KR102761698B1 - Management monitoring system for client using pattern analysys - Google Patents

Abstract

A customer management monitoring system is disclosed, which is connected to multiple client terminals and a network for each customer. The customer management monitoring system of the present invention includes a data collection module that collects management status information and sales status information including power on/off status information, CPU usage information, power consumption information, program execution information, and customer industry information of the client terminals from multiple client terminals for each customer; a sales prediction module that derives a correlation between the management status information and the sales status information through an AI learning model and calculates expected sales based on the management status information; an abnormality detection module that determines that an abnormality has occurred when the actual sales included in the sales status information is smaller than a preset first reference ratio value compared to the expected sales calculated by the sales prediction module; and an output module that outputs abnormality occurrence information detected by the abnormality detection module to a pre-designated administrator terminal among the client terminals.

Description

{MANAGEMENT MONITORING SYSTEM FOR CLIENT USING PATTERN ANALYSYS}

The present invention relates to a customer management monitoring system, and more specifically, to a customer management monitoring system through pattern analysis that analyzes in real time whether software is operating normally and whether the customer's management is operating normally for a customer who is renting and using the software, thereby detecting abnormal situations and enabling immediate response to risks.

Various types of companies, such as businesses, academies, PC rooms, and karaoke rooms, rent and use software for various purposes. In addition, the software can be installed directly on the client's computer, but it can also be provided in a cloud format where the client accesses the software from outside, installed and run on the server of the company providing the software. In some cases, customized hardware is also provided along with the software.

If a company providing software can determine whether the program provided to the client is operating normally, whether the client's sales are increasing, or whether the client's software or hardware assets are being stolen, the company can use this information to prevent the client's assets from being leaked or identify signs of management abnormalities and share this with the client's representative to resolve management risks.

However, in the prior art, only a configuration for analyzing and predicting the management status, such as estimating the customer's sales growth rate, based on the customer's financial information, size information, sales growth rate information, etc., is disclosed, and a technology for analyzing the customer's management status through the operation of a client program installed in the customer, detecting anomalies, and suggesting solutions thereto is not presented.

Korean Patent Registration No. 10-2236336 (2021.03.30)

The present invention is intended to solve the above problems and provides a client management monitoring system that collects and analyzes management status information and sales status information including hardware and software usage information from multiple client terminals installed in a client company and notifies an administrator terminal when an abnormal situation is detected.

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

According to an embodiment of the present invention for solving the above problem, a client management monitoring system connected to a plurality of client terminals and a network for each client company includes: a data collection module for collecting management status information and sales status information including power on/off status information, CPU usage information, power consumption information, program execution information, and client industry information of the client terminals from the plurality of client terminals for each client company; a sales prediction module for deriving a correlation between the management status information and the sales status information through an AI learning model and calculating expected sales based on the management status information; an abnormality detection module for determining that an abnormality has occurred when the actual sales included in the sales status information is smaller than a preset first reference ratio value compared to the expected sales calculated by the sales prediction module; and an output module for outputting abnormality occurrence information detected by the abnormality detection module to a pre-designated administrator terminal among the client terminals.

In one embodiment, the data collection module may further collect average sales information by industry via the Internet, and the sales prediction module may further consider the average sales information by industry to calculate the expected sales.

In one embodiment, the sales prediction module may calculate effective business hours using the power on/off status information, CPU usage information, power consumption information, and program execution information of the client terminal, calculate basic sales by multiplying the effective business hours by a sales unit price corresponding to the client company's industry, and calculate the expected sales by multiplying the basic sales by a pre-established correction coefficient.

In one embodiment, the sales prediction module may calculate the effective business activity time as the time when the client terminal is turned on, the power consumption is greater than a preset standard power amount, and a preset program is executed.

In one embodiment, if the above-described abnormal situation detection module determines that an abnormal situation has occurred, it performs a matching analysis of the management status information and the sales status information according to the time flow to generate abnormal situation details of the time period in which the abnormal sign occurred, and the output module can output the abnormal situation details to the administrator terminal.

In one embodiment, the customer management monitoring system of the present invention may further include a sales activity prediction module that learns the pattern of sales activities through the management status information using an AI learning model and calculates information on the presence or absence of sales activities by customer and detailed status information on sales activities.

In one embodiment, the above-described abnormal situation detection module can determine an abnormal situation when the information on the presence or absence of the above-described sales activity and the detailed status information of the above-described sales activity calculated by the above-described sales activity prediction module are greater or smaller than a second reference ratio value compared to the average value learned and constructed.

In one embodiment, the customer management monitoring system of the present invention may further include a risk management module that executes a protection mode to prevent data stored in a client terminal of a customer company where an abnormal situation has occurred from being deleted when the abnormal situation detection module determines that an abnormal situation has occurred.

In one embodiment, the risk management module can remotely install and execute a program for data forensic analysis on the client terminal where an abnormal situation has occurred to collect and analyze deleted data.

In one embodiment, the client management monitoring system of the present invention further includes a scenario generation module in which the data collection module collects more data, such as log files, document files, and image files, stored in the client terminal, and extracts and analyzes text data and metadata from the data to analyze user behavior and generate a scenario, and the abnormal situation detection module analyzes the scenario generated by the scenario generation module, and if an abnormal behavior pattern is detected, it can determine that an abnormal situation has occurred.

In one embodiment, the client management monitoring system of the present invention further includes a plurality of IP cameras connected to the client terminal via a network, and the scenario generation module can further analyze image information captured by the plurality of IP cameras to generate a scenario.

In one embodiment, the client management monitoring system of the present invention further includes a location information providing device installed in the client terminal, and the location information providing device may be configured to include a rechargeable battery that is charged using external power supplied to the client terminal and a GPS communication module that transmits GPS location information to the data collection module.

In one embodiment, the abnormal situation detection module may determine that an abnormal situation has occurred when the rechargeable battery of the location information providing device is in a non-charged state and the GPS location information of the location information providing device received through the GPS communication module deviates from preset reference location information.

In one embodiment, the location information providing device may be configured to include a speaker module, and may be configured to output an alarm sound according to a control signal of the abnormal situation detection module when the abnormal situation detection module determines that an abnormal situation has occurred.

In one embodiment, the location information providing device may be configured to include a detachment detection device including a protrusion that is pressed and compressed by a component of the client terminal when the device is first installed in the client terminal, and when the detachment detection device detects displacement of the protrusion and detects that the location information providing device is detached from the client terminal, the speaker module may be configured to output an alarm sound.

A customer management monitoring system according to one embodiment of the present invention collects and analyzes management status information and sales status information including hardware and software usage information from multiple client terminals installed in a customer company, and notifies an administrator terminal when an abnormal situation is detected, thereby minimizing risk.

The effects according to the present invention are not limited to those exemplified above, and further diverse effects are included in the present specification.

FIG. 1 is a schematic diagram of a network to which a customer management monitoring system according to one embodiment of the present invention is applied.
Figure 2 is a detailed configuration diagram of the customer management monitoring system of Figure 1.
Figure 3 is a detailed configuration diagram of the scenario generation module of Figure 2.
Figure 4 is a detailed configuration diagram of a location information providing device of a customer management monitoring system of the present invention.
Figure 5 is a flowchart explaining a method for determining the occurrence of an abnormal situation using expected sales of the customer management monitoring system of the present invention.

The advantages and features of the present invention, and the methods for achieving them, will become clearer with reference to the embodiments described in detail below together with the accompanying drawings. However, the present invention is not limited to the embodiments disclosed below, but may be implemented in various different forms, and the present embodiments are provided only to make the disclosure of the present invention complete and to fully inform those skilled in the art of the scope of the invention, and the present invention is defined only by the scope of the claims. Like reference numerals refer to like elements throughout the specification.

Hereinafter, a customer management monitoring system through pattern analysis according to embodiments of the present invention will be described with reference to drawings.

FIG. 1 is a schematic diagram of a network to which a customer management monitoring system according to one embodiment of the present invention is applied.

Referring to FIG. 1, a customer management monitoring system (100) according to one embodiment of the present invention is connected to a client terminal (200, 300) installed in a customer company via a wired or wireless network.

In Fig. 1, the customer management monitoring system (100) is illustrated in the form of a single server, but is not limited thereto and includes an embodiment in which each component described later in Fig. 2 is configured as separate hardware.

Client terminals (200) and client terminals (300) refer to terminals such as PCs, laptops, electronic whiteboards, tablets, kiosks, and karaoke machines installed in customer companies belonging to the same or different industries.

The client terminal (200, 300) may be composed of multiple terminals (210, 220, 230, 310, 320, 330).

The customer management monitoring system (100) can provide software or applications that can be executed on client terminals (200, 300) in a cloud manner or a program installation manner. The software or applications can be implemented in the form of program commands that can be executed through various computer means and recorded on a computer-readable medium. The computer-readable medium can be composed of program commands, data files, data structures, etc., either singly or in combination. The program commands recorded on the medium can be those that are specially designed and configured for the present invention or those that are known and available to those skilled in the art of computer software.

Figure 2 is a detailed configuration diagram of the customer management monitoring system of Figure 1.

Referring to FIG. 2, the customer management monitoring system (100) is configured to include a data collection module (110), a sales prediction module (120), a sales activity prediction module (130), a scenario generation module (140), an abnormal situation detection module (150), a risk management module (160), an output module (170), and a storage module (180).

The data collection module (110) collects various types of data from multiple client terminals (200, 300) for each customer.

The data collected may include business status information and sales status information.

Sales status information refers to information on actual sales received from client terminals (200, 300). Sales status information may be an electronic tax invoice or card payment approval history by a program installed on the client terminal (200, 300).

The data collection module (110) can be configured to automatically collect sales status information from the program at preset intervals.

In addition to the above method, the data collection module (100) can also collect sales status information through National Tax Service income report data, etc. that the customer's manager directly inputs into the client terminal.

Management status information refers to various data related to the client company's management, excluding sales status information, among the data collected from client terminals (200, 300).

Each of the client terminal (200) and the client terminal (300) refers to a plurality of client terminals installed in separate business operators, which are merely exemplary and connected to the customer management monitoring system (100) of the present invention. A much larger number of client terminals may be connected to the customer management monitoring system (100). For convenience, the client terminal (200) will be used as an example for explanation below.

Management status information may include power on/off status information, CPU usage information, power consumption information, program execution information, and customer industry information corresponding to the operating status information of the client terminal (200).

The power on/off status information refers to information about whether the client terminal (200) is booted or powered off.

CPU usage information refers to all information related to CPU usage, and includes information about programs run by the CPU, CPU usage share by program, CPU usage share compared to total CPU capacity, and current CPU temperature information.

The power consumption information refers to information on the real-time power consumption of the client terminal (200). The power consumption information may be power information measured by the client terminal (200) itself or power information calculated through information including the above information measured by the client terminal (200).

Program execution information refers to information such as the type of program executed after the client terminal (200) is booted, the execution time of each program, the termination time, and the execution time.

The customer industry information refers to the industry information of the customer company where the client terminal (200) is installed. For example, the industry information may include academy business, karaoke business, PC room business, general meeting business, etc.

The data collection module (110) can collect more information on average sales by industry through the Internet. Specifically, the data collection module (110) can collect information on average sales by period for each business operator by industry from a portal or an external server accessible through the portal through the Internet.

Additionally, the data collection module (110) can collect storage data such as log files, document files, and image files stored in the client terminal (200).

A log file refers to a file created in relation to an event that occurs while an operating system or software is running, and when the log file is analyzed, it can be used to estimate a series of actions performed on a client terminal (200).

Document files and image files refer to document or image files created by a user using a client terminal (200).

The sales prediction module (120) derives a correlation between management environment information and sales status information through an AI learning model and calculates expected sales based on the management environment information.

At this time, the sales prediction module (120) can be configured to calculate expected sales by further considering average sales information by industry.

The AI learning model of the sales prediction module (120) learns the management status information including power on/off status information, CPU usage information, power consumption information, program execution information, log files, image files, document files, customer industry information, and average sales amount information by industry collected from the client terminal (200) and the actual sales information included in the sales status information by time, and derives a correlation between the two data to update the learning model.

For example, the sales prediction module (120) may calculate effective business hours using power on/off status information, CPU usage information, power consumption information, and program execution information of the client terminal (200), multiply the effective business hours by the sales unit price corresponding to the client company's industry to calculate basic sales, and multiply the basic sales by a pre-established correction coefficient to calculate the expected sales.

At this time, the sales prediction module (120) can calculate the effective business activity time as the time when the client terminal (200) is turned on, the power consumption is greater than the preset standard power amount, and a pre-designated program is executed.

The above correction coefficient can be determined by an AI learning model, and can be determined in response to the customer's industry information, monthly or seasonal information, and the type of data being input.

The sales activity prediction module (130) learns sales activity patterns through management status information using an AI learning model and produces information on the presence or absence of sales activities by customer and detailed status information on sales activities.

Information on the presence or absence of sales activities is information on whether the client company is conducting sales activities. In particular, information such as the absence of any sales activities on weekdays when sales activities are supposed to be conducted can be meaningful information.

Detailed sales activity status information refers to detailed status information produced by learning the sales activity patterns of each customer company while taking into account the characteristics of each customer company's industry.

Detailed information on the status of business activities may include, for example, pattern information on the presence or absence of business activities, on/off time information of a client terminal (200) in the case of business activities, power consumption pattern information, program execution pattern information, etc.

By learning information on the presence or absence of sales activities and tax status information on sales activities, an average value for the extent of sales activities can be calculated, which means a value that quantifies the amount of sales activities.

The scenario generation module (140) extracts and analyzes text data and metadata from data such as log files, document files, and image files collected by the data collection module (110) to analyze user behavior and generate a scenario.

Referring to FIG. 3, the scenario generation module (140) is configured to include a character extraction unit (141), a metadata extraction unit (142), and a scenario generation unit (143).

The character extraction unit (141) extracts text from data such as document files and image files, and may be configured to include an optical character recognition (OCR) module so that text can be extracted from image data.

The metadata extraction unit (142) extracts metadata included in document data, etc. generated from a client terminal (200), and the metadata may be configured to include the ID information of the client terminal (200), the creation date of the data, the creator, the modification date, the modifier, etc.

The scenario generation unit (143) analyzes the extracted text and metadata, log files, etc. to generate a scenario about what actions the user took on the client terminal (200).

The abnormal situation detection module (150) detects whether an abnormal situation has occurred in the customer company's management environment based on management status information and sales status information.

For example, the abnormal situation detection module (150) can determine that an abnormal situation has occurred if the actual sales included in the sales status information is smaller than the expected sales calculated by the sales prediction module by a preset first standard ratio value.

The output module (170) plays a role in outputting various data to the client (200) terminal, and can output information on the occurrence of an abnormal situation detected by the abnormal situation detection module (150) to a pre-designated administrator terminal among the client terminals (200).

The reason why the abnormal situation detection module (150) determines that an abnormal situation has occurred when the actual sales amount is smaller than the expected sales amount by a preset first standard ratio value or more is as follows.

For example, if a PC room operator who provides services to customers by installing a client program on a client terminal (200) is a client company, the operating status of the PC terminal can be analyzed to analyze the PC room usage status by customers and estimate sales based on this. However, if there is a difference of a certain percentage or more between the estimated sales and the actual sales information based on actual electronic tax invoices or card payment details, it may be reasonable to assume that an abnormal situation has occurred.

For example, if a PC room business owner hires an employee who receives cash even though sales have occurred and does not reflect this in the sales records or manipulates the sales records, the actual sales may be significantly lower than the expected sales.

Of course, the above method is only an example, so in addition to determining that an abnormal situation has occurred when it is smaller than a preset first reference ratio value, it is also possible to determine that an abnormal situation has occurred when it is smaller than a preset amount.

The abnormal situation detection module (150) may be configured to perform a matching analysis of the time flow of the management status information and the sales status information when it is determined that an abnormal situation has occurred, and generate abnormal situation details for the time period in which the abnormal sign occurred, and the output module (170) may be configured to output the abnormal situation details to the administrator terminal.

In addition, the abnormal situation detection module (150) can determine that an abnormal situation has occurred if the average value of the activity volume of the sales activity, which is learned by the sales activity prediction module (130) and the detailed status information of the sales activity, is greater or smaller than the second standard ratio value.

The risk management module (160) can execute a protection mode to prevent data stored in a client terminal (200) where an abnormal situation has occurred from being deleted when the abnormal situation detection module (150) determines that an abnormal situation has occurred.

The risk management module (160) can be configured to remotely install and execute a program for data forensic analysis on a client terminal (200) where an abnormal situation has occurred to collect and analyze deleted data.

In addition, the abnormal situation detection module (150) can determine that an abnormal situation has occurred when an abnormal behavior pattern is detected by analyzing a scenario generated by the scenario generation module (140). For example, the abnormal situation detection module (150) can determine that an abnormal situation has occurred when a user of a client terminal (200) deletes a large amount of data that should not be deleted, or when a large amount of internal data is transmitted externally.

The storage module (180) serves to store data collected by the customer management monitoring system (100), analyzed data, data generated through analysis, etc., as well as information on client terminals (200) and industry for each customer.

Although not shown in the drawing, the customer management monitoring system (100) of the present invention may further include a plurality of IP cameras connected to the client terminal (200) via a network, and the scenario generation module (140) may further analyze image information captured by the plurality of IP cameras to generate a scenario.

At this time, the scenario generation module (140) may be configured to include an image analysis module that can distinguish objects through image analysis and identify people among the distinguished objects.

Referring to FIG. 4, the customer management monitoring system (100) of the present invention may further include a location information providing device (400) installed in a client terminal (200).

The location information providing device (400) is configured to include a rechargeable battery (410), a GPS communication module (420), a departure detection device (430), and a speaker module (440).

The rechargeable battery (410) is a battery that can be charged using external power supplied to the client terminal (200), and serves to enable the location information providing device (400) to operate even when power is cut off to the client terminal (200).

The GPS communication module (420) transmits GPS location information of the location information providing device (400) to the data collection module (110) via wireless communication. At this time, the GPS communication module (420) may be configured to include a communication module such as Wi-Fi, 3G, 4G, or Bluetooth.

The speaker module (440) outputs an alarm sound when a preset abnormal situation occurs.

The abnormal situation detection module (150) can determine that an abnormal situation has occurred when the rechargeable battery (410) of the location information providing device (400) is not charged and the GPS location information of the location information providing device (400) received through the GPS communication module (420) deviates from the preset reference location information.

At this time, the location information providing device (400) can output an alarm sound through the speaker module (440) according to a control signal received from the abnormal situation detection module (150).

The departure detection device (430) may be configured to include a protrusion (not shown) that is pressed by a component of the client terminal (200) when the location information providing device (400) is initially installed in the client terminal (200).

The detachment detection device (430) is configured to detect displacement of the protrusion, and when the displacement of the protrusion changes to a value greater than a reference value, it can be determined that the location information providing device (400) is detached from the client terminal (200). At this time, an alarm sound can be output through the speaker module (440).

Figure 5 is a flowchart explaining a method for determining the occurrence of an abnormal situation using expected sales of the customer management monitoring system of the present invention.

The client management monitoring system (100) of the present invention collects management status information including operation status information of the client terminal (200) (S510).

In addition, the customer management monitoring system (100) collects sales status information from the client terminal (200) (S520).

The customer management monitoring system (100) calculates expected sales through an AI learning model built by learning data in advance (S530).

The customer management monitoring system (100) determines whether the actual sales included in the sales status information is smaller than the first preset standard ratio value compared to the above-described expected sales (S540).

In step S540, if the actual sales are smaller than the first standard ratio value compared to the expected sales, the monitoring system (100) determines that an abnormal situation has occurred (S550) and outputs information on the occurrence of the abnormal situation to a pre-designated administrator terminal (S560).

In the above manner, the customer management monitoring system (100) of the present invention automatically collects management status information including client operation status information, determines in real time whether an abnormal situation has occurred, and provides this to the manager terminal, thereby enabling the manager of the customer company to take immediate risk management measures.

In addition, in addition to making judgments based on sales as in Fig. 5, it is possible to detect occurrence of abnormal situations based on analyzed sales activity patterns or scenario patterns, and to detect hardware leaks, etc. in real time by detecting mass movement of client terminals (200) with client programs installed through a location information providing device (400).

In addition, in cases where a user arbitrarily detaches the location information provision device (400), the system detects this and notifies the administrator terminal of this and outputs an alarm sound through the speaker module (440), thereby effectively preventing hardware asset leakage.

A person having ordinary skill in the art to which the present invention pertains will understand that the present invention can be implemented in other specific forms without changing the technical idea or essential characteristics thereof. Therefore, it should be understood that the embodiments described above are exemplary in all respects and not restrictive. The scope of the present invention is indicated by the claims described below rather than the detailed description above, and all changes or modified forms derived from the meaning and scope of the claims and their equivalent concepts should be interpreted as being included in the scope of the present invention.

100: Customer Management Monitoring System 110: Data Collection Module
120: Sales Forecasting Module 130: Sales Activity Forecasting Module
140: Scenario generation module 141: Character extraction unit
142: Metadata Extraction Section 143: Scenario Generation Section
150: Anomaly Detection Module 160: Risk Management Module
170: Output module 180: Storage module
200, 300: Client terminal 400: Location information provider
410: Rechargeable battery 420: GPS communication module
430: Departure detection device 440: Speaker module

Claims (15)

In a customer management monitoring system that is connected to multiple client terminals and networks for each customer,
A data collection module that collects management status information and sales status information including power on/off status information, CPU usage information, power consumption information, program execution information, and customer industry information from multiple client terminals for each client company, as well as log files, document files, and image files stored in the client terminals;
A location information providing device comprising: a rechargeable battery installed in the client terminal and charged using external power supplied to the client terminal; a GPS communication module transmitting GPS location information to the data collection module; and a departure detection device including a protrusion that is pressed and compressed by a component of the client terminal when first installed in the client terminal;
A plurality of IP cameras connected to the above client terminal and the network;
A scenario generation module that extracts and analyzes text data and metadata from the data collected by the above data collection module to analyze user behavior and analyzes video information captured by the multiple IP cameras to generate a scenario;
A sales prediction module that derives a correlation between the above management status information and sales status information and calculates expected sales based on the above management status information;
An abnormality detection module that determines that an abnormal situation has occurred when an abnormal behavior pattern is detected by analyzing a scenario generated by the above scenario generation module, or when the actual sales included in the above sales status information is smaller than the expected sales calculated by the above sales prediction module by a preset first reference ratio or when the rechargeable battery of the above location information providing device is not charged and the GPS location information of the above location information providing device received through the GPS communication module deviates from the preset reference location information or when the departure detection device detects a displacement of the protrusion and detects that the above location information providing device is separated from the client terminal;
A risk management module that executes a protection mode to prevent data stored in the client terminal of the customer company where the abnormal situation occurred from being deleted when the above abnormal situation detection module determines that an abnormal situation has occurred; and
Includes an output module that outputs abnormal situation occurrence information detected by the above abnormal situation detection module to a pre-designated administrator terminal among the client terminals,
The above data collection module collects more information on average sales by industry through the Internet, and the above sales prediction module calculates the expected sales by further considering the above average sales information by industry.
The above sales prediction module calculates the effective business activity time using the power on/off status information, CPU usage information, power consumption information, and program execution information of the client terminal, calculates the basic sales amount by multiplying the effective business activity time by the sales unit price corresponding to the client company's industry, and calculates the expected sales amount by multiplying the basic sales amount by a pre-established correction coefficient.
The above risk management module is a customer management monitoring system that remotely installs and executes a program for data forensic analysis on the client terminal where an abnormal situation has occurred, thereby collecting and analyzing deleted data. delete delete In the first paragraph,
The above sales prediction module is a customer management monitoring system characterized in that the effective business activity time is calculated as the time when the client terminal is turned on, the power consumption is greater than a preset standard power amount, and a preset program is executed. In paragraph 4,
The above abnormal situation detection module, when it is determined that an abnormal situation has occurred, performs a matching analysis of the above management status information and the above sales status information according to the time flow to generate abnormal situation details of the time period in which the abnormal sign occurred, and the above output module is a customer management monitoring system that outputs the above abnormal situation details to the above administrator terminal. delete delete delete delete delete delete delete delete In the first paragraph,
A customer management monitoring system characterized in that the above location information providing device is configured to include a speaker module and output an alarm sound according to a control signal of the above abnormal situation detection module when the above abnormal situation detection module determines that an abnormal situation has occurred. delete