Management effectiveness visualization system, management effectiveness visualization method, program, and recording medium.

Abstract

To provide a management effect visualization system, a management effect visualization method, a program, and a recording medium that can visualize the monetary management benefits obtained by preventing accidents. [Solution] The management effect visualization system comprises: an information acquisition unit that acquires operational information of workers and transport robots via an information acquisition means; a collision possibility prediction unit that predicts the possibility of collision between workers and transport robots and determines the accident level based on the operational information of workers and transport robots, with reference to a worker information database and a transport robot information database; an accident avoidance unit that alerts workers or controls transport robots based on the prediction results from the collision possibility prediction unit; an effect amount calculation unit that calculates the amount of management loss that would be expected if an accident occurred when accident avoidance was performed by the accident avoidance unit, using multiple element tables; and a visualization unit that visualizes the amount of management loss calculated by the effect amount calculation unit.

Description

TECHNICAL FIELD

[0001] The present disclosure relates to a management effect visualization system, a management effect visualization method, a program, and a recording medium, which visualize the monetary amount of management effect obtained by preventing accidents in advance, particularly in an environment where workers and transport robots collaborate.

BACKGROUND ART

[0002] Patent Document 1 discloses an information processing apparatus including: a first acquisition unit that acquires region information indicating a second region in which the mobile device can move within a first region around the mobile device, and a third region in which an object exists within the first region; and a second acquisition unit that acquires movement information including at least one of the speed, movement direction, and acceleration of the mobile device. This information processing apparatus is provided with an evaluation unit that, when the movement state of the mobile device is abnormal and a plurality of third regions where objects exist are present within the first region, uses the movement information and the region information to determine whether there is a movement path for the mobile device to reach each of the plurality of objects present in the plurality of third regions, acquires an evaluation value indicating damage when the mobile device collides with each of the objects for which a movement path exists, and determines the position corresponding to the first object that minimizes the damage based on the evaluation value as the target position to which the mobile device moves.

PRIOR ART DOCUMENTS

PATENT DOCUMENTS

[0003]

PATENT DOCUMENT 1

SUMMARY OF THE INVENTION

PROBLEMS TO BE SOLVED BY THE INVENTION

[0004] The present disclosure has been devised in view of the above-described conventional situation, and an object thereof is to provide a management effect visualization system, a management effect visualization method, a program, and a recording medium capable of visualizing the monetary amount of management effect obtained by preventing accidents in advance.

MEANS FOR SOLVING THE PROBLEMS

[0005] This disclosure provides a business effect visualization system for visualizing the business effects of accident avoidance in an environment in which workers and transport robots work together, comprising: an information acquisition unit that acquires worker information including worker motion information and work information via an information acquisition means; a worker information database that stores worker information including worker motion information and work information; a transport robot information database that stores transport robot information including transport robot motion information and work information of the transport robot; a collision possibility prediction unit that predicts the possibility of collision between the worker and the transport robot and determines the accident level based on the worker motion information and transport robot motion information acquired by the information acquisition unit, with reference to the worker information database and the transport robot information database; an accident avoidance unit that alerts the worker or controls the transport robot based on the prediction result by the collision possibility prediction unit; an effect amount calculation unit that calculates the amount of business losses that are expected to occur if an accident occurs when accident avoidance is performed by the accident avoidance unit, using a plurality of element tables; and a visualization unit that visualizes the amount of business losses calculated by the effect amount calculation unit.

[0006] This disclosure provides a method for visualizing business effectiveness using the aforementioned business effectiveness visualization system, comprising the steps of: acquiring worker motion information and transport robot motion information via information acquisition means; predicting the possibility of collision between the worker and the transport robot and determining the accident level based on the acquired worker motion information and transport robot motion information; issuing a warning to the worker or controlling the transport robot based on the predicted result of the possibility of collision between the worker and the transport robot; calculating the estimated amount of business losses that would occur if an accident occurred, using a plurality of element tables, when an accident was avoided by warning the worker or controlling the transport robot; and visualizing the calculated amount of business losses.

[0007] This disclosure provides a program for causing a computer to execute the aforementioned method for visualizing business effects.

[0008] This disclosure provides a recording medium on which the aforementioned program is recorded. [Effects of the Invention]

[0009] This disclosure makes it possible to visualize the monetary business benefits that can be obtained by preventing accidents. [Brief explanation of the drawing]

[0010] [Figure 1] Block diagram showing an example configuration of the management effect visualization system according to Embodiment 1. [Figure 2] Block diagram showing an example of the hardware configuration of an information processing device that executes a program for the management effect visualization method according to Embodiment 1. [Figure 3] This figure shows an example of the data structure of the worker information database for the management effectiveness visualization system according to Embodiment 1. [Figure 4] This figure shows an example of the data configuration of the transport robot information database for the management effect visualization system according to Embodiment 1. [Figure 5] This figure shows an example of the data structure of the first element table of the management effect visualization system according to Embodiment 1. [Figure 6] This figure shows an example of the data structure of the second element table of the management effect visualization system according to Embodiment 1. [Figure 7] This figure shows an example of the data structure of the third element table of the management effect visualization system according to Embodiment 1. [Figure 8] This figure shows an example of the data structure of the fourth element table of the management effect visualization system according to Embodiment 1. [Figure 9] This figure shows an example of the data structure of the fifth element table of the management effect visualization system according to Embodiment 1. [Figure 10]Diagram showing an example of the data configuration of the sixth element table of the management effect visualization system according to Embodiment 1 [Figure 11] Diagram showing an example of the data configuration of the seventh element table of the management effect visualization system according to Embodiment 1 [Figure 12] Diagram showing an example of the data configuration of the eighth element table of the management effect visualization system according to Embodiment 1 [Figure 13] Flowchart showing an example of the operation procedure of the management effect visualization system according to Embodiment 1 [Figure 14] Flowchart showing an example of the processing procedure of the accident prediction processing of the management effect visualization system according to Embodiment 1 [Figure 15] Flowchart showing an example of the processing procedure of the accident prediction processing of the management effect visualization system according to Embodiment 1 [Figure 16] Flowchart showing an example of the processing procedure of the management loss amount calculation processing of the management effect visualization system according to Embodiment 1 [Figure 17] Diagram showing a display example on the display unit in the management effect visualization system according to Embodiment 1 [Figure 18] Diagram showing a display example on the display unit in the management effect visualization system according to Embodiment 1 [Mode for Carrying Out the Invention]

[0011] (Background Leading to the Present Disclosure) In recent years, with the decline in the working population, labor shortages have become increasingly serious in various industrial fields. Particularly in factories, automation and labor saving are being advanced through the introduction of transport robots (such as AGVs) and picking robots. However, in existing factories, fundamental automation is often difficult due to infrastructure constraints and cost considerations, and environments where humans and robots collaborate have become common. Under such circumstances, safety and health management has become an important issue, and in particular, the prevention of personal accidents has become a critical management issue.

[0012] Conventional on-site safety management systems focus on preventing accidents in advance by sharing accident occurrences, near-misses (encounters with situations that could have resulted in an accident or are perceived as potentially hazardous), and risk prediction information (also referred to as KY information) on the system, making it difficult to quantitatively indicate the managerial effects resulting from successful accident avoidance. On the other hand, grasping the effects of safety measures based on specific numerical values and evaluating the return on investment is an important consideration for factory managers, on-site supervisors, and the like. That is, factory managers, on-site supervisors, and others desire the visualization of the monetary managerial effects obtained by preventing accidents in advance.

[0013] Meanwhile, as one of the empirical rules for accident occurrence, "Heinrich's Law" is particularly known in the field of occupational accidents. This Heinrich's Law is also called the "1:29:300 rule", which states that behind one serious accident, there are 29 minor injury accidents and 300 near-misses. Based on Heinrich's Law, the conventional idea is that sharing information on 300 near-misses will effectively eliminate 29 accidents. However, it is difficult to eliminate 29 substantial accidents unless 300 near-misses are eliminated.

[0014] Therefore, in the following embodiments, a managerial effect visualization system, a managerial effect visualization method, a program, and a recording medium capable of visualizing the monetary managerial effect obtained by preventing accidents in advance will be described. That is, the managerial effect visualization system, managerial effect visualization method, program, and recording medium of the present disclosure convert incidents that cause 300 near-misses that have been predicted and prevented in advance into monetary values for visualization.

[0015] The following describes in detail the embodiments of the management effect visualization system, management effect visualization method, program, and recording medium disclosed herein, with reference to the drawings as appropriate. However, unnecessary details may be omitted. For example, detailed explanations of already well-known matters and redundant explanations of substantially identical configurations may be omitted. This is to avoid the following explanation becoming unnecessarily verbose and to facilitate understanding by those skilled in the art. The accompanying drawings and the following explanation are provided to enable those skilled in the art to fully understand this disclosure and are not intended to limit the subject matter described in the claims.

[0016] (Embodiment 1) <System Configuration> Figure 1 is a block diagram showing an example configuration of the Management Effectiveness Visualization System 1 according to Embodiment 1. Note that the system configuration shown in Figure 1 is just one example; one device may be divided into multiple components, or multiple devices may be combined into one. Furthermore, multiple instances of the single device shown in Figure 1 may be provided. Moreover, the processing entities shown below are just examples; a part of the function of one device may be implemented as the function of another device.

[0017] The Management Effect Visualization System 1 is a system that visualizes the monetary management effect obtained by preventing accidents in an environment where workers and transport robots work together. However, the essence of this disclosure is not limited to transport robots (AGVs, etc.) and unmanned forklifts, but also includes automated equipment within the factory premises, such as manufacturing line robots, picking robots, gas and electrical piping and control panels, etc., that could lead to accidents if workers come into contact with them in any way. In the following explanation, for the sake of clarity, transport robots will be used as an example of a case with a high probability of accidents.

[0018] As shown in Figure 1, the management effect visualization system 1 is composed of an information acquisition unit 101, a collision probability prediction unit 102, an accident avoidance unit 103, and an effect amount calculation unit 104.

[0019] The information acquisition unit 101 acquires operator movement information and transport robot movement information via the information acquisition means 400. The information acquisition means 400 can include, for example, a camera, LiDAR, radar, ultrasonic sensor, infrared sensor, acceleration sensor, gyro sensor, etc., either individually or in combination. When a camera is used as the information acquisition means 400, image recognition technology is used to analyze the movements of the operator and transport robot. When LiDAR (TOF), radar, or ultrasonic sensor is used as the information acquisition means 400, the distance and relative speed between the operator and the transport robot are measured. The information acquisition unit 101 processes this information comprehensively to acquire 3D position information, speed information, attitude information, etc., of the operator and the transport robot.

[0020] Furthermore, the camera, which serves as the information acquisition means 400, may use AI functions or TOF sensors to determine whether the transport robot is loaded or unloaded. The camera, which serves as the information acquisition means 400, may also determine whether the worker is looking at their smartphone or looking away.

[0021] The collision probability prediction unit 102 predicts the possibility of a collision between a worker and a transport robot based on the worker's motion information and the transport robot's motion information acquired by the information acquisition unit 101, with reference to the worker information database 200 (see below) and the transport robot information database 300 (see below). The collision probability prediction unit 102 predicts the future trajectory of the worker and the transport robot using motion information such as the current position, speed, and direction of travel of the worker and the transport robot, with reference to worker information such as the worker's work content and location information, and transport robot information such as the transport robot's work information, next work process, and location information, and determines whether the trajectories will intersect or whether they will approach within a certain distance, thereby predicting the possibility of a collision between the worker and the transport robot. For example, a known collision prediction algorithm can be used for this collision probability prediction.

[0022] Furthermore, if the collision probability prediction unit 102 determines that there is a possibility of a collision, it determines the level of the accident at the time of the collision based on the information acquired by the information acquisition unit 101. In this case, for example, if the transport robot is loaded, it may not be able to stop suddenly, and the increased weight of the transport robot may be taken into consideration. Similarly, the state of the worker's distraction, such as whether the worker is looking at a smartphone or looking away, is also taken into consideration.

[0023] The accident avoidance unit 103 performs processing to avoid an accident based on the prediction results from the collision probability prediction unit 102. If the collision probability prediction unit 102 predicts a high probability of collision, the accident avoidance unit 103 will send an alert notification to the worker or control the transport robot. Alert notifications may include, for example, a screen display on a terminal carried by the worker, audio output, or vibration. Control of the transport robot may include, for example, stopping, decelerating, or changing the path of the transport robot. This control of the transport robot is performed by sending a control signal to a control device mounted on the transport robot.

[0024] The effect amount calculation unit 104 calculates the estimated business loss amount that would have occurred if the accident avoided by the accident avoidance unit 103 had actually occurred. This effect amount calculation unit 104 calculates the loss amount using each element table (see below). This loss amount calculation may be created by referring to similar accident cases of the company or other companies in the past, or it may be calculated using AI that takes market rates into account. Furthermore, the loss amount calculation here may be performed in conjunction with a workers' compensation calculation program in the company's personnel system, etc.

[0025] The information acquisition unit 101, the collision probability prediction unit 102, the accident avoidance unit 103, and the effect amount calculation unit 104 are mounted in the control unit 11 (see below) of one or more information processing devices 10. These information processing devices 10 are, for example, PCs (Personal Computers), tablet terminals, servers (including cloud-based servers), etc.

[0026] Figure 2 is a block diagram showing an example of the hardware configuration of an information processing device that executes a program for visualizing management effects according to Embodiment 1. The hardware configuration of the information processing device 10 described here is just one example, and some parts may be omitted or other parts added as needed.

[0027] As shown in Figure 2, the information processing device 10 is composed of a control unit 11, a storage unit 12, a communication unit 13, an input unit 14, and a display unit 15. Each component is configured to communicate via an internal interface 16.

[0028] The control unit 11 may be configured using, for example, a CPU (Central Processing Unit), a GPU (Graphical Processing Unit), an MPU (Micro Processing Unit), a DSP (Digital Signal Processor), or an FPGA (Field-Programmable Gate Array).

[0029] The memory unit 12 is a memory unit for storing various data and programs, and may consist of volatile / non-volatile memory units such as RAM (Random Access Memory), ROM (Read Only Memory), and HDD (Hard Disk Drive). For example, the memory unit 12 stores a program on a method for visualizing management effects. The memory unit 12 may also store a worker information database 200 and a transport robot information database 300.

[0030] The communication unit 13 is an interface for communicating with external devices such as the information acquisition means 400 via the network NW. The communication standards supported by the communication unit 13 are not particularly limited and can be wired or wireless. Furthermore, it may support multiple communication standards.

[0031] The input unit 14 accepts, for example, operations and instructions from an inspector. The input unit 14 may consist of a mouse, keyboard, touch panel display, or the like.

[0032] The display unit 15 displays various user interfaces to the user. The display unit 15 may consist of a liquid crystal display, a touch panel display, a lamp, etc. The display unit 15 corresponds to the "visualization unit" in this disclosure.

[0033] As shown in Figure 1, the management effectiveness visualization system 1 further comprises a worker information database 200. The worker information database 200 is a database that stores information about workers who work in the factory.

[0034] Figure 3 shows an example of the data structure of the worker information database 200 of the management effect visualization system 1 according to Embodiment 1. In the example shown in Figure 3, the worker information database 200 includes worker ID, name, department, skill level, employment date, telephone number, safety and health education and other hazard prediction training attendance history, training date, health status, past accident history, position at the time of the accident, and the degree of impact of the accident. In addition, the worker information database 200 also includes items such as position, work content for the day, work location, and location information. The location information here is obtained by a terminal carried by the worker or by a location detection sensor installed in the factory.

[0035] As shown in Figure 1, the management effect visualization system 1 further comprises a transport robot information database 300. The transport robot information database 300 is a database that stores information about transport robots operating within the factory.

[0036] Figure 4 shows an example of the data structure of the transport robot information database 300 of the management effect visualization system 1 according to Embodiment 1. In the example shown in Figure 4, the transport robot information database 300 includes robot ID, model name (model), operating status, last maintenance date, management department, transport capacity, failure history, sensor status, past accident history, operation at the time of the accident, and the degree of impact of the accident. In addition, the transport robot information database 300 also includes items such as year of manufacture, safety functions, operating hours, operation information (speed, direction of travel, etc.), work information, next work process, and location information. The location information here is obtained by location information sensors mounted on the transport robot or location detection sensors installed in the factory.

[0037] <Element Table> As mentioned above, the element tables (TB1 to TB8) are tables used by the effect amount calculation unit 104 when calculating the amount of business losses.

[0038] [First element table] Figure 5 shows an example of the data structure of the first element table TB1 of the management effect visualization system 1 according to Embodiment 1. The first element table TB1 is a table for evaluating the accident level according to the respective operating speeds of the worker and the transport robot immediately before they collide.

[0039] In the example shown in Figure 5, the first element table TB1 includes the AGV (automated guided vehicle) speed classification, the worker speed classification, the degree of worker injury defined in relation to the AGV and worker speed classifications, and the accident level associated with these. The accident level here is set based on the degree of worker injury. For example, if the AGV is "high speed" and the worker is "moving quickly," the accident level is defined as "level 8." In this example, the AGV speed classification is divided into three stages, but is not limited to these. Similarly, the worker speed classification is not limited to three stages.

[0040] Furthermore, when the target is manufacturing line robots, picking robots, etc., instead of transport robots, accident levels can be set by including items such as the acceleration of arm movement and whether dangerous work is being performed (e.g., high-speed arm movement, shearing, drilling, pressing, high-temperature and high-pressure work). Similarly, when the target is piping, power lines, etc., instead of transport robots, accident levels can be set by including items such as the degree of high temperature and pressure of the discharged material and the degree of current and voltage. The same applies to the following.

[0041] [Second element table] Figure 6 shows an example of the data structure of the second element table TB2 of the management effect visualization system 1 according to Embodiment 1. The second element table TB2 is a table that defines the period of absence from work and the amount of compensation for workers according to the accident level.

[0042] In the example shown in Figure 6, the second element table TB2 includes the worker's period of absence from work and the compensation amount, which are linked to the accident level. The compensation amount here includes wages, medical expenses, and compensation for pain and suffering. For example, if the accident level is "Level 3," the period of absence from work is defined as "1 month," and the compensation amount is defined as "500,000 yen."

[0043] [Third element table] Figure 7 shows an example of the data structure of the third element table TB3 of the management effect visualization system 1 according to Embodiment 1. The third element table TB3 is a table that defines the degree of impact on the production process and the resulting loss amount according to the accident level (period of time off work for the worker).

[0044] In the example shown in Figure 7, the third element table TB3 includes the degree of impact on the production process linked to the accident level, and the amount of loss. The amount of loss here includes the decrease in sales due to production delays, the cost of transferring to other processes, the cost of handling complaints, and compensation for damages. For example, if the accident level is "Level 3," the degree of impact on the production process is defined as "Moderate 1," and the amount of loss is defined as "10 million yen or more."

[0045] [Fourth element table] Figure 8 shows an example of the data structure of the fourth element table TB4 of the management effect visualization system 1 according to Embodiment 1. The fourth element table TB4 is a table that defines the need for replacement personnel and the amount of money required for them, according to the worker's position and the period of leave.

[0046] In the example shown in Figure 8, the fourth element table TB4 includes the need for replacement personnel linked to the accident level, and the cost associated with those replacement personnel. The cost associated with replacement personnel here includes dispatch costs, training costs, etc. For example, if the accident level is "Level 3," the need for replacement personnel is defined as "Necessary (person with similar work experience)," and the cost associated with replacement personnel is defined as "1.5 million yen."

[0047] [Table of the fifth element] Figure 9 shows an example of the data structure of the fifth element table TB5 of the management effect visualization system 1 according to Embodiment 1. The fifth element table TB5 is a table that defines the amount of money required for on-site investigation according to the accident level.

[0048] In the example shown in Figure 9, the fifth element table TB5 includes the level of detail of the investigation and the investigation cost, which are linked to the accident level. The investigation cost here includes the personnel costs of investigators, the cost of investigation equipment, and the cost of preparing the report. For example, if the accident level is "Level 3," the level of detail of the investigation is defined as "Detailed Investigation 1," and the investigation cost is defined as "500,000 yen."

[0049] [Table of the 6th element] Figure 10 shows an example of the data structure of the sixth element table TB6 of the management effect visualization system 1 according to Embodiment 1. The sixth element table TB6 is a table that defines the amount of money required for safety measures to prevent recurrence, according to the accident level.

[0050] In the example shown in Figure 10, the sixth element table TB6 includes the countermeasures linked to the accident level, as well as the cost of those countermeasures. The cost of countermeasures here includes equipment modification costs, system modification costs, safety training costs, manual revision costs, etc. For example, if the accident level is "Level 3," the countermeasure is defined as "Partial Modification 1," and the cost of the countermeasure is defined as "500,000 yen or more."

[0051] [Table of the 7th element] Figure 11 shows an example of the data structure of the seventh element table TB7 of the management effect visualization system 1 according to Embodiment 1. The seventh element table TB7 is a table that defines the amount of loss due to damage to the company's image according to the level of the accident.

[0052] In the example shown in Figure 11, the seventh element table TB7 includes the degree of impact on the company's image and the amount of loss, linked to the accident level. The degree of impact on the company's image here includes things like a drop in stock price, a boycott of products, suspension of business transactions, and impact on recruitment activities, while the amount of loss here is the monetary equivalent of these. For example, if the accident level is "Level 3," the degree of impact on the company's image is defined as "significant impact," and the amount of loss is defined as "10 million to several hundred million yen."

[0053] [Table of 8 elements] Figure 12 shows an example of the data structure of the eighth element table TB8 of the management effect visualization system 1 according to Embodiment 1. The eighth element table TB8 is a table that defines the degree of impact on business operations according to the job position of the target worker.

[0054] The example shown in Figure 12 includes the degree of impact on business operations linked to the job title of the worker in question. Here, the degree of impact on business operations is calculated by converting factors such as project delays, lost business opportunities, contract termination, and litigation costs into coefficients. For example, if the job title of the worker in question is "specialist," the degree of impact on business operations is defined as "1.5." Note that in the eighth element table TB8, the degree of impact on business operations may be converted into monetary value instead of coefficients.

[0055] <Example of operating procedure for a business effectiveness visualization system> Figure 13 is a flowchart showing an example of the operation procedure of the management effect visualization system 1 according to Embodiment 1. In this example, the processing unit is described as the control unit 11, but some functions may be configured to be executed by other systems. The following processing flow may be executed continuously or repeatedly at predetermined timings.

[0056] The control unit 11 (information acquisition unit 101) acquires real-time information (step Sp101). Here, it acquires operator activity information and equipment status (for example, operation information of a transport robot) via the information acquisition means 400.

[0057] The control unit 11 (collision probability prediction unit 102) performs accident prediction processing (step Sp101). Here, it predicts accidents that may occur between the worker and the equipment (e.g., a transport robot). The detailed processing here will be described later.

[0058] The control unit 11 (collision probability prediction unit 102) determines whether or not there is a possibility of an accident occurring based on the results of the accident prediction processing (step Sp103). If it is determined that there is a possibility of an accident occurring (YES in step Sp103), the control unit 11 proceeds to steps Sp104A and 104B. On the other hand, if it is determined that there is no possibility of an accident occurring (NO in step Sp103), this processing flow ends.

[0059] The control unit 11 (accident avoidance unit 103) executes accident avoidance processing (step Sp104A). Here, the unit appropriately executes processes necessary for accident avoidance, such as issuing alert notifications to the worker in question or controlling the equipment in question (e.g., a transport robot) to stop or slow down.

[0060] The control unit 11 (effect amount calculation unit 104) performs the calculation of the business loss amount (step Sp104B). In this example, this step is executed in parallel with step Sp104A, but it may also be executed sequentially after step Sp104A. The detailed processing here will be described later.

[0061] The control unit 11 displays the amount of business losses on the display unit (step Sp105). Then, this processing flow ends.

[0062] Through the above processing flow, the Management Effect Visualization System 1 can prevent accidents and visualize the amount of management benefits obtained as a result (the amount of management losses if an accident occurs).

[0063] [Accident Prediction Processing (Overview)] Figure 14 is a flowchart showing an example of the processing procedure for accident prediction in the management effect visualization system 1 according to Embodiment 1. Here, we will explain the overview of the accident prediction process. Note that the collision probability prediction unit 102 is the main processor in the following processing flow.

[0064] The collision probability prediction unit 102 calculates the worker's actions and the equipment status from real-time information (step Sp201). Here, the worker's actions and the equipment status are calculated based on the worker's movement information and the equipment status acquired via the information acquisition means 400.

[0065] The collision probability prediction unit 102 predicts the worker's next action from worker information (especially the work plan) and the calculated worker's actions, and also predicts the next state of the equipment from equipment information (especially the work plan) and the calculated state of the equipment (step Sp202). Here, for example, worker information is obtained from the worker information database 200, and equipment information is obtained from the equipment information database (for example, the transport robot information database 300).

[0066] The collision probability prediction unit 102 compares the predicted next action of the worker with the next state of the equipment (step Sp203). Here, based on the worker's next process and the equipment's next state, it checks whether there is any interference between the worker and the equipment.

[0067] The collision probability prediction unit 102 determines whether or not a danger to the worker is predicted (step Sp204). If it determines that a danger to the worker is predicted (YES in step Sp204), the collision probability prediction unit 102 proceeds to step Sp205. On the other hand, if it determines that no danger to the worker is predicted (NO in step Sp204), this processing flow ends.

[0068] The collision probability prediction unit 102 determines that there is a possibility of an accident occurring (step Sp205). The collision probability prediction unit 102 may also determine the accident level at this step. Then, this processing flow ends.

[0069] [Accident prediction processing (specific example)] Figure 15 is a flowchart showing an example of the processing procedure for accident prediction in the management effect visualization system 1 according to Embodiment 1. Here, a specific example of accident prediction processing is described, using the case where the equipment is a transport robot as an example. In the following processing flow, the collision probability prediction unit 102 is the main processor.

[0070] The collision probability prediction unit 102 calculates the position and velocity of the worker and the transport robot from real-time information (step Sp211).

[0071] The collision probability prediction unit 102 predicts the movement path from worker information (especially the work plan) and the calculated position and speed of the worker, and predicts the movement path from transport robot information (especially the work plan) and the calculated position and speed of the transport robot (step Sp212).

[0072] The collision probability prediction unit 102 compares the predicted movement paths of the worker and the transport robot (step Sp213).

[0073] The collision possibility prediction unit 102 determines whether the worker and the transport robot are approaching each other at a certain speed or higher (step Sp214). Here, the possibility of a collision between the worker and the transport robot is determined. If it is determined that the worker and the transport robot are approaching each other at a certain speed or higher (YES in step Sp214), the collision possibility prediction unit 102 proceeds to step Sp215. On the other hand, if it is determined that the worker and the transport robot are not approaching each other at a certain speed or higher (NO in step Sp214), this processing flow ends.

[0074] The collision probability prediction unit 102 determines that there is a possibility of an accident occurring (step Sp215). Then, this processing flow ends.

[0075] [Calculation process for business losses] Figure 16 is a flowchart showing an example of the processing procedure for calculating the amount of business losses in the business effectiveness visualization system 1 according to Embodiment 1. In the following processing flow, the effect amount calculation unit 104 is the main processor.

[0076] The effect amount calculation unit 104 obtains the prediction results from the collision probability prediction unit 102 (step Sp301). Here, it obtains prediction results such as the speed of the worker and the transport robot, the injury status of the worker, or the level of the accident.

[0077] The effect amount calculation unit 104 refers to the first element table TB1 and determines the accident level based on the obtained prediction results (step Sp302).

[0078] The effect amount calculation unit 104 refers to the second element table TB2 and obtains the compensation amount based on the determined accident level (step Sp303).

[0079] The effect amount calculation unit 104 refers to the third element table TB3 and obtains the loss amount based on the determined accident level (step Sp304).

[0080] The effect amount calculation unit 104 obtains the worker's job title from the worker information database 200 (step Sp305).

[0081] The cost calculation unit 104 refers to the fourth element table TB4 and obtains the amount to be paid for the replacement personnel based on the acquired worker's job title (step Sp306).

[0082] The effect amount calculation unit 104 refers to the fifth element table TB5 and obtains the verification amount based on the determined accident level (step Sp307).

[0083] The effect amount calculation unit 104 refers to the sixth element table TB6 and obtains the countermeasure amount based on the determined accident level (step Sp308).

[0084] The effect amount calculation unit 104 refers to the seventh element table TB7 and obtains the amount of loss due to the damage to the company's image (step Sp309). Here, for example, the degree to which the accident will be made public is predicted based on past cases, and the amount of loss is obtained based on that.

[0085] The effect amount calculation unit 104 obtains the worker's job title from the worker information database 200 (step Sp310).

[0086] The effect amount calculation unit 104 refers to the eighth element table TB8 and obtains the degree of impact (coefficient) on business operations based on the acquired job title of the worker (step Sp311).

[0087] The effect amount calculation unit 104 calculates the amount of business losses based on the acquired guaranteed amount, loss amount, cost of replacement personnel, verification amount, countermeasure amount, loss amount due to damage to corporate image, and impact on business operations (step Sp311). In this example, the amount of business losses can be calculated by multiplying the sum of the guaranteed amount, loss amount, cost of replacement personnel, verification amount, countermeasure amount, and loss amount due to damage to corporate image by the impact on business operations (coefficient).

[0088] [Example of display on the display unit] Figure 17 shows an example UI1 displayed on the display unit 15 in the management effect visualization system 1 according to Embodiment 1. Figure 18 shows an example UI2 displayed on the display unit 15 in the management effect visualization system 1 according to Embodiment 1.

[0089] The display unit 15, acting as a visualization unit, can visualize the calculated business loss amount, as shown in the display example UI1 in Figure 17. Furthermore, the display unit 15 can visualize the loss amount not only as a monetary value, but also in various formats such as graphs, risk levels, and messages. It can also notify users of the loss amount via voice output. The visualized information can be displayed not only on the display unit 15, but also on the administrator's mobile device or other devices as needed.

[0090] Furthermore, the display unit 15 can display images from the camera, which is the information acquisition means 400, as shown in the display example UI2 in Figure 18.

[0091] In summary, this disclosure makes it possible to visualize the monetary business benefits obtained by preventing accidents in environments where workers and transport robots collaborate, based on a detailed set of elemental tables and specific examples. This makes it possible to present more concrete and easily understandable benefits to management and on-site personnel.

[0092] Furthermore, according to this disclosure, by using element tables to grasp a detailed breakdown of losses, it is possible to individually identify a wide range of loss items, such as compensation for business interruption, project delays, replacement personnel costs, on-site inspection costs, safety measures costs, damage to corporate image, and business operation losses, and to analyze the breakdown of losses in detail.

[0093] Furthermore, according to this disclosure, by using a detailed element table that takes into account the circumstances of the accident, the attributes of the workers, their job responsibilities, etc., it becomes possible to calculate a more accurate and realistic amount of the effect.

[0094] Furthermore, this disclosure allows for an analysis of which items significantly impact the amount of losses by understanding the detailed breakdown of losses, and enables the identification of items that should be prioritized for countermeasures. This can lead to the consideration of more effective safety measures.

[0095] (Other variations) This disclosure also applies to programs and storage media that supply programs that realize the functions of the apparatus of the above-described embodiment to the apparatus via a network or various storage media, and which are read and executed by a computer within the apparatus.

[0096] Although various embodiments have been described above with reference to the drawings, it goes without saying that this disclosure is not limited to these examples. It will be clear to those skilled in the art that various modifications, alterations, substitutions, additions, deletions, and equivalents can occur within the scope of the claims, and these will naturally fall within the technical scope of this disclosure. Furthermore, the components of the various embodiments described above can be combined arbitrarily without departing from the spirit of the invention.

[0097] (Note) The following technologies are disclosed based on the above description of embodiments.

[0098] <Technology 1> A management effect visualization system 1 that visualizes the management effects of accident avoidance in an environment where workers and transport robots work together, A worker information database 200 that stores worker information including worker action information and work information, A transport robot information database 300 that stores transport robot information including operation information and work information of the transport robot, An information acquisition unit 101 acquires the operator's operation information and the transport robot's operation information via the information acquisition means 400, Based on the worker information database 200 and the transport robot information database 300, the collision probability prediction unit 102 predicts the likelihood of a collision between the worker and the transport robot and determines the accident level, based on the worker's movement information and the transport robot's movement information acquired by the information acquisition unit 101. Based on the prediction results from the collision probability prediction unit, the accident avoidance unit 103 alerts the worker or controls the transport robot, When accident avoidance is performed by the accident avoidance unit 103, the effect amount calculation unit 104 calculates the amount of business loss that would be expected if an accident occurred, using multiple element tables (for example, TB1 to TB8), A visualization unit (for example, 15) that visualizes the amount of business losses calculated by the effect amount calculation unit, A management effectiveness visualization system 1 equipped with the following features.

[0099] This configuration makes it possible to visualize the monetary business benefits that can be obtained by preventing accidents in environments where workers and transport robots (and other automated equipment) work together. This allows users such as factory managers and site supervisors to quantitatively understand the cost-effectiveness of safety measures and use this information to build more effective safety management systems. It is also expected to contribute to raising awareness of accident prevention and fostering a safety culture.

[0100] <Technology 2> The aforementioned work information includes a work plan, The collision probability prediction unit 102 predicts the likelihood of a collision between the worker and the transport robot and determines the accident level based on the next actions of the worker and the transport robot predicted based on the worker's movement information, the transport robot's movement information, the worker's work plan, and the transport robot's work plan. The management effect visualization system 1 according to claim 1.

[0101] This configuration allows for highly accurate prediction of the likelihood of collisions between workers and transport robots.

[0102] <Technology 3> The worker information database further stores at least one of the worker's job title, role, or duties. The aforementioned operation information includes the operating speed, The aforementioned element table is, A first table TB1 determines the accident level based on the movement speed of the transport robot and the worker before contact between them, A second table, TB2, defines the period of absence from work and the amount of compensation for workers due to contact accidents, according to the level of the accident. A third table, TB3, defines the impact on the process and the amount of loss corresponding to the worker's absence, according to the duration of the absence. A fourth table, TB4, defines the need for replacement personnel and the cost of those replacement personnel according to the worker's position and the duration of the leave, Table TB5 defines the amount of verification costs required for on-site investigations according to the level of the accident, Table TB6 defines the amount of money required for safety measures to prevent recurrence, according to the level of the accident, Table TB7 defines the amount of loss due to damage to the company's image according to the level of the accident, Table TB8, the eighth table, defines the degree of impact on business operations and business operating losses according to the job responsibilities of the workers involved, From among these, including at least one, A management effect visualization system 1 as described in Technology 1 or Technology 2.

[0103] This configuration allows for a highly accurate calculation of the monetary impact on management, taking all aspects into consideration.

[0104] <Technology 4> The aforementioned first table TB1 is, The speed classification of the transport robot, the speed classification of the worker, and the accident level are stored in association with each other. The speed classification of the aforementioned worker is: It can be one of the following: standing still, walking slowly, walking normally, or running. Management effect visualization system 1 as described in Technology 3.

[0105] This configuration allows for flexible adaptation to any situation and enables highly accurate calculation of the monetary impact on business operations.

[0106] <Technology 5> The aforementioned effect amount calculation unit 104 is: Referencing at least one of the second table TB2 through the eighth table TB8, calculate the amount of business loss based on at least one of the following: accident level, worker's period of absence, job title, and job responsibilities. A management effect visualization system 1 described in any one of Technology 2 to Technology 4.

[0107] This configuration allows for a highly accurate calculation of the monetary impact on business operations.

[0108] <Technology 6> The accident avoidance unit 103 is, If the collision probability prediction unit 102 predicts a high probability of collision, the system controls the system to either issue an alert notification to the worker or stop or slow down the transport robot. A management effect visualization system 1 described in any one of Technology 1 to Technology 5.

[0109] This configuration makes it possible to prevent accidents before they happen.

[0110] <Technology 7> The aforementioned visualization unit, The aforementioned amount of business losses is output visually or audibly using at least one of the following: amount, graph, risk level, and message. A management effect visualization system 1 described in any one of Technology 1 to Technology 6.

[0111] This configuration allows users to intuitively recognize the monetary impact on business operations.

[0112] <Technology 8> The aforementioned transport robot Automated guided vehicles, or manned or unmanned forklifts, A management effect visualization system 1 described in any one of Technology 1 to Technology 7.

[0113] This configuration makes it possible to visualize the monetary business benefits that can be obtained by preventing accidents between workers and transport robots, which have a high risk of accidents.

[0114] <Technology 9> The information acquisition means 400 is, Cameras, LiDAR, radar, ultrasonic sensors, infrared sensors, accelerometers, gyroscopes, or combinations thereof, A management effect visualization system 1 described in any one of the technologies 1 through 8.

[0115] This configuration allows for highly accurate acquisition of information regarding the movements of workers and transport robots.

[0116] <Technology 10> The aforementioned worker information is, This includes the worker's skill level, years of experience, or history of safety and health training. A management effect visualization system 1 described in any one of the technologies 1 through 9.

[0117] This configuration allows for flexible adaptation to worker attributes and enables highly accurate calculation of the economic benefits.

[0118] <Technology 11> A method for visualizing business effects using the business effect visualization system 1 described in any one of the technologies 1 to 10, The process involves acquiring information on the worker's actions and the transport robot's actions via the information acquisition means 400. Based on the acquired motion information of the worker and the motion information of the transport robot, the steps include predicting the likelihood of a collision between the worker and the transport robot and determining the level of the accident, Based on the predicted probability of collision between the worker and the transport robot, the steps include: alerting the worker or controlling the transport robot; The steps include: calculating the estimated business loss amount that would occur if an accident were to occur, using a multi-element table, assuming that the accident was avoided by alerting the worker or controlling the transport robot; The steps include visualizing the calculated amount of the aforementioned business loss, Methods for visualizing business effectiveness, including [specific examples of such methods].

[0119] This configuration makes it possible to visualize the monetary business benefits that can be obtained by preventing accidents in an environment where workers and transport robots (and other automated equipment) work together.

[0120] <Technology 12> A program for a computer to execute the management effect visualization method described in Technology 11.

[0121] This configuration makes it possible to visualize the monetary business benefits that can be obtained by preventing accidents in an environment where workers and transport robots (and other automated equipment) work together.

[0122] <Technology 13> A recording medium on which the program described in Technical 12 is stored.

[0123] This configuration makes it possible to visualize the monetary business benefits that can be obtained by preventing accidents in an environment where workers and transport robots (and other automated equipment) work together. [Industrial applicability]

[0124] This disclosure is useful as a management effect visualization system, management effect visualization method, program, and recording medium that can visualize the monetary management effect obtained by preventing accidents. [Explanation of Symbols]

[0125] 1. Management Effectiveness Visualization System 10 Information Processing Devices 11 Control Unit 12 Storage section 13 Communications Department 14 Input section 15 Display section 16 Internal Interface 101 Information Acquisition Department 102 Collision Probability Prediction Unit 103 Accident Avoidance Department 104 Effect Amount Calculation Section 200 Worker Information Database 300 Transport Robot Information Database 400 Information acquisition means

Claims

1. A management effect visualization system that visualizes the business effects of accident avoidance in an environment where workers and transport robots collaborate, A worker information database that stores worker information including worker action information and work information, A transport robot information database that stores transport robot information including operation information and work information of the transport robot, An information acquisition unit that acquires the operator's operation information and the transport robot's operation information via an information acquisition means, A collision probability prediction unit predicts the likelihood of a collision between the worker and the transport robot and determines the accident level based on the worker's movement information and the transport robot's movement information acquired by the information acquisition unit, with reference to the worker information database and the transport robot information database. Based on the prediction results from the collision probability prediction unit, the accident avoidance unit alerts the worker or controls the transport robot, When the accident avoidance unit has performed the aforementioned accident avoidance, the effect amount calculation unit calculates the estimated amount of business loss that would occur if an accident were to occur, using multiple element tables. A visualization unit that visualizes the amount of business losses calculated by the effect amount calculation unit, A management effectiveness visualization system equipped with [features / equipment].

2. The aforementioned work information includes a work plan, The collision probability prediction unit predicts the likelihood of a collision between the worker and the transport robot and determines the accident level based on the next actions of the worker and the transport robot predicted based on the worker's movement information, the transport robot's movement information, the worker's work plan, and the transport robot's work plan. The management effect visualization system according to claim 1.

3. The worker information database further stores at least one of the worker's job title, role, or duties. The aforementioned operation information includes the operating speed, The aforementioned element table is, A first table that determines the accident level based on the movement speed of the transport robot and the worker before contact, A second table defines the period of absence from work and the amount of compensation for workers due to contact accidents, according to the level of the accident, A third table defines the impact on the process and the amount of loss due to worker absences, according to the duration of the absence. A fourth table defines the need for replacement personnel and the cost of those replacement personnel, based on the worker's position and the duration of the leave. A fifth table defines the amount of investigation costs required for on-site investigations according to the level of the accident, A sixth table defines the amount of money required for safety measures to prevent recurrence, according to the level of the accident, A seventh table defines the amount of loss due to damage to the company's image according to the level of the accident, The eighth table defines the degree of impact on business operations and business operating losses according to the job responsibilities of the workers involved, From among these, including at least one, The management effect visualization system according to claim 1.

4. The first table mentioned above is, The speed classification of the transport robot, the speed classification of the worker, and the accident level are stored in association with each other. The speed classification of the aforementioned worker is: It can be one of the following: standing still, walking slowly, walking normally, or running. The management effect visualization system according to claim 3.

5. The aforementioned effect amount calculation unit, Referencing at least one of the tables from the second to the eighth, calculate the amount of business loss based on at least one of the following: accident level, worker's period of absence, job title, and job responsibilities. The management effect visualization system according to claim 3.

6. The aforementioned accident avoidance unit is If the collision probability prediction unit predicts a high probability of collision, the system controls the system to either issue an alert to the worker or stop or slow down the transport robot. The management effect visualization system according to claim 1.

7. The aforementioned visualization unit, The aforementioned amount of business losses is output visually or audibly using at least one of the following: amount, graph, risk level, and message. The management effect visualization system according to claim 1.

8. The aforementioned transport robot Automated guided vehicles, or manned or unmanned forklifts, The management effect visualization system according to claim 1.

9. The aforementioned information acquisition means is Cameras, LiDAR, radar, ultrasonic sensors, infrared sensors, accelerometers, gyroscopes, or combinations thereof, The management effect visualization system according to claim 1.

10. The aforementioned worker information is, This includes the worker's skill level, years of experience, or history of safety and health training. The management effect visualization system according to claim 1.

11. A method for visualizing business effectiveness using the business effectiveness visualization system described in any one of claims 1 to 10, The steps include acquiring information on the worker's movements and the transport robot's movements via an information acquisition means, Based on the acquired motion information of the worker and the motion information of the transport robot, the steps include predicting the likelihood of a collision between the worker and the transport robot and determining the level of the accident, Based on the predicted probability of collision between the worker and the transport robot, the steps include: alerting the worker or controlling the transport robot; The steps include: calculating the estimated business loss amount that would occur if an accident were to occur, using a multi-element table, assuming that the accident was avoided by alerting the worker or controlling the transport robot; The steps include visualizing the calculated amount of the aforementioned business loss, Methods for visualizing business effectiveness, including [specific examples of such methods].

12. A program for causing a computer to execute the method for visualizing the business effects described in claim 11.

13. A recording medium on which the program described in claim 12 is recorded.

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