. The container bays include 21L... 21R, 22L, 22R, 23L, 23R; pallet positions include 23P, 24P; CPT3 includes 2 container positions and 2 pallet positions, with container positions including 34L, 34R, and pallet positions including 31P, 32P; CPT4 includes 6 container positions, including 41L, 41R, 42L, 42R, 43L, 43R; CPT5 includes 1 bulk cargo position; Example Figure 2 The two bold horizontal lines at the bottom indicate the location of the hatch.

[0119] Step (4) Weight limit static data may include front and rear hold weight limit static data, compartment weight limit static data, cargo location weight limit static data, combined cargo location weight limit static data, and combined compartment weight limit static data. Front and rear hold weight limit static data defines the maximum weight of cargo that can be loaded in the front cargo hold and the rear cargo hold. Compartment weight limit static data defines the maximum weight of cargo that can be loaded in each compartment. Cargo location weight limit static data defines the maximum weight of cargo that can be loaded in each cargo location. Combined cargo location weight limit static data defines the sum of the maximum weight of cargo that can be loaded in multiple cargo locations. Combined compartment weight limit static data defines the sum of the maximum weight of cargo that can be loaded in multiple compartments.

[0120] Step (4) Static data on center of gravity constraints can include static data on takeoff center of gravity envelope, static data on zero-fuel center of gravity envelope, and static data on landing center of gravity envelope. The static data on zero-fuel center of gravity envelope defines the index range for different aircraft weights when the aircraft is not refueled; the aircraft's index value must be within this range. The static data on takeoff center of gravity envelope defines the index range for different aircraft takeoff weights; the aircraft's index value must be within this range. The static data on landing center of gravity envelope defines the index range for different aircraft landing weights; the aircraft's index value must be within this range. The static data on volume constraints defines the maximum volume of cargo that can be loaded in each bulk cargo bay. The static data on special cargo segregation rules defines the minimum distance required between two types of special cargo. The static data on optimal center of gravity defines the optimal index range for different aircraft weights; if the aircraft's index value is within this range during flight, it helps the aircraft save fuel. Optimal side balance static data includes takeoff optimal side balance static data and landing optimal side balance static data. Takeoff optimal side balance static data defines the optimal Lateral Index value for different takeoff weights of the aircraft. The closer the Lateral Index of the aircraft is to this optimal value during takeoff, the better. Landing optimal side balance static data defines the optimal Lateral Index value for different landing weights of the aircraft. The closer the Lateral Index of the aircraft is to this optimal value during landing, the better.

[0121] Step (5) Select a suitable cargo hold layout template based on the cargo information of the current flight. The specific steps are as follows: (The position and number of bulk cargo positions of each cargo hold layout template can be the same): (a) Calculate the number of container cargo and pallet cargo of the current flight; (b) Search for cargo hold layout templates from all cargo hold layout templates of the current flight that satisfy the requirement that the number of pallet cargo positions is greater than or equal to the number of pallet cargo; (c) Search for cargo hold layout templates from the cargo hold layout templates obtained in step (b) that satisfy the requirement that the number of container cargo positions is greater than or equal to the number of container cargo.

[0122] Step (6) Generate all feasible loading schemes for bulk cargo based on the bulk cargo location information and bulk cargo information. The specific steps are as follows: (a) Sort the bulk cargo in reverse order according to the weight of the bulk cargo; (b) Select the first bulk cargo and traverse all bulk cargo locations in a fixed order. Based on the bulk cargo weight, volume, cargo type information, the types of cargo that can be stored in the bulk cargo location, as well as the static data of the cargo location weight limit and volume limit, select a suitable cargo location for storage. If there is no suitable cargo location, report an error directly; (c) Select the next bulk cargo for cargo location allocation. It may be allocated to the same bulk cargo location as the previous bulk cargo. At this time, the maximum loadable weight of the bulk cargo location must be greater than or equal to the sum of the weights of all cargo in the location, and the maximum loadable volume must be greater than or equal to the sum of the volumes of all cargo in the location. If no suitable cargo location can be found, report an error directly; (d) Repeat step (c). If all bulk cargo can be allocated to a cargo location, a feasible loading scheme can be obtained; (e) Change the cargo location allocated to the cargo until all feasible loading schemes are found.

[0123] Step (7) For each cargo hold layout template obtained in step (5), generate all feasible container cargo loading schemes based on the container cargo location information and container cargo information. For this description, the steps to find all feasible container cargo loading schemes for a specific layout template are as follows: (a) Sort the container cargo in reverse order based on the weight of the container cargo; (b) Select the first container cargo, traverse all container cargo locations of the template in a fixed order, and select a suitable cargo location for storage based on the container cargo weight, cargo type, container type information, cargo type that can be stored in the container cargo location, container type, and static weight limit data of the cargo location. If there is no suitable cargo location, it means that there is no feasible loading scheme for this template; (c) Select the next container cargo for cargo location allocation. If no suitable cargo location can be found, it means that there is no feasible loading scheme for this template; (d) Repeat step (c). If all container cargo can be allocated to a cargo location, a feasible loading scheme can be obtained; (e) Change the cargo location allocated to the cargo until all feasible loading schemes are found.

[0124] Step (8) For each cargo hold layout template filtered in step (7), generate all feasible container cargo loading schemes based on container location information and container cargo information. For this description, the steps to find all feasible container cargo loading schemes for a specific layout template are as follows: (a) Sort the container cargo in reverse order based on the weight of the container cargo; (b) Select the first container cargo, traverse all container locations of the template in a fixed order, and select a suitable location for storage based on the container cargo weight, cargo type, container type information, cargo type and container type that the container location can store, and static weight limit data of the location. If there is no suitable location, it means that there is no feasible loading scheme for this template; (c) Select the next container cargo for location allocation. If no suitable location can be found, it means that there is no feasible loading scheme for this template; (d) Repeat step (c). If all container cargo can be allocated to a location, a feasible loading scheme can be obtained; (e) Change the location allocated to the cargo until all feasible loading schemes are found.

[0125] Step (9) For each cargo hold layout template filtered in step (8), combine the loading schemes generated in steps (6), (7), and (8) to obtain all complete loading schemes for each template; if a certain layout template i has NP cargo loading schemes for its container, then... i There are NC container cargo loading solutions. i If there are NB loading schemes for bulk cargo, then the total number of complete loading schemes for this layout template is T. i =NP i ×NC i ×NB, if a total of n layout templates are selected, then the number of feasible loading schemes is...

[0126] Step (10) Loading rules, including the following rules: (a) Weight limit rules: The sum of the weights of the cargo in the forward cargo hold cannot exceed the weight limit of the forward cargo hold, and the sum of the weights of the cargo in the aft cargo hold cannot exceed the weight limit of the aft cargo hold; the sum of the weights of the cargo in each compartment cannot exceed the weight limit of the corresponding compartment; for each set of static weight limit data for cargo positions, the sum of the weights of the cargo loaded in these cargo positions cannot exceed the maximum value defined by the static data; for each set of static weight limit data for compartments, the sum of the weights of the cargo loaded in these compartments cannot exceed the maximum value defined by the static data; (b) Center of gravity limit rules: Calculate the zero-fuel weight, zero-fuel center of gravity index, takeoff weight, and takeoff center of gravity index of the aircraft after loading the cargo. ex, landing weight, landing center of gravity index; based on the zero-fuel weight, find the zero-fuel center of gravity envelope static data to obtain the zero-fuel center of gravity index range, at which time the zero-fuel center of gravity index cannot exceed this range; based on the takeoff weight, find the takeoff center of gravity envelope static data to obtain the takeoff center of gravity index range, at which time the takeoff center of gravity index cannot exceed this range; based on the landing weight, find the landing center of gravity envelope static data to obtain the landing center of gravity index range, at which time the landing center of gravity index cannot exceed this range; (c) Special cargo isolation rules: all currently loaded special cargo (the cargo special cargo code is not empty) must meet the restriction rules in the special cargo isolation rules static data.

[0127] Step (11) Optimize the evaluation criteria, including: (a) Optimization criterion DH: whether the cargo, mail, and baggage arriving at the current station are closer to the cabin door than the cargo, mail, and baggage arriving at subsequent stations; (b) Optimization criterion CH: whether the baggage arriving at the current station is closer to the cabin door than the cargo and mail; (c) Optimization criteria TOI and LAI: whether the center of gravity of the aircraft during flight is within the range of the optimal center of gravity static data. Since it is impossible to obtain the center of gravity value at a certain moment during flight, this item is simplified and divided into whether the center of gravity of the aircraft is within the range of the optimal center of gravity static data (TOI) during takeoff and whether the center of gravity of the aircraft is within the range of the optimal center of gravity static data (LAI) during landing; (d) Optimization criteria TOLI and LALI: whether the side balance (horizontal center of gravity) of the aircraft is closer to the optimal side balance static data. This item is further divided into whether the side balance of the aircraft during takeoff is closer to the optimal side balance static data during takeoff (TOLI) and whether the side balance of the aircraft during landing is closer to the optimal side balance static data during landing (TOLI).

[0128] Step (11) scores each optimization evaluation item of the loading scheme obtained after screening in step (10). The specific scoring method is as follows:

[0129] (a) Count how many cargo, mail, and baggage arriving at subsequent stations are closer to the cabin door than the cargo, mail, and baggage arriving at the current station. If for a certain loading scheme j, DHj If cargo, mail, and baggage arriving at a subsequent station are closer to the hatch than cargo, mail, and baggage arriving at the current station, then the corresponding score will be SDH. j The calculation formula is:

[0130]

[0131] For example, if the current arrival station of the current flight is PEK and the second arrival station is CAN, mail in cargo bay 23L (arriving at CAN) is closer to the door than cargo in cargo bay 24L (arriving at PEK) and baggage in cargo bay 25L (arriving at PEK); mail in cargo bay 21R (arriving at CAN) and cargo in cargo bay 22R (arriving at CAN) are closer to the door than cargo in cargo bay 24R (arriving at SHA). Therefore, there are a total of 3 cargo, mail, and baggage arriving at subsequent stations (mail in cargo bay 23L, mail in cargo bay 21R, and cargo in cargo bay 22R) that are closer to the door than the cargo, mail, and baggage arriving at the current station. Calculations show DH = 3 and SDH = 1 / 4.

[0132] (b) Count how many cargo and mail arriving at the current station are closer to the cabin door than baggage arriving at the current station on the current flight. If for a certain loading scheme j, CH j If cargo or mail arriving at the current station is closer to the cabin door than baggage arriving at the current station, then the corresponding score is SCH. j The calculation formula is:

[0133]

[0134] For example, if the current arrival station of the current flight is PEK, the mail in cargo slots 13L and 21L is closer to the door than the baggage in cargo slot 25L. Therefore, there are a total of 2 cargo and mail items (mail in cargo slots 13L and 21L) that are closer to the door than the baggage in the current arrival station. The calculation results in CH=2 and SCH=1 / 3.

[0135] (c) If for a certain loading scheme j, the center of gravity of the aircraft at takeoff is TOI j The corresponding optimal centroid range is [IDI] min IDI max The range of the center of gravity corresponding to the takeoff center of gravity envelope is [TOI]. min TOI max The corresponding score is STOI. j The calculation formula is:

[0136]

[0137] (d) If, for a certain loading scheme j, the center of gravity of the aircraft upon landing is LAIj The corresponding optimal deployment range is [IDI] min IDI max The corresponding range of the center of gravity envelope is [LAI]. min LAI max The corresponding score is SLAI. j The calculation formula is:

[0138]

[0139] (e) If for a certain loading scheme j, the lateral balance value of the aircraft at takeoff is TOLI j The corresponding optimal takeoff side balance is TOLI best The corresponding score is STOLI j The calculation formula is:

[0140]

[0141] Where T sum This represents the total number of loading schemes.

[0142] (f) If, for a certain loading scheme j, the lateral balance value of the aircraft upon landing is LALI j The corresponding optimal landing-side balance is LALI best The corresponding score is SLALI j The calculation formula is:

[0143]

[0144] Among them, T sum This represents the total number of loading schemes.

[0145] Step (11) Calculate the weight of each optimization evaluation item. This invention uses the subjective analytic hierarchy process and the objective entropy weight method to obtain the comprehensive weight. The weight is determined according to the entropy weight method weight r set by the user. EWM (0≤r EWM≤ 1) Based on the importance of each optimization evaluation item set by the user, the weight of each optimization evaluation item is calculated. The specific calculation method is as follows:

[0146] (a) Based on the scores of each optimization evaluation item for all loading schemes, the weights of each optimization evaluation item are calculated using the entropy weight method. For optimization evaluation item k, the calculated weight is:

[0147] (b) Based on the importance of each optimization evaluation item set by the user, calculate the relative importance of each optimization evaluation item. Simultaneously, based on the scores of each optimization evaluation item for all loading schemes, input them into the hierarchical analysis algorithm to calculate the weight of each optimization evaluation item. For optimization evaluation item k, the calculated weight is: The relative importance of each optimization evaluation item is calculated based on the importance of each optimization evaluation item set by the user. For example, if there are three optimization evaluation items, the importance of the first optimization evaluation item is 2, the importance of the second optimization evaluation item is 1, and the importance of the third optimization evaluation item is 3, then the relative importance of each optimization evaluation item can be obtained as shown in Table 1 of Example 1.

[0148] (c) For the optimization evaluation term k, its comprehensive weight ω k The calculation formula is:

[0149]

[0150] Step (11) Calculate the overall score of the loading scheme according to the TOPSIS algorithm. The specific calculation method is as follows:

[0151] (a) Based on the scores of each optimization evaluation item for all loading schemes, the original matrix X is constructed as follows:

[0152]

[0153] (b) Obtain the positive and negative ideal loading schemes:

[0154]

[0155] (c) Calculate the correlation coefficient between each loading scheme and the positive and negative ideal loading schemes, and use this as the evaluation value of the loading scheme. For loading scheme j, its correlation coefficient C j The calculation formula is as follows:

[0156]

[0157] in:

[0158]

[0159] Where ω k To optimize the overall weight of evaluation item k.

[0160] First, this embodiment can automatically select a loading scheme, eliminating the need for loading personnel to adjust the scheme and improving their work efficiency. Second, this embodiment fully considers loading rules and eliminates human interference, reducing potential production safety hazards. Finally, this embodiment fully considers various optimization items, such as whether the cargo, mail, and baggage arriving at the current station are closer to the cabin door than the cargo, mail, and baggage arriving at subsequent stations, whether the baggage arriving at the current station is closer to the cabin door than the cargo and mail, whether the aircraft's center of gravity is within the optimal range during flight, and whether the aircraft's lateral balance (horizontal center of gravity) is closer to the optimal value. The scores of these optimization items are comprehensively evaluated to provide the optimal loading scheme.

[0161] Example 3

[0162] Embodiment 3 of the present invention provides an optional data processing device based on aircraft cargo hold loading, wherein each implementation unit in the data processing device corresponds to each implementation step in Embodiment 1. Figure 3 This is a schematic diagram of an optional data processing device based on aircraft cargo hold loading according to an embodiment of the present invention, such as... Figure 3 As shown, it includes: an acquisition unit 31, a determination unit 32, and a processing unit 33.

[0163] In the data processing device based on aircraft cargo hold loading provided in Embodiment 3 of the present invention, the cargo information of the target flight and N cargo hold layout templates associated with the target flight can be obtained by the acquisition unit 31. The cargo information includes information about the aircraft objects to be loaded associated with the target flight, where N is a positive integer. The determination unit 32 determines S initial loading strategies for the objects to be loaded based on the cargo information and the N cargo hold layout templates, where S is a positive integer. The processing unit 33 scores the S initial loading strategies to obtain a scoring result, and determines the target loading strategy based on the scoring result. The target loading strategy includes the initial loading strategy with the highest score among the S initial loading strategies. This solves the technical problem in related technologies where loading personnel load cargo in the aircraft cargo hold based on their own experience, resulting in poor loading effects. In this embodiment, based on cargo information or cargo hold layout information, a full set of initial loading strategies is generated, and each initial loading strategy is comprehensively scored to select the target loading strategy with the highest score. This avoids the situation in related technologies where cargo hold loading schemes are obtained based on the loading personnel's own experience, resulting in low efficiency and low safety, thereby achieving the technical effect of improving the efficiency and safety of aircraft cargo hold loading.

[0164] Optionally, in the data processing device based on aircraft cargo hold loading provided in this embodiment, the determining unit includes: an acquisition subunit, used to acquire static data information of the target flight, wherein the static data information includes: restriction data for limiting cargo loading; a filtering subunit, used to filter N types of cargo hold layout templates based on cargo information to obtain M types of cargo hold layout templates, wherein M is a positive integer less than N; and a determining subunit, used to determine S types of initial loading strategies based on cargo information, M types of cargo hold layout templates and static data information.

[0165] Optionally, in the data processing device based on aircraft cargo hold loading provided in this embodiment, the objects to be loaded include: containerized cargo that needs to be placed in containers, palletized cargo that needs to be placed in pallets, and loose cargo that needs to be placed in bulk. The cargo information includes: containerized cargo information, palletized cargo information, and loose cargo information. The cargo hold layout template includes: container cargo location information, palletized cargo location information, and loose cargo location information. The determining subunit includes: a generation module, used to generate a loading strategy for loose cargo based on the loose cargo information and the loose cargo location information in the M types of cargo hold layout templates; and a first processing module, used to generate a loading strategy for palletized cargo based on the palletized cargo information and palletized cargo location information for each of the M types of cargo hold layout templates, and to filter out loading strategies for palletized cargo that cannot be generated from the M types of cargo hold layout templates. The system first generates P cargo hold layout templates, where P is a positive integer less than M. The second processing module generates a loading strategy for each of the P cargo hold layout templates based on container cargo information and container location information, and filters out cargo hold layout templates that cannot generate a loading strategy for container cargo, resulting in Q cargo hold layout templates, where Q is a positive integer less than P. The combination module combines the loading strategies for bulk cargo, palletized cargo, and containerized cargo for each of the Q cargo hold layout templates to obtain all loading strategies associated with each cargo hold layout template. The first filtering module filters all loading strategies associated with the Q cargo hold layout templates based on static data information to obtain S initial loading strategies.

[0166] Optionally, the filtering subunit includes: a calculation module for calculating the quantity of container cargo and pallet cargo in the object to be loaded based on cargo information; and a second filtering module for filtering cargo hold layout templates from N cargo hold layout templates where the number of container berths is greater than or equal to the number of container cargo and the number of pallet berths is greater than or equal to the number of pallet cargo, to obtain M cargo hold layout templates.

[0167] Optionally, the generation module includes: a sorting submodule, used to sort the bulk cargo based on its weight to obtain a first sorting result, and based on the first sorting result; and a processing submodule, used to traverse all bulk cargo based on the first sorting result, and generate a loading strategy for the bulk cargo based on the weight, volume, object type, and bulk cargo location information in M ​​types of cargo hold layout templates, wherein the object type includes at least one of the following: cargo, mail, and baggage.

[0168] Optionally, the processing unit includes: a first processing subunit, used to calculate the score of multiple evaluation items associated with each initial loading strategy and determine the weight of each evaluation item, wherein the multiple evaluation items include: indicators for evaluating the initial loading strategy; a second processing subunit, used to determine the comprehensive score of each initial loading strategy based on the score of multiple evaluation items associated with each initial loading strategy and the weight of each evaluation item, using the superior-inferior solution distance method; and a third processing subunit, used to obtain the scoring result based on the comprehensive score of S initial loading strategies.

[0169] Optionally, the second processing subunit includes: a first acquisition module, used to acquire the relative position of the cargo and the aircraft door of each destination station associated with the target flight for each initial loading strategy, thereby obtaining relative position information; a second acquisition module, used to acquire the center of gravity value of the target flight at takeoff and at landing, thereby obtaining center of gravity information; a third acquisition module, used to acquire the aircraft side balance information of the target flight; and a first determination module, used to determine the score value of multiple evaluation items associated with each initial loading strategy based on the relative position information, center of gravity information, and aircraft side balance information.

[0170] Optionally, the first processing subunit includes: a second determining module, used to determine the first weight of each evaluation item based on the score values ​​of multiple evaluation items associated with each initial loading strategy, using the entropy weight method; a third determining module, used to obtain the importance of each evaluation item set by the target object, and to determine the second weight of each evaluation item based on the importance of multiple evaluation items, using the hierarchical analysis strategy; and a fourth determining module, used to determine the weight of each evaluation item based on the first weight and the second weight of each evaluation item.

[0171] The aforementioned data processing device based on aircraft cargo hold loading may also include a processor and a memory. The aforementioned acquisition unit 31, determination unit 32 and processing unit 33 are all stored in the memory as program units, and the processor executes the aforementioned program units stored in the memory to realize the corresponding functions.

[0172] The aforementioned processor contains a kernel that retrieves the corresponding program units from memory. One or more kernels can be configured. By adjusting kernel parameters, a full set of initial loading strategies can be generated based on cargo information or cargo hold layout information. Each initial loading strategy is then comprehensively evaluated, and the highest-scoring target loading strategy is selected. This avoids the inefficiency and safety issues associated with relying on the loader's experience to arrive at cargo hold loading plans, as is common in related technologies. Therefore, this approach improves both the efficiency and safety of aircraft cargo hold loading.

[0173] The aforementioned memory may include non-permanent memory in computer-readable media, such as random access memory (RAM) and / or non-volatile memory, such as read-only memory (ROM) or flash RAM, and the memory includes at least one memory chip.

[0174] According to another aspect of the present invention, an electronic device is also provided, including: a processor; and a memory for storing executable instructions of the processor; wherein the processor is configured to perform the data processing method based on aircraft cargo hold loading of any of the above-mentioned methods by executing the executable instructions.

[0175] According to another aspect of the present invention, a computer-readable storage medium is also provided, which stores a computer program, wherein the computer program controls the device where the computer-readable storage medium is located to execute the data processing method based on aircraft cargo hold loading as described above when the computer program is running.

[0176] The sequence numbers of the above embodiments of the present invention are for descriptive purposes only and do not represent the superiority or inferiority of the embodiments. In the above embodiments of the present invention, the descriptions of each embodiment have their own emphasis, and for parts not described in detail in a certain embodiment, please refer to the relevant descriptions of other embodiments.

[0177] In the several embodiments provided in this application, it should be understood that the disclosed technical content can be implemented in other ways. The device embodiments described above are merely illustrative. For example, the division of units can be a logical functional division, and in actual implementation, there may be other division methods. For instance, multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Furthermore, the displayed or discussed mutual coupling, direct coupling, or communication connection can be through some interfaces; indirect coupling or communication connection between units or modules can be electrical or other forms. The units described as separate components may or may not be physically separate. The components shown as units may or may not be physical units, i.e., they can be located in one place or distributed across multiple units. Some or all of the units can be selected to achieve the purpose of this embodiment according to actual needs. Additionally, the functional units in the various embodiments of this invention can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit. The integrated units described above can be implemented in hardware or as software functional units.

[0178] If the integrated unit is implemented as a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention, in essence, or the part that contributes to the prior art, or all or part of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods described in the various embodiments of the present invention. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, read-only memory (ROM), random access memory (RAM), portable hard drives, magnetic disks, or optical disks.

[0179] The above description is only a preferred embodiment of the present invention. It should be noted that for those skilled in the art, several improvements and modifications can be made without departing from the principle of the present invention, and these improvements and modifications should also be considered within the scope of protection of the present invention.

Claims

1. A data processing method based on aircraft cargo hold loading, characterized in that, include: Obtain cargo information of the target flight and N cargo hold layout templates associated with the target flight, wherein the cargo information includes: information on the aircraft objects to be loaded associated with the target flight, and N is a positive integer; Based on the cargo information and the N cargo hold layout templates, S initial loading strategies for the object to be loaded are determined, where S is a positive integer; The S initial loading strategies are scored to obtain a score result, and a target loading strategy is determined based on the score result, wherein the target loading strategy includes the initial loading strategy with the highest score among the S initial loading strategies.

2. The data processing method according to claim 1, characterized in that, Based on the cargo information and the N cargo hold layout templates, S initial loading strategies are determined for the object to be loaded, including: Obtain static data information of the target flight, wherein the static data information includes: restriction data on cargo loading; Based on the cargo information, N cargo hold layout templates are filtered to obtain M cargo hold layout templates, where M is a positive integer less than N; Based on the cargo information, the M types of cargo hold layout templates, and the static data information, the S types of initial loading strategies are determined.

3. The data processing method according to claim 2, characterized in that, The objects to be loaded include: containerized cargo to be placed in containers, palletized cargo to be placed on pallets, and loose cargo to be placed in bulk. The cargo information includes: containerized cargo information, palletized cargo information, and loose cargo information. The cargo hold layout template includes: container cargo location information, palletized cargo location information, and loose cargo location information. Based on the cargo information, M types of cargo hold layout templates, and the static data information, S types of initial loading strategies are determined, including: Based on the bulk cargo information and the bulk cargo location information in the M types of cargo hold layout templates, a bulk cargo loading strategy is generated. For each of the M cargo hold layout templates, a loading strategy for the pallet cargo is generated based on the pallet cargo information and the pallet location information. Cargo hold layout templates that cannot generate a loading strategy for pallet cargo are filtered out from the M cargo hold layout templates to obtain P cargo hold layout templates, where P is a positive integer less than M. For each of the P cargo hold layout templates, a loading strategy for the container cargo is generated based on the container cargo information and the container cargo location information. Cargo hold layout templates that cannot generate a loading strategy for the container cargo are filtered out from the P cargo hold layout templates to obtain Q cargo hold layout templates, where Q is a positive integer less than P. For each of the Q cargo hold layout templates, the loading strategies for bulk cargo, pallet cargo, and container cargo are combined to obtain all loading strategies associated with each cargo hold layout template. Based on the static data information, all loading strategies associated with the cargo hold layout template of type Q are filtered to obtain the initial loading strategy of type S.

4. The data processing method according to claim 3, characterized in that, Based on the cargo information, N cargo hold layout templates are filtered to obtain M cargo hold layout templates, including: Based on the cargo information, calculate the quantity of the container cargo and the quantity of the pallet cargo in the object to be loaded; From the N cargo hold layout templates, select the cargo hold layout templates where the number of container berths is greater than or equal to the number of container cargo, and the number of pallet berths is greater than or equal to the number of pallet cargo, to obtain M cargo hold layout templates.

5. The data processing method according to claim 3, characterized in that, Based on the bulk cargo information and the bulk cargo location information in the M types of cargo hold layout templates, a loading strategy for bulk cargo is generated, including: Based on the weight of the bulk cargo, the bulk cargo is sorted to obtain a first sorting result, and based on the first sorting result; Based on the first sorting result, all loose cargo is traversed, and based on the weight, volume, object type of the loose cargo and the loose cargo location information in the M types of cargo hold layout templates, a loading strategy for the loose cargo is generated, wherein the object type includes at least one of the following: cargo, mail, and baggage.

6. The data processing method according to claim 1, characterized in that, The initial loading strategies described in S are scored to obtain the scoring results, including: Calculate the score value of multiple evaluation items associated with each of the initial loading strategies, and determine the weight of each of the evaluation items, wherein the multiple evaluation items include: metrics for evaluating the initial loading strategy; Based on the scores of multiple evaluation items associated with each initial loading strategy and the weight of each evaluation item, the comprehensive score of each initial loading strategy is determined using the superior-inferior solution distance method. The scoring result is obtained based on the comprehensive score of the S initial loading strategies.

7. The data processing method according to claim 6, characterized in that, Calculate the score values ​​for multiple evaluation items associated with each of the initial loading strategies, including: For each of the initial loading strategies, the relative positions of the cargo and the aircraft door at each destination associated with the target flight are obtained to acquire relative position information; Obtain the center of gravity value of the target flight at takeoff and at landing to obtain the center of gravity information. Obtain the aircraft side balance information of the target flight; Based on the relative position information, the center of gravity information, and the aircraft side balance information, the score values ​​of multiple evaluation items associated with each of the initial loading strategies are determined.

8. The data processing method according to claim 6, characterized in that, Determine the weight of each of the evaluation items, including: Based on the scores of multiple evaluation items associated with each of the initial loading strategies, the first weight of each evaluation item is determined using the entropy weight method. The importance of each of the evaluation items set by the target object is obtained, and based on the importance of multiple evaluation items, a hierarchical analysis strategy is used to determine the second weight of each evaluation item; The weight of each evaluation item is determined based on the first weight and the second weight of each evaluation item.

9. A data processing device based on aircraft cargo hold loading, characterized in that, include: The acquisition unit is used to acquire cargo information of the target flight and N cargo hold layout templates associated with the target flight. The cargo information includes information about the aircraft objects to be loaded onto the target flight. N is a positive integer; The determining unit is configured to determine S initial loading strategies for the object to be loaded based on the cargo information and the N cargo hold layout templates, where S is a positive integer; The processing unit is used to score the S initial loading strategies, obtain the scoring results, and determine the target loading strategy based on the scoring results, wherein the target loading strategy includes the initial loading strategy with the highest score among the S initial loading strategies.

10. An electronic device, characterized in that, It includes one or more processors and a memory, the memory being used to store one or more programs, wherein when the one or more programs are executed by the one or more processors, the one or more processors cause the one or more processors to implement the data processing method based on aircraft cargo hold loading as described in any one of claims 1 to 7.

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