KR102451127B1 - Method, device and system for providing management strategy of company based on pay stub information of company - Google Patents

Abstract

One embodiment of the present invention relates to a method for providing a management strategy for a company based on pay stub information on the company, which is performed by a device. The method for providing a management strategy for a company based on pay stub information on the company according to the present invention comprises the steps of: collecting pay stub information on employees working at a first company; analyzing a first employment indicator indicating a state of employment for the first company for a first pre-set period, based on the pay stub information collected for the first period; applying the first employment indicator to a first pre-trained artificial neural network to generate a first analysis result obtained by analyzing a fund which the first company can apply for, based on the state of employment for the first period by using the output of the first artificial neural network; checking whether the first company has ever applied for the fund of a first category, based on application of the first company for the fund when the first company is found to be able to apply for the fund of the first category through the first analysis result; and providing a management strategy which recommends application for the fund of the first category to a first user terminal when the first company is found not to have applied for the fund of the first category.

Description

Method, device and system for providing business strategy based on company pay stub information {METHOD, DEVICE AND SYSTEM FOR PROVIDING MANAGEMENT STRATEGY OF COMPANY BASED ON PAY STUB INFORMATION OF COMPANY}

The following embodiments relate to a technology for providing a business strategy for a company based on company payslip information.

A pay stub is a statement that records the details of payment of salaries and various allowances to workers. Pay stubs contain details of payments, deductions, or actual payments, etc., and such pay stubs can be used as evidence in various ways, so it is an important issue for workers to receive pay stubs.

Accordingly, the issuance of payslips is gradually expanding, and business owners who run companies need to find ways to systematically manage payslips.

Since these pay stubs contain information on the salaries of employees, the information on the pay stubs can determine the employment status of the company.

Accordingly, there is an increasing demand to establish a management strategy of a company based on information on a company's payslip, and research on related technologies is required.

Korean Patent Publication No. 10-2017-0070348 Korean Patent Publication No. 10-2018-0059986 Korean Patent Publication No. 10-2022-0043712 Korean Patent No. 10-1635573

According to an embodiment, collect payslip information of employees working in a first company, and based on the payslip information collected for a first preset period, the first company indicating the employment status of the first company for the first period 1 Analyze the employment index, apply the first employment index to the pre-trained first artificial neural network, and based on the output of the first artificial neural network, based on the employment status of the first period, the first company can apply for the subsidy. A first analysis result is generated, which is a result of analyzing the It is checked whether the first user has applied for the subsidy of the first category, and if it is confirmed that the first company has never applied for the subsidy of the first category, the first user An object of the present invention is to provide a method, apparatus and system for providing a management strategy of a company based on company payslip information, which is provided to a terminal.

Objects of the present invention are not limited to the objects mentioned above, and other objects not mentioned will be clearly understood from the description below.

According to one embodiment, there is provided a method of providing a management strategy of a company based on company payslip information, performed by a device, the method comprising: collecting payslip information of employees working in a first company; analyzing a first employment indicator indicating an employment status of the first company for the first period based on the payslip information collected for a first preset period; By applying the first employment indicator to the pre-trained first artificial neural network, based on the output of the first artificial neural network, based on the employment status of the first period, the subsidy that the first company can apply for is analyzed. generating a first analysis result that is a result; If it is confirmed through the first analysis result that the first company can apply for the subsidy of the first category, based on the details of the subsidy application of the first company, the first company applies for the subsidy of the first category. checking whether there are ever; and if it is confirmed that the first company has never applied for the subsidy of the first category, providing a management strategy for recommending an application for the subsidy of the first category to the first user terminal. A method of providing a management strategy of an enterprise based on payslip information is provided.

The method of providing a management strategy of a company based on the payslip information of the company includes: Based on the payslip information collected for a second period after the first period, analyzing a second employment indicator indicative of the employment status; Applying the second employment indicator to the first artificial neural network, based on the output of the first artificial neural network, based on the employment status of the second period, the result of analyzing the subsidy that the first company can apply for generating a second analysis result; By comparing the first analysis result and the second analysis result, if it is confirmed that the first company can apply for the subsidy of the second category only through the result of the second analysis, based on the subsidy application details of the first company , checking whether the first company has ever applied for the subsidy of the second category; providing, to the first user terminal, a management strategy for recommending an application for the second category of the subsidy when it is confirmed that the first company has never applied for the subsidy of the second category; predicting a third employment indicator representing the employment status of the first enterprise for a third period after the second period by comparing the first employment indicator and the second employment indicator; Applying the third employment index to the first artificial neural network, based on the output of the first artificial neural network, based on the employment status of the third period, the result of analyzing the subsidy that the first company can apply for generating a third analysis result; Comparing the second analysis result and the third analysis result, if it is confirmed that the first company can apply for the third category subsidy only through the third analysis result, based on the subsidy application details of the first company , checking whether the first company has ever applied for the third category of subsidy; and if it is confirmed that the first company has never applied for the third category of subsidy, the management strategy for recommending achievement of the third employment indicator and the third category of subsidy when the third employment indicator is achieved The method may further include providing a notification message guiding that the application is possible to the first user terminal.

The method of providing a management strategy of a company based on the payslip information of the company includes: collecting sales data for the first company; checking first sales, which is sales of the first company during the first period, based on the sales data collected during the first period; checking a first salary amount that is a total salary amount paid to employees working in the first company during the first period based on the first employment index; calculating a first productivity representing the productivity of the first company during the first period through a value obtained by dividing the first sales by the first salary; when a second company is matched as a competitor of the first company, checking a second productivity indicating the productivity of the second company during the first period; calculating a first difference value by subtracting the second productivity from the first productivity; checking whether the first difference value is out of a preset reference range; if it is confirmed that the first difference value does not deviate from the reference range, providing a management strategy for recommending maintenance of salaries of employees working in the first company to the first user terminal; when it is determined that the first difference value is out of the reference range, determining whether the first difference value is greater than 0; If it is confirmed that the first difference value is greater than 0, providing a management strategy for recommending a salary increase of employees working in the first company to the first user terminal; and if it is determined that the first difference value is less than 0, providing a management strategy for recommending a reduction in salaries of employees working in the first company to the first user terminal.

According to an embodiment, collect payslip information of employees working in a first company, and based on the payslip information collected for a first preset period, the first company indicating the employment status of the first company for the first period 1 Analyze the employment index, apply the first employment index to the pre-trained first artificial neural network, and based on the output of the first artificial neural network, based on the employment status of the first period, the first company can apply for the subsidy. A first analysis result is generated, which is a result of analyzing the It is checked whether the first user has applied for the subsidy of the first category, and if it is confirmed that the first company has never applied for the subsidy of the first category, the first user By providing the terminal, there is an effect that can present the business strategy of the company.

On the other hand, the effects according to the embodiments are not limited to those mentioned above, and other effects not mentioned will be clearly understood by those of ordinary skill in the art from the following description.

1 is a diagram schematically showing the configuration of a system according to an embodiment.
2 is a flowchart for explaining a process of providing a management strategy of a company based on company payslip information according to an exemplary embodiment.
3 is a flowchart illustrating a process of providing a business strategy for each period according to an exemplary embodiment.
4 is a flowchart illustrating a process of providing a management strategy of a company through prediction of employment status according to an exemplary embodiment.
5 is a flowchart illustrating a process of comparing productivity with a competitor according to an exemplary embodiment.
6 is a flowchart for explaining a process of providing a management strategy of a company with respect to salary through comparison of competitors according to an exemplary embodiment.
7 is a flowchart illustrating a process of analyzing a change trend for sales according to an exemplary embodiment.
8 is a flowchart illustrating a process of analyzing a change trend for the number of employees according to an exemplary embodiment.
9 is a flowchart for explaining a process of providing a business strategy for employment when a change trend for sales is an increase in sales, according to an exemplary embodiment.
10 is a flowchart for explaining a process of providing a business strategy for employment when a change trend for sales is a decrease in sales according to an exemplary embodiment.
11 is a flowchart illustrating a process of providing a business strategy for employment when a change trend for sales is maintenance of sales according to an exemplary embodiment.
12 is a flowchart illustrating a process of controlling policy settings of a host firewall based on artificial intelligence according to an embodiment.
13 is a flowchart illustrating a process of detecting an approach estimated as an attack and providing a countermeasure according to the number of attacks, according to an exemplary embodiment.
14 is a diagram for explaining learning of an artificial neural network according to another embodiment.
15 is an exemplary diagram of a configuration of an apparatus according to an embodiment.

Hereinafter, embodiments will be described in detail with reference to the accompanying drawings. However, since various changes may be made to the embodiments, the scope of the patent application is not limited or limited by these embodiments. It should be understood that all modifications, equivalents and substitutes for the embodiments are included in the scope of the rights.

Specific structural or functional descriptions of the embodiments are disclosed for purposes of illustration only, and may be changed and implemented in various forms. Accordingly, the embodiments are not limited to a specific disclosure form, and the scope of the present specification includes changes, equivalents, or substitutes included in the technical spirit.

Although terms such as first or second may be used to describe various elements, these terms should be interpreted only for the purpose of distinguishing one element from another. For example, a first component may be termed a second component, and similarly, a second component may also be termed a first component.

When a component is referred to as being “connected” to another component, it may be directly connected or connected to the other component, but it should be understood that another component may exist in between.

Terms used in the examples are used for the purpose of description only, and should not be construed as limiting. The singular expression includes the plural expression unless the context clearly dictates otherwise. In the present specification, terms such as “comprise” or “have” are intended to designate that a feature, number, step, operation, component, part, or combination thereof described in the specification exists, but one or more other features It is to be understood that this does not preclude the possibility of the presence or addition of numbers, steps, operations, components, parts, or combinations thereof.

Unless defined otherwise, all terms used herein, including technical or scientific terms, have the same meaning as commonly understood by one of ordinary skill in the art to which the embodiment belongs. Terms such as those defined in a commonly used dictionary should be interpreted as having a meaning consistent with the meaning in the context of the related art, and should not be interpreted in an ideal or excessively formal meaning unless explicitly defined in the present application. does not

In addition, in the description with reference to the accompanying drawings, the same components are assigned the same reference numerals regardless of the reference numerals, and the overlapping description thereof will be omitted. In the description of the embodiment, if it is determined that a detailed description of a related known technology may unnecessarily obscure the gist of the embodiment, the detailed description thereof will be omitted.

The embodiments may be implemented in various types of products, such as personal computers, laptop computers, tablet computers, smart phones, televisions, smart home appliances, intelligent cars, kiosks, wearable devices, and the like.

In an embodiment, the artificial intelligence (AI) system is a computer system that implements human-level intelligence, and is a system in which a machine learns and makes decisions on its own, unlike an existing rule-based smart system. The more the AI system is used, the better the recognition rate and the more accurate understanding of user preferences, and the existing rule-based smart systems are gradually being replaced by deep learning-based AI systems.

Artificial intelligence technology consists of machine learning and element technologies using machine learning. Machine learning is an algorithm technology that categorizes/learns characteristics of input data by itself, and element technology uses machine learning algorithms such as deep learning to simulate functions such as cognition and judgment of the human brain. It consists of technical fields such as understanding, reasoning/prediction, knowledge expression, and motion control.

The various fields where artificial intelligence technology is applied are as follows. Linguistic understanding is a technology for recognizing and applying/processing human language/text, and includes natural language processing, machine translation, dialogue system, question and answer, and speech recognition/synthesis. Visual understanding is a technology for recognizing and processing objects like human vision, and includes object recognition, object tracking, image search, human recognition, scene understanding, spatial understanding, image improvement, and the like. Inferential prediction is a technology for logically reasoning and predicting by judging information, and includes knowledge/probability-based reasoning, optimization prediction, preference-based planning, and recommendation. Knowledge expression is a technology that automatically processes human experience information into knowledge data, and includes knowledge construction (data generation/classification) and knowledge management (data utilization). Motion control is a technology for controlling autonomous driving of a vehicle and movement of a robot, and includes motion control (navigation, collision, driving), manipulation control (action control), and the like.

In general, in order to apply the machine learning algorithm to real life, learning is performed in the Trial and Error method due to the characteristics of the basic methodology of machine learning. In particular, deep learning requires hundreds of thousands of iterations. It is impossible to execute this in the actual physical external environment, so instead, the actual physical external environment is implemented on a computer and learning is performed through simulation.

1 is a diagram schematically showing the configuration of a system according to an embodiment.

Referring to FIG. 1 , a system according to an embodiment may include a plurality of user terminals 100 and devices 200 capable of communicating with each other through a communication network.

First, the communication network may be configured regardless of its communication mode, such as wired and wireless, and may be implemented in various forms so that communication between a server and a server and communication between a server and a terminal are performed.

Each of the plurality of user terminals 100 may be a desktop computer, a notebook computer, a tablet, a smart phone, or the like. Preferably, as shown in FIG. 1 , the first user terminal 110 , the second user terminal 120 , the third user terminal 130 , etc. may be smartphones, and may be employed differently depending on the embodiment. .

The plurality of user terminals 100 are terminals used by business owners who run companies, and are used by a first user terminal 110 used by a first business owner running a first business, and a second business owner running a second business. a second user terminal 120 , a third user terminal 130 used by a third business owner who operates a third company, and the like.

Each of the plurality of user terminals 100 may be configured to perform all or part of an arithmetic function, a storage/referencing function, an input/output function, and a control function of a typical computer. The plurality of user terminals 100 may be configured to communicate with the device 200 by wire or wireless.

Each of the plurality of user terminals 100 is connected to a web page operated by a person or group that provides a service using the device 200 , or is developed and distributed by a person or group that provides a service using the device 200 . One application can be installed. Each of the plurality of user terminals 100 may be linked with the device 200 through a web page or an application.

Each of the plurality of user terminals 100 may access the device 200 through a web page or an application provided by the device 200 .

Hereinafter, for convenience of description, the operation of the first user terminal 110 will be mainly described, but other user terminals such as the second user terminal 120 may perform the operation of the first user terminal 110 instead. to be.

The device 200 may be a self-server owned by a person or organization that provides services using the device 200, a cloud server, or a peer-to-peer (p2p) set of distributed nodes. may be The device 200 may be configured to perform all or part of an arithmetic function, a storage/referencing function, an input/output function, and a control function of a typical computer. The device 200 may include at least one artificial neural network that performs an inference function.

The device 200 may be configured to communicate with the plurality of user terminals 100 by wire or wireless, and controls the operation of each of the plurality of user terminals 100 , and displays which information on the screen of each of the plurality of user terminals 100 . You can control what is displayed.

Meanwhile, for convenience of explanation, only the first user terminal 110 , the second user terminal 120 , and the third user terminal 130 are shown in FIG. 1 among the plurality of user terminals 100 , but the number of terminals is not It may vary depending on the example. As long as the processing capacity of the apparatus 200 allows, the number of terminals is not particularly limited.

According to an embodiment, the device 200 determines whether there is a subsidy that the company can apply for, based on the company's pay stub information, and provides a service so that the company can guide the business owner of the subsidy that can be applied and apply. can In this case, the device 200 may check whether a company can apply for various subsidies, such as employment subsidies and government subsidies, and inform the business owner of the types of subsidies that can be applied for. In addition, the device 200 may also guide the business owner of the type of business that can be applied in connection with the business application.

The device 200 determines whether there is a subsidy that the worker can apply for for that employee, based on the pay stub information of the specific employee, and provides a service so that the worker can apply for the subsidy that the worker can apply for. can

The device 200 may analyze the subsidy that the company can apply for based on artificial intelligence. To this end, the apparatus 200 may include a plurality of artificial neural networks that have been pre-trained to perform a machine learning algorithm.

In the present invention, artificial intelligence (AI) refers to a technology that imitates human learning ability, reasoning ability, perceptual ability, etc., and implements it with a computer, and the concepts of machine learning, symbolic logic, etc. may include Machine Learning (ML) is an algorithm technology that classifies or learns characteristics of input data by itself. Artificial intelligence technology is an algorithm of machine learning that can analyze input data, learn the results of the analysis, and make judgments or predictions based on the results of the learning. In addition, technologies that use machine learning algorithms to simulate functions such as cognition and judgment of the human brain can also be understood as a category of artificial intelligence. For example, technical fields such as verbal comprehension, visual comprehension, reasoning/prediction, knowledge expression, and motion control may be included.

Machine learning may refer to the process of training a neural network model using experience of processing data. With machine learning, computer software could mean improving its own data processing capabilities. The neural network model is constructed by modeling the correlation between data, and the correlation may be expressed by a plurality of parameters. A neural network model extracts and analyzes features from given data to derive correlations between data, and repeating this process to optimize parameters of a neural network model can be called machine learning. For example, a neural network model may learn a mapping (correlation) between an input and an output with respect to data given as an input/output pair. Alternatively, the neural network model may learn the relationship by deriving regularity between the given data even when only input data is given.

The artificial intelligence learning model or neural network model may be designed to implement a human brain structure on a computer, and may include a plurality of network nodes that simulate neurons of a human neural network and have weights. A plurality of network nodes may have a connection relationship with each other by simulating a synaptic activity of a neuron through which a neuron sends and receives a signal through a synapse. In the AI learning model, a plurality of network nodes can exchange data according to a convolutional connection relationship while being located in layers of different depths. The artificial intelligence learning model may be, for example, an artificial neural network model, a convolutional neural network model, or the like. As an embodiment, the AI learning model may be machine-learned according to a method such as supervised learning, unsupervised learning, reinforcement learning, or the like. Machine learning algorithms for performing machine learning include Decision Tree, Bayesian Network, Support Vector Machine, Artificial Neural Network, Ada-boost. , Perceptron, Genetic Programming, Clustering, etc. may be used.

Among them, CNNs are a type of multilayer perceptrons designed to use minimal preprocessing. CNN consists of one or several convolutional layers and general artificial neural network layers on top of it, and additionally utilizes weights and pooling layers. Thanks to this structure, CNN can fully utilize the input data of the two-dimensional structure. Compared with other deep learning structures, CNN shows good performance in both video and audio fields. CNNs can also be trained through standard back-passing. CNNs are easier to train than other feed-forward neural network techniques and have the advantage of using fewer parameters.

Convolutional networks are neural networks that contain sets of nodes with bound parameters. The increased size of available training data and the availability of computational power, combined with advances in algorithms such as piecewise linear units and dropout training, have greatly improved many computer vision tasks. For huge data sets, such as those available for many tasks today, overfitting is not important, and increasing the size of the network improves test accuracy. Optimal use of computing resources becomes a limiting factor. To this end, a distributed, scalable implementation of deep neural networks may be used.

2 is a flowchart for explaining a process of providing a management strategy of a company based on company payslip information according to an exemplary embodiment.

Referring to FIG. 2 , first, in step S201 , the device 200 may collect payslip information of employees working in a first company. Here, the pay stub information may be managed separately for each employee, and may include information such as the employee's name, department, position, date of birth, gender, salary payment date, number of working days, working hours, salary payment amount, deduction amount, actual amount received, etc. .

Specifically, the device 200 may collect pay stub information of employees working in the first company from the first user terminal 110 used by the first business owner who operates the first company, and the account of the first company It is possible to collect pay stub information of employees working in the first company from the accountant terminal in charge of the .

When collecting payslip information of employees working in the first company, the device 200 may collect payslip information according to a period in which a salary is paid. For example, when the salaries are paid to employees working in the first company on a weekly basis, the device 200 may collect pay stub information of employees working in the first company weekly, and If the employees are paid monthly, pay stub information of the employees working in the first company may be collected monthly.

In step S202 , the device 200 may check whether payslip information of employees working in the first company has been collected during the first period. Here, the first period may be set differently depending on the period in which the salary is paid. For example, when wages are paid to employees working in the first company on a weekly basis, the first period is set to one week, , when the salaries are paid as monthly wages to the employees working in the first company, the first period may be set to one month.

If it is confirmed in step S202 that the payslip information of the employees working in the first company has not been collected during the first period, the process returns to step S201, and the device 200 adds the payslip information of the employees working in the first company. can be collected with

If it is confirmed in step S202 that the payslip information of the employees working in the first company has been collected for the first period, in step S203 , the device 200 based on the payslip information collected for the first period, the first period A first employment indicator indicating the employment status of the first company for , may be analyzed. Here, the first employment index is an index indicating the number of employees who worked at the first company during the first period, the total salaries paid to the employees working at the first company during the first period, and the like.

For example, if 10 employees work in the first company during the first period and the total salary paid to the 10 employees during the first period is 200 million, the device 200 employs (1.0, 200 million) as the first employment indicators can be analyzed.

In step S204 , the device 200 may apply the first employment indicator to the first artificial neural network previously learned in the device 200 .

According to an embodiment, the first artificial neural network may be an algorithm that, after receiving an employment index, analyzes and outputs a subsidy that a company can apply for. To this end, the first artificial neural network may be in a state in which qualification targets, qualification conditions, etc. have been previously learned for each type of subsidy.

For example, if the first employment indicator is (1, 200 million), the first artificial neural network receives (1, 200 million) as an input, and based on 10 people and 200 million, the eligible target and qualification for each type of subsidy By checking the conditions, it is possible to analyze whether a company with 10 employees and a total salary of 200 million can apply for a subsidy, and analyze and print the subsidy that the company can apply for.

In step S205 , the device 200 may generate a first analysis result based on the output of the first artificial neural network. Here, the first analysis result is a result of analyzing the subsidy that the first company can apply for based on the employment status of the first period, and may include the result of analyzing whether there is a subsidy that the first company can apply for, If there is a subsidy that the first company can apply for, the result of analyzing which kind of subsidy can be applied may be included.

For example, the device 200 applies the first employment indicator to the first artificial neural network and checks the output of the first artificial neural network. 1 can generate an analysis result, and when the output value is confirmed as 1, the first company can generate a first analysis result as being able to apply for the subsidy of the first category, and when the output value is confirmed as 2, the first company The first analysis result may be generated as it is possible to apply for the second category of support funds. In this case, the first category and the second category may be set to any one of employment subsidy, government subsidy, government-linked business, and the like.

The first artificial neural network may be trained to analyze the subsidy that the company can apply for through the employment index. The first artificial neural network may be learned through a method described later with reference to FIG. 14 .

In step S206 , the device 200 may check whether there is a subsidy that the first company can apply for through the first analysis result.

If it is confirmed in step S206 that there is a subsidy that the first company can apply for, in step S207 , the device 200 may confirm that the first company can apply for the subsidy of the first category through the first analysis result.

In step S208 , the device 200 may check whether there is a history of the first company applying for the subsidy of the first category based on the subsidy application details of the first company. To this end, the database of the device 200 stores subsidy application details for each company, and the subsidy application details may consist of a list indicating which subsidy was applied at which point in time.

If it is confirmed in step S208 that the first company has never applied for the subsidy of the first category, in step S209, the device 200 applies a management strategy for recommending an application for the subsidy of the first category to the first user terminal 110 ) can be provided. In this case, the device 200 may further provide link information associated with the application page of the first category subsidy to the first user terminal 110 .

3 is a flowchart illustrating a process of providing a business strategy for each period according to an exemplary embodiment.

Referring to FIG. 3 , first, in step S301 , the device 200 may collect payslip information of employees working in a first company.

In step S302 , the device 200 may check whether payslip information of employees working in the first company has been collected during the second period. Here, the second period may be set to have the same length as the first period from a time point after the end of the first period. For example, when the first period is set to January, the second period may be set to February.

If it is confirmed in step S302 that the payslip information of the employees working in the first company has not been collected for the second period, the process returns to step S301, and the device 200 adds the payslip information of the employees working in the first company. can be collected with

If it is confirmed in step S302 that the payslip information of the employees working in the first company has been collected for the second period, in step S303, the device 200 based on the payslip information collected for the second period, the second period A second employment indicator indicating the employment status of the first company for , may be analyzed.

In step S304 , the device 200 may apply the second employment indicator to the first artificial neural network previously learned in the device 200 .

In step S305 , the device 200 may generate a second analysis result based on the output of the first artificial neural network. Here, the second analysis result is the result of analyzing the subsidy that the first company can apply for based on the employment status of the second period.

In step S306 , the device 200 may compare the first analysis result generated in step S205 with the second analysis result generated in step S305 to determine whether there is a subsidy that can be applied only for the second analysis result.

If it is confirmed in step S306 that there is a subsidy that can be applied only for the second analysis result, in step S307 , the device 200 may confirm that the first company can apply for the subsidy of the second category only through the second analysis result.

That is, when it is confirmed through the first analysis result that it is possible to apply for the support of the first category and it is confirmed that it is possible to apply for the support of the second category through the second analysis result, the device 200 performs the first analysis result and the second analysis result. By comparing the 2 analysis results, it can be confirmed that the first company can apply for the second category subsidy only through the second analysis result.

In step S308 , the device 200 may check whether there is a history of the first company applying for the subsidy of the second category based on the subsidy application details of the first company.

If it is confirmed in step S308 that the first company has never applied for the subsidy of the second category, in step S309, the device 200 applies a management strategy for recommending an application for the subsidy of the second category to the first user terminal 110 ) can be provided. In this case, the device 200 may further provide link information associated with the application page of the second category subsidy to the first user terminal 110 .

4 is a flowchart illustrating a process of providing a management strategy of a company through prediction of employment status according to an exemplary embodiment.

Referring to FIG. 4 , first, in step S401 , the device 200 analyzes the first employment indicator indicating the employment status of the first company for the first period based on the payslip information collected for the first period. can

In step S402 , the device 200 may analyze a second employment indicator indicating the employment status of the first company for the second period based on the payslip information collected for the second period.

In step S403 , the device 200 may compare the first employment index and the second employment index to predict a third employment index indicating the employment status of the first company for the third period. Here, the third period may be set to have the same length as the first period and the second period from a time point after the end of the second period. For example, when the first period is set to January and the second period is set to February, the third period may be set to March.

When predicting the third employment indicator, the device 200 may compare the first employment indicator and the second employment indicator to confirm a change trend in the number of employees and the total salary from the first period to the second period, and in the first period The number of employees and total salaries in the third period can be predicted using the trend of fluctuations from to the second period.

For example, in the first period, 10 employees work in the first company, the total salary paid to the 10 employees in the first period is 200 million, and in the second period, 11 employees work in the first company and the second If the total salaries paid to 11 employees during the second period are 220 million, the device 200 predicts that the number of employees who will work in the first company for the third period is 12, and 12 in the first company during the third period It is possible to predict the total salary to be paid to two employees at 240 million, and through this, (12,240 million) can be predicted as the third employment indicator.

In step S404 , the device 200 may apply the third employment indicator to the first artificial neural network previously learned in the device 200 .

In step S405 , the device 200 may generate a third analysis result based on the output of the first artificial neural network. Here, the third analysis result is the result of analyzing the subsidy that the first company can apply for based on the employment status of the third period.

In step S406 , the device 200 may compare the second analysis result generated in step S305 with the third analysis result generated in step S405 to determine whether there is a subsidy that can be applied only for the third analysis result.

If it is confirmed in step S406 that there is a subsidy that can be applied only for the third analysis result, in step S407 , the device 200 may confirm that the first company can apply for the subsidy of the third category only through the third analysis result.

That is, when it is confirmed through the second analysis result that it is possible to apply for the support of the second category, and it is confirmed that it is possible to apply for the support of the third category through the result of the third analysis, the device 200 provides the second analysis result and the second analysis result. By comparing the 3 analysis results, it can be confirmed that the first company can apply for the 3rd category subsidy only through the 3rd analysis result.

In step S408 , the device 200 may check whether there is a history of the first company's application for the third category of the subsidy based on the subsidy application details of the first company.

If it is confirmed in step S408 that the first company has never applied for the subsidy of the third category, in step S409, the device 200 provides a management strategy for recommending achievement of the third employment index and the third employment index upon achievement of the third employment index. A notification message guiding that an application for a subsidy of 3 categories is possible may be provided to the first user terminal 110 .

For example, if the second employment indicator is (11, 220 million) and the third employment indicator is (12, 240 million), the device 200 hires 1 additional person and adds 2,000 To the first user terminal 110 , a notification message guiding the management strategy that the third employment index can be achieved, and the possibility of applying for the third category of subsidy when the third employment index is achieved, is sent to the first user terminal 110 can provide

5 is a flowchart illustrating a process of comparing productivity with a competitor according to an exemplary embodiment.

Referring to FIG. 5 , first, in step S501 , the device 200 may collect sales data for a first company. Here, the sales data may include information such as a sales place, a sales date, a sales item, and a sales price.

Specifically, the device 200 may collect sales data for the first company from the first user terminal 110 used by the first business owner who operates the first company, and is responsible for managing the sales of the first company. Sales data for the first company may be collected from the terminal in charge.

In step S502 , the device 200 may determine whether sales data for the first company has been collected during the first period.

If it is confirmed in step S502 that the sales data for the first company has not been collected for the first period, the process returns to step S501, and the device 200 may additionally collect sales data for the first company.

If it is confirmed in step S502 that the sales data for the first company has been collected for the first period, in step S503, the device 200 is based on the sales data collected for the first period, the sales data of the first company during the first period. The first sales, which are sales, may be identified.

Meanwhile, in step S504 , the device 200 may analyze the first employment indicator indicating the employment status of the first company for the first period based on the payslip information collected for the first period.

In step S505 , the device 200 may check the first salary amount, which is the total salary amount paid to the employees working in the first company for the first period based on the first employment index.

In step S506, the device 200 uses the first sales amount confirmed in step S503 and the first salary amount confirmed in step S505, through the value obtained by dividing the first sales by the first salary amount, the first company during the first period A first productivity representing the productivity of can be calculated.

For example, when the first sales amount is determined to be 400 million won and the first salary amount is confirmed to be 200 million won, the apparatus 200 may calculate the first productivity as 2.0.

Meanwhile, in step S507 , the device 200 may confirm that the second company is matched as a competitor of the first company. In this case, the competitor may be set separately through the request of the business owner, or may be automatically set as a company of a similar size in the same industry.

In step S508 , when the second company is matched as a competitor of the first company, the device 200 may check the second productivity indicating the productivity of the second company during the first period. To this end, the database of the device 200 stores productivity details classified for each company, and the productivity details may be composed of a list indicating how much productivity is in a period. When the second productivity is not confirmed as a result of inquiring the productivity details, the device 200 calculates the second productivity indicating the productivity of the second company during the first period through the same method as the method of calculating the first productivity can do.

In step S509 , the device 200 may calculate a first difference value by subtracting the second productivity from the first productivity.

6 is a flowchart for explaining a process of providing a management strategy of a company with respect to salary through comparison of competitors according to an exemplary embodiment.

Referring to FIG. 6 , first, in step S601 , the apparatus 200 may calculate a first difference value by subtracting the second productivity from the first productivity.

In step S602 , the device 200 may determine whether the first difference value is out of a reference range. Here, the reference range may be set differently depending on the embodiment, for example, the reference range may be set from -1.0 to 1.0.

If it is confirmed in step S602 that the first difference value does not deviate from the reference range, in step S604 , the device 200 transmits a management strategy for recommending maintenance of the salaries of employees working in the first company to the first user terminal 110 . can provide

That is, when it is confirmed that the first difference value is within the reference range, the device 200 analyzes that the productivity of the first company and the productivity of the second company are similar, and recommends maintaining the salaries of the employees working in the first company. A management strategy may be provided to the first user terminal 110 .

If it is determined in step S602 that the first difference value is out of the reference range, in step S603 , the device 200 may determine whether the first difference value is greater than zero.

If it is confirmed in step S603 that the first difference value is greater than 0, in step S605, the device 200 provides the first user terminal 110 with a management strategy for recommending a salary increase of employees working in the first company. can

That is, when it is confirmed that the first difference value is greater than 0 while out of the reference range, the device 200 may determine that the first difference value is greater than the maximum value of the reference range, and the first difference value is the maximum value of the reference range If it is confirmed that the value is greater than the value, it is analyzed that the productivity of the first company is higher than that of the second company, and a management strategy for recommending a salary increase of the employees working in the first company is provided to the first user terminal 110 . can

If it is confirmed in step S603 that the first difference value is less than 0, in step S606, the device 200 provides the first user terminal 110 with a management strategy for recommending a pay cut of employees working in the first company. can

That is, when it is confirmed that the first difference value is smaller than 0 while deviating from the reference range, the device 200 may determine that the first difference value is smaller than the minimum value of the reference range, and the first difference value is greater than the minimum value of the reference range If it is confirmed as small, it is possible to analyze that the productivity of the first company is lower than that of the second company, and provide a management strategy for recommending a reduction in salaries of employees working in the first company to the first user terminal 110 . .

7 is a flowchart illustrating a process of analyzing a change trend for sales according to an exemplary embodiment.

Referring to FIG. 7 , first, in step S701 , the device 200 may collect sales data for a first company during a first period.

In step S703 , the device 200 may check the first sales, which is the sales of the first company during the first period, based on the sales data collected during the first period.

Meanwhile, in step S702 , the device 200 may collect sales data for the first company during the second period.

In step S704 , the device 200 may check the second sales, which is the sales of the first company during the second period, based on the sales data collected during the second period.

In step S705, the device 200 may compare the first sales and the second sales using the first sales confirmed in step S703 and the second sales confirmed in step S704, and compare the first sales and the second sales. By comparison, it is possible to analyze the trend of fluctuations in the sales of the first company.

When the device 200 analyzes the fluctuation trend of the sales of the first company, based on the result of comparing the first sales and the second sales, it is possible to check the fluctuation trend of the sales from the first period to the second period. . In this case, the trend of fluctuations in sales may be analyzed as any one of an increase in sales, a decrease in sales, and maintenance of sales.

8 is a flowchart illustrating a process of analyzing a change trend for the number of employees according to an exemplary embodiment.

Referring to FIG. 8 , first, in step S801 , the device 200 analyzes the first employment indicator indicating the employment status of the first company for the first period based on the payslip information collected for the first period. can

In step S803 , the device 200 may check the first number of employees, which is the number of employees working in the first company for the first period, based on the first employment index.

Meanwhile, in step S802 , the device 200 may analyze a second employment indicator indicating the employment status of the first company for the second period based on the payslip information collected during the second period.

In step S804 , the device 200 may check the second number of employees, which is the number of employees working in the first company for the second period, based on the second employment indicator.

In step S805 , the device 200 may compare the first number of employees and the second number of employees using the first number of employees identified in step S803 and the second number of employees identified in step S804 , and the first number of employees And by comparing the second number of employees, it is possible to analyze a change trend for the number of employees of the first company.

When the device 200 analyzes the change trend of the number of employees of the first company, based on the result of comparing the number of first and second employees, the trend of change in the number of employees from the first period to the second period can be checked. At this time, the change trend of the number of employees may be analyzed as any one of an increase in the number of people, a decrease in the number of people, and a maintenance of the number of employees.

9 is a flowchart for explaining a process of providing a business strategy for employment when a change trend for sales is an increase in sales, according to an exemplary embodiment.

Referring to FIG. 9 , first, in step S901 , the device 200 compares the first sales and the second sales, and as a result of analyzing the fluctuation trend of the sales of the first company, the sales of the first company The change trend can be confirmed by the increase in sales.

In step S902 , the device 200 may determine whether the change trend for the number of employees of the first company is an increase in the number of employees.

If the change trend for the number of employees of the first company is confirmed as an increase in the number of employees in step S902, in step S903, the device 200 provides a management strategy for recommending maintaining the employment of the first company to the first user terminal 110 can do.

That is, the device 200 is a management that recommends maintaining the employment of the first company when the change trend in the sales of the first company is confirmed as an increase in sales, and the change trend in the number of employees of the first company is confirmed as an increase in the number of people The strategy may be provided to the first user terminal 110 .

If the change trend for the number of employees of the first company is not confirmed as an increase in the number of employees in step S902, in step S904, the device 200 transmits a management strategy for recommending employment expansion of the first company to the first user terminal 110 can provide

That is, the device 200 confirms that the trend of change in the sales of the first company is increased sales, and when the trend of change in the number of employees of the first company is confirmed as maintaining or decreasing the number of employees, the employment expansion of the first company is confirmed. A recommended management strategy may be provided to the first user terminal 110 .

10 is a flowchart for explaining a process of providing a business strategy for employment when a change trend for sales is a decrease in sales according to an exemplary embodiment.

Referring to FIG. 10 , first, in step S1001 , the device 200 compares the first sales and the second sales, and as a result of analyzing the fluctuation trend of the sales of the first company, the sales of the first company The change trend can be confirmed as a decrease in sales.

In step S1002 , the device 200 may determine whether the change trend for the number of employees of the first company is a decrease in the number of employees.

If the change trend for the number of employees of the first company is confirmed as a decrease in the number of employees in step S1002, in step S1003, the device 200 provides a management strategy for recommending maintaining the employment of the first company to the first user terminal 110 can do.

That is, the device 200 is a management that recommends maintaining the employment of the first company when the change trend of the sales of the first company is confirmed as a decrease in sales, and the change trend of the number of employees of the first company is confirmed as a decrease in the number of employees The strategy may be provided to the first user terminal 110 .

If the change trend for the number of employees of the first company is not confirmed as a reduction in the number of employees in step S1002, in step S1004, the device 200 transmits a management strategy for recommending a reduction in employment of the first company to the first user terminal 110 can provide

That is, the device 200 confirms that the trend of change in the sales of the first company is reduced sales, and when the trend of change in the number of employees of the first company is confirmed as maintaining or increasing the number of employees, the employment reduction of the first company is confirmed. A recommended management strategy may be provided to the first user terminal 110 .

11 is a flowchart illustrating a process of providing a business strategy for employment when a change trend for sales is maintenance of sales according to an exemplary embodiment.

Referring to FIG. 11 , first, in step S1101 , the device 200 compares the first sales and the second sales, and as a result of analyzing the fluctuation trend of the sales of the first company, the sales of the first company The change trend can be confirmed by maintaining sales.

In step S1102 , the device 200 may determine whether the trend of change in the number of employees of the first company is the retention of employees.

If the change trend for the number of employees of the first company is confirmed in step S1102 as staff retention, in step S1104, the device 200 provides a management strategy for recommending retention of employment of the first company to the first user terminal 110 can do.

That is, the device 200 is a management that recommends maintaining the employment of the first company when the trend of change in the sales of the first company is confirmed as maintaining sales, and the trend of change in the number of employees of the first company is confirmed as retention of personnel The strategy may be provided to the first user terminal 110 .

If the trend of change in the number of employees of the first company is not confirmed as the number of employees in step S1102, in step S1103, the device 200 may determine whether the trend of change in the number of employees of the first company is a decrease in the number of employees.

If, in step S1103, the trend of change in the number of employees of the first company is confirmed as a reduction in the number of employees rather than as a maintenance of the number, in step S1105, the device 200 applies a management strategy for recommending the employment expansion of the first company to the first user. It may be provided to the terminal 110 .

That is, the device 200 is a management that recommends employment expansion of the first company when the trend of change in the sales of the first company is confirmed as maintaining sales, and the trend of change in the number of employees of the first company is confirmed as the reduction in the number of employees The strategy may be provided to the first user terminal 110 .

If the trend of change in the number of employees of the first company is not confirmed as a decrease in the number of employees in step S1103, the trend of change in the number of employees of the first company may be confirmed as an increase in the number of employees, and the trend of change in the number of employees of the first company If it is confirmed as the increase in the number of people instead of the maintenance of the number, in step S1106 , the device 200 may provide the first user terminal 110 with a management strategy for recommending the reduction of the employment of the first company.

That is, the device 200 is a management that recommends reducing the employment of the first company when it is confirmed that the trend of change in the sales of the first company is maintained as sales, and the trend of change in the number of employees of the first company is confirmed as the increase in the number of employees The strategy may be provided to the first user terminal 110 .

12 is a flowchart illustrating a process of controlling policy settings of a host firewall based on artificial intelligence according to an embodiment.

First, the device 200 may be connected to a plurality of servers in order to provide a management strategy of a company based on company payslip information, and the plurality of servers may include a first server, a second server, and the like. Each of the plurality of servers is a host connected to the device 200 through an internal network, and may transmit/receive data to and from the device 200 through a network connected to the internal network.

Referring to FIG. 12 , in step S1201 , the device 200 may receive operating system information of the first server and network setting information of the first server from the first server. Here, the operating system information of the first server may include information for identifying the type and version of the operating system installed and operated in the first server, and the network setting information of the first server is the network setting state of the first server. may include information indicating

In step S1202 , the device 200 may analyze the network connection state of the first server based on the operating system information of the first server and the network setting information of the first server. In this case, the device 200 may analyze the network connection state of the first server and generate an analysis result of the network connection state of the first server. Here, the analysis result of the network connection state of the first server may include a result of analyzing in which operating system the first server is operated and through which network port the first server is connected.

In step S1203 , the device 200 may apply the analysis result of the network connection state of the first server to the second artificial neural network previously learned in the device 200 .

According to an embodiment, the second artificial neural network may be an algorithm that analyzes and outputs a firewall policy suitable for the server after receiving an analysis result of the server's network connection state.

In step S1204, the device 200 may select a firewall policy suitable for the first server based on the output of the second artificial neural network.

For example, the device 200 applies the analysis result of the network connection state of the first server to the second artificial neural network, and as a result of checking the output of the second artificial neural network, when the output value is 0, the first server When an appropriate firewall policy is selected as the first policy and the output value is 1, a firewall policy suitable for the first server may be selected as the second policy.

The second artificial neural network may be trained to analyze a firewall policy suitable for the server through the analysis result of the server's network connection state. The second artificial neural network may be learned through a method described later with reference to FIG. 14 . Through this, the second artificial neural network may analyze and output a firewall policy suitable for the server in consideration of the network connection state of the server.

In step S1205, the device 200 may check whether the policy selected in step S1204 is included in the firewall policy of the first server. For example, when the first policy is selected as the firewall policy suitable for the first server, the device 200 may check whether the first policy is included in the firewall policy of the first server.

To this end, information on the firewall policy set in each of the plurality of servers may be stored separately for each server in the database of the device 200 . The number of information about the firewall policy may be different depending on the firewall policy set in the server, and when a plurality of firewall policies are set in one server, a priority for each of the plurality of firewall policies may be set .

If it is determined in step S1205 that the first policy is included in the firewall policy of the first server, in step S1206 , the device 200 may determine that a change to the firewall policy of the first server is not required.

That is, since the first policy selected as the firewall policy suitable for the first server is already set in the first server, the device 200 may determine that maintenance of the firewall policy of the first server is necessary.

After step S1206, after a certain period of time, the process returns to step S1201, the device 200 receives the operating system information of the first server and the network setting information of the first server again from the first server, and a firewall policy suitable for the first server can be selected again.

Meanwhile, if it is determined in step S1205 that the first policy is not included in the firewall policy of the first server, in step S1207 , the device 200 may determine that a change to the firewall policy of the first server is necessary.

That is, since the first policy selected as the firewall policy suitable for the first server is not set in the first server, the device 200 may determine that the firewall policy of the first server needs to be changed.

In step S1208, the device 200 may register by adding the selected policy to the firewall policy of the first server. For example, if the first policy is selected as a firewall policy suitable for the first server, the device 200 may register the first policy by adding the first policy to the firewall policy of the first server. In this case, when the second policy is already registered in the firewall policy of the first server, the first policy may be additionally registered in the firewall policy of the first server. Through this, when a plurality of policies are registered in the firewall policy of the first server, the device 200 may set a priority for each of the plurality of policies.

The process of adding and registering the first policy to the firewall policy of the first server is to add and update the first policy to the information about the firewall policy of the first server stored in the database of the device 200, and to the firewall policy. The setting may be performed through step S1209.

In step S1209, when the first policy is additionally registered in the firewall policy of the first server, the device 200 transmits a setting change command for the firewall policy of the first server to the first server, so that the first policy is the first It can be controlled to be set by the server's firewall policy. Here, the setting change command for the firewall policy of the first server is a command for setting by adding the first policy to the firewall policy of the first server, and the first server is configured to change the settings for the firewall policy of the first server through the setting change command. , can be set by adding the first policy as a firewall policy of the first server.

After step S1209, when a certain period elapses, the process returns to step S1201, and the device 200 receives the operating system information of the first server and the network setting information of the first server again from the first server, and a firewall policy suitable for the first server can be selected again.

13 is a flowchart illustrating a process of detecting an approach estimated as an attack and providing a countermeasure according to the number of attacks, according to an exemplary embodiment.

Referring to FIG. 13 , first, in step S1301 , the device 200 may receive monitoring information on network traffic of the first server from the first server. Here, the monitoring information on the network traffic of the first server includes monitoring information on which port was connected to and generated traffic from the first server, and how much traffic was generated per hour for each IP address connected to the first server. It may include monitoring information and the like.

In step S1302 , the device 200 may apply the monitoring information for the network traffic of the first server to the third artificial neural network previously learned in the device 200 .

According to an embodiment, the third artificial neural network may be an algorithm that receives monitoring information on network traffic of a server and outputs a detection result of whether an approach estimated as an attack is detected to the server. Here, the attack is an attack on the server, and may include ransomware, hacking, and the like.

In step S1303 , the device 200 may detect whether an approach estimated as an attack to the first server is detected based on the output of the third artificial neural network.

For example, the device 200 applies monitoring information for network traffic of the first server to the third artificial neural network, and as a result of checking the output of the third artificial neural network, if the output value is 0, it attacks the first server When it is detected that the approach estimated as ' is not detected and the output value is 1', it may be detected that the approach presumed to be an attack by the first server is detected.

The third artificial neural network may be trained to analyze whether an approach estimated as an attack is detected to the server through monitoring information on network traffic of the server. The third artificial neural network may be learned through a method described later with reference to FIG. 14 . Through this, the third artificial neural network may analyze and output whether an approach estimated as an attack is detected to the server in consideration of the change state of the server's network traffic.

In step S1304 , the device 200 may determine how many times an attack estimated to be approached to the first server during the reference period is detected, and calculate the first number of attacks, which is the number of times the first server has been attacked during the reference period. Here, the reference period may be set differently depending on the embodiment, and steps S1301 to S1303 may be repeatedly performed during the reference period. Through this, the device 200 may calculate the first number of attacks by checking how many times the approach estimated as an attack is detected by the first server during the reference period.

In step S1305 , the device 200 may determine whether the first number of attacks is less than the first reference number. Here, the first reference number may be set differently in proportion to the length of the reference period.

If it is determined in step S1305 that the number of first attacks is less than the first reference number, in step S1307 , the device 200 may determine the state of the first server as a normal state.

After a certain period of time after step S1307, the process returns to step S1301, and the device 200 may again receive monitoring information for network traffic of the first server from the first server, and recalculate the first number of attacks.

If it is determined in step S1305 that the first number of attacks is greater than the first reference number, in step S1306 , the device 200 may determine whether the first number of attacks is less than the second reference number. Here, the second reference number may be set to a value greater than the first reference number.

If it is determined in step S1306 that the number of first attacks is less than the second reference number, in step S1308, the device 200 may determine the state of the first server as a warning state.

If it is determined in step S1306 that the number of first attacks is greater than the second reference number, in step S1309, the device 200 may determine the state of the first server as a dangerous state.

In step S1310 , when it is determined that the state of the first server is a warning state, the device 200 may transmit a notification message warning of an attack of the first server to the first manager terminal. Here, the first manager terminal means a terminal used by the first manager registered as a manager of the first server, and the database of the device 200 stores contact information of the first manager terminal, and through this, the device 200 may transmit a notification message to the first manager terminal.

In step S1311, when the state of the first server is determined to be a critical state, the device 200 transmits a connection blocking command of the first port to which the connection is allowed through the first policy to the first server, and in the first server Connection through the first port may be controlled to be blocked.

That is, in the first server, the first policy is set as a firewall policy, and the first policy includes a setting to allow a connection through the first port, so that the network connection state of the first server is connected through the first port. In a state in which traffic is being generated through the first port, when the number of attacks through the first port is too large, the device 200 transmits a connection blocking command of the first port to the first server. , it is possible to control the first server to block the connection through the first port. In this case, the first server may block the connection of the first port through the connection blocking command of the first port, so that traffic does not occur through the first port.

14 is a diagram for explaining learning of an artificial neural network according to another embodiment.

According to an embodiment, the artificial neural network may be any one of a first artificial neural network, a second artificial neural network, and a third artificial neural network. Here, the first artificial neural network is an algorithm that analyzes and outputs the subsidy that a company can apply for after receiving an employment index, and the second artificial neural network receives the analysis result of the server's network connection state, and then a firewall suitable for the server It is an algorithm that analyzes and outputs a policy, and the third artificial neural network may be an algorithm that receives monitoring information on network traffic of a server and outputs a detection result of whether an approach estimated as an attack is detected to the server.

The learning device in which the artificial neural network is trained may be the same device as the device 200 or a separate device. Hereinafter, a process in which an artificial neural network is trained will be described.

First, in step S1401, the learning apparatus may generate an input to be input to the artificial neural network. In this case, the learning apparatus may generate an input based on the employment index to input to the first artificial neural network, and to input to the second artificial neural network, generate an input based on the analysis result of the network connection state of the server In order to input to the third artificial neural network, the input may be generated based on monitoring information on network traffic of the server.

Specifically, the learning device may perform a process of preprocessing the employment index. The employment index on which the preprocessing has been performed may be used as it is as an input of the first artificial neural network, or an input of the first artificial neural network may be generated through a normal process of removing unnecessary information.

In addition, the learning apparatus may perform a process of pre-processing the analysis result of the network connection state of the server. The analysis result of the network connection state on which the pre-processing has been performed may be used as it is as an input of the second artificial neural network, or an input of the second artificial neural network may be generated through a normal process of removing unnecessary information.

Also, the learning apparatus may perform a process of pre-processing monitoring information for network traffic of the server. The monitoring information on the network traffic of the server on which the preprocessing has been performed may be used as it is as an input to the third artificial neural network, or an input of the third artificial neural network may be generated through a normal process of removing unnecessary information.

In step S1402, the learning apparatus may apply an input to the artificial neural network. In this case, the learning apparatus applies the input to the first artificial neural network when generating the input of the first artificial neural network, applies the input to the second artificial neural network when generating the input of the second artificial neural network, and applies the input to the third artificial neural network. When the input of the neural network is generated, the input may be applied to the third artificial neural network.

The artificial neural network may be an artificial neural network trained according to reinforcement learning. The artificial neural network may be a Q-Network, DQN (Depp Q-Network), or relational network (RL) structure suitable for outputting abstract reasoning through reinforcement learning.

An artificial neural network trained according to reinforcement learning can be updated and optimized by reflecting the evaluation in various rewards. For example, the first artificial neural network is updated and optimized through the first reward and the second reward, the second artificial neural network is updated and optimized through the third reward and the fourth reward, and the third artificial neural network is updated and optimized through the fifth reward and through the sixth compensation can be updated and optimized.

For example, the first compensation may be higher as the number of employees is selected, and the second compensation may be higher as the amount of support that can be applied is selected in consideration of the total salary.

In addition, the third reward may increase as a firewall policy suitable for the server is selected in consideration of the server's network connection state, and the fourth reward may increase as a firewall policy unsuitable for the server is not selected in consideration of the server's network connection state. can rise

In addition, as for the fifth reward, the higher the amount of network traffic of the server, the higher the reward value may be when it detects that an approach that is presumed to be an attack on the server is detected. If an approach presumed to be an attack is detected as being detected, the reward value may be increased.

In step S1403, the learning apparatus may obtain an output from the artificial neural network.

The output of the first artificial neural network may be information on a result of analyzing the subsidy that the company can apply for. In this case, the first artificial neural network may analyze whether there is a subsidy that a company can apply for based on the employment index, and may output information on the analyzed result. The first artificial neural network may output information on a result of analyzing which types of subsidies can be applied for when there is a subsidy that a company can apply for.

The output of the second artificial neural network may be information on a firewall policy suitable for the server. In this case, the second artificial neural network may select a firewall policy suitable for the server in consideration of the network connection state of the server, and may output information on the firewall policy most suitable for the server.

The output of the third artificial neural network is a detection result of whether an approach estimated as an attack is detected by the server. At this time, the third artificial neural network analyzes whether an approach estimated as an attack is detected to the server through the monitoring information on the server's network traffic, and outputs a detection result as to whether an approach estimated as an attack is detected to the server. have.

In step S1404, the learning device may evaluate the output of the artificial neural network and pay a reward.

The evaluation of the output of the first artificial neural network may be divided into a first reward and a second reward. For example, the learning apparatus may award a larger amount of the first reward as the number of applicants is selected in consideration of the number of employees, and may award a greater amount of the second reward as the amount of support that can be applied for is selected in consideration of the total salary.

The evaluation of the output of the second artificial neural network may be divided into a third reward and a fourth reward. For example, the learning device gives a higher third reward as it selects a firewall policy suitable for the server in consideration of the server's network connection status, and does not select a firewall policy that is not suitable for the server in consideration of the server's network connection status. The more the 4th reward, the more can be awarded.

The evaluation of the output of the third artificial neural network may be divided into a fifth reward and a sixth reward. For example, if the learning device detects that the server's network traffic is excessive, the server receives a 5th reward when it detects that an attack presumed to be an attack. Detecting that the presumed approach has been detected may award a large sixth reward.

In step S1405, the learning apparatus may update the artificial neural network based on the evaluation.

In an environment where the first artificial neural network analyzes the subsidy that a company can apply for through an employment index, the learning device is configured to maximize the expected value of the sum of rewards in specific states. The first artificial neural network may be updated through a process of optimizing a policy for determining actions to be taken.

In addition, the learning device performs the action to be taken in specific states so that the expectation of the sum of rewards is maximized in the environment in which the second artificial neural network analyzes the firewall policy most suitable for the server. The second artificial neural network may be updated through a process of optimizing a policy for determining actions.

In addition, in an environment in which the third artificial neural network analyzes whether an approach estimated as an attack to the server is detected through the monitoring information on the server's network traffic, the learning apparatus expects a sum of rewards (rewards) The third artificial neural network can be updated through the process of optimizing the policy that determines the actions to be taken in specific states so that expectation) is maximized.

Meanwhile, the process of optimizing the policy may be performed through the process of estimating the maximum value of the expected value of the sum of rewards or the maximum value of the Q-function, or estimating the minimum value of the loss function of the Q-function. Estimation of the minimum value of the loss function may be performed through stochastic gradient descent (SGD) or the like. The process of optimizing the policy is not limited thereto, and various optimization algorithms used in reinforcement learning may be used.

The learning apparatus may gradually update the artificial neural network by repeating the learning process of the artificial neural network as described above.

Specifically, the learning device collects information on the current subsidy in order to set the analysis criteria for the subsidy that companies can apply for, checks the eligibility targets, qualification conditions, etc. by type of subsidy, and uses the confirmed information The first artificial neural network can be trained, and when the subsidy that can be applied by the company is analyzed through the employment index, the first artificial neural network that outputs the analyzed result can be trained,

That is, the learning device can learn the first artificial neural network by adjusting the analysis criteria by reflecting reinforcement learning through the first reward, the second reward, etc. when analyzing the subsidy that can be applied by a company through the employment index. .

In addition, in selecting a firewall policy suitable for the server, the learning device considers all items such as forwarding type, protocol, external allowed IP address, external blocked IP address, virtual port, and operation port, and selects the most suitable firewall policy for the server. After selection, the second artificial neural network that outputs information about the selected firewall policy may be trained.

Through this, after receiving the analysis result of the network connection state of the server, the learning apparatus may learn the second artificial neural network that analyzes and outputs a firewall policy suitable for the server.

That is, when analyzing the firewall policy suitable for the server through the analysis result of the server's network connection state, the learning device reflects reinforcement learning through the third reward, the fourth reward, etc., by adjusting the analysis criteria, 2 Artificial neural networks can be trained.

In addition, the learning apparatus may train a third artificial neural network that outputs a detection result of whether an approach estimated as an attack is detected to the server through monitoring information on network traffic of the server.

That is, the learning device reflects reinforcement learning through the 5th reward, the 6th reward, etc. when analyzing whether an access estimated as an attack is detected to the server through the monitoring information on the server's network traffic, analysis criteria By adjusting , the third artificial neural network can be trained.

15 is an exemplary diagram of a configuration of an apparatus according to an embodiment.

The device 200 according to an embodiment includes a processor 210 and a memory 220 . The processor 210 may include at least one of the devices described above with reference to FIGS. 1 to 14 , or perform at least one method described above with reference to FIGS. 1 to 14 . A person or organization using the apparatus 200 may provide a service related to some or all of the methods described above with reference to FIGS. 1 to 14 .

The memory 220 may store information related to the above-described methods or a program in which methods to be described below are implemented. The memory 220 may be a volatile memory or a non-volatile memory.

The processor 210 may execute a program and control the device 200 . The code of the program executed by the processor 210 may be stored in the memory 220 . The device 200 may be connected to an external device (eg, a personal computer or a network) through an input/output device (not shown), and may exchange data through wired/wireless communication.

The device 200 may be used to train an artificial neural network or to use a learned artificial neural network. The memory 220 may include a learning or learned artificial neural network. The processor 210 may learn or execute an artificial neural network algorithm stored in the memory 220 . The apparatus 200 for learning an artificial neural network and the apparatus 200 for using the learned artificial neural network may be the same or may be separate.

The embodiments described above may be implemented by a hardware component, a software component, and/or a combination of a hardware component and a software component. For example, the apparatus, methods and components described in the embodiments may include, for example, a processor, a controller, an arithmetic logic unit (ALU), a digital signal processor, a microcomputer, a field programmable gate (FPGA). array), a programmable logic unit (PLU), a microprocessor, or any other device capable of executing and responding to instructions, may be implemented using one or more general purpose or special purpose computers. The processing device may execute an operating system (OS) and one or more software applications running on the operating system. The processing device may also access, store, manipulate, process, and generate data in response to execution of the software. For convenience of understanding, although one processing device is sometimes described as being used, one of ordinary skill in the art will recognize that the processing device includes a plurality of processing elements and/or a plurality of types of processing elements. It can be seen that may include For example, the processing device may include a plurality of processors or one processor and one controller. Other processing configurations are also possible, such as parallel processors.

The method according to the embodiment may be implemented in the form of program instructions that can be executed through various computer means and recorded in a computer-readable medium. The computer-readable medium may include program instructions, data files, data structures, etc. alone or in combination. The program instructions recorded on the medium may be specially designed and configured for the embodiment, or may be known and available to those skilled in the art of computer software. Examples of the computer-readable recording medium include magnetic media such as hard disks, floppy disks and magnetic tapes, optical media such as CD-ROMs and DVDs, and magnetic such as floppy disks. - includes magneto-optical media, and hardware devices specially configured to store and execute program instructions, such as ROM, RAM, flash memory, and the like. Examples of program instructions include not only machine language codes such as those generated by a compiler, but also high-level language codes that can be executed by a computer using an interpreter or the like. The hardware devices described above may be configured to operate as one or more software modules to perform the operations of the embodiments, and vice versa.

The software may comprise a computer program, code, instructions, or a combination of one or more thereof, which configures a processing device to operate as desired or is independently or collectively processed You can command the device. The software and/or data may be any kind of machine, component, physical device, virtual equipment, computer storage medium or device, to be interpreted by or to provide instructions or data to the processing device. , or may be permanently or temporarily embody in a transmitted signal wave. The software may be distributed over networked computer systems and stored or executed in a distributed manner. Software and data may be stored in one or more computer-readable recording media.

As described above, although the embodiments have been described with reference to the limited drawings, those skilled in the art may apply various technical modifications and variations based on the above. For example, the described techniques are performed in a different order than the described method, and/or the described components of the system, structure, apparatus, circuit, etc. are combined or combined in a different form than the described method, or other components Or substituted or substituted by equivalents may achieve an appropriate result.

Therefore, other implementations, other embodiments, and equivalents to the claims are also within the scope of the following claims.

Claims (3)

A method of providing a business strategy for a company based on company payslip information, performed by a device, the method comprising:
Collecting pay stub information of employees working in a first company;
analyzing a first employment indicator indicating an employment status of the first company for the first period based on the payslip information collected for a first preset period;
By applying the first employment indicator to the pre-trained first artificial neural network, based on the output of the first artificial neural network, based on the employment status of the first period, the subsidy that the first company can apply for is analyzed. generating a first analysis result that is a result;
If it is confirmed through the first analysis result that the first company can apply for the subsidy of the first category, based on the details of the subsidy application of the first company, the first company applies for the subsidy of the first category. checking whether there are ever;
providing, to a first user terminal, a management strategy for recommending an application for the subsidy of the first category when it is confirmed that the first company has never applied for the subsidy of the first category;
analyzing a second employment indicator indicating an employment status of the first company for the second period based on the payslip information collected for a second period after the first period;
Applying the second employment indicator to the first artificial neural network, based on the output of the first artificial neural network, based on the employment status of the second period, the result of analyzing the subsidy that the first company can apply for generating a second analysis result;
By comparing the first analysis result and the second analysis result, if it is confirmed that the first company can apply for the subsidy of the second category only through the result of the second analysis, based on the subsidy application details of the first company , checking whether the first company has ever applied for the subsidy of the second category;
providing, to the first user terminal, a management strategy for recommending an application for the second category of the subsidy when it is confirmed that the first company has never applied for the subsidy of the second category;
predicting a third employment indicator representing the employment status of the first enterprise for a third period after the second period by comparing the first employment indicator and the second employment indicator;
Applying the third employment index to the first artificial neural network, based on the output of the first artificial neural network, based on the employment status of the third period, the result of analyzing the subsidy that the first company can apply for generating a third analysis result;
Comparing the second analysis result and the third analysis result, if it is confirmed that the first company can apply for the third category subsidy only through the third analysis result, based on the subsidy application details of the first company , checking whether the first company has ever applied for the third category of subsidy;
If it is confirmed that the first company has never applied for the subsidy of the third category, the management strategy recommending achievement of the third employment indicator and the application for the subsidy of the third category upon achievement of the third employment indicator providing a notification message guiding that this is possible to the first user terminal;
collecting sales data for the first company;
checking first sales, which is sales of the first company during the first period, based on the sales data collected during the first period;
checking a first salary amount that is a total salary amount paid to employees working in the first company during the first period based on the first employment index;
calculating a first productivity representing the productivity of the first company during the first period through a value obtained by dividing the first sales by the first salary;
when a second company is matched as a competitor of the first company, checking a second productivity indicating the productivity of the second company during the first period;
calculating a first difference value by subtracting the second productivity from the first productivity;
checking whether the first difference value is out of a preset reference range;
if it is confirmed that the first difference value does not deviate from the reference range, providing a management strategy for recommending maintenance of salaries of employees working in the first company to the first user terminal;
when it is determined that the first difference value is out of the reference range, determining whether the first difference value is greater than 0;
If it is confirmed that the first difference value is greater than 0, providing a management strategy for recommending a salary increase of employees working in the first company to the first user terminal;
if it is confirmed that the first difference value is less than 0, providing a management strategy for recommending a pay cut of employees working in the first company to the first user terminal;
confirming a second sales amount that is a sales amount of the first company during the second period based on the sales data collected during the second period;
Comparing the first sales and the second sales, analyzing a change trend of the sales of the first company;
determine a first number of employees that is the number of employees working in the first company during the first period based on the first employment indicator, and work at the first company during the second period based on the second employment indicator confirming a second number of employees, which is the number of employees;
comparing the first number of employees and the second number of employees to analyze a change trend in the number of employees of the first company;
When it is confirmed that the trend of change in the sales of the first company is an increase in sales, it is checked whether the trend of change in the number of employees of the first company is an increase in the number of employees, and the trend of change in the number of employees of the first company is If it is confirmed as the increase in the number of people, a management strategy for recommending maintaining the employment of the first company is provided to the first user terminal, and if the change trend in the number of employees of the first company is not confirmed as the increase in the number of people, the first providing a management strategy for recommending employment expansion of a company to the first user terminal;
When the trend of change in the sales of the first company is confirmed as a decrease in sales, it is checked whether the trend of change in the number of employees of the first company is a decrease in the number of employees, and the trend of change in the number of employees of the first company is If it is confirmed as a reduction in the number of employees, a management strategy for recommending maintaining the employment of the first company is provided to the first user terminal, and if the change trend in the number of employees of the first company is not confirmed as a reduction in the number of people, the first providing a management strategy for recommending employment reduction of a company to the first user terminal; and
When it is confirmed that the trend of change in the sales of the first company is maintenance of sales, it is checked whether the trend of change in the number of employees of the first company is the retention of employees, and the trend of change in the number of employees of the first company is When it is confirmed that the number of employees is maintained, a management strategy for recommending the retention of the employment of the first company is provided to the first user terminal, and the change trend of the number of employees of the first company is confirmed as a decrease in the number of people rather than as a maintenance of the number of people If it is, a management strategy for recommending employment expansion of the first company is provided to the first user terminal, and when the trend of change in the number of employees of the first company is confirmed as an increase in the number of employees rather than as a maintenance of the number of employees, the second 1 Comprising the step of providing a management strategy for recommending employment reduction of the company to the first user terminal,
A method of providing a company's management strategy based on the company's pay stub information. delete delete

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