JP2019200625A - Danger prediction activity support system, danger prediction activity support method, and program - Google Patents

Abstract

To provide assistance so that effective danger prediction activities can be carried out at a construction site or the like.SOLUTION: The prediction activity support system comprises: a case database storing cases of occupational accidents occurred in the past in association with result information on meteorological elements; a case extracting unit that extracts the cases based on forecast information on the meteorological elements at a predetermined site; and an output unit that outputs the extracted cases and the forecast information. The prediction activity support system may further comprise an explanation database storing explanation information explaining influences of the meteorological elements on living bodies, and an explanation acquisition unit that reads the explanation information on the forecast elements corresponding to the extracted cases, and the output unit may further output the read explanation information.SELECTED DRAWING: Figure 9

Description

  The present invention relates to a risk prediction activity support system, a risk prediction activity support method, and a program for supporting a risk prediction activity.

  At construction and civil engineering sites, risk prediction activities are undertaken before work begins to prevent occupational accidents. For example, Patent Document 1 stores work accidents that occurred in the past in association with the work that was performed at the time of occurrence, the situation at the time of occurrence, and the cause of occurrence, and when the work to be performed is input, In the list, a safety information system that displays an outline of construction, disaster victim information, and an outline of the disaster in order from the most important disaster is disclosed.

JP-A-6-44211

  However, in general, there are many occupational accidents to be aware of, and it is difficult to determine what kind of accidents should be particularly noted. Therefore, there is a question about the effect of the risk prediction activity in the alerting by the above safety information list.

  The present invention has been made in view of such a background, and provides a risk prediction activity support system, a risk prediction activity support method, and a program capable of supporting so that an effective risk prediction activity can be performed. The purpose is to provide.

  The main invention of the present invention for solving the above-mentioned problems is based on a case database storing cases of occupational accidents that occurred in the past in association with actual performance information of weather elements, and prediction information of the weather elements at a predetermined site. A case extracting unit that extracts the case, and an output unit that outputs the extracted case and the prediction information.

  Other problems and solutions to be disclosed by the present application will be made clear by the embodiments of the invention and the drawings.

  ADVANTAGE OF THE INVENTION According to this invention, it can assist so that an effective danger prediction activity can be performed.

1 is a diagram illustrating an example of the overall configuration of a system including a user terminal 10, a danger prediction activity (KY) support system 20, a weather system 30, and a safety system 40 in the present embodiment. It is a figure which shows the hardware structural example of the computer used as the danger prediction activity support system. It is a figure which shows the example of a software structure of the danger prediction activity support system. It is a figure which shows the structural example of a case database (DB). It is a figure which shows the structural example of a weather results database (DB). It is a figure which shows the structural example of an evaluation model memory | storage part. It is a figure which shows the structural example of description database (DB). It is a flowchart which shows the flow of an analysis process. It is a flowchart which shows the flow of an information delivery process. It is a flowchart which shows the flow of a calculation process of a risk level. It is explanatory drawing of a deviation degree. It is a flowchart which shows the flow of the acquisition process of description.

  Hereinafter, a risk prediction activity support system according to an embodiment of the present invention will be described with reference to the drawings. The risk prediction activity support system of the present embodiment is supposed to support the risk prediction activity at the construction site, but the present invention is not limited to the support of the risk prediction activity at the construction site.

(overall structure)
With reference to FIG. 1, the overall configuration of the system in this embodiment will be described.
FIG. 1 is a diagram illustrating an overall configuration example of a system including a user terminal 10, a risk prediction activity (KY) support system 20, a weather system 30, and a safety system 40 in the present embodiment. Each of the user terminal 10, the weather system 30, and the safety system 40 is connected to the danger prediction activity support system 20 so as to be communicable in a wired or wireless manner.

  In this embodiment, it is assumed that the user terminal 10, the danger prediction activity support system 20, the weather system 30, and the safety system 40 are independent from each other. However, for example, the user terminal 10 may include the danger prediction activity support system 20, and the danger prediction activity support system 20 may include the safety system 40.

  The user terminal 10 is a terminal used by a person (user) who performs a risk prediction activity, such as an on-site worker or supervisor. In this embodiment, although the portable terminal like a smart phone or a tablet terminal is assumed as the user terminal 10, it is not restricted to this. The user terminal 10 receives and displays information on the weather forecast and the risk prediction in the field from the risk prediction activity support system 20. Here, “display” includes not only e-mail notification but also general information output such as pop-up display and audio output.

  The danger prediction activity support system 20 is one or a plurality of computers that support danger prediction activities by the user. The specific configuration and operation of the danger prediction activity support system 20 will be described later.

  The weather system 30 is a computer that provides weather data to the danger prediction activity support system 20, and is, for example, a server of a weather company. Here, the meteorological data may include measured values, analyzed values, and predicted values of meteorological elements for each predetermined time zone in a corresponding place such as a site. The analysis value is a value obtained by calculating an actual measurement value at a predetermined point, for example, by performing interpolation (objective analysis) on a time axis or a space axis based on irregularly distributed observation data. The meteorological element may include temperature, atmospheric pressure, wind speed, humidity, sunshine duration, trunk temperature, discomfort index, and the like. In addition, the weather element value may include statistical information such as a maximum value, a minimum value, an average value, a previous day difference, a day difference, a slope, and a standard deviation.

  The safety system 40 is a computer that collects case data including the date and time of occurrence of an occupational accident, place of occurrence, type and content, and provides the case data to the risk prediction activity support system 20 as required. The safety system 40 is, for example, a construction company server. The date and time of occurrence of occupational accident, location, type, and content will be described in detail in relation to the case database shown in FIG.

(Configuration of risk prediction activity support system)
With reference to FIGS. 2-7, the structure of the danger prediction activity support system 20 is demonstrated in detail.
FIG. 2 is a diagram illustrating a hardware configuration example of a computer used as the danger prediction activity support system 20. The danger prediction activity support system 20 includes a CPU 201, a memory 202, a storage device 203, a communication device 204, an input device 205, and an output device 206.

The CPU 201 implements various functions of the risk prediction activity support system 20 described later by reading various programs into the memory 202 and executing them.
The storage device 203 is, for example, a hard disk drive, a solid state drive, or a flash memory that stores various data and programs.
The communication device 204 is an interface for connecting to a communication network. For example, an adapter for connecting to Ethernet (registered trademark), a modem for connecting to a public telephone line network, a wireless communication device for performing wireless communication, A USB (Universal Serial Bus) connector for serial communication, an RS232C connector, or the like.
The input device 205 is, for example, a keyboard, a mouse, a touch panel, a button, or a microphone that inputs data.
The output device 206 is, for example, a display, a printer, or a speaker that outputs data.

  FIG. 3 is a diagram illustrating a software configuration example of the risk prediction activity support system 20. The risk prediction activity support system 20 includes a case registration unit 211, a prediction model creation unit 212, a weather prediction acquisition unit 213, an evaluation value calculation unit 214, a case extraction unit 215, an explanation acquisition unit 216, and a risk prediction activity (KY) support information transmission. A unit 217, a case database (DB) 231, a weather performance database (DB) 232, an evaluation model storage unit 233, and an explanation database (DB) 234 are configured.

  Each function of the case registration unit 211, the prediction model creation unit 212, the weather prediction acquisition unit 213, the evaluation value calculation unit 214, the case extraction unit 215, and the explanation acquisition unit 216 stores the execution program stored in the storage device 203 by the CPU 201. It implement | achieves by reading to 202 and performing. The function of the KY support information transmission unit 217 is realized by the communication device 204. Each function of the case DB 231, the weather performance DB 232, the evaluation model storage unit 233, and the explanation DB 234 corresponds to the function of the storage device 203. Hereinafter, these functional units will be described.

The case registration unit 211 receives case data from the safety system 40 and registers it in the case DB 231. The case DB 231 will be described with reference to FIG.
FIG. 4 is a diagram illustrating a configuration example of the case DB. The case DB 231 is a collection of occupational accident cases such as accidents and near misses, for example, which stores the occupational accidents included in the example data in association with the position on the map where the occupational accident occurred.

  Specifically, the case registration unit 211 includes information indicating the date and time of occurrence of an occupational accident (accident), the location, type, and content. Here, the place may be an administrative division such as a municipality or a latitude and longitude. The type is a classification of occupational accidents such as “cut / rub” and is set in advance. The content is a specific content of an occupational accident such as “an operator accidentally cut his hand”.

  Next, the prediction model creation unit 212 creates a statistical model (prediction model) for risk evaluation described later, and stores the statistical model in the evaluation model storage unit 233. In the present embodiment, as the prediction model, the distribution of each weather element corresponding to the type of occupational accident is assumed. However, the present invention is not limited to this, and statistics indicating the relationship between each weather element and the occurrence of each type of occupational accident. Any information may be used. A specific operation example of the prediction model creation unit 212 will be described with reference to FIG.

  The weather prediction acquisition unit 213 receives prediction data indicating the predicted value of the weather element in the field from the weather system 30. This prediction data is used for calculation of the risk level described later. It is assumed that the weather prediction acquisition unit 213 receives this prediction data when the danger prediction activity support system 20 is activated or at a predetermined time (for example, 8:00 am).

  The evaluation value calculation unit 214 evaluates the risk of occurrence of the type of work accident according to the type of work accident based on the predicted value of the weather element described above. Here, the degree of risk is an index indicating the probability (risk) that the type of occupational accident will occur under the predicted weather factors. In the present embodiment, the degree of risk is evaluated by comparing the predicted value of the weather element with the distribution of the weather element at the time of occurrence of a specific type of work accident indicated by the evaluation model created by the prediction model creation unit 212. A specific procedure for calculating the degree of risk will be described with reference to FIGS. 10 and 11.

  The case extraction unit 215 reads a specific case related to the selected type of work accident from the case DB 231. In the present embodiment, the case extraction unit 215 reads one case corresponding to the type with the highest degree of risk at random. However, as long as the user's attention is not excessively distributed, a plurality of cases may be read. Good. In addition, the case extraction unit 215 may read one or a plurality of cases corresponding to a plurality of types in descending order of risk.

  The explanation acquisition unit 216 acquires, from the explanation DB 234, explanations of meteorological elements (explanation data) that can cause the selected type of work accident. In the present embodiment, since it is assumed that an explanation for one weather element is registered in the explanation DB 234, the explanation acquisition unit 216 obtains an explanation for one weather element. However, when the description DB 234 registers the description in association with a combination of a plurality of weather elements, the description acquisition unit 216 may acquire descriptions for the plurality of weather elements. An explanation acquisition procedure by the explanation acquisition unit 216 will be described with reference to FIG.

  The KY support information transmitting unit 217 transmits information for supporting risk prediction (hereinafter referred to as KY support information) to the user terminal 10. In the present embodiment, it is assumed that the KY support information includes a predicted value of a weather element, a selected occupational accident example, and a description of the weather element.

Next, functions related to the storage device 203 will be described with reference to FIGS.
FIG. 5 is a diagram illustrating a configuration example of the weather performance DB. The meteorological record DB 232 stores record data received from the meteorological system 30 and indicating the record of meteorological elements. Here, the results of meteorological elements are observed values and analytical values of meteorological elements (such as temperature) at a predetermined location (such as the location of the site) at a predetermined date and time, and are explanatory variables for correlation analysis described later. Become.

  FIG. 6 is a diagram illustrating a configuration example of the evaluation model storage unit 233. The evaluation model storage unit 233 stores correlated weather elements and information indicating the correlation for each type of occupational accident. In the present embodiment, it is assumed that the degree of variation in weather elements is assumed as information indicating the correlation, and the maximum value, average value, and minimum value of the weather elements are employed as an example.

For example, in FIG. 6, a maximum value “24”, an average value “14”, and a minimum value “10” are stored for the weather element “maximum temperature” that can be related to the type of work accident of “cut / rub”. However, the first quartile may be adopted instead of the maximum value, or the mode value may be adopted instead of the average value. Or you may make it the evaluation model memory | storage part 233 memorize | store the regression coefficient by multiple regression as correlation information. Further, as correlation information, for example, values indicating various probability distributions such as average and variance can be stored.
As will be described later, in the present embodiment, scoring is performed by assigning a higher score to each weather element closer to the average of the distribution, thereby evaluating the risk of occurrence of a specific type of occupational accident.

  FIG. 7 is a diagram illustrating a configuration example of the explanation DB 234. The explanation DB 234 is a database that stores comments that explain the reasons that can cause the occupational accident that is assumed when an occupational accident occurs under the condition of a predetermined weather element.

  The comment may be described from the viewpoint of biometeorology, for example. Biometeorology is an academic field that examines the effects of meteorological phenomena on living bodies. In this case, the explanation DB 234 describes what kind of body reaction can occur when each meteorological element is in what condition. For example, in FIG. 7, when the meteorological element “day difference in atmospheric pressure” reaches “−10 hPa” or “+10 hPa”, “when the pressure difference from the previous day is large, the balance of the autonomic nerve is ...”. Explained. In addition, the weather element may be an explanation for a value at a specific time point instead of a difference. For example, it may be an explanation that a temperature is 0 ° C. and the object is simply dropped and the hand is not moved. The comment may be an explanation that is not based on biometeorology, for example, an explanation that an object is blown off due to a strong wind.

  In the present embodiment, it is assumed that an explanation for one weather element is registered in the explanation DB 234, but the explanation may be registered in the explanation DB 234 in association with a combination of a plurality of weather elements.

(Operation of the risk prediction activity support system)
The operation of the danger prediction activity support system 20 will be described with reference to FIGS.
The operation of the risk prediction activity support system 20 is largely performed, a statistical analysis is performed based on case data of occupational accidents and actual data of meteorological elements, and a procedure for creating a risk evaluation model (prediction model), and prediction of meteorological elements The procedure is divided into the procedure for evaluating the risk by applying the data to the evaluation model. Hereinafter, these procedures will be described in order.

With reference to FIG. 8, a procedure for creating an evaluation model by statistical analysis will be described.
FIG. 8 is a flowchart showing the flow of analysis processing. Here, the weather record is assumed to be registered in the weather record DB 232 in advance (see FIG. 5).

  First, the case registration unit 211 receives work accident case data from the safety system 40 and registers it in the case DB 231 as shown in FIG. 4 (step S11).

  Next, the prediction model creation unit 212 extracts the actual value of the weather element from the weather result DB 232 (step S12), and creates a weather element for analysis from the extracted actual value of the weather element (step S13). Here, the creation of the weather element for analysis includes calculating a calculated value such as a difference between the weather elements. As a specific example, using the actual data stored in the meteorological result DB 232, the actual value of the previous day's pressure is subtracted from the actual value of the previous day's atmospheric pressure, and the previous day's pressure difference is calculated. For example, the average value may be calculated, or the variance, average, and trend (slope of the approximate straight line) may be calculated from the actual temperature values for 72 hours. In addition, what is necessary is just to utilize this provision data, when the data which show such a calculated value are previously provided from the weather system 30. FIG.

  And the prediction model preparation part 212 calculates | requires distribution of each meteorological element when the said working accident occurs for every kind of working accident (step S14). This procedure is an example of statistical analysis for determining the relationship between each weather element and the occurrence of occupational accidents by type. As a result, the evaluation model (distribution) illustrated in FIG. 6 is obtained.

  Alternatively, as a modified example, the prediction model creation unit 212 may cause a specific type of occupational accident under each weather element by using an analysis method / learning method such as multiple regression analysis or neural network (deep learning). You may create the evaluation model which predicts.

  The prediction model creation unit 212 registers the created evaluation model in the evaluation model storage unit 233 (step S15), and the series of evaluation model creation procedures ends.

  Next, a procedure in which the KY support system 20 distributes information to the user terminal 10 will be described with reference to FIGS. This information distribution process may be executed at a preset time (for example, every morning at 6 o'clock), or may be performed in response to a request from the user terminal 10.

FIG. 9 is a flowchart showing the overall flow of the information distribution process.
First, the weather prediction acquisition unit 213 acquires a predicted value of a weather element from the weather system 30 (step S21).

  Next, the evaluation value calculation unit 214 creates a weather element for analysis (step S22). The creation of the meteorological element for analysis includes processing for calculating various calculated values such as the difference and variance of the meteorological elements, as in step S13 of FIG. Here, however, the forecast value of the weather element is used.

  Then, the evaluation value calculation unit 214 calculates the risk level for each type of occupational accident (step S23). The degree of risk is calculated based on the distribution (that is, the evaluation model) of each weather element that can be related to each type of work accident and the predicted value of the weather element. In the present embodiment, the similarity between the meteorological element at the time of occurrence of this type of occupational accident and the meteorological element on the prediction target day is evaluated as the risk level. Specifically, for each meteorological element, the risk level is calculated by how close it is to the average of the distribution of the meteorological element when the type of occupational accident occurred in the past (how small the divergence is) (See FIG. 11).

Here, with reference to FIG.10 and FIG.11, the calculation process of a risk degree is demonstrated concretely.
FIG. 10 is a flowchart showing the flow of the risk degree calculation process. FIG. 11 is an explanatory diagram of the divergence degree.

  First, the evaluation value calculation unit 214 calculates the degree of deviation from the average of predicted values of each weather element that can be related to a certain type of occupational accident (step S31 in FIG. 10).

  In the present embodiment, the divergence degree is a value obtained by standardizing divergence from the average of predicted values (absolute value of the difference) in a weather element that may be related to a corresponding type of work accident. For example, in an evaluation model, for an occupational accident of “cut / rub”, the weather element “maximum temperature” has a distribution of a maximum value “24”, an average value “14”, and a minimum value “10”. (See FIG. 6). As shown in FIG. 11, when the predicted value of the maximum temperature at a certain site is 12 ° C., the deviation of the predicted value from the average in this distribution is 2 ° C., and the range from the highest value to the lowest value is 12 ° C. Therefore, the deviation degree obtained by standardizing the deviation as a percentage is 14.3% (= (average 14 ° C.−predicted value 12 ° C.) / Width from the highest value to the lowest value 14 ° C.).

  Returning to the risk degree calculation process, the evaluation value calculation unit 214 averages the calculated divergence degrees (step S32), and calculates the risk degree from the average divergence degree (step S33). Here, the degree of divergence may be expressed as a percentage. In this case, the degree of risk is calculated by a calculation formula of “risk degree = 1− (total of divergence degrees)”.

  Then, the evaluation value calculation unit 214 repeats steps S31 to S33 for each type of occupational accident, and the series of risk level calculation processing ends.

  Returning further to the information distribution process, the evaluation value calculation unit 214 selects the type of occupational accident with the highest degree of risk (step S24 in FIG. 9).

  Then, the explanation acquisition unit 216 acquires the explanation of the weather element having the smallest degree of divergence (calculated in step S31 in FIG. 10) regarding the type of work accident selected in step S24 (step S25).

Here, with reference to FIG. 12, the acquisition process of the description of a specific weather element is demonstrated concretely.
FIG. 12 is a flowchart showing the flow of the explanation acquisition process.

  First, the explanation acquiring unit 216 selects a weather element having the smallest degree of divergence (hereinafter, also referred to as “selection element”) among the weather elements corresponding to the selected type of work accident (step S41). That is, the explanation acquisition unit 216 extracts, as selection elements, weather elements that are predicted to have the greatest risk of causing the selected type of work accident based on the degree of divergence.

  Next, the explanation acquisition unit 216 searches the explanation DB 234 for explanations corresponding to the selected elements (step S42). If there is no explanation corresponding to the selected element, the explanation acquiring unit 216 extracts the weather element having the next smallest divergence as the selected element (step S43), and executes the above-described step S42 again. And the description acquisition part 216 will complete | finish a series of description acquisition processes, if applicable description is discovered in step S42.

In the present embodiment, the description acquisition process corresponding to the selected element has been described on the assumption that the description for one weather element is registered in the description DB 234, but the present invention is not limited to this. For example, if the explanation is registered in the explanation DB 234 in association with a combination of a plurality of weather elements, instead of searching for the weather elements one by one as in the process described here, What is necessary is just to search description DB234 for the description corresponding to a combination. For example, the explanation acquisition unit 216 may search explanations for all combinations of meteorological elements, or, for all records in the explanation DB 234, determines whether meteorological elements are satisfied and corresponds to the satisfied records. You may make it select the description to carry out.
The explanation acquisition unit 216 may select explanations corresponding to as many weather elements as possible. For example, the explanation acquisition unit 216 selects a predetermined number of weather elements (for example, any value such as 3, 5, etc.) in ascending order of the divergence degree, and selects a predetermined weather element from the selected weather elements. The explanation for n combinations less than or equal to the number is registered in the explanation DB 234, and it is determined whether the meteorological elements of the combination satisfy the conditions indicated by the range of the explanation DB 234, and the largest number of meteorological elements satisfying the conditions The description corresponding to the combination of can be selected.

  When returning to the information distribution process again, the case extraction unit 215 reads out the case of the selected type of work accident (step S26 in FIG. 9). In the present embodiment, one case is read, but the case extraction unit 215 may read a plurality of cases as long as the user's attention is not distracted.

  Then, the KY support information transmitting unit 217 transmits KY support information that combines the predicted value of the weather element described above, the selected occupational accident example, and the description of the selected weather element to the user terminal 10 (step S27), A series of information distribution processing ends. The KY support information transmission unit 217 can transmit the KY support information to the user terminal 10 by, for example, an e-mail, a short mail, a message using SNS, a push notification to the smartphone, or the like.

As a result, for example, the following KY support information is displayed on the screen of the user terminal 10.
“Today ’s weather near the scene:
The clouds will spread slightly due to atmospheric pressure valleys and moist air, but there will be sunny days.
High-risk work accidents:
Today, the highest temperature is expected to be around 15 degrees. Since the temperature has risen from the last few days, the body cannot keep up with changes in temperature and the balance of autonomic nerves seems to be disturbed. Attention may be distracted and a property damage accident may occur.
Examples of occupational accidents during past similar weather:
When I parked while going backward on the truck, I hit the rear fence. "

  As described above, in this embodiment, an example of an occupational accident is presented based on the predicted value of the weather element. By outputting cases of past occupational accidents in the same weather element, it is possible to refer to cases that should be particularly referred to from among huge past accident cases on the site. Therefore, it is possible to have a sense of danger at the site, and the risk prediction activity can be more effectively advanced.

  Moreover, in this embodiment, the description regarding the meteorological element corresponding to the presented example (for example, comment from the viewpoint of biometeorology) is also presented. By explaining why the meteorological factors lead to occupational accidents, for example, from the viewpoint of biometeorology, it is possible to get a sense of conviction for the connection between meteorological factors and occupational accidents.

  In the present embodiment, the weather element to be considered includes the time change of the weather element. For example, even if the temperature and pressure are the same, the influence of the habituation on the worker's body and the influence on the body / mental spirit and behavior will change according to the magnitude of the change. Therefore, it is possible to appropriately evaluate the risk of occupational accidents by taking into account the time difference between weather factors.

  In this embodiment, in evaluating the risk of occupational accidents, statistical analysis such as correlation analysis between types of occupational accidents and meteorological elements is performed, and an evaluation model (for example, distribution) and weather obtained as an analysis result are analyzed. The risk of occupational accidents is evaluated based on the predicted values of the elements. There are a wide variety of weather factors that can cause occupational accidents, and the risk of occupational accidents can be appropriately evaluated by comparing the distribution for each weather element and a predicted value for a specific type of occupational accident.

Although the present embodiment has been described above, the above embodiment is intended to facilitate understanding of the present invention and is not intended to limit the present invention. The present invention can be changed and improved without departing from the gist thereof, and the present invention includes equivalents thereof.
For example, in the present embodiment, it is assumed that the risk prediction activity at the construction site is supported. However, the present invention is not limited to this, and can also be applied to support the risk prediction activity at the production site such as a factory. . In addition, it is possible to notify the student of the danger as an activity for informing the danger at the time of going to and from school at the school. Furthermore, in service facilities such as shopping centers, it is possible to provide support for employees to conduct risk prediction activities, and it can also be used as an activity for notifying customers in advance of danger.
In the present embodiment, the KY support information includes the predicted value of the weather element, the selected example of the occupational accident, and the description of the selected meteorological element, but includes only the selected example of the occupational accident. Two cases of the case and the predicted value of the weather element may be included, or two of the case and the description may be included.
In the present embodiment, the risk prediction support is a process up to once transmitting the KY support information to the user terminal 10. For example, a response indicating that the user has confirmed the KY support information is received from the user terminal 10. Thus, the KY support information transmitting unit 217 may retransmit the KY support information by the same method or a different method when a reply is not received from the user terminal 10 within a predetermined time.
In the present embodiment, the KY support information is a sentence expressed by a character string, but is not limited thereto, and may be graphic information using a graph or a chart. For example, displaying a mark representing the weather, displaying a pictogram representing a high-risk occupational accident, displaying a graph indicating the risk of one or more occupational accidents, representing characteristic weather conditions The KY support information can include at least one of displaying an illustration, a graph, a weather map, and the like, and displaying an illustration indicating the state of an occupational accident.
In the present embodiment, when the explanation corresponding to the selected element is not registered in the explanation DB 234, the explanation is searched by changing the selected element (see FIG. 12). However, the explanation corresponding to the selected element is used. May not be registered in the explanation DB 234, the fact may be notified, an explanation input from the operator may be accepted, and the accepted explanation may be registered in the explanation DB 234.
In addition, regarding the type of occupational accident included in the KY support information, the user terminal 10 is used to input an action (measure) for avoiding the risk determined as the risk prediction activity by the user, and the countermeasure is taken from the user terminal 10. You may make it transmit to the danger prediction activity support system 20, and the danger prediction activity support system 20 may be provided with the countermeasure storage part which accumulate | stores the said countermeasure.
In addition, before transmitting the KY support information, the risk prediction activity support system 20 receives an action (measure) for avoiding the risk determined as the risk prediction activity by the user from the user terminal 10, and Is attached to the types of occupational accidents to be avoided, and the risk prediction activity support system 20 selects and selects a predetermined number of types of occupational accidents in descending order of risk calculated in step S23 of FIG. A determination unit that determines whether or not a countermeasure against a work accident with a high risk has been considered in the field depending on whether or not a countermeasure for the type has been received from the user terminal 10 can be provided. In this case, when the determination unit determines that a countermeasure for a high-risk work accident is not considered at the site, the KY support information transmission unit 217 notifies the user terminal 10 to that effect and predicts the risk. You may make it alert about activity.

DESCRIPTION OF SYMBOLS 10 User terminal 20 Danger prediction activity (KY) support system 30 Weather system 40 Safety system 212 Prediction model creation part 214 Evaluation value calculation part 215 Case extraction part 216 Explanation acquisition part 231 Case database (DB)
232 Weather database (DB)
233 Evaluation model storage unit 234 Explanation database (DB)

Claims (7)

A case database that stores past cases of occupational accidents that occurred in association with the results information of meteorological elements,
A case extraction unit that extracts the case based on the forecast information of the weather element at a predetermined site;
An output unit for outputting the extracted case;
A risk prediction activity support system comprising: An explanation database storing explanation information explaining the influence of the weather element on the living body,
An explanation acquisition unit that reads the explanation information of the meteorological element corresponding to the extracted case;
The danger prediction activity support system according to claim 1, wherein the output unit further outputs the read explanation information. The track record information includes a difference between the track record information at the site of occurrence at the time of occurrence of the industrial accident and the track record information at the site of occurrence at a time point before the time of the occurrence of the accident,
The danger prediction activity support system according to claim 1, wherein the case extraction unit extracts the case based on a difference between the prediction information and the performance information. For each type of occupational accident, a predictive model creation unit that creates a statistical model indicating the relationship between the type of occupational accident and the performance information;
An evaluation unit that evaluates the probability of occurrence of each type of work accident based on the prediction information and the statistical model;
The danger prediction activity support system according to any one of claims 1 to 3, wherein the case extraction unit extracts the disaster case based on a result of the evaluation. The risk prediction activity support system according to claim 4, wherein the evaluation unit performs the evaluation based on a degree of deviation of the prediction information of the weather element from a representative value of the weather element for each type of the work accident. Computer
Extracting the case from a database that stores past examples of occupational accidents that occurred in the past in association with the weather element based on the forecast information of the weather element at a predetermined site;
Outputting the extracted case and the prediction information;
To support risk prediction activities. Against the computer,
Extracting the case from a database that stores cases of occupational accidents that occurred in the past in association with the weather element based on the predicted value of the weather element at a predetermined site;
Outputting the extracted case and the prediction information;
A program for running