KR102447159B1 - Method and apparatus for providing customized vacancy management information to improve the vacancy rate of warehouse - Google Patents

Abstract

A method performed as a processor of a computer executes one or more instructions stored in the memory of the computer, the method comprising: acquiring, by the processor, attribute information and rental history information of a plurality of warehouses according to a predetermined period; The processor, calculating the first vacancy rate of the first distribution warehouse from the rental history information of the first distribution warehouse, and determining a predicted vacancy rate based on the first vacancy rate, wherein the processor, the first distribution warehouse Selecting, by the processor, attribute information and rental history information of the second warehouse using an artificial intelligence model, having attribute information similar to attribute information, and having a vacancy rate lower than the first vacancy rate From, including the step of generating customized vacancy management information that can improve the predicted vacancy rate of the first logistics warehouse, and the step of generating the customized vacancy management information is, rent, management fee, facility usage fee of the second logistics warehouse and cost factor information including labor costs; Lease factor information, including lease term, lease use, and leased area; and generating a second relationship graph for sales by period of a second distribution warehouse using building factor information including internal facilities, external environment information, location information, and building information, and a second logistics from the second relationship graph Creating a reference weight by setting a weight for each factor affecting the sales by period of the warehouse, and comparing the second relationship graph with the first relationship graph of the first distribution warehouse, the reference weight in the first relationship graph Including the step of generating customized vacancy management information that can increase the sales of the first distribution warehouse by using a factor having a lower weight, and the step of generating the customized vacancy management information is the property of the first distribution warehouse Comparing the information with attribute information of other logistics warehouses to determine the attribute similarity, and analyzing the sales cause from the rental history information of the second logistics warehouse with the highest sales among other logistics warehouses whose attribute similarity is equal to or greater than a threshold and generating customized vacancy management information that can increase sales of the first logistics warehouse from the sales causes of the second logistics warehouse. to start

Description

Method and device for providing customized vacancy management information to improve the vacancy rate of warehouses

The present invention relates to a method and apparatus for providing customized vacancy management information for improving the vacancy rate of a distribution warehouse.

Artificial intelligence technology includes machine learning (machine learning) technology, and among machine learning technologies, it includes deep learning technology, which is widely used to analyze images.

Deep learning is a set of machine learning algorithms that attempt high-level abstractions (summarizing key contents or functions in large amounts of data or complex data) through a combination of several nonlinear transformation methods. Defined. Deep learning can be viewed as a field of machine learning that teaches computers to think in a broad framework.

When there is any data, it is represented in a form that the computer can understand (for example, in the case of an image, pixel information is expressed as a column vector), and many studies (how to better express it) How to build a technique and how to build a model to learn it) is underway. As a result of these efforts, various deep learning techniques have been developed. Examples of deep learning techniques include Deep Neural Networks (DNN), Convolutional Deep Neural Networks (CNN), Reccurent Neural Network (RNN), and Deep Belief Networks (DBN). have.

A deep neural network (DNN) is an artificial neural network (ANN) composed of a plurality of hidden layers between an input layer and an output layer.

Although such artificial intelligence technology is rapidly being applied to various fields, there are many cases where it is still not properly applied to industrial sites despite its necessity.

Due to the development of the distribution industry, the high-yield logistics warehouse rental business is increasing. However, as the supply of distribution warehouses increased, the vacancy rate of distribution warehouses in non-preferred areas also increased, resulting in losses. Therefore, the need for a method that can improve the vacancy rate by analyzing the vacancy rate and vacancy causes of the distribution warehouse has increased.

The problem to be solved by the present invention is to provide a method and apparatus for providing customized vacancy management information for improving the vacancy rate of a distribution warehouse.

The problems to be solved by the present invention are not limited to the problems mentioned above, and other problems not mentioned will be clearly understood by those skilled in the art from the following description.

In order to solve the above problems, the present invention provides a method performed by a computer according to a first embodiment, comprising the steps of: acquiring attribute information and rental history information of a plurality of warehouses according to a predetermined period; a first logistics warehouse Calculating the first vacancy rate of the first distribution warehouse from the rental history information of, and determining the predicted vacancy rate based on the first vacancy rate, has attribute information similar to the attribute information of the first distribution warehouse, and has a vacancy rate lower than the first vacancy rate Selecting a second logistics warehouse with It is possible to provide a method of managing the vacancy rate of the logistics warehouse using an artificial intelligence model including

According to a second embodiment, a memory for storing at least one or more instructions and a processor for executing one or more instructions stored in the memory, wherein the processor executes the one or more instructions, thereby performing the method of the present application using an artificial intelligence model It is possible to provide a device for managing the vacancy rate of the logistics warehouse.

According to the third embodiment, it is possible to provide a computer program stored in a computer-readable recording medium in combination with a computer, which is hardware, to perform the method of the present invention.

A warehouse vacancy rate management program using an artificial intelligence model according to an aspect of the present invention for solving the above problems is combined with a computer that is hardware, and a computer-readable record to perform the method according to the disclosed embodiment stored on the medium.

Other specific details of the invention are included in the detailed description and drawings.

According to the disclosed embodiment, there is an effect of predicting the vacancy rate of the logistics warehouse using an artificial intelligence model, and further predicting the sales of the logistics warehouse, thereby providing information necessary for the operation and management of the logistics warehouse.

According to one disclosure, the present invention can provide customized management information that can improve the vacancy rate of the distribution warehouse, and can derive a method for increasing the sales of the distribution warehouse by using the customized management information.

According to one disclosure, in the present invention, it is possible to provide a method for matching a consumer and a supplier of a logistics warehouse by using the information of the logistics warehouse.

Effects of the present invention are not limited to the effects mentioned above, and other effects not mentioned will be clearly understood by those skilled in the art from the following description.

1 is a diagram illustrating a system for executing a method for managing a vacancy rate of a logistics warehouse using an artificial intelligence model according to an embodiment.
2 is a view showing the flow of a method for managing the vacancy rate of a logistics warehouse using an artificial intelligence model according to one disclosure.
Figure 3 is a flow chart for explaining in detail the characteristics of generating customized vacancy management information by one disclosure.
4 is a view for explaining the characteristics of generating and using customized vacancy management information that can improve the vacancy rate by using the relationship graph generated using the artificial intelligence model according to the disclosure.
5 is a view for explaining the characteristics of matching the warehouse rental application information and the distribution warehouse using the customized vacancy management information by the start of work.
6 is a view showing the configuration of the vacancy rate management apparatus of the logistics warehouse using an artificial intelligence model according to the disclosure.

Advantages and features of the present invention and methods of achieving them will become apparent with reference to the embodiments described below in detail in conjunction with the accompanying drawings. However, the present invention is not limited to the embodiments disclosed below, but may be implemented in various different forms, and only these embodiments allow the disclosure of the present invention to be complete, and those of ordinary skill in the art to which the present invention pertains. It is provided to fully inform those skilled in the art of the scope of the present invention, and the present invention is only defined by the scope of the claims.

The terminology used herein is for the purpose of describing the embodiments and is not intended to limit the present invention. In this specification, the singular also includes the plural, unless specifically stated otherwise in the phrase. As used herein, “comprises” and/or “comprising” does not exclude the presence or addition of one or more other components in addition to the stated components. Like reference numerals refer to like elements throughout, and "and/or" includes each and every combination of one or more of the recited elements. Although "first", "second", etc. are used to describe various elements, these elements are not limited by these terms, of course. These terms are only used to distinguish one component from another. Therefore, it goes without saying that the first component mentioned below may be the second component within the spirit of the present invention.

Unless otherwise defined, all terms (including technical and scientific terms) used herein will have the meaning commonly understood by those of ordinary skill in the art to which this invention belongs. In addition, terms defined in a commonly used dictionary are not to be interpreted ideally or excessively unless clearly specifically defined.

As used herein, the term “unit” or “module” refers to a hardware component such as software, FPGA, or ASIC, and “unit” or “module” performs certain roles. However, “part” or “module” is not meant to be limited to software or hardware. A “part” or “module” may be configured to reside on an addressable storage medium or may be configured to reproduce one or more processors. Thus, by way of example, “part” or “module” refers to components such as software components, object-oriented software components, class components and task components, processes, functions, properties, Includes procedures, subroutines, segments of program code, drivers, firmware, microcode, circuitry, data, databases, data structures, tables, arrays, and variables. Components and functionality provided within “parts” or “modules” may be combined into a smaller number of components and “parts” or “modules” or additional components and “parts” or “modules”. can be further separated.

In this specification, a computer means all types of hardware devices including at least one processor, and may be understood as encompassing software configurations operating in the corresponding hardware device according to embodiments. For example, a computer may be understood to include, but is not limited to, smart phones, tablet PCs, desktops, notebooks, and user clients and applications running on each device.

Hereinafter, embodiments of the present invention will be described in detail with reference to the accompanying drawings.

Each step described in this specification is described as being performed by a computer, but the subject of each step is not limited thereto, and at least a portion of each step may be performed in different devices according to embodiments.

According to one disclosure, the vacancy rate management apparatus 100 of the logistics warehouse using the artificial intelligence model is abbreviated and described as the vacancy rate management apparatus 100 .

1 is a diagram illustrating a system for executing a method for managing a vacancy rate of a logistics warehouse using an artificial intelligence model according to an embodiment.

Referring to FIG. 1 , a system and its components are disclosed for explaining a method for predicting a vacancy rate of a logistics warehouse using an artificial intelligence model according to the disclosed embodiment.

The system shown in FIG. 1 includes a distribution warehouse 10 , a vacancy rate management apparatus 100 , and a manager terminal 200 .

In one embodiment, the type of the distribution warehouse represented by the distribution warehouse 10 is not limited, and the distribution warehouse 10 collects information such as the type of item stored in the distribution warehouse 10, inventory, stock, and shipment amount. It may include a management terminal to manage.

The vacancy rate management apparatus 100 may collect information about the distribution warehouse 10 from the management terminal of the distribution warehouse 10, and generate information necessary for management, operation and management of the distribution warehouse 10 based on this. . The vacancy rate management apparatus 100 may include a server, a mobile terminal, a cloud, an electronic device, and the like, and may include any type of device capable of executing the vacancy rate prediction method of the logistics warehouse using the artificial intelligence model of the present application. .

The vacancy rate management apparatus 100 by one disclosure collects information about the distribution warehouse 10 from the management terminal of the distribution warehouse 10, and based on this, information necessary for the management, operation and management of the distribution warehouse 10 can create

The information generated by the vacancy rate management apparatus 100 may be transmitted to the manager terminal 200 . For example, the manager terminal 200 may mean a terminal used by a manager or manager of the distribution warehouse 10 , but is not limited thereto.

In an embodiment, the vacancy rate management apparatus 100 may predict the vacancy rate of the distribution warehouse 10 . In addition, in one embodiment, the vacancy rate management apparatus 100 may record the daily vacancy rate of the distribution warehouse 10, and predict the vacancy rate at a future time by using this.

For example, the vacancy rate management apparatus 100 may predict the vacancy rate at a future time point with reference to the change trend of the daily vacancy rate and the vacancy rate at a past time point.

In addition, the vacancy rate management device 100 collects information about one or more other logistics warehouses (1), and the vacancy rate and logistics warehouses of other logistics warehouses having a location similar to the logistics warehouse 10, an architectural outline, and main storage items. The vacancy rate in (10) can be compared.

When a difference occurs in the vacancy rate between the distribution warehouse 10 and other distribution warehouses (for example, when the vacancy rate of other distribution warehouses is low), the vacancy rate management device 100 is between the distribution warehouse 10 and other distribution warehouses. Analyze the difference, and based on this, it is possible to analyze the cause of the vacancy of the warehouse (10).

For example, the cause of vacancy in the logistics warehouse 10 is the distance from the highway entrance and exit, the year of construction of the building, dock, freight elevator, warehouse facilities such as temperature control device, main storage items, auxiliary facilities such as office, kitchen, rent and rental period, etc., but is not limited thereto.

In addition, the vacancy rate management apparatus 100 may predict sales of the distribution warehouse 10 based on the vacancy rate of the distribution warehouse 10 . In this case, the vacancy rate management device 100 also obtains information on the highest sales among other logistics warehouses having a location and land area similar to that of the logistics warehouse 10, and analyzes the difference with the logistics warehouse 10 and may provide the corresponding information to the manager terminal 200 .

The information provided to the manager terminal 200 may be utilized as information for reconstructing the distribution warehouse 10 .

According to an embodiment, the vacancy rate management apparatus 100 may also perform a shipment forecast based on an external factor. In one embodiment, the vacancy rate management device 100 collects external information such as the day of the week, weather, season, and whether or not promotions for the item are in progress at a point in the future, and analyzes the correlation between each external information and the amount of shipment can learn

The vacancy rate management apparatus 100 may predict the shipment amount of a specific item based on external factors at a point in time in the future, based on the learning result.

The vacancy rate management apparatus 100 may perform an operation for inventory and logistics management, such as performing a preemptive warehousing request based on the predicted shipment amount information.

In addition, the vacancy rate management apparatus 100 may obtain information for updating the learned model by storing the predicted data in the database, and later obtaining the actual shipment amount information at the corresponding time point and comparing it with the predicted data.

2 is a view showing the flow of a method for managing the vacancy rate of a logistics warehouse using an artificial intelligence model according to one disclosure.

In block 201 by one disclosure, the vacancy rate management apparatus 100 may acquire attribute information and rental history information of a plurality of distribution warehouses according to a predetermined period.

In block 202 by one disclosure, the vacancy rate management apparatus 100 may calculate the first vacancy rate of the first distribution warehouse from the rental history information of the first distribution warehouse, and determine the predicted vacancy rate based on the first vacancy rate.

In block 203 by one disclosure, the vacancy rate management apparatus 100 has attribute information similar to the attribute information of the first distribution warehouse, and may select the second distribution warehouse having a vacancy rate lower than the first vacancy rate.

According to the commencement of work, the property information of the distribution warehouse includes the area, location, use, available internal facilities, external environment, internal environment, use, cause of rental, insurance coverage, certification acquisition, available logistics services, etc. can, but is not limited thereto.

The vacancy rate management device 100 by the disclosure of the first warehouse area, location, use, available internal facilities, external environment, internal environment, use, attribute information including attribute information and other logistics warehouses including the cause of rental By comparison, the attribute similarity can be determined.

According to one disclosure, the vacancy rate management apparatus 100 may select at least one distribution warehouse whose attribute similarity is greater than or equal to a threshold value among other distribution warehouses. Here, the threshold value represents a predetermined value or more among values comparing similarities of physical information of distribution warehouses. When the attribute similarity between warehouses is greater than or equal to the threshold, it indicates that the warehouses can provide similar locations, uses, facilities, services, and the like.

By one disclosure, the vacancy rate management apparatus 100 may analyze the vacancy rate for a predetermined period of at least one distribution warehouse. According to one disclosure, the vacancy rate management apparatus 100 may select at least one distribution warehouse in which the analyzed vacancy rate is lower than the first vacancy rate as the second distribution warehouse. That is, among other distribution warehouses having a high similarity to the first distribution warehouse, a distribution warehouse having a higher rental rate than that of the first distribution warehouse may be determined as the second distribution warehouse. According to one disclosure, the second distribution warehouse may be at least one distribution warehouse.

In block 204 by the start of the work, the vacancy rate management device 100 uses an artificial intelligence model from the attribute information and rental history information of the second logistics warehouse, customized vacancy management information that can improve the predicted vacancy rate of the first logistics warehouse. can create

The vacancy rate management apparatus 100 by the start may generate customized vacancy management information including monitoring information for reducing the vacancy rate of the first distribution warehouse, and the customized vacancy management information provides information to improve the problems of the distribution warehouse By doing so, it is possible to include information that can increase the sales of the warehouse.

By the start, the vacancy rate management apparatus 100 may determine the rate of return and the profit cause of the first distribution warehouse from the correlation between the period-specific sales volume and the first vacancy rate of the first distribution warehouse.

At this time, the vacancy rate management apparatus 100 by the start of the work uses the customized vacancy management information to change the use that can improve the profitability of the first distribution warehouse, obtain certification, purchase insurance, provide services, add facilities inside the warehouse, warehouse Monitoring information can be provided, including how to improve operations, such as adding external facilities.

That is, the vacancy rate management device 100 can provide a method for determining the predicted vacancy rate by analyzing the information of the first logistics warehouse, and reducing the predicted vacancy rate through customized vacancy management information, so that the warehouse can be efficiently provided. have.

Figure 3 is a flow chart for explaining in detail the characteristics of generating customized vacancy management information by one disclosure.

According to one disclosure, the vacancy rate management apparatus 100 in block 301 may determine the attribute similarity by comparing attribute information of the first logistics warehouse with attribute information of other logistics warehouses. According to the disclosure, the vacancy rate management apparatus 100 may determine the attribute similarity for each factor such as location, use, rental period, and cost of the warehouse, and may determine the attribute similarity from a combination of complex factors.

In block 302 by the start, the vacancy rate management apparatus 100 may analyze the sales cause from the rental history information of the second logistics warehouse with the highest sales among other logistics warehouses whose attribute similarity is equal to or greater than the threshold. According to one disclosure, the threshold of attribute similarity may indicate a case in which the similarity of physical conditions between warehouses is 85% or more. Also, the threshold of the attribute similarity may be determined by the user.

In block 303 by one disclosure, the vacancy rate management apparatus 100 may generate customized vacancy management information that can increase the sales of the first distribution warehouse from the sales causes of the second distribution warehouse. That is, by the start of the vacancy rate management apparatus 100 can manage the vacancy rate by providing an improvement method for increasing the sales of the first distribution warehouse.

The customized vacancy management information by the start may include consulting information for the rate of return of the first distribution warehouse, a solution for improving the rate of return through analysis of the overall state of the first distribution warehouse, and the expected future rate of return.

4 is a view for explaining the characteristics of generating and using customized vacancy management information that can improve the vacancy rate by using the relationship graph generated using the artificial intelligence model according to the disclosure.

The artificial intelligence model by one disclosure may be a learned relationship between attribute information, rental history information, and sales information and vacancy rate of a plurality of warehouses. The AI model may be re-learned using the vacancy rate and sales information of the warehouse acquired according to the time-series flow by the start.

By one disclosure, the vacancy rate management apparatus 100 may generate a relationship graph using the cost factor information, the rental factor information, and the building factor information of the distribution warehouse. According to one disclosure, the relationship graph may be an index indicating the correlation between a plurality of factor information and sales generated by the artificial intelligence model.

The vacancy rate management device 100 by the start of the second logistics warehouse rent, management cost, cost factor information including facility usage fee and labor cost, rental factor information including rental period, rental use and rental area, and internal facilities, external A second relationship graph for sales by period of the second distribution warehouse may be generated by using building factor information including environmental information, location information, and building information. Here, the method of generating the first relationship graph and the second relationship graph is the same.

By the start, the vacancy rate management apparatus 100 may generate a reference weight by setting a weight for each factor affecting the sales for each period of the second distribution warehouse from the second relationship graph.

By one disclosure, the vacancy rate management apparatus 100 compares the second relationship graph with the first relationship graph of the first distribution warehouse, and uses a factor having a weight lower than the reference weight in the first relationship graph of the first distribution warehouse. You can create customized vacancy management information that can increase sales.

By one disclosure, the vacancy rate management apparatus 100 may generate a plurality of nodes corresponding to a plurality of factors occurring during the rental period of the distribution warehouse in order to generate a relationship graph. Here, the sales of the distribution warehouse may be generated as a node of the relationship graph.

According to one disclosure, the vacancy rate management apparatus 100 may generate at least one line connecting each of the plurality of factors in order to indicate the relationship of each of the plurality of factors. In this case, a line connecting each of the plurality of factors indicates the influence of each of the plurality of factors, and the influence may be a criterion that each of the factors has on the sales.

By one disclosure, the vacancy rate management apparatus 100 may display the strength of the correlation between a plurality of factors affecting sales during the rental period of the distribution warehouse by adjusting the thickness of at least one line. The high strength of the association indicates that when sales occur in the warehouse, the probability that they occur at the same time is high.

In an embodiment, the correlation between factors corresponding to a specific node may be displayed and stored using not only the thickness of the line but also various types of lines. For example, when there is insufficient information to determine the correlation between specific factors (for example, when the number of collected cases is less than or equal to a preset number), the line connecting the nodes corresponding to the factor can be displayed as a dashed line. have.

As another example, when the correlation between specific factors is different under specific conditions, the shape of a line connecting the nodes may be set differently. For example, when factors having high correlation with each other but the correlation between the factors are low only in regions in which annual precipitation is greater than or equal to a preset value, a line connecting nodes of the factors may be in the form of a wave. In addition, a wave-shaped filter corresponding to an area in which annual precipitation is greater than or equal to a preset value may be stored. On the other hand, nodes of factors independent of annual precipitation can be connected by a straight line.

The vacancy rate management apparatus 100 may analyze the contribution rate of factors affecting sales for each rental period of the first distribution warehouse from the generated first relationship graph by the start. For example, the vacancy rate management apparatus 100 may determine that a specific use and location have a high contribution to sales during a predetermined period as a result of analyzing the relationship graph of a predetermined period in which sales of the distribution warehouse are high.

For example, the vacancy rate management apparatus 100 may determine the contribution rate of each factor based on the thickness of the line inside the relationship graph, and also determine the contribution rate of each factor according to the shape of the line (eg, broken line). can

In addition, the vacancy rate management apparatus 100 may apply a pre-stored ripple filter to all lines included in the graph when trying to obtain the contribution rate of factors for an area in which annual precipitation is greater than or equal to a preset value. By applying the filter, the line in the portion overlapping with the pre-stored wavy shape may be deleted, the color may be blurred, or the thickness may be reduced. For example, in the case of a straight line, only a portion overlapping the wavy shape of the filter may be deleted to form a broken line, and in the case of a wavy line, the entire line may be deleted as it overlaps the wavy shape of the filter.

Based on the state of the lines of the graph to which the filter is applied, the vacancy rate management apparatus 100 may determine that the contribution rate is high when the deleted portion is small, and that the contribution rate is high as the thickness of the non-deleted portion increases.

5 is a view for explaining the characteristics of matching the warehouse rental application information and the distribution warehouse using the customized vacancy management information by the start of work.

According to one disclosure, the vacancy rate management apparatus 100 may match a user who needs a distribution warehouse and an owner of the distribution warehouse in order to manage the vacancy rate of the distribution warehouse. The vacancy rate management apparatus 100 by one start receives a request for a distribution warehouse application from the user terminal 300, selects a distribution warehouse 10 that matches the distribution warehouse application among a plurality of distribution warehouses 1, and selects the selected logistics Information on the warehouse 10 may be transmitted to the user terminal 300 . Furthermore, the competitiveness of the logistics warehouse 10 can be increased by notifying the necessary improvement points for the logistics warehouse 10 to the manager terminal 300 . In addition, by transmitting information summarizing the advantages of the distribution warehouse 10 to the user terminal 300 , it is possible to increase the matching rate of the distribution warehouse.

By the start of the work, the vacancy rate management apparatus 100 may use the attribute information of the distribution warehouse and information on the leaseable period to find a user who needs the distribution warehouse. In addition, when the application information of the distribution warehouse and the information of the distribution warehouse do not completely match, it is possible to provide information that can reduce the vacancy rate of the distribution warehouse by providing necessary improvements to the distribution warehouse.

The vacancy rate management apparatus 100 by the start of the work includes a plurality of distribution warehouse application information including the area where the warehouse is required, the rental use, the form of the distribution warehouse, the area of the distribution warehouse, the desired rental cost, the required service, and the rental request period. can be obtained According to one disclosure, the vacancy rate management apparatus 100 may acquire the distribution warehouse application information for requesting the distribution warehouse through an application for distribution warehouse matching, a web page, or the like.

By the start of the work, the vacancy rate management apparatus 100 may select at least one distribution warehouse application information matching the attribute information and the leaseable period of the first distribution warehouse from among the plurality of distribution warehouse application information.

By one disclosure, the vacancy rate management apparatus 100 may analyze the service of the first distribution warehouse that can be additionally provided to each of the at least one distribution warehouse application information. For example, if the information on the first warehouse has a high matching rate with the information on the application for the logistics office applied by the user, but the first warehouse requires specific authentication, information on the method for obtaining a specific certification from the first warehouse is provided. can create

According to one disclosure, the vacancy rate management apparatus 100 may transmit attribute information and service information about the first logistics warehouse to the terminal of the user who has requested at least one logistic warehouse application information. Service information by one disclosure may include information requested to match the warehouse application information, and may include information that has been improved in order to match the logistics warehouse application information. In addition, the service information may include information about the advantages of the first distribution warehouse compared to other distribution warehouses.

6 is a view showing the configuration of the vacancy rate management apparatus of the logistics warehouse using an artificial intelligence model according to the disclosure.

According to one disclosure, the vacancy rate management apparatus 100 may include a memory 1200 that stores one or more instructions and a processor 1100 that executes one or more instructions stored in the memory 1200 . The processor 1100 included in the vacancy rate management apparatus 100 according to one disclosure may perform the method of the present application by executing one or more instructions.

However, the vacancy rate management apparatus 100 may be implemented by more components than the components shown in FIG. 6 or may be implemented by fewer components.

The processor 1100 generally controls the overall operation of the vacancy rate management apparatus 100 . For example, the processor 1100 may generally control other components included in the vacancy rate management apparatus 100 by executing programs stored in the memory 1200 . In addition, the processor 1100 may perform the function of the vacancy rate management apparatus 100 by executing programs stored in the memory 1200 . The processor 1100 may include at least one processor. The processor 1100 may include a plurality of processors or a single processor in an integrated form according to functions and roles thereof. In an embodiment, the processor 1100 may include at least one processor that provides a notification message by executing at least one program stored in the memory 1200 .

The memory 1200 may store a program for processing and control of the processor 1100 , and may store data input to or output from the vacancy rate management apparatus 100 .

The memory 1200 is a flash memory type, a hard disk type, a multimedia card micro type, a card type memory (eg, SD or XD memory 1200 ), etc. ), RAM (Random Access Memory), SRAM (Static Random Access Memory), ROM (Read-Only Memory), EEPROM (Electrically Erasable Programmable Read-Only Memory), PROM (Programmable Read-Only Memory), magnetic memory , a magnetic disk, and an optical disk may include at least one type of storage medium.

Programs stored in the memory 1200 may be classified into a plurality of modules according to their functions, where the plurality of modules are software, not hardware, and refer to modules that are functionally operated.

The processor 1100 according to an embodiment may include a data learning unit 11001 and a data recognition unit 1102 .

At least one of the data learning unit 1101 and the data recognition unit 1102 may be manufactured in the form of at least one hardware chip and mounted in an electronic device. For example, at least one of the data learning unit 1101 and the data recognition unit 1102 may be manufactured in the form of a dedicated hardware chip for artificial intelligence (AI), or an existing general-purpose processor (eg, CPU). Alternatively, it may be manufactured as a part of an application processor) or a graphics-only processor (eg, GPU) and mounted on the various electronic devices described above.

In this case, the data learning unit 1101 and the data recognition unit 1102 may be mounted on one electronic device or may be mounted on separate electronic devices, respectively. For example, one of the data learning unit 1101 and the data recognition unit 1102 may be included in the electronic device, and the other one may be included in the server. In addition, the data learning unit 1101 and the data recognition unit 1102 may provide the model information built by the data learning unit 1101 to the data recognition unit 1102 through wired or wireless communication, and the data recognition unit ( Data input to 1102 may be provided to the data learning unit 1101 as additional learning data.

Meanwhile, at least one of the data learning unit 1101 and the data recognition unit 1102 may be implemented as a software module. When at least one of the data learning unit 1101 and the data recognition unit 1102 is implemented as a software module (or a program module including instructions), the software module is a computer-readable non-transitory readable It may be stored in a recording medium (non-transitory computer readable media). Also, in this case, the at least one software module may be provided by an operating system (OS) or may be provided by a predetermined application. Alternatively, a part of the at least one software module may be provided by an operating system (OS), and the other part may be provided by a predetermined application.

The above-described method for predicting the vacancy rate and sales of the warehouse may be performed based on an artificial intelligence model learned based on the existing history and one or more external factors, and learning data including information on the vacancy rate and sales accordingly. The learning method of the artificial intelligence model and the type of learning data used therein are not limited.

The steps of the method or algorithm described in relation to the embodiment of the present invention may be implemented directly in hardware, implemented as a software module executed by hardware, or implemented by a combination thereof. A software module may include random access memory (RAM), read only memory (ROM), erasable programmable ROM (EPROM), electrically erasable programmable ROM (EEPROM), flash memory, hard disk, removable disk, CD-ROM, or It may reside in any type of computer-readable recording medium well known in the art to which the present invention pertains.

The components of the present invention may be implemented as a program (or application) to be executed in combination with a computer, which is hardware, and stored in a medium. Components of the present invention may be implemented as software programming or software components, and similarly, embodiments may include various algorithms implemented as data structures, processes, routines, or combinations of other programming constructs, including C, C++ , Java, assembler, etc. may be implemented in a programming or scripting language. Functional aspects may be implemented in an algorithm running on one or more processors.

As mentioned above, although embodiments of the present invention have been described with reference to the accompanying drawings, those skilled in the art to which the present invention pertains can realize that the present invention can be embodied in other specific forms without changing its technical spirit or essential features. you will be able to understand Therefore, it should be understood that the embodiments described above are illustrative in all respects and not restrictive.

Claims (1)

A method performed as a processor of a computer executes one or more instructions stored in a memory of the computer, the method comprising:
obtaining, by the processor, attribute information and rental history information of a plurality of distribution warehouses according to a predetermined period;
calculating, by the processor, a first vacancy rate of the first distribution warehouse from rental history information of the first distribution warehouse, and determining a predicted vacancy rate based on the first vacancy rate;
selecting, by the processor, a second distribution warehouse having similar property information as the property information of the first distribution warehouse, and having a lower vacancy rate than the first vacancy rate; and
The processor, from the attribute information and rental history information of the second logistics warehouse using an artificial intelligence model, generating customized vacancy management information that can improve the predicted vacancy rate of the first logistics warehouse,
The step of generating the customized vacancy management information is,
cost factor information including rent, management cost, facility usage fee, and labor cost of the second distribution warehouse; Lease factor information, including lease term, lease use, and leased area; and generating a second relationship graph for sales by period of a second distribution warehouse using building factor information including internal facilities, external environment information, location information, and building information;
generating a reference weight by setting a weight for each factor affecting the sales for each period of the second distribution warehouse from the second relationship graph; and
By comparing the second relationship graph and the first relationship graph of the first distribution warehouse, a customized vacancy that can increase the sales of the first distribution warehouse by using a factor having a weight lower than the reference weight in the first relationship graph generating management information;
The step of generating the customized vacancy management information is,
determining attribute similarity by comparing attribute information of the first logistics warehouse with attribute information of other logistics warehouses;
analyzing a sales cause from rental history information of a second logistics warehouse having the highest sales among other logistics warehouses whose attribute similarity is equal to or greater than a threshold value; and
A method of providing customized vacancy management information for improving the vacancy rate of the distribution warehouse, comprising the step of generating customized vacancy management information that can increase the sales of the first distribution warehouse from the source of sales of the second distribution warehouse.

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