Method for judging path offset of radiation detector

Abstract

The invention belongs to the technical field of radiation detection, and particularly relates to a method for judging path deviation of a radiation detector. The method comprises the following steps: S1, dividing a preset path into broken line segments formed by straight line segments formed by connecting a plurality of adjacent path points; s2, the offset distance is defined as the distance PD between the real-time position point P and a certain point D on the corresponding straight line segment, and PD is the minimum distance between the point P and the straight line segment; the maximum value of the allowable path offset is set as Lmax; s3, surveying and mapping longitude and latitude coordinates of preset path points in advance and storing the longitude and latitude coordinates in a preset path database; s4, collecting longitude and latitude coordinates of a real-time position point P of the radiation detector, and calculating an offset distance PD; s5, the offset distance PD is compared with the maximum value Lmax of the allowable path offset, and if PD is smaller than or equal to Lmax, it is regarded that the set path is not offset; if PD is greater than Lmax, it is regarded that the path deviates from the set path, and path deviation early warning is carried out. According to the invention, all the portable radiation detectors of the nuclear power station can be managed in a unified manner through the network.

Description

Technical Field

[0001] The present invention belongs to the technical field of radiation detection, and particularly relates to a method for judging path deviation of a radiation detector. Background Art

[0002] To ensure the safe and reliable operation of nuclear power facilities, it is necessary to use portable radiation detectors to regularly or temporarily inspect the radiation levels at key points within the control area of the nuclear power plant. In the traditional detection method, the detection personnel carry radiation detection instruments to each detection point in turn to detect and record the radiation level values, and then manually input them into the computer after returning to the control center. This method cannot monitor the walking path of the detection personnel in real time, and cannot give an early warning in time when the detection personnel deviate from the path and enter the dangerous area, which poses a potential hazard to the personal safety of the detection personnel; at the same time, the radiation level data cannot be uploaded to the control center in time, and the technical personnel cannot timely understand the radiation situation on site. In addition, multiple portable radiation detectors are used simultaneously in a nuclear power plant, and it is required to uniformly manage all the in-use detectors at the same time, such as configuring parameters for these devices, monitoring the positions of each detector in real time online, the radiation levels at the detection points, etc. Summary of the Invention

[0003] The purpose of the present invention is to provide a method for judging path deviation of a radiation detector to solve the problems existing in the prior art.

[0004] To achieve the above purpose, the technical solution adopted by the present invention is as follows:

[0005] A method for judging path deviation of a radiation detector:

[0006] S1: Divide the pre-set path into a broken line segment composed of a plurality of straight line segments connected by adjacent path points;

[0007] S2: Define the offset distance as: the distance PD between the real-time position point P and a certain point D on the corresponding straight line segment, and PD is the minimum distance between the point P and the straight line segment; set the maximum value of the allowable path offset as: L max ;

[0008] S3: Map and store the latitude and longitude coordinates of the pre-set path points in a pre-set path database in advance;

[0009] S4: Collect the latitude and longitude coordinates of the real-time position point P of the radiation detector, and calculate the offset distance PD;

[0010] S5: Compare the offset distance PD with the maximum value L of the allowable path offset max If PD ≤ L max , it is regarded as not deviating from the set path; if PD > L max, it is regarded as deviating from the set path, and a path deviation warning is issued.

[0011] Perpendicular to the intersection of two adjacent straight line segments, draw perpendicular lines to the two straight line segments respectively. With this intersection as the center, draw a circle with a radius of L max The circle intersects the two perpendicular lines at point P' and point P'' respectively; the fan-shaped area surrounded by the connection lines between this intersection and point P', point P'' and the circumference of the circle is used as the first area, and the area surrounded by the extension lines of the connection lines between this intersection and point P', point P'' and the circumference of the circle is used as the second area; if the real-time position point P is within the first area, and the intersection of two adjacent straight line segments is point D, it is regarded as not deviating from the set path; if the real-time position point P is within the second area, and the intersection of two adjacent straight line segments is point D, it is regarded as deviating from the set path; if the real-time position point P is outside the first area and the second area, then draw a perpendicular line from point P to the corresponding straight line segment and intersect the straight line segment at point D, and compare the offset distance PD with the maximum value L max of the allowable path offset.

[0012] The method for judging that the real-time position point P is within the first area and the second area is: the angles between the connection line of the real-time position point P and this intersection and the two straight line segments are both greater than 90 degrees; when the real-time position point P is outside the first area and the second area, the offset distance PD is solved according to the distances between point P and the two path points of the straight line segment and the trigonometric relationship.

[0013] It also includes predicting the path deviation trend based on the historical position information of the radiation detector to issue an early warning; setting the position of the radiation detector at the previous moment as The offset distance of the radiation detector at the previous moment before migration is compared with the offset distance PD of the radiation detector at the current moment. If , the trend of deviating from the set path is increasing, and a warning prompt is issued; if then the trend of deviating from the set path is decreasing, and no warning is issued.

[0014] The calculation method of the offset distance PD is:

[0015] T1: Select a rectangular area A1B1C1D1 on the ground, fix one side A1B1 and take the two endpoints A1, B1 of this side as fixed points. Select any moving point P1 within the area, obtain the longitudes and latitudes of the three points A1, B1, P1, measure the distances between the three points in pairs and obtain three distance values;

[0016] T2: Keep points A1, B1 fixed, and randomly move point P1 to another position P2, obtain the longitudes and latitudes of the three points A1, B1, P2, measure the distances between the three points in pairs and obtain three distance values;

[0017] T3: Fix points A1 and B1 in place, move the moving point to P3, …, P n , repeat step T2 to obtain n groups of samples;

[0018] T4: Move to the new area A2B2C2D2, repeat the above steps to obtain another n groups of samples;

[0019] T5: Repeat the above steps within m areas, and a dataset consisting of m×n groups of samples can be obtained in total, and they are divided into training samples and test samples;

[0020] T6: Use the longitude and latitude coordinates of 3 points in each group of samples, a total of 6 values, as the input of the neural network, and the corresponding 3 distance values as the output of the neural network to construct a 6-X-3 BP neural network; put the test samples into the constructed BP neural network and train it to determine the parameters of the neural network model: weights and biases;

[0021] T7: Use the trained neural network model to perform regression prediction on the distance between points; the input is the real-time position longitude and latitude coordinates of the portable radiation detector, the longitude and latitude coordinates of the starting point and the ending point of a certain set path, and the output is the distance between the portable radiation detector and the starting point of the path, the distance from the ending point, and the distance between the starting point and the ending point of the path.

[0022] A radiation detector management system includes an Internet of Things cloud platform, a management computing system, several portable radiation detectors and several mobile terminals. The portable radiation detectors, mobile terminals and management computing system are connected to the Internet of Things cloud platform and communicate using the MQTT network communication protocol; each portable radiation detector has a unique identification ID number, is built-in with a GPS / Beidou positioning module, can obtain the longitude and latitude coordinate information of the position of the radiation detector in real time, and is built-in with a radiation level detection module to obtain the radiation level value of the detection point.

[0023] The management computing system includes a preset path database, a portable radiation detector attribute database, a detection data database, and a user management database.

[0024] The management computing system is built-in with a system management module, a detector management module, a path management module, an online monitoring module, and a data analysis module.

[0025] The system management module can update and modify the data in the user management database; the detector management module can configure or modify the instrument parameters in the portable radiation detector attribute database and add or delete instruments; the path management module can implement the planning of the inspection path within the nuclear power plant inspection area in the path database and the portable radiation detector attribute database and the setting of path point parameters, as well as the setting of the inspection path for each portable radiation detector; the online monitoring module can monitor the traveling path and the radiation level of the detection points of the portable radiation detectors that are currently carrying out inspection work in real time; the data analysis module analyzes the historical data of the radiation levels of each detection point in the detection data database.

[0026] X1: Add a pre-set inspection path in the management computing system;

[0027] X2: Configure the network parameters and management parameters for each portable radiation detector to access the Internet of Things cloud platform, and configure the network parameters for the management computing system and each mobile terminal to access the Internet of Things cloud platform;

[0028] X3: Establish a neural network model for calculating distances;

[0029] X4: The portable radiation detector, the management computing system, and the mobile terminal are powered on and connected to the Internet of Things cloud platform, showing an online state;

[0030] X5: The portable radiation detector uploads real-time location messages to the Internet of Things cloud platform at fixed intervals, and the Internet of Things cloud platform distributes them to the management computing system and the mobile terminal;

[0031] X6: The management computing system and the mobile terminal parse the real-time location messages of the portable radiation detector received, and call up the map based on the ID number and longitude and latitude to display the real-time location of the portable radiation detector;

[0032] X7: The management computing system stores the real-time location messages of the portable radiation detector received in the database and parses them, calculates the distance and deviation trend from the preset path according to the ID number in the message; and judges whether a path deviation warning is required according to the preset deviation distance limit of the portable radiation detector. If so, upload a warning message to the Internet of Things cloud platform, and the Internet of Things cloud platform distributes it to the portable radiation detector for path deviation warning processing;

[0033] X8: After arriving at the detection point, the portable radiation detector obtains the detection point coding information, measures the radiation level, and then combines the detection point coding and radiation level information with a time stamp to form a detection point radiation level message and upload it to the Internet of Things cloud platform, which is distributed to the management computing system and the mobile terminal. The management computing system and the mobile terminal parse the message and display the radiation level of the current detection point in real time on the network map according to the ID number;

[0034] X9: The management computing system stores the received radiation level message in the database and parses it. According to the ID number of the portable radiation detector and the detection point number, it looks up the preset upper threshold value of the radiation rate of the radiation detector at this detection point, and extracts the actual radiation level value in the message to judge whether the radiation level exceeds the standard; it judges whether radiation overrun warning is needed. If so, it uploads the warning message to the Internet of Things cloud platform, and the Internet of Things cloud platform sends it to the portable radiation detector for radiation overrun warning processing;

[0035] X10: Loop and repeat X5 - X9.

[0036] The beneficial effects achieved by the present invention are as follows:

[0037] The present invention can realize unified management of all portable radiation detectors in a nuclear power plant through the network. By using the path deviation determination method to judge whether the walking path of the detector is on the specified route, and giving a warning when there is a deviation, it can ensure the personal safety of the detector. It can monitor the radiation level exceeding the standard at the detection point in real time, ensure that technicians can view the on-site data in the control room in real time, lay a foundation for the automation, networking and intelligence of nuclear power plant radiation detection, and solve the urgent problems faced by enterprises. Description of the Drawings

[0038] Figure 1 It is a schematic diagram of the path deviation judgment method of the present invention (point P is located in the first area);

[0039] Figure 2 It is a schematic diagram of the path deviation judgment method of the present invention (point P is located in the second area);

[0040] Figure 3 It is a schematic diagram of the path deviation judgment method of the present invention (point P is located outside the first and second areas);

[0041] Figure 4 It is a schematic diagram of the warning of the path deviation trend according to the historical position information of the radiation detector in the present invention;

[0042] Figure 5 It is a schematic diagram of the selection of the sample set of the BP neural network of the present invention. Detailed Embodiments

[0043] The present invention will be described in detail below with reference to the drawings and specific embodiments.

[0044] A method for judging the path deviation of a radiation detector includes the following steps:

[0045] S1: Approximate the preset path as a broken line composed of a plurality of adjacent path points connected into straight line segments;

[0046] S2: Define the offset distance as the distance PD between the real-time position point P and a point D on the corresponding straight-line segment, and PD is the minimum distance between point P and the straight-line segment; set the maximum value of the allowable path offset as: L max ;

[0047] S3: Survey and store the latitude and longitude coordinates of the preset path points in advance in a preset path database;

[0048] S4: Collect the latitude and longitude coordinates of the real-time position point P of the radiation detector, and calculate the offset distance PD;

[0049] S5: Compare the offset distance PD with the maximum value L of the allowable path offset max If PD ≤ L max , it is regarded as not deviating from the set path; if PD > L max , it is regarded as having deviated from the set path, and a path offset warning is given.

[0050] Preferably, the method for judging path offset is: respectively draw perpendicular lines to two straight-line segments perpendicular to the intersection of two adjacent straight-line segments, take the intersection as the center of the circle, and draw a circle with a radius of L max to intersect the two perpendicular lines at point P' and point P'' respectively;

[0051] The sector area surrounded by the connection lines between the intersection and point P', point P'' and the circumference of the circle is used as the first area, and the area surrounded by the extension lines of the connection lines between the intersection and point P', point P'' and the circumference of the circle is used as the second area;

[0052] If the real-time position point P is within the first area, and the intersection of two adjacent straight-line segments is point D, it is regarded as not deviating from the set path;

[0053] If the real-time position point P is within the second area, and the intersection of two adjacent straight-line segments is point D, it is regarded as deviating from the set path;

[0054] If the real-time position point P is outside the first area and the second area, then draw a perpendicular line from point P to the corresponding straight-line segment to intersect the straight-line segment at point D, and compare the offset distance PD with the maximum value L max for comparison.

[0055] Preferably, the method for judging that the real-time position point P is within the first area and the second area is: the angles between the connection line of the real-time position point P and the intersection and the two straight-line segments are both greater than 90 degrees; when the real-time position point P is outside the first area and the second area, the offset distance PD is solved according to the distances between point P and the two path points of the straight-line segment and the trigonometric relationship.

[0056] Preferably, it further includes predicting the path deviation trend based on the historical position information of the radiation detector to give an early warning;

[0057] Set the position of the radiation detector at the previous moment as The deviation distance of the radiation detector at the previous moment before migration is Compare it with the deviation distance PD at the current moment of the radiation detector. If then the trend of deviating from the set path is increasing, and an early warning prompt is given; if then the trend of deviating from the set path is decreasing, and no early warning is given.

[0058] Preferably, the calculation method of the deviation distance PD is as follows:

[0059] T1: Select a rectangular area A1B1C1D1 on the ground. Fix one side A1B1 and take the two endpoints A1 and B1 of this side as fixed points. Select any moving point P1 within the area, obtain the longitude and latitude of the three points A1, B1, and P1, measure the distances between the three points pairwise, and obtain three distance values;

[0060] T2: Keep the two points A1 and B1 fixed, and randomly move the P1 point to another position P2, obtain the longitude and latitude of the three points A1, B1, and P2, measure the distances between the three points pairwise, and obtain three distance values;

[0061] T3: Keep the two points A1 and B1 fixed, move the moving point to P3,..., P n , repeat step T2 to obtain n groups of samples;

[0062] T4: Move to a new area A2B2C2D2, repeat the above steps to obtain another n groups of samples;

[0063] T5: Repeat the above steps in m areas, and a total of m×n groups of samples can be obtained to form a data set, which is divided into training samples and test samples;

[0064] T6: Use the longitude and latitude coordinates of the three points in each group of samples, a total of 6 values, as the input of the neural network, and the corresponding three distance values as the output of the neural network to construct a 6-X-3 BP neural network; put the test samples into the constructed BP neural network and train it to determine the parameters of the neural network model: weights and biases;

[0065] T7: Use the trained neural network model to perform regression prediction on the distances between points; the input is the real-time position longitude and latitude coordinates of the portable radiation measuring instrument, the longitude and latitude coordinates of the starting point and the ending point of a certain set path, and the output is the distance between the portable radiation measuring instrument and the starting point of this path, the distance from the ending point, and the distance between the starting point and the ending point of the path.

[0066] The present invention also provides a radiation detector management system, which includes an Internet of Things cloud platform, a management computing system, a number of portable radiation detectors, and a number of mobile terminals. The portable radiation detectors, mobile terminals, and management computing system are connected to the Internet of Things cloud platform and communicate using the MQTT network communication protocol. Each portable radiation detector has a unique identification ID number and is built-in with a GPS / Beidou positioning module, which can obtain the longitude and latitude coordinate information of the location of the radiation detector in real time. It is built-in with a radiation level detection module, which can obtain the radiation level value of the detection point. The management computing system includes a preset path database, a portable radiation detector attribute database, a detection data database, and a user management database, and is built-in with a system management module, a detector management module, a path management module, an online monitoring module, and a data analysis module.

[0067] Preferably, the system management module can update and modify the data in the user management database; the detector management module can configure or modify the instrument parameters in the portable radiation detector attribute database and add or delete instruments; the path management module can realize the planning of the inspection path within the nuclear power plant detection area in the path database and the portable radiation detector attribute database and the setting of path point parameters, as well as the setting of the inspection path for each portable radiation detector; the online monitoring module can monitor the travel path and the radiation level of the detection point of the portable radiation detector currently performing the inspection work in real time; the data analysis module analyzes the historical data of the radiation levels of each detection point in the detection data database.

[0068] Preferably, a radiation detector management system includes the following work processes:

[0069] X1: Add a preset inspection path in the management computing system;

[0070] X2: Configure the network parameters and management parameters for each portable radiation detector to access the Internet of Things cloud platform, and configure the network parameters for the management computing system and each mobile terminal to access the Internet of Things cloud platform;

[0071] X3: Establish a neural network model for calculating distance;

[0072] X4: The portable radiation detector, management computing system, and mobile terminal are powered on and connected to the Internet of Things cloud platform, showing an online state;

[0073] X5: The portable radiation detector uploads real-time location messages to the Internet of Things cloud platform at fixed intervals, and the Internet of Things cloud platform distributes them to the management computing system and mobile terminal;

[0074] X6: The management computing system and the mobile terminal parse the real-time location messages of the received portable radiation detectors. Based on the ID number and the longitude and latitude, they call up the map to display the real-time locations of the portable radiation detectors.

[0075] X7: The management computing system stores the real-time location messages of the received portable radiation detectors in the database and parses them. It calculates the distance and deviation trend from the preset path according to the ID number in the message. And according to the preset deviation distance limit of the portable radiation detector, it judges whether a path deviation warning is needed. If so, it uploads the warning message to the Internet of Things cloud platform, and the Internet of Things cloud platform sends it to the portable detector for path deviation warning processing.

[0076] X8: After arriving at the detection point, the portable detector obtains the detection point coding information and measures the radiation level. Then it combines the detection point coding and the radiation level information with time stamps to form a detection point radiation level message and uploads it to the cloud platform, which is sent to the management computing system and the mobile terminal. The management computing system and the mobile terminal parse the message and display the radiation level of the current detection point in real time on the network map according to the ID number.

[0077] X9: The management computing system stores the received radiation level messages in the database and parses them. It finds the preset radiation rate upper limit threshold of the radiation detector at this detection point according to the ID number of the portable radiation detector and the detection point number, and extracts the actual radiation level value in the message to judge whether the radiation level exceeds the standard. It judges whether a radiation overlimit warning is needed. If so, it uploads the warning message to the Internet of Things cloud platform, and the Internet of Things cloud platform sends it to the portable detector for radiation overlimit warning processing.

[0078] X10: Repeat X5 - X9 in a loop.

[0079] A method for judging the path deviation of a radiation detector, comprising the following steps:

[0080] S1: Approximate the preset path as a broken line composed of several adjacent straight line segments connected by path points.

[0081] S2: Define the deviation distance as: the distance PD between the real-time position point P and a point D on the corresponding straight line segment, and PD is the minimum distance between point P and the straight line segment; set the maximum value of the allowable path deviation as: L max ;

[0082] S3: Survey and store the longitude and latitude coordinates of the preset path points in advance in a preset path database.

[0083] S4: Collect the longitude and latitude coordinates of the real-time position point P of the radiation detector and calculate the deviation distance PD.

[0084] S5: Compare the offset distance PD with the maximum value L of the allowable path offset max If PD ≤ L max , it is regarded as not deviating from the set path; if PD > L max , it is regarded as having deviated from the set path, and a path offset warning is given.

[0085] The method for judging path offset is as follows: Perpendiculars to two straight line segments are respectively made at the intersection of two adjacent straight line segments. Taking this intersection as the center, a circle with a radius of L max intersects the two perpendiculars at points P' and P'' respectively;

[0086] The sector area surrounded by the connecting lines of this intersection with points P' and P'' and the circle is used as the first area, and the area surrounded by the extension lines of the connecting lines of this intersection with points P' and P'' and the circle is used as the second area;

[0087] If the real-time position point P is within the first area, and the intersection of two adjacent straight line segments is point D, it is regarded as not deviating from the set path;

[0088] If the real-time position point P is within the second area, and the intersection of two adjacent straight line segments is point D, it is regarded as deviating from the set path;

[0089] If the real-time position point P is outside the first area and the second area, then a perpendicular is made from point P to the corresponding straight line segment and intersects the straight line segment at point D. Compare the offset distance PD with the maximum value L max of the allowable path offset.

[0090] The method for judging that the real-time position point P is within the first area and the second area is as follows: The angles between the connecting line of the real-time position point P and this intersection and the two straight line segments are both greater than 90 degrees; when the real-time position point P is outside the first area and the second area, the offset distance PD is solved according to the distances between point P and the two path points of the straight line segment and the trigonometric relationship.

[0091] In this embodiment, the above calculation method is specifically as follows:

[0092] (1) In the figure, AB - BC is the set path, and the longitude and latitude coordinates of points A, B, and C are measured in advance and stored in the preset path database;

[0093] (2) Point P is the longitude and latitude of the real-time position parsed from the real-time position message of the portable radiation detector by the management computer system;

[0094] (3) In the figure, PD is defined as the path deviation amount, and the calculation method of PD is as follows:

[0095] PD = bsin(∠PAB),

[0096] (4) When point D coincides with point B, that is, when the portable radiation detector is located in the area to the right of line P'B and to the left of line P''B, if the portable radiation detector (point P) is located within the fan-shaped area (the fan-shaped radii P'B = P''B = L max ), that is, PB ≤ L max , it is considered not to deviate from the path, otherwise it is determined to deviate from the path.

[0097] Method for determining whether the portable radiation detector is within the fan-shaped area: Both ∠PBA and ∠PBC are greater than 90°, that is, there is PA 2 > AB 2 + PB 2 and PC 2 > CB 2 + PB 2

[0098] (5) When the portable radiation detector is located in the area to the right of line P''B, the method for determining path deviation is the same as in (3).

[0099] It also includes predicting the path deviation trend based on the historical position information of the radiation detector to give an early warning in advance;

[0100] Set the position of the radiation detector at the previous moment as The offset distance of the radiation detector at the previous moment before migration is Compare it with the current offset distance PD of the radiation detector at the current moment. If , then the trend of deviating from the set path is increasing, and an early warning prompt is given; if then the trend of deviating from the set path is decreasing, and no early warning is given.

[0101] According to the foregoing method, predict the path deviation trend based on the historical position information of the portable detector to give an early warning in advance to prevent entering a dangerous area. For example, the previous PD = 0.6. If the current value is less than or equal to 0.6, no warning is given; otherwise, if it is greater than 0.6, a warning is given. When judging based on the initially received real-time position, PD = 1, which is the maximum allowable deviation value.

[0102] Use a neural network to calculate the distance between the real-time position and the standard path point. The method is as follows:

[0103] T1: Select a rectangular area A1B1C1D1 on the ground. Fix one side A1B1 and take the two endpoints A1 and B1 of this side as fixed points. Select any moving point P1 within the area, obtain the longitudes and latitudes of the three points A1, B1, and P1, measure the distances between the three points pairwise, and obtain three distance values;

[0104] T2: Fix points A1 and B1 stationary, randomly move point P1 to another position P2, obtain the longitudes and latitudes of the three points A1, B1, and P2, measure the distances between every two of the three points and obtain three distance values;

[0105] T3: Fix points A1 and B1 stationary, move the moving point to P3, ……, P n , repeat step T2 to obtain n groups of samples;

[0106] T4: Move to the new area A2B2C2D2, repeat the above steps to obtain another n groups of samples;

[0107] T5: Repeat the above steps in m areas, a total of m×n groups of samples can be obtained to form a data set, and it is divided into training samples and test samples;

[0108] T6: Use the six values of the longitudes and latitudes of the three points of each group of samples as the input of the neural network, and the corresponding three distance values as the output of the neural network to construct a 6-X-3 BP neural network; put the test samples into the constructed BP neural network and train it to determine the parameters of the neural network model: weights and biases;

[0109] T7: Use the trained neural network model to perform regression prediction on the distance between points; the input is the real-time position longitude and latitude coordinates of the portable radiation measuring instrument, the longitude and latitude coordinates of the starting point and the ending point of a certain set path, and the output is the distance between the portable radiation measuring instrument and the starting point of the path, the distance from the ending point, and the distance between the starting point and the ending point of the path.

[0110] Specifically, in the first area A1B1C1D1, obtain the longitude and latitude (x A1 , y A1 ) of point A1, the longitude and latitude (x B1 , y B1 ) of point B1, the longitude and latitude of point P1 is (x P1 , y P1 ), measure the distances A1B1, P1A1, and P1B1 between the three points A1, B1, and P1, and use [(x A1 , y A1 )(x B1 , y B1 )(x P1 , y P1 )] as the input of the neural network, [A1B1, P1A1, P1B1] as the output of the neural network, [(x A1 , y A1 )(x B1 , y B1 )(x P1 , y P1)],[A1B1, P1A1, P1B1] form the first sample of the neural network;

[0111] The moving point P moves from P1 to P2, and the longitude and latitude of point P2 are obtained as (x P2 , y P2 ). The distances P2A1 and P2B1 between A1, B1 and P2 are measured. Taking [(x A1 , y A1 )(x B1 , y B1 )(x P2 , y P2 )] as the input of the neural network and [A1B1, P2A1, P2B1] as the output of the neural network, [(x A1 , y A1 )(x B1 , y B1 )(x P2 , y P2 )],[A1B1, P2A1, P2B1] form the second sample of the neural network; Let the point P continue to move in this way, move a total of n - 1 times, and obtain n samples in total;

[0112] A radiation detector management system includes an Internet of Things cloud platform, a management computing system, several portable radiation detectors and several mobile terminals. The portable radiation detectors, mobile terminals and management computing system are connected to the Internet of Things cloud platform and communicate using the MQTT network communication protocol; Each portable radiation detector has a unique 4-digit identification ID number, is built-in with a GPS / Beidou positioning module, and can obtain the longitude and latitude coordinate information of the position of the radiation detector in real time; It is built-in with a radiation level detection module and can obtain the radiation level value of the detection point; It has a 4G network communication function and can connect the portable radiation detector to the Internet. The management computing system includes a preset path database, a portable radiation detector attribute database, a detection data database, and a user management database. The formats and contents are as follows:

[0113] Preset path database

[0114] Path Encoding Creation Time Modification Time Point 1 Point 2 Point 3 ------ Point n Allowed Deviation

[0115] Portable detector attribute database

[0116] Device ID Online / Offline Last Online Time Preset Path Dose Limit 1234 Online 2023.8.10 path1 0.2

[0117] Detection data database

[0118]

[0119] User management database

[0120] Username Creation Time Password Permissions Modification Time Last Login Time

[0121] The management computing system is built-in with a system management module, a detector management module, a path management module, an online monitoring module, and a data analysis module. Among them, the system management module mainly realizes the management of users of the management computing system and portable radiation detectors, such as adding, deleting, operating permissions and scopes of users, etc., and records the operations of each user in the form of logs for traceability in case of problems;

[0122] The detector management module mainly realizes the management and parameter setting of portable radiation detectors used in nuclear power. It mainly realizes functions such as viewing relevant information of detectors in the system, adding or deleting a detector to the management system, and configuring or modifying parameters of each detector;

[0123] The path management module can realize the planning of the inspection path in the nuclear power plant detection area in the path database and the portable radiation detector attribute database, the setting of path point parameters, and the setting of the inspection path for each portable radiation detector;

[0124] The online monitoring module can monitor the traveling path and the radiation level of the detection points of the portable radiation detectors that are currently carrying out inspection work in real time;

[0125] The data analysis module can select the radiation level data of the detection points within a certain time period according to time conditions for statistical analysis, calculate parameters such as maximum value, minimum value, mean value and variance, and draw a historical curve to analyze the historical data of the radiation level of each detection point in the detection data database.

[0126] The radiation detector management system includes the following working processes:

[0127] X1: Add a preset inspection path in the management computing system;

[0128] X2: Configure the network parameters (username, password) and management parameters (ID number, radiation level limit, preset path, etc.) for each portable radiation detector to access the Internet of Things cloud platform, and configure the network parameters (username, password) for the management computing system and each mobile terminal to access the Internet of Things cloud platform;

[0129] X3: Establish a neural network model for calculating distance;

[0130] X4: The portable radiation detector, the management computing system and the mobile terminal are powered on and connected to the Internet of Things cloud platform, showing an online state;

[0131] X5: The portable radiation detector uploads real-time location messages to the Internet of Things cloud platform at fixed intervals, and the Internet of Things cloud platform distributes them to the management computing system and the mobile terminal;

[0132] X6: The management computing system and the mobile terminal parse the real-time location messages of the received portable radiation detectors. Based on the ID number and the longitude and latitude, they call up the map to display the real-time locations of the portable radiation detectors.

[0133] X7: The management computing system stores the received real-time location messages of the portable radiation detectors in the database and parses them. It calculates the distance and deviation trend from the preset path according to the ID number in the message. And according to the preset deviation distance limit of the portable radiation detector, it judges whether a path deviation warning is needed. If so, it uploads the warning message to the Internet of Things cloud platform, and the Internet of Things cloud platform sends it to the portable detector for path deviation warning processing.

[0134] X8: After arriving at the detection point, the portable detector obtains the detection point coding information and measures the radiation level. Then it combines the detection point coding and the radiation level information with a time stamp to form a detection point radiation level message and uploads it to the cloud platform, which is sent to the management computing system and the mobile terminal. The management computing system and the mobile terminal parse the message and display the radiation level of the current detection point in real time on the network map according to the ID number.

[0135] X9: The management computing system stores the received radiation level message in the database and parses it. It finds the preset radiation rate upper limit threshold of the radiation detector at this detection point according to the ID number of the portable radiation detector and the detection point number, and extracts the actual radiation level value in the message to judge whether the radiation level exceeds the standard. It judges whether a radiation overlimit warning is needed. If so, it uploads the warning message to the Internet of Things cloud platform, and the Internet of Things cloud platform sends it to the portable detector for radiation overlimit warning processing.

[0136] X10: Repeat X5 - X9 in a loop.

[0137] Specifically, at every set fixed time interval, the radiation detector uploads its own ID number and the longitude and latitude coordinate information of the location to the cloud platform in the format of {ID:”1234”,GPS:”longitude,latitude”} message, and then the cloud platform sends it to the management computing system for path deviation judgment and prediction.

[0138] If there is a deviation, an alarm message in the format of {ID:”1234”,PathAlarm:”A”} will be uploaded to the cloud platform; the cloud platform sends the path deviation alarm message uploaded by the management computing to all the portable detectors on the network through the communication network. After each portable detector receives the alarm message sent by the cloud platform, it will extract the ID number part and compare it with its own ID number to judge whether the alarm message is its own. If the ID number in the alarm message is different from its own ID number, it will not respond to the alarm message. If the ID number in the alarm message is the same as its own ID number, it will respond to the alarm message.

[0139] After reaching the detection point, the portable detector obtains the detection point coding information, measures the radiation level, and then combines the detection point coding and radiation level information with time stamps to form a detection point radiation level message {ID: "1234", date: "2023-07-08-16:08:25", Checkpoint: "123456", Radiance: "0.31"} and uploads it to the cloud platform. Then it is sent to the management computing system. The management calculation looks up the preset upper threshold of the radiation rate of the radiation instrument at this detection point according to the ID number of the portable radiation instrument and the detection point number, and extracts the actual radiation level value in the message to judge whether the radiation level exceeds the standard. Take out the ID number from the message, such as "1234", then take out the radiation level of the current detection point, such as 0.31, and then read the maximum allowable radiation level of the detector with this ID number from the portable detector status attribute database, such as 0.2. At this time, 0.31 is greater than 0.2, and a radiation overlimit warning is required.

[0140] If the actual radiation level value is greater than the set upper threshold, it is determined that the standard is exceeded, and an alarm message with the upload format {ID: "1234", RadianceAlarm: "A"} is uploaded to the cloud platform; the cloud platform sends the radiation level overlimit alarm message uploaded by the management computing system to all portable detectors on the network through the communication network. After each portable detector receives the radiation level overlimit alarm message sent by the cloud platform, it will extract the ID number part and compare it with its own ID number to judge whether the alarm message is its own. If the ID number in the alarm message is different from its own ID number, it will not respond to the alarm message. If the ID number in the alarm message is the same as its own ID number, it will respond to the alarm message.

Claims

1. A method for determining path deviation of a radiation detector, characterized in that: S1: Divide the preset path into broken line segments consisting of straight line segments connected by a number of adjacent path points; S2: Define the offset distance as: the distance PD between the real-time position point P and a point D on the corresponding straight line segment, and PD is the minimum distance between point P and the straight line segment; set the maximum value of the allowed path offset to: L max ; S3: mapping the longitude and latitude coordinates of the preset path points in advance and storing them in a preset path database; S4: Collect the longitude and latitude coordinates of the real-time position point P of the radiation detector and calculate the offset distance PD; S5: Divide the offset distance PD by the maximum allowed path offset L max Compare, if PD≤L max , it is considered that the set path is not offset; PD>L max , it is considered to have deviated from the set path and a path deviation warning is issued.

2. The method for determining path deviation of a radiation detector according to claim 1, characterized in that: Draw perpendicular lines to the intersection of two adjacent straight line segments, and draw a circle with a radius of L with the intersection as the center. max The circle intersects with the two perpendicular lines at points P′ and P″ respectively; the sector area formed by the line connecting the intersection point, points P′, and P″ and the circle is taken as the first area, and the area formed by the extension line of the line connecting the intersection point, points P′, and P″ and the circle is taken as the second area; if the real-time position point P is located in the first area, the intersection point of the two adjacent straight line segments is point D, and it is regarded as not deviating from the set path; if the real-time position point P is located in the second area, the intersection point of the two adjacent straight line segments is point D, and it is regarded as deviating from the set path; if the real-time position point P is located outside the first area and the second area, a perpendicular line is made through point P to intersect the corresponding straight line segment at point D, and the offset distance PD is equal to the maximum value L of the allowed path offset. max For comparison.

3. The method for determining path deviation of a radiation detector according to claim 2, characterized in that: The method for determining whether the real-time position point P is located within the first area and the second area is as follows: the angles between the real-time position point P and the line connecting the intersection point and the two straight line segments are both greater than 90 degrees; if the real-time position point P is located outside the first area and the second area, the offset distance PD is solved based on the distance between point P and the two path points of the straight line segment and the trigonometric relationship.

4. The method for determining path deviation of a radiation detector according to claim 3, characterized in that: The method also includes predicting the path deviation trend based on the historical position information of the radiation detector to provide early warning; setting the position of the radiation detector at the previous moment as The offset distance of the radiation detector before migration is Compare with the offset distance PD of the radiation detector at the current moment. , the tendency of deviation from the set path is increasing, and an early warning is issued; if The tendency of deviation from the set path is decreasing and no warning is issued.

5. The method for determining path deviation of a radiation detector according to claim 4, characterized in that: The calculation method of the offset distance PD is: T1: Select a rectangular area A1B1C1D1, fix one side A1B1 and take the two endpoints A1 and B1 of the side as fixed points, select any moving point P1 in the area, obtain the longitude and latitude of the three points A1, B1, and P1, measure the distances between the three points and obtain three distance values; T2: Fix points A1 and B1 and move point P1 randomly to another position P2. Obtain the longitude and latitude of three points A1, B1, and P2. Measure the distances between the three points and obtain three distance values. T3: Fix points A1 and B1 and move the moving point to P3, ..., P n , repeat step T2 to obtain n groups of samples; T4: Move to the new area A2B2C2D2 and repeat the above steps to obtain another n groups of samples; T5: Repeat the above steps in m regions to obtain a data set consisting of m×n groups of samples, which are divided into training samples and test samples; T6: Take the latitude and longitude coordinates of the three points of each sample group as the input of the neural network, and the corresponding three distance values ​​as the output of the neural network to construct a 6-X-3 BP neural network; Put the test sample into the constructed BP neural network, train it, and determine the parameters of the neural network model: weights and biases; T7: Use the trained neural network model to perform regression prediction on the distance between points; The input is the real-time position latitude and longitude coordinates of the portable radiation detector, and the latitude and longitude coordinates of the starting point and end point of a set path. The output is the distance between the portable radiation detector and the starting point of the path, the distance to the end point, and the distance between the starting point and the end point of the path.

6. A radiation detector management system, characterized in that: It includes an Internet of Things cloud platform, a management computing system, several portable radiation detectors and several mobile terminals. The portable radiation detectors, mobile terminals and management computing system are connected to the Internet of Things cloud platform and communicate using the MQTT network communication protocol. Each portable radiation detector has a unique identity identification ID number and a built-in GPS / Beidou positioning module, which can obtain the latitude and longitude coordinate information of the location of the radiation detector in real time. It also has a built-in radiation level detection module to obtain the radiation level value of the detection point.

7. The radiation detector management system according to claim 6, characterized in that: The management and computing system includes a preset path database, a portable radiation detector attribute database, a detection data database, and a user management database.

8. The radiation detector management system according to claim 7, characterized in that: The management computing system has built-in system management module, detector management module, path management module, online monitoring module and data analysis module.

9. The radiation detector management system according to claim 8, characterized in that: The system management module can update and modify the data in the user management database; the detector management module can configure or modify the instrument parameters in the portable radiation detector attribute database and add or delete instruments; the path management module can realize the planning of the inspection path in the nuclear power plant detection area in the path database and the portable radiation detector attribute database, the setting of the path point parameters, and the setting of the inspection path for each portable radiation detector; The online monitoring module can monitor the travel path and radiation level of the detection point of the portable radiation detector currently undergoing inspection in real time; The data analysis module analyzes the historical data of radiation levels at each detection point in the detection data database.

10. The radiation detector management system according to claim 6, characterized in that: X1: Add a pre-set inspection path in the management computing system; X2: Configure the network parameters and management parameters of each portable radiation detector connected to the IoT cloud platform, and configure the network parameters of the management computing system and each mobile terminal connected to the IoT cloud platform; X3: Establish a neural network model for calculating distance; X4: The portable radiation detector, management computing system and mobile terminal are turned on and connected to the IoT cloud platform, and are online; X5: The portable radiation detector uploads real-time location information to the IoT cloud platform at regular intervals, and the IoT cloud platform sends it to the management computing system and mobile terminals; X6: The management computing system and the mobile terminal parse the real-time location message of the portable radiation detector received, and call the map to display the real-time location of the portable radiation detector according to the ID number and longitude and latitude; X7: The management computing system stores the received real-time location message of the portable radiation detector in the database and analyzes it, and calculates the distance and deviation trend from the preset path according to the ID number in the message; And according to the preset deviation distance limit of the portable radiation detector, it is judged whether a path deviation warning is needed. If necessary, the warning message is uploaded to the IoT cloud platform, and the IoT cloud platform sends it to the portable radiation detector for path deviation warning processing; X8: After arriving at the detection point, the portable radiation detector obtains the detection point code information and measures the radiation level. Then, the detection point code and radiation level information are combined with a time stamp to form a detection point radiation level message, which is uploaded to the IoT cloud platform and sent to the management computing system and mobile terminal. The management computing system and mobile terminal parse the message and display the radiation level of the current detection point in real time on the network map according to the ID number. X9: The management computing system stores the received radiation level message in the database and parses it, searches for the radiation rate upper limit threshold preset by the portable radiation detector at the detection point according to the ID number and detection point number of the portable radiation detector, and extracts the actual radiation level value in the message to determine whether the radiation level exceeds the standard; Determine whether a radiation over-limit warning is needed. If necessary, upload the warning message to the IoT cloud platform, which will then send it to the portable radiation detector for radiation over-limit warning processing; X10: Repeat X5-X9 in a loop.

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