JP6833605B2 - Surface contamination density distribution calculation device, method and program - Google Patents

Description

An embodiment of the present invention relates to a technique for calculating the surface contamination density distribution of radioactivity based on the measured air dose rate.

When radioactive contamination occurs in facilities that handle radioactive substances, the surface contamination density is evaluated to manage the leaked radioactive substances, and measures such as decontamination, shielding, and access restrictions are taken as necessary. To reduce the exposure of workers working there.
However, when the pollution range is wide, the decontamination and shielding work itself causes an increase in the exposure of workers. Therefore, by formulating a work plan based on an appropriately evaluated surface contamination density distribution, the decontamination and shielding work will be made more efficient and the exposure of workers will be reduced.

The surface contamination density distribution is generally calculated by measuring radiation while scanning the surface of a controlled area with a radiation inspection device such as a GM tube detector.
However, since it takes time to scan the inspection device over the entire controlled area, a technique for calculating the surface contamination density distribution based on the air dose rate, which can be measured relatively easily, has been proposed. ..

For example, there is known a technique for estimating the surface pollution density using a change in air dose rate data measured at different heights and a function that can be calculated in advance by assuming the shape of the pollution source. There is. According to this technique, the surface contamination density can be estimated very easily because the measured dose data is only input to the pre-calculated mathematical formula.
Further, in the same manner as described above, a technique is known in which measurement data of a plurality of air dose rates having different heights and horizontal directions are used as input information, a response function thereof is created, and the surface contamination concentration is calculated by the maximum likelihood method. ing.

JP 2015-001500 Japanese Unexamined Patent Publication No. 2016-0756332

However, in the actual measurement data of the air dose rate, not only the radiation directly incident from the radiation source but also the radiation scattered or transmitted through the structure such as the wall or the ceiling is counted. For this reason, the above-mentioned technique has a problem that a surface contamination density distribution satisfying the required accuracy cannot be obtained in a building having a complicated structure.
On the other hand, it is possible to improve the accuracy of the surface contamination density distribution by creating a response function based on information on structures and the like. However, in the prior art, there was an antinomy relationship between the creation time and the evaluation accuracy of the response function that expresses the relationship between the air dose rate and the contamination concentration. Therefore, it is not realistic to individually determine the response function having sufficient accuracy for each of the objects having various environmental conditions from the viewpoint of the measurement efficiency of the surface contamination density distribution.

The embodiment of the present invention has been made in consideration of such circumstances, and even in a controlled area where a complicated structure exists, the surface contamination density distribution of radioactivity is increased based on the measured air dose rate. It is an object of the present invention to provide a technique capable of calculating with accuracy and easily.

In the surface contamination density distribution calculation device, a first memory area for holding the first air dose rate distribution data created by measuring air dose rates at a plurality of positions in the target area and a first structure arranged in the target area. The second memory area where the first structure data in which the object is set in the same coordinate system as the first air dose rate distribution data is held, the second air dose rate distribution data in the model area, and the second in the model area. A third memory area in which an arithmetic expression showing the relationship between the structure data and the second surface contamination density distribution data of the model area is held, and the first air dose rate distribution data and the first structure data in the arithmetic expression. Is provided, and a first calculation unit for outputting the first surface contamination density distribution data of the target area is provided.

An embodiment of the present invention provides a technique capable of easily and accurately calculating the surface contamination density distribution of radioactivity based on the measured air dose rate.

The block diagram which shows the surface pollution density distribution calculation apparatus which concerns on 1st Embodiment of this invention. A perspective view of a target area where the surface contamination density distribution of radioactive substances is calculated based on the measured first air dose rate distribution data. Top view of the model area where the relationship between the second air dose rate distribution, the second structure and the surface contamination density distribution is known. A graph showing the average value of the dose rate distribution with respect to the maximum value of the dose rate distribution in the data group with less pollution and the data group with strong pollution. A graph showing the distribution of air dose with respect to the measurement position in the data group with less pollution and the data group with strong pollution. The block diagram which shows the surface pollution density distribution calculation apparatus which concerns on 3rd Embodiment. The flowchart explaining the surface pollution density distribution calculation method and a program.

(First Embodiment)
Hereinafter, embodiments of the present invention will be described with reference to the accompanying drawings.
As shown in FIG. 1, the surface contamination density distribution calculation device 10 according to the first embodiment measures the air dose rate at a plurality of positions in the target area 31 (FIG. 2) and creates the first air dose rate distribution data D1. The first memory area 11 in which the data is stored and the first structure data D2 in which the first structure 32 arranged in the target area 31 is set in the same coordinate system as the first air dose rate distribution data D1 are stored. The relationship between the second memory area 12 and the second air dose rate distribution data S1 in the model area 35 (FIG. 3), the second structure data S2 in the model area 35, and the second surface contamination density distribution data S3 in the model area 35. The first memory dose rate distribution data D1 and the first structure data D2 are input to the third memory area 13 in which the calculation formula T shown is held, and the first surface pollution density distribution data D3 in the target area 31. It is provided with a first calculation unit 21 for outputting the above.

As shown in FIG. 2, a plurality of dosimeters 36 (only one in the figure) are arranged two-dimensionally or three-dimensionally in the target area 31 for evaluating the surface contamination density. The plurality of air dose rate values measured by these dosimeters 36 are associated with the coordinate values of the corresponding dosimeters to form the first air dose rate distribution data D1. In this embodiment, an example in which a plurality of dosimeters 36 are arranged two-dimensionally or three-dimensionally is shown, but the air dose rate at a plurality of two-dimensional or three-dimensional positions in the target area is measured. I don't mind if possible. That is, in the present embodiment, one dosimeter 36 may be sequentially moved to a plurality of two-dimensional or three-dimensional positions to measure a plurality of air dose rates.

The interval between the plurality of dosimeters arranged in the target area 31 is not particularly limited, but the more the first air dose rate distribution data D1 obtained by increasing the number of points and making this interval denser, the more final. The accuracy of the first surface contamination density distribution data D3 guided by the above is improved.
The first air dose rate distribution data D1 measured in this way is held in the first memory area 11.

The first structure 32 is arranged around and / or inside the target area 31, and the first structure data D2 set in the same coordinate system as the first air dose rate distribution data D1 is the second. It is held in the memory area 12.
The first structure data D2 includes shape information and position information of a structure that interacts with gamma rays such as a ceiling and a wall, and further interacts (transmission) with gamma rays such as the density and material of the structure. , Scattering, etc.) are also included.

Since gamma rays emitted from radioactive substances existing as a dotted line source in space are emitted isotropically, the air dose rate (Sv / h) is attenuated in inverse proportion to the square of the distance from this dotted line source. I will go. Further, as shown in FIG. 2, in the target area 31 actually contaminated with radioactivity, the radioactive substance 33 may be present in a plane spread over a certain range, and does not always exist as a dotted source.

The air dose rate measured in the target area 31 is counted not only when the gamma rays emitted from the radioactive material 33 are directly counted, but also when the gamma rays scattered by the first structure 32 such as a wall are superimposed. It is a thing. Further, the radioactive sources existing in the target area 31 may be dispersed in a plurality of radiation sources.
^{Therefore, when obtaining the surface contamination density (Bq / m 2} ), which is the contamination concentration of the radioactive substance 33 existing in the target area 31 by conversion from the measured air dose rate, it is necessary to consider the above-mentioned conditions. ..

The model area 35 shown in FIG. 3 is different from the target area 31, and the relationship between the second air dose rate distribution data S1, the second structure data S2, and the second surface contamination density distribution data S3 is known. It is a system.
These three data S1, S2, and S3 can be obtained based on simulation analysis as well as the case based on actual measurement data. Monte Carlo analysis, which is an example of popular simulation analysis, virtually arranges the second structure data S2 in the model area 35 by considering the interaction between substances such as permeation and shielding. It is possible to calculate the second air dose rate distribution data S1 by the gamma rays emitted from the set second surface pollution density distribution data S3 with high accuracy.

More specifically, the floor surface of the model area 35 and the horizontal plane from the floor surface to a predetermined height are divided into an arbitrary number of meshes, and the air dose at which the surface contamination density at the attention position 37 of the floor surface contributes to each mesh. The rate can be determined by analysis. The air dose rate distribution is represented by superimposing the air dose rate due to gamma rays emitted from the contamination existing in the entire model area 35 for each mesh.

Furthermore, multiple regression analysis is applied to the system of a plurality of model areas 35 (35a, 35b, 35c ...), And the size information, distance information, material information, etc. of the second air dose rate distribution data S1 and the second structure data S2 are obtained. As an input parameter, machine learning processing is performed so that the surface contamination density at the attention position 37 becomes an output, and the calculation formula T is created.

There is a correlation between the surface contamination density and the air dose rate distribution in the area where various structures are arranged, and it is possible to formulate the relationship between the two by machine learning such as a regression equation.
As such machine learning, the correlation equation may be calculated by multiple regression analysis or deep learning may be used, and not only the continuous value is output but also the contamination concentration is classified according to the level. Good.
In this way, the arithmetic expression T showing the relationship between the second air dose rate distribution data S1, the second structure data S2, and the second surface contamination density distribution data S3 of the model area 35 is a plurality of model areas 35 (35a, 35a, It is created by machine learning from 35b, 35c ...). Then, this arithmetic expression T is held in the third memory area 13.

The second air dose rate distribution data S1 can be set by rearranging according to the relative position with respect to the attention position 37 or by sorting by the absolute position.
Further, as the nuclides of the radioactive substances constituting the second surface contamination density distribution data S3, nuclides having different energies and release rates such as Cs137, Cs134 and Co60 can be set.

Further, as the second structure data S2, a plurality of parameters can be set and learned for the material of the structure and the position of the attention position 37 within the range of the assumed conditions, and the calculation formula T can be calculated.
This makes it possible to derive an arithmetic expression T that reflects the size of the pollution source and the effects of scattering and transmission by the structure, in addition to the effect that gamma rays spread with the range and the intensity decreases. This arithmetic expression T is generalized without being limited to a specific area or structure.

The calculation formula T defines the distance information in the target area 31 of the first structure 32 as a parameter, defines the average dose value in the first air dose rate distribution data D1 as a parameter, and defines the first air dose rate distribution data. The distance information in D1 is specified as a parameter, the ratio of the average dose value to the maximum dose value in the first air dose rate distribution data D1 is specified as a parameter, and the type of radioactive nuclei existing in the target area 31 is specified as a parameter. Can be specified.

The first calculation unit 21 acquires the first air dose rate distribution data D1 from the first memory area 11 and acquires the first structure data D2 from the second memory area 12. Then, the first calculation unit 21 inputs these data into the calculation formula T and outputs the first surface contamination density distribution data D3 of the target area 31. The output first surface contamination density distribution data D3 is overwritten and displayed on the map of the target area 31 on the display unit 28.

According to the first embodiment, an arithmetic expression T representing the relationship between the air dose rate distribution data, the structure data, and the surface contamination density distribution data is created in advance by machine learning for a plurality of model areas 35. Then, the surface contamination density distribution data in the target area 31 can be derived only by inputting the value of the air dose rate distribution data measured in the target area 31 into the calculation formula T.
Therefore, it is not necessary to accurately obtain the response function between the air dose rate distribution data and the surface contamination density distribution data in consideration of the structure arranged in the target area 31 as in the conventional case. As a result, even in a controlled area where a complicated structure exists, for example, the surface contamination density distribution data considering the complicated interaction such as scattering and transmission of gamma rays in the structure arranged in the target area 31 can be obtained. It can be guided instantly without taking time.

(Second Embodiment)
Next, a second embodiment of the present invention will be described with reference to FIG.
In the third memory area 13 of the surface contamination density distribution calculation device according to the second embodiment, a plurality of arithmetic expressions T selected based on at least one of the first air dose rate distribution data D1 and the first structure data D2. (T1, T2, T3 ...) Are held, and the first calculation unit 21 selects and executes the calculation formula T from a plurality of meshes for each mesh in which the target area 31 is divided.

When only one calculation formula T for calculating the surface pollution density, which is the pollution concentration, is calculated by machine learning, the parameters are diverse, so it is calculated depending on the variation of various conditions such as pollution strength and measurement conditions. The accuracy of surface contamination density may deteriorate.
Therefore, the data for creating the calculation formula T (second air dose rate distribution data S1, second structure data S2, second surface contamination density distribution data S3) are classified into a plurality of groups according to the conditions. Then, the calculation formula T is calculated for each of the classified groups.

As a classification method of this group, for example, a classification based on the degree of gamma ray scattering in which a structure such as a wall affects the air dose rate may be considered. This gamma-ray scattering affects the air dose rate within a few meters near the wall, but the effect diminishes away from this range.

In order to obtain the arithmetic expression T in which such an influence is surely corrected, the model area 35 is classified into a group having a large influence of gamma ray scattering in the structure and a group having a small influence, and then machine learning is performed to obtain the arithmetic expression T ( It is effective to calculate T1, T2, T3 ...). In this case, when incorporating the distance from the structure (wall, etc.) as the parameter of the regression equation, the reciprocal of the distance or the power of the distance, etc., is applied according to the classified group.

An error is included in the first surface contamination density distribution data D3 that is output by inputting the first air dose rate distribution data D1 and the first structure data D2 into the arithmetic expression T. One of the causes of this error is that the attention position 37, where the level of the surface contamination density is zero or low, is affected by the gamma rays emitted from the surrounding contamination points, and the level of the surface contamination density is overestimated.

In order to reduce such error factors, the first air dose rate distribution data D1 is evaluated in advance in mesh units, classified into groups according to the distribution method, and the calculation formula T (T1, T1) corresponding to each group. T2, T3 ...) Are selected and the calculation is executed in the first calculation unit 21.
Examples of such a group classification method include a method of evaluating the correlation between the average value and the maximum value of the dose data of the first air dose rate distribution data D1.

The graph of FIG. 4 shows the average value of the dose rate distribution with respect to the maximum value of the dose rate distribution in the data group with less pollution and the data group with strong pollution.
When there is little contamination around the attention position 37 (FIG. 3) as described above, the ratio between the maximum value and the average value does not increase, and a substantially linear correlation can be obtained. On the other hand, when there is contamination in the vicinity of the attention position 37, the air dose rate near the contaminated location changes significantly, so that the ratio of the maximum value to the average value becomes large.
Using this relationship, the model area 35 mesh is classified into several groups according to the threshold value set for the maximum / average value of the air dose rate distribution, and calculation is performed for each group. Formula T (T1, T2, T3 ...) Is created. This makes it possible to improve the evaluation accuracy of pollution.

The graph of FIG. 5 shows the distribution of the air dose with respect to the measurement position in the data group with less pollution and the data group with strong pollution.
In the vicinity of the contaminated location, the rate of change in the air dose rate is greatly measured. Therefore, the evaluation accuracy of the surface contamination density can be improved by using the values of the first-order and second-order differentials of the air dose rate depending on the position as the threshold value of the group classification as the rate of change of the air dose rate.
Such classification of the model area 35 into groups can be combined with classification by distance from the structure and classification by air dose rate distribution as shown in FIG. 4, and for each group classified in this way. It is also possible to calculate the calculation formula T. Furthermore, it is possible to combine classifications based on nuclide data.

According to the second embodiment, it is possible to reduce the influence of gamma ray scattering by a structure such as a wall and the overestimation of the surface contamination density of the evaluation position due to the surrounding contamination. That is, the evaluation accuracy can be improved by creating a plurality of arithmetic expressions T corresponding to the conditions in which an error is likely to occur.

(Third Embodiment)
Next, a third embodiment of the present invention will be described with reference to FIG. In FIG. 6, the parts having the same configuration or function as those in FIG. 1 are indicated by the same reference numerals, and duplicate description will be omitted.
The surface pollution density distribution calculation device 10 according to the third embodiment further inputs the first structure data D2 and the first surface pollution density distribution data D3 into the calculation formula T and outputs the virtual air dose rate distribution data D4. The second calculation unit 22, the difference calculation unit 25 that calculates the difference value D5 between the virtual air dose rate distribution data D4 and the first air dose rate distribution data D1, and the calculation formula T based on the difference value D5. A correction unit 26 for correction is provided.

Since the calculation formula T is created from the data of a plurality of model areas 35 (35a, 35b, 35c), the relationship between the air dose rate distribution and the surface contamination density distribution of the target area 31 to be evaluated for contamination There is no guarantee as to whether or not it conforms.
Therefore, the second calculation unit 22 inputs the first structure data D2 and the first surface pollution density distribution data D3 which is the output of the first calculation unit 21, and outputs the virtual air dose rate distribution data D4. Perform the calculation.

If the formula T matches the relationship between the air dose rate distribution of the target area 31 and the surface contamination density distribution, the virtual air dose rate distribution data D4 and the first air dose rate distribution data D1 match. Should do. On the other hand, when the deviation between the virtual air dose rate distribution data D4 and the first air dose rate distribution data D1 is large, it is determined that the calculation formula T is not suitable for the contamination evaluation of the target area 31.

The nonconformity / conformity determination is determined by whether the difference value D5 between the virtual air dose rate distribution data D4 and the first air dose rate distribution data D1 exceeds / does not exceed the threshold value in the difference calculation unit 25. .. This threshold is appropriately determined by the target accuracy.
If it is determined that the product is non-conforming, the correction unit 26 newly applies the correction calculation formula T', which is a correction of the calculation formula T based on the difference value D5.
In this modified calculation formula T', another calculation formula T may be reselected, or the machine learning unit 15 may relearn to create a new calculation formula T.

According to the third embodiment, the surface contamination density distribution is derived from the air dose rate distribution based on the arithmetic expression T suitable for the target area 31, so that the calculation accuracy of the surface contamination density can be improved.

An embodiment of the surface contamination density distribution calculation method and the surface contamination density distribution calculation program will be described with reference to the flowchart of FIG. 7 (see FIGS. 1, 2 and 3 as appropriate).
The air dose rate at a plurality of positions in the target area 31 is measured (S11). Then, the first air dose rate distribution data D1 of the target area 31 is created and held in the first memory area 11 (S12). Next, the first structure data D2 in which the first structure 32 arranged in the target area 31 is set in the coordinate system common to the first air dose rate distribution data D1 is held in the second memory area 12 (S13).

The second structure data S2 and the second surface contamination density distribution data S3 are set in each of the plurality (n) model areas 35, and the second air dose rate distribution data S1 in each model area 35 is analytically used. Obtain (S14, S15, S16).
By machine learning, an arithmetic expression T showing the relationship between the second air dose rate distribution data S1, the second structure data S2, and the second surface contamination density distribution data S3 of a plurality of (n) model areas 35 was created. It is held in the third memory area 13 (S17).

Next, the first air dose rate distribution data D1 and the first structure data D2 are input to this calculation formula T, and the first surface contamination density distribution data D3 of the target area 31 is calculated (S18) (the above is the first. 1 Embodiment).

Further, the first structure data D2 and the first surface contamination density distribution data D3 are input to the calculation formula T, and the virtual air dose rate distribution data D4 is calculated (S19).
Next, the difference value D5 between the virtual air dose rate distribution data D4 and the first air dose rate distribution data D1 is calculated (S20), and the suitability of the calculation formula T is determined based on this difference value D5 (S21). ). If the nonconformity is determined here, the calculation formula T is corrected and the calculation of (S18) is tried again. Then, if the conformity is determined, the first surface contamination density distribution data D3 calculated most recently in (S18) is adopted as it is, and the flow ends (END).

According to the surface contamination density distribution calculation device of at least one embodiment described above, a model area separate from the target area for evaluating the surface contamination density is assumed, and the air dose rate distribution, the structure and the surface in this model area are assumed. Create an arithmetic expression that expresses the relationship of pollution density distribution by machine learning. Then, using an arithmetic expression, it is possible to output the surface contamination density distribution with high accuracy and easily by using the air dose rate distribution measured in the target area and the structure arranged in the target area as input information. ..

Although some embodiments of the present invention have been described, these embodiments are presented as examples and are not intended to limit the scope of the invention.
For example, in the above description, it is assumed that the area surrounded by the wall as a structure is radioactively contaminated, and an embodiment for deriving the surface contamination density distribution is shown. However, the application of the present invention is this. It is not limited to such a case, and can be applied to any structure arrangement form.

In addition, these embodiments can be implemented in various other embodiments, and various omissions, replacements, changes, and combinations can be made without departing from the gist of the invention. These embodiments and modifications thereof are included in the scope and gist of the invention, as well as in the scope of the invention described in the claims and the equivalent scope thereof.
Further, the components of the surface contamination density distribution calculation device can be realized by a computer processor, and can be operated by a surface contamination density distribution calculation program.

The surface contamination density distribution calculation device described above includes a dedicated chip, a control device in which processors such as an FPGA (Field Programmable Gate Array), a GPU (Graphics Processing Unit), or a CPU (Central Processing Unit) are highly integrated. Storage devices such as ROM (Read Only Memory) and RAM (Random Access Memory), external storage devices such as HDD (Hard Disk Drive) and SSD (Solid State Drive), display devices such as displays, mice and keyboards, etc. It is equipped with an input device and a communication I / F, and can be realized by a hardware configuration using a normal computer.

Further, the program executed by the surface contamination density distribution calculation device is provided by incorporating it into a ROM or the like in advance. Alternatively, the program is provided as a file in an installable or executable format stored on a computer-readable storage medium such as a CD-ROM, CD-R, memory card, DVD, or flexible disk (FD). You may try to do it.

Further, the program executed by the surface contamination density distribution calculation device according to the present embodiment may be stored on a computer connected to a network such as the Internet, and may be downloaded and provided via the network.
In addition, the surface contamination density distribution calculation device can also be configured by connecting separate modules that independently exert each function of the components to each other by a network or a dedicated line and combining them.

D1 ... 1st air dose rate distribution data, D2 ... 1st structure data, D3 ... 1st surface pollution density distribution data, D4 ... virtual air dose rate distribution data, D5 ... difference value, S1 ... 2nd air dose rate Distribution data, S2 ... Second structure data, S3 ... Second surface pollution density distribution data, T (T1, T2, T3) ... Calculation formula, 10 ... Surface pollution density distribution calculation device, 11 ... First memory area, 12 ... 2nd memory area, 13 ... 3rd memory area, 15 ... Machine learning unit, 21 ... 1st calculation unit, 22 ... 2nd calculation unit, 25 ... Difference calculation unit, 26 ... Correction unit, 28 ... Display unit, 31 ... Target area, 32 ... Structure, 33 ... Radioactive material, 35 (35a, 35b, 35c) ... Model area, 36 ... Dose meter, 37 ... Attention position.

Claims (10)

The first memory area that holds the first air dose rate distribution data created by measuring the air dose rate at multiple locations in the target area, and
A second memory area for holding the first structure data in which the first structure arranged in the target area is set in a coordinate system common to the first air dose rate distribution data, and
A third memory area in which an arithmetic expression showing the relationship between the second air dose rate distribution data of the model area, the second structure data of the model area, and the second surface contamination density distribution data of the model area is held, and
The calculation formula is characterized by including a first calculation unit for inputting the first air dose rate distribution data and the first structure data and outputting the first surface contamination density distribution data of the target area. Surface pollution density distribution calculation device. In the surface contamination density distribution calculation device according to claim 1,
In the third memory area, a plurality of the arithmetic expressions selected based on at least one of the first air dose rate distribution data and the first structure data are held.
The first calculation unit is a surface contamination density distribution calculation device, characterized in that the calculation formula is selected from a plurality of meshes for dividing the target area and executed. In the surface contamination density distribution calculation device according to claim 1 or 2.
A second calculation unit that inputs the first structure data and the first surface contamination density distribution data to the calculation formula and outputs virtual air dose rate distribution data, and
A difference calculation unit that calculates a difference value between the virtual air dose rate distribution data and the first air dose rate distribution data,
A surface contamination density distribution calculation device including a correction unit that corrects the calculation formula based on the difference value. In the surface contamination density distribution calculation device according to any one of claims 1 to 3.
The calculation formula is a surface contamination density distribution calculation device characterized in that distance information in the target area of the first structure is defined as a parameter. In the surface contamination density distribution calculation device according to any one of claims 1 to 4.
The calculation formula is a surface contamination density distribution calculation device characterized in that the average dose value in the first air dose rate distribution data is defined as a parameter. In the surface contamination density distribution calculation device according to any one of claims 1 to 5.
The calculation formula is a surface contamination density distribution calculation device characterized in that distance information in the first air dose rate distribution data is defined as a parameter. In the surface contamination density distribution calculation device according to any one of claims 1 to 6.
The calculation formula is a surface pollution density distribution calculation device characterized in that the ratio of the average dose value and the maximum dose value in the first air dose rate distribution data is defined as a parameter. In the surface contamination density distribution calculation device according to any one of claims 1 to 7.
The calculation formula is a surface contamination density distribution calculation device characterized in that the type of radionuclide existing in the target area is defined as a parameter. The step of holding the first air dose rate distribution data created by measuring the air dose rate at multiple positions in the target area, and
A step of holding the first structure data in which the first structure arranged in the target area is set in the same coordinate system as the first air dose rate distribution data, and
A step in which an arithmetic expression showing the relationship between the second air dose rate distribution data of the model area, the second structure data of the model area, and the second surface contamination density distribution data of the model area is held, and
The calculation formula includes a step of inputting the first air dose rate distribution data and the first structure data and outputting the first surface pollution density distribution data of the target area. Density distribution calculation method. On the computer
A step of measuring the air dose rate at multiple positions in the target area and holding the first air dose rate distribution data created.
A step of holding the first structure data in which the first structure arranged in the target area is set in the same coordinate system as the first air dose rate distribution data.
A step of holding an arithmetic expression showing the relationship between the second air dose rate distribution data of the model area, the second structure data of the model area, and the second surface contamination density distribution data of the model area.
Surface contamination, which comprises inputting the first air dose rate distribution data and the first structure data into the calculation formula and outputting the first surface contamination density distribution data of the target area. Density distribution calculation program.

Images