Military aviation part supply management method and system based on artificial intelligence

Abstract

The invention relates to the technical field of warehouse management, and particularly discloses a military aviation part supply management method and system based on artificial intelligence, and the method comprises the steps: obtaining a part storage assignment value of each type of aviation parts at a current moment based on all part storage data of each type of aviation parts within a preset time period before the current moment; based on the component storage assignment values of all types of aviation components at the current moment, obtaining a reasonable goods allocation value of each type of aviation components at the current moment, and further obtaining a predicted goods allocation point of each type of aviation components at the current moment; and obtaining an aviation part supply management result at the current moment based on the predicted goods allocation distribution point of each type of aviation parts at the current moment. According to the method, the optimal path which should be adopted in the warehousing process of each type of aviation parts at the current moment is accurately predicted, so that warehouse resources are more effectively utilized, and the risk of resource idleness and waste is reduced.

Description

Technical Field

[0001] The present invention relates to the technical field of warehouse management, and in particular to a military aviation parts supply management method and system based on artificial intelligence. Background Art

[0002] Currently, the diverse variety and specifications of aviation parts make supply management extremely complex. Traditional management methods often rely on manual experience and intuition, making it difficult to efficiently and accurately process large amounts of data and information. With the rapid development of the aviation industry, the frequency and quantity of parts stored are increasing, placing higher demands on supply management efficiency. A key aspect of supply management is inventory management, particularly in the current military aviation parts manufacturing sector. The variety and specifications of military aviation parts produced in factories are even more specialized, requiring more meticulous inventory management and placing higher demands on storage space allocation. Haphazard storage space allocation leads to significant waste of space and storage difficulties. This not only increases the operating costs of military enterprises but also reduces overall operational efficiency.

[0003] However, the existing AI-based military aviation parts supply management methods and systems only execute different inspection processes according to the classification of the goods to be inspected and track the inspection results. They generate warehousing data for the goods that have passed the inspection of the waiting inspection module and match the cargo location information to reduce the time workers spend traveling back and forth between the main warehouse area and the production site. They do not consider accurately predicting the optimal path that each type of aviation parts should take during the warehousing process at the current moment to more effectively utilize warehouse resources and reduce the risk of idle resources and waste. For example, the patent publication number is "CN110717708A" and the name of the patent is "A Military Aviation In-and-Out Warehousing System and Its Operation Method", and its method includes the following steps: an arrival inspection module, which is used to identify the information of the goods to be inspected, execute different inspection processes according to the classification of the goods to be inspected, and track the inspection results; an entry module, which generates entry data for the goods that have passed the inspection of the arrival inspection module and matches the cargo location information; an exit module, which reads the information of the existing inventory and updates the inventory information according to the exit order; a main warehouse area, which is used to store the goods after the inspection of the arrival inspection module is completed; an on-site container, which is set in the production site area, and the on-site container is filled with the goods required for production; a rear container, which is located in the main warehouse area, and the rear container corresponds one-to-one with the on-site container, and the rear container is used to replenish the on-site container. The above patent greatly reduces the time workers spend traveling back and forth between the main warehouse area and the production site, increases the effective working time of workers, and improves production efficiency. However, this patent only reduces the time workers spend traveling back and forth between the main warehouse area and the production site by executing different inspection processes and tracking the inspection results according to the classification of the goods to be inspected, generating warehousing data for the goods that have passed the inspection of the goods waiting to be inspected module, and matching the cargo location information. It does not consider accurately predicting the optimal path that each type of aviation parts should take during the warehousing process at the current moment to more effectively utilize warehouse resources and reduce the risk of idle and wasted resources.

[0004] Therefore, the present invention proposes a military aviation parts supply management method and system based on artificial intelligence. Summary of the Invention

[0005] The present invention provides a military aviation parts supply management method and system based on artificial intelligence, which is used to obtain the component storage score of each type of aviation parts at the current moment according to all the component storage data of each type of aviation parts within a preset time period before the current moment, thereby quantifying the difficulty of storing components for each type of aviation parts at the current moment, and obtaining the storage cargo location node diagram of each type of aviation parts at the current moment according to all the storage cargo location areas of each type of aviation parts at the current moment, so as to facilitate the calculation of the reasonable value of subsequent cargo location allocation, and then obtaining the reasonable cargo location allocation value of each type of aviation parts at the current moment according to the storage cargo location node diagram and component storage score of all types of aviation parts at the current moment, thereby quantifying the reasonable degree of cargo location allocation of each type of aviation parts at the current moment, and according to each The reasonable value of cargo location allocation of aviation parts of a certain type at all times in the preset time period before the current moment is obtained, and all predicted cargo location allocation points of each type of aviation parts at the current moment are obtained, and the most reasonable cargo location node for storing parts of each type of aviation parts at the current moment is accurately predicted. Then, based on all predicted cargo location allocation points and the preset handling path model of each type of aviation parts at the current moment, the predicted optimal warehousing path of each type of aviation parts at the current moment is obtained, and the optimal path that each type of aviation parts should take during the warehousing process at the current moment is accurately predicted. Finally, based on the predicted optimal warehousing path of all types of aviation parts at the current moment, the aviation parts supply management result at the current moment is obtained, which effectively improves the warehousing efficiency and inventory management efficiency, makes more effective use of warehouse resources, and reduces the risk of idle and wasted resources.

[0006] The present invention provides a military aviation parts supply management method based on artificial intelligence, comprising:

[0007] S1: Based on all pieces of component storage data of each type of aviation component within a preset time period before the current moment, obtain a component storage score value of each type of aviation component at the current moment;

[0008] S2: Based on all storage locations of each type of aviation parts at the current moment, obtain the storage location node diagram of each type of aviation parts at the current moment, and based on the storage location node diagram and component storage score of all types of aviation parts at the current moment, obtain the reasonable value of the storage location allocation of each type of aviation parts at the current moment;

[0009] S3: Based on the reasonable values of cargo space allocation for each type of aviation parts at all times in a preset time period before the current moment, obtain all predicted cargo space allocation points for each type of aviation parts at the current moment;

[0010] S4: Based on all predicted cargo location allocation points and preset handling path models for each type of aviation parts at the current moment, the predicted optimal warehousing path for each type of aviation parts at the current moment is obtained, and based on the predicted optimal warehousing paths for all types of aviation parts at the current moment, the aviation parts supply management results at the current moment are obtained.

[0011] Preferably, the artificial intelligence-based military aviation parts supply management method, S1: based on all parts storage data of each type of aviation parts within a preset time period before the current moment, obtain the parts storage score value of each type of aviation parts at the current moment, including:

[0012] Obtain all pieces of component storage data for each type of aviation component within a preset time period before the current moment, where each piece of component storage data includes component pickup and delivery time, component pickup and delivery distance, and component pickup and delivery energy consumption;

[0013] Based on the number of component storage data of each type of aviation component in a preset time period before the current moment, the component storage score value of each type of aviation component at the current moment is obtained.

[0014] Preferably, the artificial intelligence-based military aviation parts supply management method obtains the parts storage score of each type of aviation parts at the current moment based on the number of parts storage data of each type of aviation parts within a preset time period before the current moment, including:

[0015] If the number of component storage data items for each type of aviation component within the preset time period before the current moment is zero, the three component storage data items of the corresponding type of aviation component closest to the current moment in terms of time sequence will be used as the component storage data items for the corresponding type of aviation component within the preset time period before the current moment;

[0016] Based on all the component storage data of each type of aviation parts in the preset time period before the current moment, the component storage score of the corresponding type of aviation parts at the current moment is obtained, which is:

[0017]

[0018] Where β is the component storage score of the current computing aviation component at the current moment, z0 is the average value of the component pickup and delivery time of all component storage data of the current computing aviation component within the preset time period before the current moment, σ z is the standard deviation of the component pickup and delivery time values of all pieces of component storage data of the current computing aviation component within the preset time period before the current moment, m is the number of pieces of component storage data of the current computing aviation component within the preset time period before the current moment, z maxThe maximum value of the component pickup and delivery time in all the component storage data of the current calculation type aviation component within the preset time period before the current moment, z min is the minimum value of the component pickup and delivery time in all the component storage data of the current calculation-type aviation parts within the preset time period before the current moment, c0 is the mean value of the component pickup and delivery distance in all the component storage data of the current calculation-type aviation parts within the preset time period before the current moment, σ c The standard deviation of the value of the component delivery distance of all the component storage data of the current calculation type aviation parts within the preset time period before the current moment, c max The maximum value of the component pickup and delivery distance in all the component storage data of the current calculation type aviation component within the preset time period before the current moment, c min is the minimum value of the component pickup and delivery distance in all the component storage data of the current calculation-type aviation parts in the preset time period before the current moment, b0 is the average value of the component pickup and delivery energy consumption in all the component storage data of the current calculation-type aviation parts in the preset time period before the current moment, σ b The standard deviation of the energy consumption of all stored data of the current computing aviation parts in the preset time period before the current moment, b max The maximum value of the energy consumption of the current calculation aviation parts in all the parts storage data within the preset time period before the current moment, b min It is the minimum value of the energy consumption of the current calculation aviation parts in all the parts storage data within the preset time period before the current moment. ln is the natural logarithm, and the value of the natural constant e is 2.718.

[0019] Preferably, the artificial intelligence-based military aviation parts supply management method, S2: based on all storage cargo area of each type of aviation parts at the current moment, obtain the storage cargo node diagram of each type of aviation parts at the current moment, and based on the storage cargo node diagram of all types of aviation parts at the current moment and the component storage score, obtain the reasonable cargo location allocation value of each type of aviation parts at the current moment, including:

[0020] The sub-areas of all sub-areas of the cargo storage area at the current moment, which store each type of aviation parts, are regarded as the storage location areas of the corresponding type of aviation parts at the current moment;

[0021] The sub-areas of all sub-areas of the cargo storage area at the current moment that do not store each type of aviation parts are regarded as unstored cargo areas of the corresponding type of aviation parts at the current moment;

[0022] Each storage cargo area of each type of aviation parts at the current moment is used as a cargo location node of the corresponding type of aviation parts at the current moment, and each non-storage cargo area of each type of aviation parts at the current moment is used as a non-cargo location node of the corresponding type of aviation parts at the current moment;

[0023] Based on all cargo location nodes and all non-cargo location nodes of each type of aviation parts at the current moment, a storage cargo location node graph of each type of aviation parts at the current moment is obtained;

[0024] Based on the storage location node graph of all aviation parts at the current moment, the entropy value of the storage location allocation of all aviation parts at the current moment is obtained;

[0025] Based on the entropy values of cargo location allocation and component storage scores of all types of aviation parts at the current moment, the reasonable value of cargo location allocation for each type of aviation parts at the current moment is obtained.

[0026] Preferably, the artificial intelligence-based military aviation parts supply management method obtains the storage location allocation entropy value of all types of aviation parts at the current moment based on the storage location node diagram of all types of aviation parts at the current moment, including:

[0027] Taking each storage location node in the current storage location node diagram of each type of aviation parts as the origin and the preset distance as the radius, the allocation circle of the corresponding storage location node is obtained, and the number of remaining storage location nodes covered by the allocation circle of each storage location node is regarded as the number of associated points of the corresponding storage location node;

[0028] The storage location node with the largest number of associated points among all the storage location nodes in the storage location node graph of each type of aviation parts at the current moment is regarded as the central storage location node of the storage location node graph of the corresponding type of aviation parts at the current moment, and the storage location nodes other than the central storage location node among all the storage location nodes in the storage location node graph of each type of aviation parts at the current moment are regarded as the non-central storage location nodes of the storage location node graph of the corresponding type of aviation parts at the current moment;

[0029] The distance between each non-central storage location node and the central storage location node in the storage location node graph of each type of aviation parts at the current moment is regarded as the connection distance of the corresponding non-central storage location node in the storage location node graph of the corresponding type of aviation parts at the current moment, and all non-central storage location nodes are defined by increasing ordinal numbers starting from 1 according to the order of the connection distances of all non-central storage location nodes in the storage location node graph of each type of aviation parts at the current moment, to obtain an ordinal definition result;

[0030] Based on the storage location node graph and ordinal definition results of each type of aviation parts at the current moment, the entropy value of the storage location allocation of each type of aviation parts at the current moment is obtained.

[0031] Preferably, the artificial intelligence-based military aviation parts supply management method obtains the storage location allocation entropy value of each type of aviation parts at the current moment based on the storage location node diagram and ordinal definition result of each type of aviation parts at the current moment, including:

[0032]

[0033] Among them, γ is the entropy value of the cargo space allocation of the current computing aviation parts at the current moment, δ i is the number of adjacent storage nodes of the non-central storage node with ordinal number i in the storage storage node graph of the current computing aviation parts at the current moment, ε i is the number of non-location nodes adjacent to the non-center location node with ordinal number i in the storage location node graph of the current computing aviation parts at the current moment, n is the total number of non-center location nodes in the storage location node graph of the current computing aviation parts at the current moment, δ max is the maximum number of adjacent storage nodes in all non-central storage nodes of the storage storage node graph of the current computing aviation parts at the current moment, δ min is the minimum number of adjacent storage nodes in all non-central storage nodes of the storage storage node graph of the current computing aviation parts at the current moment, ε max is the maximum number of adjacent non-storage location nodes in the non-central storage location node with ordinal number i in the storage location node graph of the current computing aviation parts at the current moment, ε min is the minimum number of adjacent non-storage location nodes in the non-central storage location node with ordinal number i in the storage location node graph of the current computing aviation parts at the current moment. ln is the natural logarithm, and the value of the natural constant e is 2.718.

[0034] Preferably, the artificial intelligence-based military aviation parts supply management method obtains a reasonable value for the cargo location allocation of each type of aviation parts at the current moment based on the cargo location allocation entropy value and component storage score value of all types of aviation parts at the current moment, including:

[0035]

[0036] Among them, μ is the reasonable value of the cargo space allocation of the current computing aviation parts at the current moment, S β The sum of the scores assigned to all aviation parts at the current moment, S γ is the sum of the entropy values of the cargo space allocation of all aviation parts at the current moment, β is the component storage score of the current calculation aviation parts at the current moment, γ is the entropy value of the cargo space allocation of the current calculation aviation parts at the current moment, σ βThe standard deviation of the scores assigned to the storage of all aviation parts at the current moment, σ γ The standard deviation of the entropy values for the cargo space allocation of all types of aviation parts at the current moment is ln, which is the natural logarithm, and the value of the natural constant e is 2.718.

[0037] Preferably, in the artificial intelligence-based military aviation parts supply management method, S3: based on the reasonable values of cargo space allocation for each type of aviation parts at all times within a preset time period before the current moment, all predicted cargo space allocation points for each type of aviation parts at the current moment are obtained, including:

[0038] Obtain the reasonable value of cargo space allocation for each type of aviation parts at all times within a preset time period before the current moment;

[0039] The time corresponding to the largest reasonable cargo space allocation value among all reasonable cargo space allocation values of each type of aviation parts at all times in a preset time period before the current time is used as the predicted time for the corresponding type of aviation parts;

[0040] All cargo location nodes of each type of aviation parts at the predicted time are regarded as the predicted cargo location allocation points of the corresponding type of aviation parts at the current moment.

[0041] Preferably, in the artificial intelligence-based military aviation parts supply management method, S4: based on all predicted cargo location allocation points and preset handling path models for each type of aviation parts at the current moment, obtaining the predicted optimal warehousing path for each type of aviation parts at the current moment, and based on the predicted optimal warehousing paths for all types of aviation parts at the current moment, obtaining the aviation parts supply management results at the current moment, including:

[0042] Get the initial location of each type of aviation parts in the warehouse at the current moment;

[0043] The predicted cargo location allocation point of each type of aviation parts at the current moment, which is closest to the initial location of the corresponding type of aviation parts entering the warehouse at the current moment, is used as the warehouse-entering cargo location allocation point of the corresponding type of aviation parts at the current moment;

[0044] Based on the inbound storage location allocation point, initial position and preset handling path model of each type of aviation parts at the current moment, the predicted optimal inbound storage path of the corresponding type of aviation parts at the current moment is obtained, and based on the predicted optimal inbound storage path of all types of aviation parts at the current moment, the aviation parts supply management result at the current moment is obtained.

[0045] The present invention provides an artificial intelligence-based military aviation parts supply management system, which is used to implement any one of the artificial intelligence-based military aviation parts supply management methods in Examples 1 to 9, including:

[0046] A scoring module, configured to obtain a component storage scoring value of each type of aviation component at the current moment based on all component storage data of each type of aviation component within a preset time period before the current moment;

[0047] a calculation module for obtaining a storage location node diagram for each type of aviation parts at the current moment based on all storage location areas for each type of aviation parts at the current moment, and obtaining a reasonable value for the storage location allocation for each type of aviation parts at the current moment based on the storage location node diagrams and component storage scores for all types of aviation parts at the current moment;

[0048] A prediction module, configured to obtain all predicted cargo location allocation points for each type of aviation parts at the current moment based on reasonable cargo location allocation values for each type of aviation parts at all moments in a preset time period before the current moment;

[0049] The management module is used to obtain the predicted optimal warehousing path for each type of aviation parts at the current moment based on all predicted cargo location allocation points and preset handling path models for each type of aviation parts at the current moment, and to obtain the aviation parts supply management results at the current moment based on the predicted optimal warehousing paths for all types of aviation parts at the current moment.

[0050] The beneficial effects of the present invention compared with the prior art are as follows: based on all the component storage data of each type of aviation parts in the preset time period before the current moment, the component storage score value of each type of aviation parts at the current moment is obtained, which realizes the quantification of the difficulty of storing components for each type of aviation parts at the current moment; based on all the storage cargo area of each type of aviation parts at the current moment, the storage cargo node diagram of each type of aviation parts at the current moment is obtained, which is convenient for the calculation of the reasonable value of subsequent cargo location allocation; and then based on the storage cargo node diagram and component storage score value of all types of aviation parts at the current moment, the reasonable value of cargo location allocation of each type of aviation parts at the current moment is obtained, which realizes the quantification of the reasonable degree of cargo location allocation of each type of aviation parts at the current moment; The reasonable values of cargo location allocation at all times within the preset time period before the current moment are obtained, and all predicted cargo location allocation points for each type of aviation parts at the current moment are obtained, and the most reasonable cargo location node for storing parts of each type of aviation parts at the current moment is accurately predicted. Then, based on all predicted cargo location allocation points and the preset transportation path model for each type of aviation parts at the current moment, the predicted optimal warehousing path for each type of aviation parts at the current moment is obtained, and the optimal path that each type of aviation parts should take during the warehousing process at the current moment is accurately predicted. Finally, based on the predicted optimal warehousing path for all types of aviation parts at the current moment, the aviation parts supply management result at the current moment is obtained, which effectively improves the warehousing efficiency and inventory management efficiency, makes more effective use of warehouse resources, and reduces the risk of idle and wasted resources.

[0051] Other features and advantages of the present invention will be described in the following description, and in part will become apparent from the description, or will be understood by practicing the present invention. The purpose and other advantages of the present invention can be realized and obtained by the structures particularly pointed out in the written application documents.

[0052] The technical solution of the present invention is further described in detail below through the accompanying drawings and embodiments. BRIEF DESCRIPTION OF THE DRAWINGS

[0053] The accompanying drawings are used to provide a further understanding of the present invention and constitute a part of the specification. Together with the embodiments of the present invention, they are used to explain the present invention and do not constitute a limitation of the present invention. In the accompanying drawings:

[0054] Figure 1 This is a flow chart of a military aviation parts supply management method based on artificial intelligence in an embodiment of the present invention;

[0055] Figure 2 Schematic diagram of an artificial intelligence-based military aviation parts supply management system in an embodiment of the present invention. DETAILED DESCRIPTION

[0056] The preferred embodiments of the present invention are described below with reference to the accompanying drawings. It should be understood that the preferred embodiments described herein are only used to illustrate and explain the present invention, and are not used to limit the present invention.

[0057] Example 1:

[0058] The present invention provides a military aviation parts supply management method based on artificial intelligence, referring to Figure 1 ,include:

[0059] S1: Based on all pieces of component storage data of each type of aviation component within a preset time period before the current moment, obtain a component storage score value of each type of aviation component at the current moment;

[0060] S2: Based on all storage locations of each type of aviation parts at the current moment, obtain the storage location node diagram of each type of aviation parts at the current moment, and based on the storage location node diagram and component storage score of all types of aviation parts at the current moment, obtain the reasonable value of the storage location allocation of each type of aviation parts at the current moment;

[0061] S3: Based on the reasonable values of cargo space allocation for each type of aviation parts at all times in a preset time period before the current moment, obtain all predicted cargo space allocation points for each type of aviation parts at the current moment;

[0062] S4: Based on all predicted cargo location allocation points and preset handling path models for each type of aviation parts at the current moment, the predicted optimal warehousing path for each type of aviation parts at the current moment is obtained, and based on the predicted optimal warehousing paths for all types of aviation parts at the current moment, the aviation parts supply management results at the current moment are obtained.

[0063] In this embodiment, aviation components are various parts and assemblies that constitute the aircraft body, engine, airborne equipment, etc.

[0064] In this embodiment, the preset time period is a preset time period for obtaining component storage data of each type of aviation component.

[0065] In this embodiment, the component storage data includes all component storage data of each type of aviation component within a preset time period before the current moment (each component storage data is data generated each time an aviation component is put into storage), including component pickup and delivery time, component pickup and delivery distance, and component pickup and delivery energy consumption data.

[0066] In this embodiment, the component storage score value for each type of aviation component at the current moment is obtained based on all component storage data of each type of aviation component within a preset time period before the current moment, and can represent the difficulty of storing components for each type of aviation component at the current moment.

[0067] In this embodiment, the storage cargo area is all sub-areas of the cargo storage area at the current moment, including sub-areas storing each type of aviation parts.

[0068] In this embodiment, the storage cargo location node diagram of each type of aviation parts at the current moment is a node diagram obtained based on all storage cargo location areas of each type of aviation parts at the current moment, which can reflect the storage status of the cargo storage area of each type of aviation parts at the current moment.

[0069] In this embodiment, the reasonable value of the cargo location allocation of each type of aviation parts at the current moment is a numerical value obtained based on the storage cargo location node diagram and component storage score values of all types of aviation parts at the current moment, which can characterize the reasonableness of the cargo location allocation of each type of aviation parts at the current moment.

[0070] In this embodiment, all moments are moments selected from a preset time period before the current moment, and the time lengths between each adjacent moment are the same.

[0071] In this embodiment, the predicted cargo location allocation point for each type of aviation parts at the current moment is the most reasonable cargo location node for storing each type of aviation parts at the current moment, which is predicted based on the reasonable cargo location allocation value of each type of aviation parts at all moments in a preset time period before the current moment.

[0072] In this embodiment, the preset transportation path model is composed of the pre-collected storage location allocation points of multiple types of aviation parts at multiple times (the most suitable storage location nodes screened out from all predicted storage location allocation points of each type of aviation parts at the current time) and the initial storage locations of the corresponding types of aviation parts at the corresponding times, as model input, and the manually marked parts transportation trajectory between the storage location allocation points of each type of aviation parts at each time and the initial storage locations of the corresponding types of aviation parts at the corresponding times, as model output. The trained neural network model can input the storage location allocation points of each type of aviation parts at each time and the initial storage locations of the corresponding types of aviation parts at the corresponding times, and can output the parts transportation trajectory between the storage location allocation points of each type of aviation parts at each time and the initial storage locations of the corresponding types of aviation parts at the corresponding times (the predicted optimal storage path for each type of aviation parts at the current time).

[0073] In this embodiment, the predicted optimal warehousing path for each type of aviation parts at the current moment is the predicted optimal path that each type of aviation parts should take during the warehousing process at the current moment.

[0074] In this embodiment, the aviation parts supply management result at the current moment is the management result obtained by warehousing all types of aviation parts (all types of aviation parts put into storage at the current moment) according to the predicted optimal warehousing path of the corresponding types of aviation parts at the current moment.

[0075] The beneficial effects of the above technology are: according to all the parts storage data of each type of aviation parts in the preset time period before the current moment, the parts storage score value of each type of aviation parts at the current moment is obtained, and the difficulty of storing parts for each type of aviation parts at the current moment is quantified; according to all the storage cargo area of each type of aviation parts at the current moment, the storage cargo node diagram of each type of aviation parts at the current moment is obtained, which is convenient for the calculation of the reasonable value of subsequent cargo location allocation; and then according to the storage cargo node diagram and parts storage score value of all types of aviation parts at the current moment, the reasonable value of cargo location allocation of each type of aviation parts at the current moment is obtained, which realizes the quantification of the rationality of cargo location allocation of each type of aviation parts at the current moment, and according to the storage cargo node diagram and parts storage score value of all types of aviation parts at the current moment, the reasonable degree of cargo location allocation of each type of aviation parts at the current moment is quantified. The reasonable values of cargo location allocation at all times in the preset time period before are obtained, and all predicted cargo location allocation points of each type of aviation parts at the current moment are obtained, and the most reasonable cargo location node for storing parts of each type of aviation parts at the current moment is accurately predicted. Then, based on all predicted cargo location allocation points and the preset handling path model of each type of aviation parts at the current moment, the predicted optimal warehousing path of each type of aviation parts at the current moment is obtained, and the optimal path that each type of aviation parts should take during the warehousing process at the current moment is accurately predicted. Finally, based on the predicted optimal warehousing path of all types of aviation parts at the current moment, the aviation parts supply management result at the current moment is obtained, which effectively improves the warehousing efficiency and inventory management efficiency, makes more effective use of warehouse resources, and reduces the risk of idle and wasted resources.

[0076] Example 2:

[0077] Based on Example 1, the artificial intelligence-based military aviation parts supply management method, S1: Based on all parts storage data of each type of aviation parts within a preset time period before the current moment, obtain the parts storage score value of each type of aviation parts at the current moment, including:

[0078] Obtain all pieces of component storage data for each type of aviation component within a preset time period before the current moment, where each piece of component storage data includes component pickup and delivery time, component pickup and delivery distance, and component pickup and delivery energy consumption;

[0079] Based on the number of component storage data of each type of aviation component in a preset time period before the current moment, the component storage score value of each type of aviation component at the current moment is obtained.

[0080] In this embodiment, the component pickup and delivery time is the time (in seconds) consumed in the warehousing process of the aviation component corresponding to each component storage data.

[0081] In this embodiment, the component pickup and delivery distance is the length (in meters) of the transportation path during the warehousing process of the aviation component corresponding to each component storage data.

[0082] In this embodiment, the energy consumption for picking up and delivering components is the energy consumption (in joules) during the warehousing process of the aviation component corresponding to each component storage data.

[0083] The beneficial effect of the above technology is: according to the number of parts storage data of each type of aviation parts in the preset time period before the current moment, the parts storage score value of each type of aviation parts at the current moment is obtained, which facilitates the calculation of the reasonable value of subsequent cargo space allocation.

[0084] Example 3:

[0085] On the basis of Example 2, the artificial intelligence-based military aviation parts supply management method obtains the parts storage score of each type of aviation parts at the current moment based on the number of parts storage data of each type of aviation parts within a preset time period before the current moment, including:

[0086] If the number of component storage data items for each type of aviation component within the preset time period before the current moment is zero, the three component storage data items of the corresponding type of aviation component closest to the current moment in terms of time sequence will be used as the component storage data items for the corresponding type of aviation component within the preset time period before the current moment;

[0087] Based on all the component storage data of each type of aviation parts in the preset time period before the current moment, the component storage score of the corresponding type of aviation parts at the current moment is obtained, which is:

[0088]

[0089] Where β is the component storage score of the current computing aviation component at the current moment, z0 is the average value of the component pickup and delivery time of all component storage data of the current computing aviation component within the preset time period before the current moment, σ z is the standard deviation of the component pickup and delivery time values of all pieces of component storage data of the current computing aviation component within the preset time period before the current moment, m is the number of pieces of component storage data of the current computing aviation component within the preset time period before the current moment, z max The maximum value of the component pickup and delivery time in all the component storage data of the current calculation type aviation component within the preset time period before the current moment, z min is the minimum value of the component pickup and delivery time in all the component storage data of the current calculation-type aviation parts within the preset time period before the current moment, c0 is the mean value of the component pickup and delivery distance in all the component storage data of the current calculation-type aviation parts within the preset time period before the current moment, σ c The standard deviation of the value of the component delivery distance of all the component storage data of the current calculation type aviation parts within the preset time period before the current moment, c maxThe maximum value of the component pickup and delivery distance in all the component storage data of the current calculation type aviation component within the preset time period before the current moment, c min is the minimum value of the component pickup and delivery distance in all the component storage data of the current calculation-type aviation parts in the preset time period before the current moment, b0 is the average value of the component pickup and delivery energy consumption in all the component storage data of the current calculation-type aviation parts in the preset time period before the current moment, σ b The standard deviation of the energy consumption of all stored data of the current computing aviation parts in the preset time period before the current moment, b max The maximum value of the energy consumption of the current calculation aviation parts in all the parts storage data within the preset time period before the current moment, b min It is the minimum value of the energy consumption of the current calculation aviation parts in all the parts storage data within the preset time period before the current moment. ln is the natural logarithm, and the value of the natural constant e is 2.718.

[0090] The beneficial effects of the above technology are: based on all the component storage data of each type of aviation parts within a preset time period before the current moment, the component storage score value of each type of aviation parts at the current moment is obtained, thereby quantifying the difficulty of storing components for each type of aviation parts at the current moment. This embodiment provides in detail a specific method for quantifying the difficulty of storing components for each type of aviation parts at the current moment.

[0091] Example 4:

[0092] Based on Example 1, the artificial intelligence-based military aviation parts supply management method, S2: Based on all storage location areas of each type of aviation parts at the current moment, a storage location node diagram of each type of aviation parts at the current moment is obtained, and based on the storage location node diagrams and component storage scores of all types of aviation parts at the current moment, a reasonable value for the storage location allocation of each type of aviation parts at the current moment is obtained, including:

[0093] The sub-areas of all sub-areas of the cargo storage area at the current moment, which store each type of aviation parts, are regarded as the storage location areas of the corresponding type of aviation parts at the current moment;

[0094] The sub-areas of all sub-areas of the cargo storage area at the current moment that do not store each type of aviation parts are regarded as unstored cargo areas of the corresponding type of aviation parts at the current moment;

[0095] Each storage cargo area of each type of aviation parts at the current moment is used as a cargo location node of the corresponding type of aviation parts at the current moment, and each non-storage cargo area of each type of aviation parts at the current moment is used as a non-cargo location node of the corresponding type of aviation parts at the current moment;

[0096] Based on all cargo location nodes and all non-cargo location nodes of each type of aviation parts at the current moment, a storage cargo location node graph of each type of aviation parts at the current moment is obtained;

[0097] Based on the storage location node graph of all aviation parts at the current moment, the entropy value of the storage location allocation of all aviation parts at the current moment is obtained;

[0098] Based on the entropy values of cargo location allocation and component storage scores of all types of aviation parts at the current moment, the reasonable value of cargo location allocation for each type of aviation parts at the current moment is obtained.

[0099] In this embodiment, the cargo storage area is a warehouse area for storing military aviation parts.

[0100] In this embodiment, the sub-area is a sub-storage area pre-divided from the cargo storage area.

[0101] In this embodiment, based on all cargo location nodes and all non-cargo location nodes of each type of aviation parts at the current moment, a storage cargo location node graph of each type of aviation parts at the current moment is obtained, namely:

[0102] Select any two nodes (not the same node) from all cargo location nodes and all non-cargo location nodes of each type of aviation parts at the current moment as a node group, and obtain all node groups of each type of aviation parts;

[0103] If the sub-areas corresponding to the two nodes in each node group of each type of aviation parts are connected (whether the actual ranges are connected), the two nodes in the corresponding node group of the corresponding type of aviation parts will be connected to obtain the storage location node diagram of each type of aviation parts at the current moment.

[0104] In this embodiment, the entropy value of the cargo location allocation of aviation parts at the current moment is a value obtained based on the storage cargo location node diagram of all types of aviation parts at the current moment, which can characterize the degree of chaos in the cargo location allocation status of each type of aviation parts at the current moment. The larger the cargo location allocation entropy value, the more chaotic the cargo location allocation.

[0105] The beneficial effects of the above technology are: based on all storage cargo area areas of each type of aviation parts at the current moment, all cargo location nodes and all non-cargo location nodes of each type of aviation parts at the current moment are obtained, and then based on all cargo location nodes and all non-cargo location nodes of each type of aviation parts at the current moment, the storage cargo location node diagram of each type of aviation parts at the current moment is obtained. This embodiment provides in detail a specific method for drawing the storage cargo location node diagram of each type of aviation parts at the current moment, which facilitates the calculation of reasonable values for subsequent cargo location allocation.

[0106] Example 5:

[0107] On the basis of Example 4, the military aviation parts supply management method based on artificial intelligence obtains the storage location allocation entropy value of all types of aviation parts at the current moment based on the storage location node diagram of all types of aviation parts at the current moment, including:

[0108] Taking each storage location node in the current storage location node diagram of each type of aviation parts as the origin and the preset distance as the radius, the allocation circle of the corresponding storage location node is obtained, and the number of remaining storage location nodes covered by the allocation circle of each storage location node is regarded as the number of associated points of the corresponding storage location node;

[0109] The storage location node with the largest number of associated points among all the storage location nodes in the storage location node graph of each type of aviation parts at the current moment is regarded as the central storage location node of the storage location node graph of the corresponding type of aviation parts at the current moment, and the storage location nodes other than the central storage location node among all the storage location nodes in the storage location node graph of each type of aviation parts at the current moment are regarded as the non-central storage location nodes of the storage location node graph of the corresponding type of aviation parts at the current moment;

[0110] The distance between each non-central storage location node and the central storage location node in the storage location node graph of each type of aviation parts at the current moment is regarded as the connection distance of the corresponding non-central storage location node in the storage location node graph of the corresponding type of aviation parts at the current moment, and all non-central storage location nodes are defined by increasing ordinal numbers starting from 1 according to the order of the connection distances of all non-central storage location nodes in the storage location node graph of each type of aviation parts at the current moment, to obtain an ordinal definition result;

[0111] Based on the storage location node graph and ordinal definition results of each type of aviation parts at the current moment, the entropy value of the storage location allocation of each type of aviation parts at the current moment is obtained.

[0112] In this embodiment, the preset distance is a preset distance threshold for obtaining an allocation circle for each cargo location node.

[0113] In this embodiment, the number of association points of the cargo location node is the number of other cargo location nodes covered by the allocation circle of the cargo location node.

[0114] The beneficial effects of the above technology are: according to the storage location node diagram of each type of aviation parts at the current moment, all non-central location nodes and central location nodes of the storage location node diagram of each type of aviation parts at the current moment are obtained, and then according to all non-central location nodes and central location nodes of the storage location node diagram of each type of aviation parts at the current moment, the ordinal definition results are obtained, which facilitates the calculation of the subsequent location allocation entropy value.

[0115] Example 6:

[0116] On the basis of Example 5, the military aviation parts supply management method based on artificial intelligence obtains the storage location allocation entropy value of each type of aviation parts at the current moment based on the storage location node diagram and ordinal definition results of each type of aviation parts at the current moment, including:

[0117]

[0118] Among them, γ is the entropy value of the cargo space allocation of the current computing aviation parts at the current moment, δ i is the number of adjacent storage nodes of the non-central storage node with ordinal number i in the storage storage node graph of the current computing aviation parts at the current moment, ε i is the number of non-location nodes adjacent to the non-center location node with ordinal number i in the storage location node graph of the current computing aviation parts at the current moment, n is the total number of non-center location nodes in the storage location node graph of the current computing aviation parts at the current moment, δ max is the maximum number of adjacent storage nodes in all non-central storage nodes of the storage storage node graph of the current computing aviation parts at the current moment, δ min is the minimum number of adjacent storage nodes in all non-central storage nodes of the storage storage node graph of the current computing aviation parts at the current moment, ε max is the maximum number of adjacent non-storage location nodes in the non-central storage location node with ordinal number i in the storage location node graph of the current computing aviation parts at the current moment, ε min is the minimum number of adjacent non-storage location nodes in the non-central storage location node with ordinal number i in the storage location node graph of the current computing aviation parts at the current moment. ln is the natural logarithm, and the value of the natural constant e is 2.718.

[0119] In this embodiment, adjacent means that two nodes are connected in the corresponding storage location node graph.

[0120] The beneficial effect of the above technology is: based on the storage location node diagram and ordinal definition results of each type of aviation parts at the current moment, the location allocation entropy value of each type of aviation parts at the current moment is accurately obtained. This embodiment provides in detail a specific method for accurately quantifying the degree of chaos in the location allocation status of each type of aviation parts at the current moment.

[0121] Example 7:

[0122] On the basis of Example 4, the artificial intelligence-based military aviation parts supply management method obtains a reasonable value for the current cargo location allocation of each type of aviation parts based on the cargo location allocation entropy values and component storage scores of all types of aviation parts at the current moment, including:

[0123]

[0124] Among them, μ is the reasonable value of the cargo space allocation of the current computing aviation parts at the current moment, S β The sum of the scores assigned to all aviation parts at the current moment, S γ is the sum of the entropy values of the cargo space allocation of all aviation parts at the current moment, β is the component storage score of the current calculation aviation parts at the current moment, γ is the entropy value of the cargo space allocation of the current calculation aviation parts at the current moment, σ β The standard deviation of the scores assigned to the storage of all aviation parts at the current moment, σ γ The standard deviation of the entropy values for the cargo space allocation of all types of aviation parts at the current moment is ln, which is the natural logarithm, and the value of the natural constant e is 2.718.

[0125] The beneficial effects of the above technology are: based on the storage location node diagram and component storage score values of all types of aviation parts at the current moment, the reasonable value of the location allocation of each type of aviation parts at the current moment is obtained, and the rationality of the location allocation of each type of aviation parts at the current moment is quantified, which helps to allocate cargo locations more efficiently, improve storage efficiency, and reduce transportation time and costs.

[0126] Example 8:

[0127] Based on Example 1, the artificial intelligence-based military aviation parts supply management method, S3: Based on the reasonable values of cargo space allocation for each type of aviation parts at all times within a preset time period before the current time, obtain all predicted cargo space allocation points for each type of aviation parts at the current time, including:

[0128] Obtain the reasonable value of cargo space allocation for each type of aviation parts at all times within a preset time period before the current moment;

[0129] The time corresponding to the largest reasonable cargo space allocation value among all reasonable cargo space allocation values of each type of aviation parts at all times in a preset time period before the current time is used as the predicted time for the corresponding type of aviation parts;

[0130] All cargo location nodes of each type of aviation parts at the predicted time are regarded as the predicted cargo location allocation points of the corresponding type of aviation parts at the current moment.

[0131] The beneficial effects of the above technology are: based on the reasonable value of cargo location allocation for each type of aviation parts at all times within a preset time period before the current moment, all predicted cargo location allocation points for each type of aviation parts at the current moment are obtained, and the most reasonable cargo location nodes for storing parts of each type of aviation parts at the current moment are accurately predicted, which facilitates the subsequent prediction of the best warehousing path and improves storage efficiency.

[0132] Example 9:

[0133] Based on Example 1, the artificial intelligence-based military aviation parts supply management method, S4: Based on all predicted cargo location allocation points and preset transportation path models for each type of aviation parts at the current moment, obtain the predicted optimal storage path for each type of aviation parts at the current moment, and based on the predicted optimal storage paths for all types of aviation parts at the current moment, obtain the aviation parts supply management results at the current moment, including:

[0134] Get the initial location of each type of aviation parts in the warehouse at the current moment;

[0135] The predicted cargo location allocation point of each type of aviation parts at the current moment, which is closest to the initial location of the corresponding type of aviation parts entering the warehouse at the current moment, is used as the warehouse-entering cargo location allocation point of the corresponding type of aviation parts at the current moment;

[0136] Based on the inbound storage location allocation point, initial position and preset handling path model of each type of aviation parts at the current moment, the predicted optimal inbound storage path of the corresponding type of aviation parts at the current moment is obtained, and based on the predicted optimal inbound storage path of all types of aviation parts at the current moment, the aviation parts supply management result at the current moment is obtained.

[0137] In this embodiment, the initial storage location is the location where each type of aviation component is just moved into the warehouse (the warehouse has multiple entrances and exits) after it is produced.

[0138] The beneficial effects of the above technology are: based on all the predicted cargo location allocation points and preset handling path models of each type of aviation parts at the current moment, the predicted optimal warehousing path for each type of aviation parts at the current moment is obtained, and the optimal path that each type of aviation parts should take during the warehousing process at the current moment is accurately predicted. Finally, based on the predicted optimal warehousing path for all types of aviation parts at the current moment, the aviation parts supply management results at the current moment are obtained, which effectively improves the warehousing efficiency and inventory management efficiency, makes more effective use of warehouse resources, and reduces the risk of idle and wasted resources.

[0139] Example 10:

[0140] The present invention provides a military aviation parts supply management system based on artificial intelligence, which is used to implement any one of the military aviation parts supply management methods based on artificial intelligence in embodiments 1 to 9, with reference to Figure 2 ,include:

[0141] A scoring module, configured to obtain a component storage scoring value of each type of aviation component at the current moment based on all component storage data of each type of aviation component within a preset time period before the current moment;

[0142] a calculation module for obtaining a storage location node diagram for each type of aviation parts at the current moment based on all storage location areas for each type of aviation parts at the current moment, and obtaining a reasonable value for the storage location allocation for each type of aviation parts at the current moment based on the storage location node diagrams and component storage scores for all types of aviation parts at the current moment;

[0143] A prediction module, configured to obtain all predicted cargo location allocation points for each type of aviation parts at the current moment based on reasonable cargo location allocation values for each type of aviation parts at all moments in a preset time period before the current moment;

[0144] The management module is used to obtain the predicted optimal warehousing path for each type of aviation parts at the current moment based on all predicted cargo location allocation points and preset handling path models for each type of aviation parts at the current moment, and to obtain the aviation parts supply management results at the current moment based on the predicted optimal warehousing paths for all types of aviation parts at the current moment.

[0145] The beneficial effects of the above technology are: according to all the parts storage data of each type of aviation parts in the preset time period before the current moment, the parts storage score value of each type of aviation parts at the current moment is obtained, and the difficulty of storing parts for each type of aviation parts at the current moment is quantified; according to all the storage cargo area of each type of aviation parts at the current moment, the storage cargo node diagram of each type of aviation parts at the current moment is obtained, which is convenient for the calculation of the reasonable value of subsequent cargo location allocation; and then according to the storage cargo node diagram and parts storage score value of all types of aviation parts at the current moment, the reasonable value of cargo location allocation of each type of aviation parts at the current moment is obtained, which realizes the quantification of the rationality of cargo location allocation of each type of aviation parts at the current moment, and according to the storage cargo node diagram and parts storage score value of all types of aviation parts at the current moment, the reasonable degree of cargo location allocation of each type of aviation parts at the current moment is quantified. The reasonable values of cargo location allocation at all times in the preset time period before are obtained, and all predicted cargo location allocation points of each type of aviation parts at the current moment are obtained, and the most reasonable cargo location node for storing parts of each type of aviation parts at the current moment is accurately predicted. Then, based on all predicted cargo location allocation points and the preset handling path model of each type of aviation parts at the current moment, the predicted optimal warehousing path of each type of aviation parts at the current moment is obtained, and the optimal path that each type of aviation parts should take during the warehousing process at the current moment is accurately predicted. Finally, based on the predicted optimal warehousing path of all types of aviation parts at the current moment, the aviation parts supply management result at the current moment is obtained, which effectively improves the warehousing efficiency and inventory management efficiency, makes more effective use of warehouse resources, and reduces the risk of idle and wasted resources.

[0146] Obviously, those skilled in the art can make various changes and modifications to the present invention without departing from the spirit and scope of the present invention, and the present invention is also intended to include these changes and modifications.

Claims

1. A military aviation parts supply management method based on artificial intelligence, characterized in that: include: S1: Based on all pieces of component storage data of each type of aviation component within a preset time period before the current moment, obtain a component storage score value of each type of aviation component at the current moment; S2: Based on all storage locations of each type of aviation parts at the current moment, obtain the storage location node diagram of each type of aviation parts at the current moment, and based on the storage location node diagram and component storage score of all types of aviation parts at the current moment, obtain the reasonable value of the storage location allocation of each type of aviation parts at the current moment; S3: Based on the reasonable values of cargo space allocation for each type of aviation parts at all times in a preset time period before the current moment, obtain all predicted cargo space allocation points for each type of aviation parts at the current moment; S4: Based on all predicted cargo location allocation points and preset handling path models for each type of aviation parts at the current moment, the predicted optimal warehousing path for each type of aviation parts at the current moment is obtained, and based on the predicted optimal warehousing paths for all types of aviation parts at the current moment, the aviation parts supply management results at the current moment are obtained.

2. The military aviation parts supply management method based on artificial intelligence according to claim 1 is characterized in that: S1: Based on all pieces of component storage data for each type of aviation component within a preset time period before the current moment, obtain a component storage score for each type of aviation component at the current moment, including: Obtain all pieces of component storage data for each type of aviation component within a preset time period before the current moment, where each piece of component storage data includes component pickup and delivery time, component pickup and delivery distance, and component pickup and delivery energy consumption; Based on the number of component storage data of each type of aviation component in a preset time period before the current moment, the component storage score value of each type of aviation component at the current moment is obtained.

3. The military aviation parts supply management method based on artificial intelligence according to claim 2 is characterized in that: Based on the number of component storage data of each type of aviation component in a preset time period before the current moment, the component storage score of each type of aviation component at the current moment is obtained, including: If the number of component storage data items for each type of aviation component within the preset time period before the current moment is zero, the three component storage data items of the corresponding type of aviation component closest to the current moment in terms of time sequence will be used as the component storage data items for the corresponding type of aviation component within the preset time period before the current moment; Based on all the component storage data of each type of aviation parts in the preset time period before the current moment, the component storage score of the corresponding type of aviation parts at the current moment is obtained, which is: Where β is the component storage score of the current computing aviation component at the current moment, z0 is the average value of the component pickup and delivery time of all component storage data of the current computing aviation component within the preset time period before the current moment, σ z is the standard deviation of the component pickup and delivery time values of all pieces of component storage data of the current computing aviation component within the preset time period before the current moment, m is the number of pieces of component storage data of the current computing aviation component within the preset time period before the current moment, z max The maximum value of the component pickup and delivery time in all the component storage data of the current calculation type aviation component within the preset time period before the current moment, z min is the minimum value of the component pickup and delivery time in all the component storage data of the current calculation-type aviation parts within the preset time period before the current moment, c0 is the mean value of the component pickup and delivery distance in all the component storage data of the current calculation-type aviation parts within the preset time period before the current moment, σ c The standard deviation of the value of the component delivery distance of all the component storage data of the current calculation type aviation parts within the preset time period before the current moment, c max The maximum value of the component pickup and delivery distance in all the component storage data of the current calculation type aviation component within the preset time period before the current moment, c min is the minimum value of the component pickup and delivery distance in all the component storage data of the current calculation-type aviation parts in the preset time period before the current moment, b0 is the average value of the component pickup and delivery energy consumption in all the component storage data of the current calculation-type aviation parts in the preset time period before the current moment, σ b The standard deviation of the energy consumption of all stored data of the current computing aviation parts in the preset time period before the current moment, b max The maximum value of the energy consumption of the current calculation aviation parts in all the parts storage data within the preset time period before the current moment, b min It is the minimum value of the energy consumption of the current calculation aviation parts in all the parts storage data within the preset time period before the current moment. ln is the natural logarithm, and the value of the natural constant e is 2.

718.

4. The military aviation parts supply management method based on artificial intelligence according to claim 1 is characterized in that: S2: Based on all storage location areas of each type of aviation parts at the current moment, obtain the storage location node diagram of each type of aviation parts at the current moment, and based on the storage location node diagram and component storage score values of all types of aviation parts at the current moment, obtain the reasonable value of the storage location allocation of each type of aviation parts at the current moment, including: The sub-areas of all sub-areas of the cargo storage area at the current moment, which store each type of aviation parts, are regarded as the storage location areas of the corresponding type of aviation parts at the current moment; The sub-areas of all sub-areas of the cargo storage area at the current moment that do not store each type of aviation parts are regarded as unstored cargo areas of the corresponding type of aviation parts at the current moment; Each storage cargo area of each type of aviation parts at the current moment is used as a cargo location node of the corresponding type of aviation parts at the current moment, and each non-storage cargo area of each type of aviation parts at the current moment is used as a non-cargo location node of the corresponding type of aviation parts at the current moment; Based on all cargo location nodes and all non-cargo location nodes of each type of aviation parts at the current moment, a storage cargo location node graph of each type of aviation parts at the current moment is obtained; Based on the storage location node graph of all aviation parts at the current moment, the entropy value of the storage location allocation of all aviation parts at the current moment is obtained; Based on the entropy values of cargo location allocation and component storage scores of all types of aviation parts at the current moment, the reasonable value of cargo location allocation for each type of aviation parts at the current moment is obtained.

5. The military aviation parts supply management method based on artificial intelligence according to claim 4 is characterized in that: Based on the storage location node graph of all aviation parts at the current moment, the location allocation entropy values of all aviation parts at the current moment are obtained, including: Taking each storage location node in the current storage location node diagram of each type of aviation parts as the origin and the preset distance as the radius, the allocation circle of the corresponding storage location node is obtained, and the number of remaining storage location nodes covered by the allocation circle of each storage location node is regarded as the number of associated points of the corresponding storage location node; The storage location node with the largest number of associated points among all the storage location nodes in the storage location node graph of each type of aviation parts at the current moment is regarded as the central storage location node of the storage location node graph of the corresponding type of aviation parts at the current moment, and the storage location nodes other than the central storage location node among all the storage location nodes in the storage location node graph of each type of aviation parts at the current moment are regarded as the non-central storage location nodes of the storage location node graph of the corresponding type of aviation parts at the current moment; The distance between each non-central storage location node and the central storage location node in the storage location node graph of each type of aviation parts at the current moment is regarded as the connection distance of the corresponding non-central storage location node in the storage location node graph of the corresponding type of aviation parts at the current moment, and all non-central storage location nodes are defined by increasing ordinal numbers starting from 1 according to the order of the connection distances of all non-central storage location nodes in the storage location node graph of each type of aviation parts at the current moment, to obtain an ordinal definition result; Based on the storage location node graph and ordinal definition results of each type of aviation parts at the current moment, the entropy value of the storage location allocation of each type of aviation parts at the current moment is obtained.

6. The artificial intelligence-based military aviation parts supply management method according to claim 5, characterized in that: Based on the storage location node graph and ordinal definition results of each type of aviation parts at the current moment, the entropy value of the storage location allocation of each type of aviation parts at the current moment is obtained, including: Among them, γ is the entropy value of the cargo space allocation of the current computing aviation parts at the current moment, δ i is the number of adjacent storage nodes of the non-central storage node with ordinal number i in the storage storage node graph of the current computing aviation parts at the current moment, ε i is the number of non-location nodes adjacent to the non-center location node with ordinal number i in the storage location node graph of the current computing aviation parts at the current moment, n is the total number of non-center location nodes in the storage location node graph of the current computing aviation parts at the current moment, δ max is the maximum number of adjacent storage nodes in all non-central storage nodes of the storage storage node graph of the current computing aviation parts at the current moment, δ min is the minimum number of adjacent storage nodes in all non-central storage nodes of the storage storage node graph of the current computing aviation parts at the current moment, ε max is the maximum number of adjacent non-storage location nodes in the non-central storage location node with ordinal number i in the storage location node graph of the current computing aviation parts at the current moment, ε min is the minimum number of adjacent non-storage location nodes in the non-central storage location node with ordinal number i in the storage location node graph of the current computing aviation parts at the current moment. ln is the natural logarithm, and the value of the natural constant e is 2.

718.

7. The method for supply management of military aviation parts based on artificial intelligence according to claim 4, characterized in that: Based on the entropy values of the cargo location allocation and the component storage scores of all types of aviation parts at the current moment, the reasonable value of the cargo location allocation for each type of aviation parts at the current moment is obtained, including: Among them, μ is the reasonable value of the cargo space allocation of the current computing aviation parts at the current moment, S β The sum of the scores assigned to all aviation parts at the current moment, S γ is the sum of the entropy values of the cargo space allocation of all aviation parts at the current moment, β is the component storage score of the current calculation aviation parts at the current moment, γ is the entropy value of the cargo space allocation of the current calculation aviation parts at the current moment, σ β The standard deviation of the scores assigned to the storage of all aviation parts at the current moment, σ γ The standard deviation of the entropy values for the cargo space allocation of all types of aviation parts at the current moment is ln, which is the natural logarithm, and the value of the natural constant e is 2.

718.

8. The military aviation parts supply management method based on artificial intelligence according to claim 1 is characterized in that: S3: Based on the reasonable values of cargo space allocation for each type of aviation parts at all times within a preset time period before the current moment, all predicted cargo space allocation points for each type of aviation parts at the current moment are obtained, including: Obtain the reasonable value of cargo space allocation for each type of aviation parts at all times within a preset time period before the current moment; The time corresponding to the largest reasonable cargo space allocation value among all reasonable cargo space allocation values of each type of aviation parts at all times in a preset time period before the current time is used as the predicted time for the corresponding type of aviation parts; All cargo location nodes of each type of aviation parts at the predicted time are regarded as the predicted cargo location allocation points of the corresponding type of aviation parts at the current moment.

9. The method for supply management of military aviation parts based on artificial intelligence according to claim 1, characterized in that: S4: Based on all the predicted cargo location allocation points and preset handling path models for each type of aviation parts at the current moment, the predicted optimal warehousing path for each type of aviation parts at the current moment is obtained. Based on the predicted optimal warehousing paths for all types of aviation parts at the current moment, the aviation parts supply management results at the current moment are obtained, including: Get the initial location of each type of aviation parts in the warehouse at the current moment; The predicted cargo location allocation point of each type of aviation parts at the current moment, which is closest to the initial location of the corresponding type of aviation parts entering the warehouse at the current moment, is used as the warehouse-entering cargo location allocation point of the corresponding type of aviation parts at the current moment; Based on the inbound storage location allocation point, initial position and preset handling path model of each type of aviation parts at the current moment, the predicted optimal inbound storage path of the corresponding type of aviation parts at the current moment is obtained, and based on the predicted optimal inbound storage path of all types of aviation parts at the current moment, the aviation parts supply management result at the current moment is obtained.

10. A military aviation parts supply management system based on artificial intelligence, characterized in that: A method for managing the supply of military aviation parts based on artificial intelligence, for executing any one of claims 1 to 9, comprising: A scoring module, configured to obtain a component storage scoring value of each type of aviation component at the current moment based on all component storage data of each type of aviation component within a preset time period before the current moment; a calculation module for obtaining a storage location node diagram for each type of aviation parts at the current moment based on all storage location areas for each type of aviation parts at the current moment, and obtaining a reasonable value for the storage location allocation for each type of aviation parts at the current moment based on the storage location node diagrams and component storage scores for all types of aviation parts at the current moment; A prediction module, configured to obtain all predicted cargo location allocation points for each type of aviation parts at the current moment based on reasonable cargo location allocation values for each type of aviation parts at all moments in a preset time period before the current moment; The management module is used to obtain the predicted optimal warehousing path for each type of aviation parts at the current moment based on all predicted cargo location allocation points and preset handling path models for each type of aviation parts at the current moment, and to obtain the aviation parts supply management results at the current moment based on the predicted optimal warehousing paths for all types of aviation parts at the current moment.

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